The Critical Patients Localization Algorithm Using ... - IEEE Xplore

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The Critical Patients Localization Algorithm Using Sparse Representation for Mixed Signals in Emergency Healthcare System Liangtian Wan, Guangjie Han, Member, IEEE, Lei Shu, Member, IEEE, and Naixing Feng

Abstract—In this paper, a new architecture for the emergency healthcare system based on mobile cloud computation (MCC) and fifth-generation (5G) wireless link is proposed. Based on the processing speed of MCC and the communication rate of 5G scheme, this system can monitor and locate patients in real time. Multiple base stations of the massive multiple input multiple output cooperate with each other to locate the patients using cross-location method. Then, a new direction-of-arrival (DOA) estimation algorithm is proposed in the presence of unknown mutual coupling. The uncorrelated signals are first estimated using the estimation of signal parameters via rotational invariance technique algorithm. Then, these estimators construct the manifold matrix of uncorrelated signals to estimate the mutual coupling coefficients. The information about uncorrelated signals and mutual coupling is eliminated based on oblique projection technique in order to estimate the DOAs of coherent signals using the original array. Finally, the DOAs of coherent signals are achieved based on sparse representation theory. Simulation results demonstrate the effectiveness and performance of the proposed algorithm. Index Terms—Direction-of-arrival (DOA) estimation, emergency healthcare system, fifth-generation (5G) wireless link, mobile cloud computation (MCC).

I. I NTRODUCTION

W

ITH the rapid development of body area networks (BANs) and wireless communication, remote diagnoses and monitoring of patients have been gaining interest in telemedicine [1]. The idea is to monitor several vital signs parameters by different sensors placed on the body surface or even by implanted sensors and that all signals are collected by a receiver (i.e., mobile phone and PC) to transmit the recordings to a doctor or a caregiver [2]. According to the American Heart Manuscript received December 20, 2014; revised February 15, 2015; accepted March 6, 2015. This work was supported in part by the Qing Lan Project, by the Educational Commission of Guangdong Province, China Project No. 2013KJCX0131, by the Guangdong High-Tech Development Fund No. 2013B010401035, by the National Science Foundation of China under Grant 61401107, and by the Aviation Science Foundation of China under Grant 201401P6001. (Corresponding author: G. Han.) L. Wan is with the Department of Information and Communication Systems, Hohai University, Changzhou 213022, China (e-mail: [email protected]). G. Han is with the Department of Information and Communication Systems, Hohai University, Changzhou 213022, China (e-mail: hanguangjie@ gmail.com). L. Shu is with the Guangdong Petrochemical Equipment Fault Diagnosis Key Laboratory, Guangdong University of Petrochemical Technology, Guangdong 525000, China (e-mail: [email protected]). N. Feng is with the Institute of Electromagnetics and Acoustics, Xiamen University, Xiamen 361005, China (e-mail: [email protected]). Digital Object Identifier 10.1109/JSYST.2015.2411745

Association [3], an estimated 83.6 million American adults have one or more types of cardiovascular diseases (CVDs), and over 2150 Americans die of CVD each day. If the patients can be monitored and diagnosed in real time, many lives could be saved in time. Thus, the real-time communication between patients’ BANs and remote servers will become a very important problem to solve. At present, the emergency healthcare system is not well constructed, particularly in the developing countries. The CVDs can relapse suddenly. Time is the most important thing for the patients. Many people have died without timely treatment. Thus, the construction of the emergency healthcare system aims at saving more lives that are in emergency condition. Recent advances in BANs and mobile technologies have promoted the use of mobile-based health monitoring and alert systems. Such systems aim at providing real-time feedback about patients’ health condition, while alerting in case of healththreatening condition [4]. According to the report, 88% of adults in the United States are cellphone owners [5]. These mobile devices give new opportunities for “pervasive healthcare” [6], and many mobile-based medical monitoring devices have been developed [7], [8]. However, the processing capacity and battery life of the mobile phone limit the resource-intensive application [9]. As a rapid development area, mobile cloud computation (MCC) has became a promising technology. MCC is a special part of cloud computing, which is suitable for the computing on mobile devices. The massive computing, storage, and software services can be executed flexibly using much lower energy consumption in a scalable and virtualized manner. Based on MCC, many applications with large computational complexity can be executed in the mobile devices [10]–[12]. As shown in Fig. 1, based on MCC framework, more accurate offsite personalized medical diagnosis and treatment can be provided. In future communication architecture, the fifth generation (5G) will need a paradigm shift that includes very high carrier frequencies with massive bandwidths, extreme base station (BS) and device densities, and unprecedented numbers of antennas [13]. As a key technology of 5G, massive multiple input multiple output (MIMO) can tremendously improve the performance of wireless networks. Massive MIMO BSs are equipped with a very large number of antennas, possibly tens to hundreds of antennas, and simultaneously communicate with multiple users on the same frequency band [14]. With large-scale distribution of BSs and antennas in massive MIMO system, the cross-location scheme gives another possible

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Fig. 1. Framework for emergency healthcare system based on MCC.

approach to confirm the location of patient, instead of Global Positioning System (GPS). Thus, the direction-of-arrival (DOA) estimation algorithms based on massive MIMO have to be designed. In order to estimate the DOAs for incoherently distributed sources in massive MIMO systems, an approach based on an estimation of signal parameters via rotational invariance technique (ESPRIT) [15] was proposed with low computational complexity [16]. However, the problem of estimating the DOAs of mixed signals (including uncorrelated and coherent signals) is more important in real application, where multipath propagation is usually encountered due to reflections. The conventional DOA estimation algorithms require the signals to be uncorrelated or lowly correlated, which will fail in such multipath propagation environments. The most famous technique is the spatial smoothing technique [17], which partitions the array into several overlapped subarrays, and then, the rank of covariance matrix of array output can be recovered to decorrelate the coherency of signals. Based on the structural properties of the source signal covariance matrix or the noise covariance matrix, many differencing methods were proposed to estimate the DOAs of uncorrelated and coherent signals separately [18]–[20]. However, these algorithms may fail to resolve the coherent signals in some cases, the resulting matrix containing the coherent signals may be canceled completely [21]. Then, several oblique projector-based algorithms were carried out to eliminate the effect of uncorrelated signals [22]–[24]; the cancelation phenomenon is avoided. However, the algorithm proposed in [24] suffered from tremendous computational complexity. In massive MIMO, the BS is equipped with large numbers of antennas; thus, the mutual coupling will decrease the performance of the algorithm in [18]–[24]. Without prior information of the array manifold, the eigenstructure-based

method proposed in [25] can calibrate the array parameters, including the mutual coupling. A novel online mutual coupling compensation algorithm was proposed for uniform linear array (ULA), and the estimated calibration matrix can be embedded within any classical superresolution direction-finding method [26]. Without the spectrum peak search and iteration, a generalized eigenvalues utilizing signal subspace eigenvectors algorithm for ULA was proposed to eliminate the effect of mutual coupling [27]. There is little literature to consider the effect of mutual coupling in multipath environment. Based on the special structure of mutual coupling matrix (MCM) for ULA, the effect of mutual coupling was eliminated both in coherent signals [28] and mixed signals [23]. The main contribution of this paper is stated as follows. First, we propose a system architecture for the emergency healthcare system based on MCC and 5G wireless link. Second, the data of multiple BANs can be processed and monitoring in real time based on cloud server and massive MIMO. Third, we proposed a localization algorithm for critical patients based on multiple BSs’ cooperation using cross-location. The details are elaborated as follows. In this paper, first, we propose a system architecture for the emergency healthcare system based on MCC and 5G wireless link and promote the use of mobile devices in healthcare, leveraging the emerging cloud computing. Thus, the machine learning techniques, the location algorithms, and other applications with tremendous computational complexity can be processed in cloud service. Goals for the 5G edge rate range from 100 Mb/s (easily enough to support high-definition streaming) to as much as 1 Gb/s [13]. The emergency healthcare system can provide patients’ real-time health condition and diagnosis, particularly in emergency condition. Second, based on the massive MIMO

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technique, there will be extreme BS and device densities and unprecedented numbers of antennas [13]. Thus, the data of multiple BANs can be processed and monitoring in real time based on cloud server and massive MIMO. Third, we proposed a localization algorithm for critical patients based on multiple BSs’ cooperation using cross-location. The DOA estimation algorithm in presence of mutual coupling is utilized in BSs, which can locate multiple patients’ locations simultaneously. Based on the special structure of MCM and ESPRIT algorithm, the effect of mutual coupling is eliminated, and the DOAs of uncorrelated signals are obtained. These estimates are then utilized to get the mutual coupling coefficients. The DOAs of coherent signals are estimated by original array using sparse representation by eliminating the effect of mutual coupling and uncorrelated signals. The location algorithm is processed based on MCC, and the multiple patients’ locations can be obtained simultaneously in emergency condition finally. Compared with the existing DOA estimation algorithms for mixed signals [18]–[24], the proposed algorithm has higher estimation accuracy than the other algorithms based on the sparse representation technique. This paper is organized as follows. The system architecture is designed in Section II. The localization algorithm based on MIMO is given in Section III. The proposed localization algorithm is given in Section IV. The simulation results are shown and analyzed in Section V. The conclusions are drawn in Section VI. Notation: In this paper, the operators (·)−1 , (·)T , (·)H , and E{·} denote conjugate, Moore–Penrose inverse, transpose, conjugate transpose, and expectation, respectively. The boldface uppercase letters and boldface lowercase letters denote matrices and column vectors, respectively. The symbol diag{z1 , z2 } stands for a diagonal matrix whose diagonal entries are z1 and z2 . The symbol blkdiag{Z1 , Z2 } stands for a block diagonal matrix whose diagonal entries are matrices Z1 and Z2 . IM stands for the M × M . angle(·) stands for the phase operator, i.e., it returns the phase angle of a complex number, in radians. II. S YSTEM A RCHITECTURE A. System Construction The wearable or even the implantable body sensors have been widely used to monitor the health status of patients or the elderly people, which bridge the physical world and electronic systems [29]. These wearable and implantable body sensors construct the BANs; and the accelerometer, pulse oximetry, electroencephalogram, ECG, electromyogram, blood pressure, and some other physiological signals can be recorded by their corresponding sensors. In the emergency healthcare system, the most prominent physiological phenomena that can affect the person’s physical status are ECG and blood pressure, and other physiological parameters can be utilized as the auxiliary judgment basis [4]. As shown in Fig. 1, the mobile devices such as mobile phones can be wirelessly connected to physiological body sensors to collect data using wireless local area network, Bluetooth, and 5G. These medical monitoring data can be routed to the doctors for detailed evaluation or to a program that has the ability to identify

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any abnormalities in physiological measurement automatically based on intelligent diagnostic mechanism (IDM), and the alerts or the warnings can be sent to the caregivers for timely response. The data transmission is almost through the wireless way. Thus, the 5G network solutions can give possibilities of entirely new ways of patient monitoring [30]. The transmission rate and capacity of the channel will be improved dramatically based on new technology. The real-time data transmission can be accomplished in the future in order to monitor patients efficiently. In recent years, the machine learning techniques [31] have been widely researched, which can be used to compare and recognize the abnormal behaviors from a tremendous amount of physiological data automatically. These machine learning algorithms need to train tremendous data in order to obtain a superior training performance [32]. However, the machine learning algorithms cannot be utilized effectively in mobile device, because these algorithms spend large computational complexity on data iteration training processing. Thus, other alternate strategies have to be proposed in order to execute these algorithms in mobile device efficiently. In MCC, the powerful configuration is not needed in mobile devices, such as high-speed central processing unit or larger memory capacity, since their data and complicated computing modules such as the training processing of machine learning approaches can be stored and processed in the cloud [33], [34]. The seamless integration between BANs and MCC can be accomplished by 5G infrastructure and Internet (bothwirelessandwire). The mobile devices serve as gateways for BANs and access the Internet through 5G link or WiFi to coordinate with application servers or make decisions on the offloading strategy. The mobile devices can offload the healthcare tasks to the cloud accordingly. Once the requests from the mobile devices have been received, the cloud controller will schedule the healthcare task on a virtual machine (VM), and the results are sent to mobile devices. The cloud service framework is based on powerful VM resources in order to provide pervasive healthcare services such as automatic diagnosis and alarm, location-based services, andIDM services [35]. Different users such as patients, doctors, hospitals, and researchers acquire multiple cloud services by various interfaces such as PCs, mobile phones, and other mobile devices. Exchange service application acts as a firewall, which connects Internet and cloud services [36]. All related patients’ data have to pass this firewall to access to cloud services. The main function is to check the authorization actors (doctors, hospitals, etc.), and the illegal access is inadmissible. The assistant function is that it allows sensors to store data locally for preprocessing. In other words, it aggregates or simplifies the data analysis before the transmission to cloud service. All related patients’ ID are registered in the cloud for future research and analysis; thus, doctors or researchers can manage related patients easily. B. Emergency Condition The most important purpose of this system is to deal with the emergency condition. When a person’s health status is in critical condition, he or she, in all probabilities, falls into a coma. The ECG and blood pressure sensors can detect abnormalities;

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Fig. 2. Mobile device localization of massive MIMO systems.

then, these abnormal data will be delivered to the mobile phone. Some preprocessing technologies and applications could be executed in the mobile phone; then, these processed data will be delivered to the Internet through a BS using 5G link. In this emergency condition, the data related to patients are transmitted to cloud service directly, and the authorization is not needed. All the data analysis and processing are completed rapidly in the cloud based on IDM, and the alerts or the warnings can be sent to the caregivers (doctors, hospitals, etc.) for timely response. The doctors can realize the patients’ condition by executing the application of the mobile devices (the large computation applications are offloaded to the cloud based on MCC). The hospitals will send emergency care personnel and ambulance to the place of the incident. The essential problem of this system is the critical patients’ localization. If the actual location is known, the nearest hospital can send the ambulance immediately. The doctors in hospital could prepare for emergency adequately. In traditional framework, GPS can be utilized to confirm the patients’ localization. However, with the rapid development of 5G technology, massive MIMO has been widely studied, which refers to the idea equipping cellular BSs with a very large number of antennas, and has been shown to potentially allow for orders of magnitude improvement in spectral and energy efficiency using relatively simple (linear) processing. Based on the cooperation among multiple BSs, the patients’ localizations can be acquired. III. L OCALIZATION A LGORITHM BASED ON M ASSIVE MIMO A. Localization Framework As shown in Fig. 2, three BSs construct a group, and DOA estimation algorithm is used in each BS. Each patient has an exclusive ID; thus, multiple patients can be distinguished. If the elevation estimations are ignored and only azimuth estimation is considered, then any two of three BSs can give two distinguishing directions. As shown in Fig. 3, the crossover point of two rays from two directions measured by two BSs in the identical plane can be regarded as the patients’ localization. The function of another BS is to ensure the accuracy and stabilization of patients’ localizations. Thus, without GPS, patients’ localizations can be obtained accurately. Then, the patients can be sent to the nearest hospital for the emergency treatment in the shortest time.

Fig. 3. Patients’ localizations based on BSs’ cooperation.

Fig. 4. Signals propagation in multipath.

B. Problem Formulation As aforementioned, we can know that the key technology of patients’ localizations in emergency healthcare system is the DOA estimation algorithm. As shown in Fig. 4, during the course of signals propagation, the patients’ distress signals may suffer from various surfaces such as buildings in urban areas. The resulting multipath propagation will make the signals received by the BS highly correlated or coherent. In addition, the mutual coupling among the elements would inevitably affect the DOA estimation performance, particularly with large number of antennas. Thus, the snapshot data model has to consider the effect of multipath and mutual coupling simultaneously. Consider N narrow far-field signals sent by the patients’ mobile phones impinging on the ULA of BS from different directions. For the sake of simplicity, assume that the BS equipped with smart antennas is ULA with M elements and the distance between adjacent elements is equal to half the wave length. In real multipath propagation condition, the most common circumstances are mixed signals, i.e., the incident signals contain coherent and uncorrelated signals synchronously. There are Nu uncorrelated signals undergoing single-path propagation, and each signal sk (t) comes from direction θk with power σk2 , k = 1, . . . , Nu . The remaining Nc signals are coherent signals undergoing multipath propagation; thus, N = Nu + Nc . Assume that there are P groups in the coherent signals. In the pth group, the coherent signal from θpl corresponds to the lth multipath propagation of sp (t) with power σp2 , and the complex fading coefficient is ρpl , with |ρ pl | ≤ 1, l = 1, . . . , Lp , p = Nu + 1, . . . , Nu + P ; thus, Nc = p Lp , and these coherent signals can be

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expressed as s1,1 (t), . . . , s1,L1 (t), . . . , sP,1 (t), . . . , sP,LP (t), where spl (t) = ρpl sp (t). Assume that the coherent signals in different groups are uncorrelated with each other and the uncorrelated signals. The M × 1 array output vector x(t) can be given by [18] x(t) =

Nu 

Ca(θk )sk (t)+

N u +P

Lp 

Ca(θpl )ρpl sp (t)+n(t)

p=Nu +1 l=1

k=1

= CAu su (t) + CAc Γsc (t) + n(t) = CAEs(t) + n(t)

(1)

where a(θ) = [1, e−jπ sin θ , . . . , e−j(M −1)π sin θ ]T is the M × 1 steering vector; A = [Au , Ac ] is the M × N array manifold matrix; Au = [a(θ1 ), . . . , a(θNu )] is the M × Nu array manifold matrix for uncorrelated signals; Ac = [Ac1 , Ac2 , . . . , AcP ] is the M × Nc array manifold matrix for coherent signals; Acp = [a(θp1 ), a(θp2 ), . . . , a(θpLp )]; su (t) = [s1 (t), s2 (t), . . . , sNu (t)]T and sc (t) = [sNu +1 (t), sNu +2 (t), . . . , sNu +P (t)]T are the Nu × 1 and P × 1 signal vectors of uncorrelated and coherent signals, respectively; s(t) = [sTu (t), sTc (t)]T ; E = blkdiag{INu , Γ}; Γ = blkdiag{ρ1 , ρ2 , . . . , ρP }; n(t) is the M × 1 noise vector with power of each entry equal to σn2 ; and C is the M × M MCM. By assumption, the entry of s(t) and n(t) are zero-mean wide-sense stationary random processes, and the entries of n(t) are uncorrelated with each other and the signals. The mutual coupling coefficient between two elements is inversely proportional to their distance. When the distance is far enough, the value can be approximated as zero. The MCM C is constructed as a banded symmetric Toeplitz matrix, whose first row is c = [1, c1 , c2 , . . . , cM0 , 0, . . . , 0] satisfying 0 < |cM0 | < |cM0 −1 | < · · · < c0 = 1. In general, the relation between the number of elements and the number of mutual coupling coefficients satisfies M > 2M0 , and the number of mutual coupling coefficients is M0 + 1. Thus, the MCM C can be expressed as [25] C = Toeplitz(c) ⎡ 1 ⎢ c1 ⎢ =⎢ .. ⎣ . ⎡ ⎢ ⎢ =⎢ ⎣

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IV. P ROPOSED A LGORITHM A. DOA Estimation of Uncorrelated Signals In general, C is a nonsingular matrix. However, when C is unknown, it is difficult to estimate DOA. No linear combination of steering vectors can result in another steering vector since Au and Ac are Vandermonde matrices. The column of CAE are linearly independent; thus, the rank of CAERs EH AH CH is Nu + P . In order to estimate DOAs of uncorrelated signals, the effect of mutual coupling and coherent signals has to be eliminated or compensated. In order to eliminate the effect of mutual coupling, the first and ¯ = (M −2M0 )-elements ULA are selast M0 elements of the M lected as instrumental elements [37]. Then, the effect of mutual coupling can be eliminated by these instrumental elements. Define a selection matrix F=[0(M −2M0)×M0 IM −2M0 0(M −2M0)×M0 ], the center array output is expressed as ¯ (t) = Fx(t) x = FCAu su (t) + FCAc Γsc (t) + FN1 (t) ¯ c Γsc (t) + N ¯ 1 (t) ¯ u su (t) + CA = CA

¯ 1 (t)= FN1 (t). The M ¯ ×M new MCM can be written as where N ¯ = FC C ⎡ c M0 ⎢ 0 ⎢ =⎢ . ⎣ .. 0

··· c M0 .. .

1 ··· .. .

···

0

c1 1 .. .

cM −2 cM −1 1 c1 ··· 1 c1 c1 .. .. .. . . . 0 ··· ···

··· ··· .. .

cM −1 cM −2 .. .

··· c M0 ··· .. .

1 0

0

⎥ ⎥ ⎥ ⎦

c M0 .. .

··· 0 .. .

··· ··· .. .

0 0 .. .

c M0

···

c1

1

⎤ ⎥ ⎥ ⎥. ⎦ (2)

Based on the array output vector (1), the array covariance matrix can be expressed as

 Rx = E x(t)xH (t) = CAERs EH AH CH + σn2 IM H H H H 2 = CAu Ru AH u C + CAc ΓRc Γ Ac C + σn IM (3)

where Rs = blkdiag{Ru , Rc }, Ru = E{su (t)sH u (t)}, and (t)} is the signal matrix. Due to the assumpRc = E{sc (t)sH c 2 tion, it can be known that Ru = diag{σ12 , σ22 , . . . , σN } and u 2 2 2 , σ , . . . , σ }. Rc = diag{σN Nu +2 Nu +P u +1

··· 1 ··· c M0

c M0 ··· .. . ···

0 c M0 ··· 1

··· ··· .. .

0 0 .. .

···

c M0

⎤ ⎥ ⎥ ⎥. ⎦ (5)

There is an important relationship between MCM and steering vector, which can be represented as ⎡

⎤

(4)

⎤

⎡ ⎤ 1 ⎥ ⎢ ⎢ ⎥ ⎢ ⎥ vk ⎢ ⎥ ⎢ ⎥ ¯ Ca(θ ⎥=C⎢ ⎥ .. k) = ⎢ ⎥ ⎢ ⎣ ⎦ . ⎣ CM0 −1 ⎦ M0 −1 vk C M0 = vkM0 D¯ a(θk ) a(θk ) = c(θk )¯ C1 = cM0 + · · · + c1 vkM0 −1 + vkM0 + c1 vkM0 +1 + · · · + cM0 vk2M0 C2 = cM0 vk + · · · + c1 vkM0 + vkM0 +1 + c1 vkM0 +2 + · · · + cM0 vk2M0 +1 CM0 −1 = cM0 vkM −2M0 −2 + · · · + c1 vkM −M0 −2 + vkM −M0 −1 + c1 vkM −M0 + · · · + cM0 vkM −2 CM0 = cM0 vkM −2M0 −1 + · · · + c1 vkM −M0 −1 + vkM −M0 + c1 vkM −M0 +1 + · · · + cM0 vkM −1 C = cM0 + · · · + c1 vkM0 −1 + vkM0 + c1 vkM0 +1 + · · · + cM0 vk2M0 M0  D =2 cm0 cos (2m0 π cos(θk )d/λ) + 1 (6) C1 C2 .. .

m0 =1

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¯(θk ) is the ideal steering vector of where vk = e−jπ sin θk , a the center array (M0 + 1 ∼ M − M0 elements), and c(θk ) is a scalar function only concerned with the mutual coupling coefficients and the direction of the incident signal θk , which is defined as c(θk ) =

vkM0 D.

(7)

¯ u Bu = A ˆ u and CA ¯ cΓ = ¯ u=A When D = 0, we have CA ¯ ¯ ¯c = ¯ ¯ ¯(θ2 ), . . . , a ¯(θNu )], A a(θ1 ), a Ac Bc Γ = Ac Γ, where Au = [¯ ¯ (θNu +2 ), . . . , a ¯(θN )], and Bu = diag{c(θ1 ), [¯ a(θNu +1 ), a c(θ2 ), . . . , c(θNu )}. Bc = blkdiag{B1 , B2 , . . . , BP }, Bp = ¯ = blkdiag{ρ¯1 , ρ¯2 , diag{c(θp1 ), c(θp2 ), . . . , c(θpLp )}, and Γ . . . , ρ¯P }, where ρ¯p = Bp ρp . Then, (4) can be written in another form as ¯ c Γsc (t) + N ¯ 1 (t) ¯ u su (t) + CA ¯ (t) = CA x ¯ c Γs ¯ 1 (t) ˆ u su (t) + A ¯ c (t) + N =A ¯ 1 (t) ¯ Es(t) ¯ =A +N

(8)



¯ c ], and E ¯ = blkdiag Iu , Γ ¯ = [A ˆ u, A ¯ . The effect of where A mutual coupling is eliminated; then, the coherent signals can be detected by hard thresholding method. The eigenvalue decomposition (EVD) of the covariance ma¯ x of the center array is given by trix R ¯x = R

M 

¯ ¯ ¯H ¯ ¯ ¯H λi ei eH i = U s Σs U s + U n Σn U n

(9)

i=1

where λi and ei stand for the ith eigenvalue and eigenvector, ¯ n stand for the signal subspace and the ¯ s and U respectively. U noise subspace constructed by eigenvectors corresponding to Nu + P larger eigenvalues and M − 2M0 − (Nu + P ) smaller eigenvalues, respectively. Σs and Σn are the diagonal matrices constructed by their corresponding eigenvalues. ¯ s span the Nu + P dimensional Thus, the columns of U ¯ E. ¯ Then, there is a (Nu + P ) × (Nu + P ) full subspace of A rank T, which satisfies [38] ¯s = A ¯ ET. ¯ U

(10)

Based on the standard ESPRIT algorithm, the center array can be divided into two identical arrays. Accordingly, the subspace ¯ s is also divided into two (M − 2M0 − 1) × (Nu + P ) maU trices, which can be expressed as ¯ s (1 : M − 1, :) = a ¯ ¯f =U ¯1 ET U ¯ ¯ ¯ ¯2 ET Ub = Us (2 : M, :) = a

(11) (12)

¯ 1 and A ¯ 2 are the array manifold matrices correspondwhere A ing to the former and the latter of the (M − 2M0 − 1) rows of ¯ respectively. The relationship between A ¯ 1 and the matrix A, ¯ 2 can be expressed as A ¯2 ¯ 1Φ = A A

−jϕ1

−jϕ2

where Φ = diag e ,e According to (13), we have

,...,e

−jϕN

¯ 1 ΦET. ¯ ¯b = A U

(13) 

with ϕk = π sin θ. (14)

¯ b can be trans¯ f and U Then, the relationship between U formed as −1 ¯ † ¯ ¯ =U ¯†U ¯ U f b = T E ΦET

(15)

¯ is a new (Nu + P ) × (Nu + P ) matrix. Referring to where U the derivation in [39], we have the following conclusion:

Λu 0 ¯ † ΦE ¯= E =Λ (16) 0 Λc where Λu = diag{η1 , . . . , ηNu } and Λc = diag{ηNu +1 , . . . , ηNu +P } correspond to the DOAs of the uncorrelated and ¯ is given by coherent signals, respectively. The EVD of U ¯ = G−1 ΛG U

(17)

where G = [g1 , . . . , gNu , gNu +1 , . . . , gNu +P ] are the eigen¯ and their corresponding eigenvalues are vectors of U, [η1 , . . . , ηNu , ηNu +1 , . . . , ηNu +P ]. Gu and Gc are constructed by eigenvectors corresponding to uncorrelated and coherent signals. The property of the diagonal entries of Λ is given by 1 = |η1 | = |η2 | = · · · = |ηNu | > |ηNu +1 | ≥ |ηNu +2 | ≥ · · · ≥ |ηNu +P |.

(18)

Thus, the hard thresholding 1 can be set to distinguish uncorrelated and coherent signals. The details can be found in [39, Appendix 1], which gives the proof of (18) in detail. Based on (18), the DOA estimation of each uncorrelated signal can be obtained from the following equation:   angle(ηk ) (19) θk = arcsin , k = 1, . . . , Nu . π B. Estimation of Mutual Coupling Coefficients Based on the DOA estimation of the uncorrelated signals, the estimations of mutual coupling coefficients can be obtained. The rank of CAERs EH AH CH is Nu + P . The EVD of Rx in (3) is given by Rx =

M 

H H γi ui uH i = U s Σs U s + U n Σn U n

(20)

i=1

where γi and ui stand for the ith eigenvalue and eigenvector, respectively. Us and Un stand for the signal subspace and the noise subspace constructed by eigenvectors u1 , . . . , uNu +P and uNu +P +1 , . . . , uM corresponding to Nu + P larger eigenvalues and M − (Nu + P ) smaller eigenvalues, respectively. The signal subspace spanned by CAc and CAu jointly is orthogonal to the noise subspace; we have UH n Ca(θk ) = 0, UH n CAcp ρp = 0,

k = 1, 2, . . . , Nu

(21)

p = Nu + 1, . . . , Nu + P

(22)

where Un = [uNu +P +1 , . . . , uM ]. Ca(θ) can be expressed as (23), shown at the bottom of the next page [40], where T(θ) is the sum of two M × (M0 + 1) matrices [25].

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Then, an M × M oblique projection operator P can be defined as [24]  −1 H H ⊥ H ⊥ Au C PCAc Γ (29) P = CAu AH u C PCAc Γ CAu

Based on (23) and the estimates of θ1 , . . . , θNu , we have UH n T(θk )c = 0,

k = 1, 2, . . . , Nu

(24)

which is the linear equations of mutual coupling coefficients c. The coefficient matrix can be defined as [37] ⎤ ⎡ H Un T(θ1 ) ⎥ ⎢ .. Q=⎣ (25) ⎦. .

where P⊥ CAc Γ stands for the orthogonal complement space spanned by the column of CAc Γ. However, without priori information of Ac , the oblique projection operator P is difficult to calculate. Then, an alternative method for computation of oblique projector is used here. Referring to the derivation in [22], when Nu + P ≤ M , the oblique projector is expressed in another form as

UH n T(θNu ) Then, (24) can be written as Qc = 0

H † −1 H H † P = CAu (AH u C Rx CAu ) Au C Rx

(26)

H Z = (IM − P)RY (IM − P)H = CAc ΓRc ΓH AH c C . (31)

In order to eliminate the effect of mutual coupling, we can multiply C−1 on both sides of Z, and we can obtain an M × M matrix as   ¯ c = C−1 Z C−1 H = Ac ΓRc ΓH AH . (32) R c

When Nu (M − Nu − P ) ≥ M0 − 1, the least square solution is given by [23]

¯ c is given by The EVD of R

Λcs ¯ c = Ucs ΛU ¯ H = [U U ] R cs cn cs 0

(28)

In order to estimate the DOA of coherent signals, the information of uncorrelated signals has to be eliminated from the covariance matrix of the array output. The DOA estimation of coherent signals can be accomplished by the reduced array. However, the loss of array aperture exists. In order to solve this problem, the original array is used here. The uncorrelated signal steering matrix Au and the MCM C can be constructed by the estimators of θ1 , . . . , θNu and c1 , c2 , . . . , cM0 , respectively. Based on the oblique projection technique, the information of coherent signals can be separated from the uncorrelated signals.

⎢ ⎢ Ca(θ) = ⎢ ⎣ ⎡

c1 1 .. .

··· ··· .. .

cM −1 cM −2 .. .

cM −1

cM −2

···

1

1

⎢ ejτ (θ) ⎢ ⎢ ej2τ (θ) ⎢ =⎢ .. ⎢ . ⎢ ⎣ ej(M −2)τ (θ) ej(M −1)τ (θ) = T(θ)c

(33)

Ucs = [τ 1 , . . . , τ P ] .

⎤⎡ ⎥⎢ ⎥⎢ ⎥⎢ ⎦⎣

H Ucs UH cn

where Ucs and Ucn are constructed by eigenvectors corresponding to P large eigenvalues and M − P small eigenvalues, respectively. The signal subspace spanned by Ac Γ. Λcs and Λcn are diagonal matrices constructed by P large eigenval¯= ues and M − P small eigenvalues, respectively, where Λ ¯ blkdiag {Λcs , Λcn }. The covariance matrix Rc of coherent signals is rank deficient; Nc large eigenvalues corresponding to ¯ c . Only Nc related signals cannot be obtained by the EVD of R P large eigenvalues corresponding to P groups of coherent source can be obtained. Assume that the pth large eigenvalue corresponds to the eigenvector τ p , p = 1, . . . , P ; we have

C. DOA Estimation of Coherent Signals

1 c1 .. .

0 Λcn

H = Ucs Λcs UH cs +Ucn Λcn Ucn

The mutual coupling coefficients estimation is completed.

⎡

(30)

where R = Rx − σn2 IM . Define a new matrix Z, exploit the property of oblique projection, then we have

where Q is a Nu (M − Nu − P ) × (M0 + 1) matrix, and Q = [q1 , q2 , . . . , qNu ]. Due to c(1) = 1, we have ⎡ ⎤ 1 ⎢ c1 ⎥ ⎢ ⎥ (27) Qc = [q1 , q2 , . . . , qM0 +1 ] ⎢ . ⎥ = 0. . ⎣ . ⎦ c M0

[c1 , c2 , . . . , cM0 ] = −[q2 , . . . , qM0 +1 ]† q1 .

7

(34)

⎤

1 ejτ (θ) .. .

⎥ ⎥ ⎥ ⎦

ej(M −1)τ (θ)

ejτ (θ) 1 + ej2τ (θ) jτ (θ) e + ej3τ (θ) .. .

··· ··· ··· .. .

ej(M −2)τ (θ) ej(M −1)τ (θ) 0 .. .

ej(M −3)τ (θ) + ej(M −1)τ (θ) ej(M −2)τ (θ)

··· ···

1 ejτ (θ)

ej(M −1)τ (θ) 0 0 0 0 1

⎤

⎡ ⎥ 1 ⎥ ⎥ ⎢ c1 ⎥⎢ ⎥⎢ .. ⎥⎣ . ⎥ ⎦ cM −1

⎤ ⎥ ⎥ ⎥ ⎦

(23)

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¯ the relationship between Define a P × P nonsingular matrix T; Ucs and Ac Γ can be expressed as ¯ = Ac G 0 Ucs = Ac ΓT

(35)

¯ is a M × P full column matrix. Based on where G0 = ΓT sparse representation, we have ¯c Gcs Ucs = Ac G0 = a

(36)

¯ c = [¯ ¯(θˆ2 ) . . . , a ¯ (θˆK )] conwhere the M × K matrix A a(θˆ1 ), a tains all possible source locations, and the sparsity of each column of Gcs is Nc with the same sparse property, i.e., the support of each column is identical [41]. The kth row of Gcs is nonzero and equal to sk (t), k = 1, . . . , Nc , if source n comes from θˆk , and zero otherwise. Then, (36) can be transformed into an l1 -norm minimum problem as   ¯ c Gcs 2 + λ2 Gcs  . ˆ = arg min Ucs − A (37) H 2,1 F H

It can be known that (37) is a multiple measurement vector problem. However, when the coherent signals are all coherent, i.e., P = 1, the signal subspace Ucs degenerates into an M × 1 vector. Then, (37) is transformed into a single measurement vector problem, which is given by ¯ c Gs Ucs = A

(38)

where Gcs = Gs is the K × 1 vector with the sparsity Nc . Then, the DOAs of coherent signals are acquired finally. D. Summary of the Algorithm and Discussion Here, first, the proposed algorithm is summarized, and then, some discussions are given. For the sake of simplicity, the source number is assumed to be known in the following steps and in simulations in Section V. The steps of the proposed algorithms are summarized as follows: Algorithm 1 DOA Estimation for Mixed Signals 1: Eliminate the effect of mutual coupling by multiplying the ¯ (t) selection F; the output of center array is expressed as x in (8); ¯f ¯ s of the center array into U 2: Partition the signal subspace U ¯ b , and then, based on (15), the matrix U ¯ is obtained; and U ¯ the eigenvalues are obtained, the DOA 3: Perform EVD of U, estimate of uncorrelated signals is obtained by (19); 4: Rewrite Ca(θ) based on (23), and construct linear equations (26) of mutual coupling coefficients. The mutual coupling coefficients estimation is obtained based (28); 5: Perform oblique projector operator based on (29); the coherent signals remain by calculating (31); 6: Take factorization of Ucs in (36) based on sparse representation theory; the DOAs of uncorrelated signals can be obtained based on (37) finally. As aforementioned, the DOAs of uncorrelated and coherent signals are estimated separately. Then, the uncorrelated and

coherent signals coming from the same direction can be distinguished by this two-stage process. Multiple snapshot data are used to construct the covariance matrix of the array output. The signal subspace is utilized to construct the measurement data based on sparse representation model. The proposed algorithm can estimate DOA in parallel, which is suitable for practical application. However, there is a phenomenon called “blind angles,” which means that the array cannot receive any signals from some particular angles when we estimate uncorrelated signals [37]. ¯ x will a(θk ) = 0, R It is caused by an angle of θk making c(θk )¯ be rank deficient one, and θk will be lost in the DOAs of uncorrelated signals. The number of lost angles can be determined by the different numbers of large eigenvalues between Rx and ¯ x . Assume that the number of lost angles is N0 . Then, the R Nu − N0 DOAs of uncorrelated signals can be estimated to obtain MCM C. The oblique projection operator that does not contain the N0 angles is calculated. Fortunately, the covariance information of the corresponding N0 uncorrelated signals is ¯ c , and the DOAs of N0 uncorrelated signals can contained in R be obtained based on (37). The GPS needs that the mobile device searches the GPS signals for location. Three satellites can locate the position of the patient. The GPS signals are too weak in a cloudy or a rainy day, which cannot be searched by the mobile device. The distance from the satellite to the patient is much farther than that from the BS to the patient, which means that the mobile device has to cost much more energy to search the GPS signal than the cost of the communication between the BS and the patient. However, the proposed algorithm can be used even in a cloudy or a rainy day with much low energy cost. In multipath propagation circumstance, the buildings may reflect the distress signal sent by the patient’s mobile device, as shown in Fig. 4. Then, the cross-location method may make mistakes about the location of patients. However, the locations of buildings and other environmental information can be regarded as prior information. The propagation path of distress signal can be confirmed in a timely manner. Then, the crosslocation can be used, as shown in Fig. 3. If this system records the information of the patients in normal life, this can be regarded as auxiliary information to distinguish the patient’s location. The information can be the places where the patient always stays, the positions of residences of the caregivers, etc. When the patient is in emergency condition, the location of the patient can be confirmed quickly. When multiple patients are in emergency condition at the same time, the processing mode is similar to a single patient in emergency condition. The proposed algorithm can locate multiple patients in a certain region at the same time. Then, more ambulances should be sent to the patients’ locations in time. The cloud is divided into private cloud and public cloud [35]. The sensitive data such as the monitoring data of patients, the personal information, and the medical plans are stored and processed in the local private cloud to guarantee security. The testing, maintenance, and updating of the cloud system, which are not sensitive, can be done in public cloud. The parclose between private cloud and public cloud has to be set, which plays the role as the firewall in the cloud. Some nonsensitive data in

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private cloud can be transformed into public cloud through the parclose. The data request and response between private cloud and public cloud has to be permitted by the firewall. In normal life (not in emergency condition), the caregivers can know the location and condition of the patients as well. The basic workflow of the computation offloading process is stated as follows [10]. The workflow starts with the execution of an application followed by checking the user’s offloading permission. If offloading is enabled, then application can connect to the cloud resources and note the available resources by checking the connectivity. If the offloading is favorable, then the computation offloading is performed. For example, the relative or the doctor of the patient wants to know to the patient’s information, they can execute the relative application in mobile phone based on MCC. If the number of patients is small, the doctor can perform all computations of relative application locally based on PC. However, the hospitals need to monitor large numbers of patients, and the computation of data processing is tremendous; thus, the cloud computation is indispensable. When the emergency condition happens, if the resource present in the dashed line box is not available, then the resource must be obtained from other executive applications at this moment. There should be a concept of priority, and the emergency condition has the highest priority. Thus, the resource can be guaranteed available in emergency condition.

Fig. 5.

RMSE of the DOA estimation of uncorrelated signals versus SNR.

Fig. 6.

RMSE of the mutual coupling coefficients estimation versus SNR.

9

V. S IMULATION R ESULTS Here, we will illustrate the performance of the proposed algorithm by simulation. Here, two methods are selected for the performance comparison. One is the method proposed in [38], and the other is the forward and backward spatial smoothing (FBSS) algorithm [17], which estimates the uncorrelated and coherent signals individually. The mutual coupling elimination mechanism is identical to the method in [28]. The orthogonal matching pursuit algorithm [42] is used to solve the problem of (37). The number of array elements is 12. Two hundred Monte Carlo trials are taken from the simulation. The average rootmean-square error (RMSE) is defined as   200  I  1  2 (ˆ αk (m) − αk )2 +(βˆk (m)−βk ) RMSE =  200I m=1 k=1

   RMSEc = 

(39) 200  1 ˆ c1 (m) − c1  × 100% 200 c1  m=1

(40)

ˆ1 (m) are the estimates of αk , βk , where α ˆ k (m), βˆk (m), and c and c1 of the mth Monte Carlo trial, respectively. I is the number of all the uncorrelated signals or all the coherent signals.  ·  is defined as the Frobenius norm. Five far-field narrow-field signals impinge on the ULA from different directions. The mutual coupling coefficients are [1, j0.4290 − 0.5322]. The signals impinging from [50◦ , 75◦ ] are the coherent signals, and their fading coefficients are [1, 0.7211 − j0.4100]. The remaining three signals impinging

from [16◦ , 76◦ , 86◦ ] are the uncorrelated signals. The number of snapshots is L = 500. For the proposed algorithm, a multiresolution grid refinement strategy is adopted for DOA estimation [43]. At first, a rough grid is applied to estimate DOA, and then, a refined grid is applied around the spectrum peak, and DOA estimation is updated until the grid is fine enough. Here, the resolution of the rough grid is selected as 1◦ , and the refined grid is set as 0.1◦ , 0.01◦ , and 0.001◦ , respectively. The RMSE of the DOA estimation of uncorrelated signals versus input SNR is shown in Fig. 5. The RMSE of the mutual coupling coefficients estimation versus input SNR is shown in Fig. 6. The RMSE of the DOA estimation of coherent signals versus input SNR is shown in Fig. 7. It is shown in Fig. 5 that the proposed algorithm outperforms the method in [38] for DOA estimation of uncorrelated signals at low SNR. Although the proposed algorithm loses some array apertures to compensate the effect of mutual coupling, it still has high estimation precision. As shown in Fig. 6, the RMSE of the mutual coupling coefficients estimation of the proposed algorithm is smaller that that of the method in [38] when SNR is less than 6 dB. In Fig. 7, it is shown that the RMSE of coherent signals of the proposed

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Fig. 7.

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RMSE of the DOA estimation of coherent signals versus SNR. Fig. 9. RMSE of the mutual coupling coefficients estimation versus snapshot number.

Fig. 8. RMSE of the DOA estimation of uncorrelated signals versus snapshot number. Fig. 10. number.

algorithm is less than that of the method in [38] when SNR is less than 6 dB. This result is caused by that the estimation precision of the uncorrelated signal steering matrix Au and the MCM C of the proposed algorithm is higher than that of the method in [38], which is used to eliminate their effect in (3). Thus, the proposed algorithm outperforms the method in [38] for coherent signals. In addition, the sparse representation used in the proposed algorithm is another reason that the proposed algorithm achieves an excellent estimation performance. The RMSE of the DOA estimation of uncorrelated signals versus snapshot number is shown in Fig. 8. The RMSE of the mutual coupling coefficients estimation versus snapshot number is shown in Fig. 9. The RMSE of the DOA estimation of coherent signals versus snapshot number is shown in Fig. 10. The SNR is set to be −5 dB. Other simulation conditions are the same as that of the RMSE versus SNR. It is shown in Fig. 8 that the proposed algorithm outperforms the method in [38] for DOA estimation of uncorrelated signals at small snapshot number. As the snapshot number increases, the RMSE of these algorithms gets smaller. For the uncorrelated signals, when the snapshot number approximates to 200, the RMSE of the proposed is smaller than 0.01◦ . However, the method

RMSE of the DOA estimation of coherent signals versus snapshot

proposed in [38] needs about a snapshot number of 700 to achieve the same RMSE. FBSS outperforms the other two algorithms. From Fig. 9, we can know that the RMSE of the proposed algorithm is smaller than that of the method in [38]. This is mainly caused by the high DOA estimation accuracy of the uncorrelated signals, which are used for calculating the mutual coupling coefficients. As shown in Fig. 10, the proposed algorithm achieves high DOA estimation accuracy because of using sparse representation. When the snapshot number approximates to 200, the RMSE of the proposed is smaller than 0.01◦ . However, the method proposed in [38] needs about a snapshot number of 500 to achieve the same RMSE. Thus, the proposed algorithm has better estimation performance. VI. C ONCLUSION In this paper, we have proposed a new system architecture for the emergency healthcare system based on MCC and 5G wireless link. This system can monitor and locate multiple BANs (patients) simultaneously, and huge amount of BAN data can be

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processed in real time. Then, a new location scheme is proposed based on the DOA estimation in the scenario that uncorrelated and coherent signals coexist with unknown mutual coupling. The method estimates the uncorrelated and coherent signals in parallel. The sparse representation is used for estimating the DOAs of coherent signals in order to improve the estimation precision. With the help of multiple BSs’ cooperation, the patients’ locations can be determined based on cross-location with high DOA estimation precision in emergency condition. In future work, other source models should be considered for DOA estimation. In addition, the beamforming method of the massive MIMO system is an excellent research area as well. R EFERENCES [1] X. Lin, R. Lu, X. Shen, Y. Nemoto, and N. Kato, “SAGE: A strong privacy-preserving scheme against global eavesdropping for ehealth systems,” IEEE J. Sel. Areas Commun., vol. 27, no. 4, pp. 365–378, May 2009. [2] M. M. Baig and H. Gholamhosseini, “Smart health monitoring systems: An overview of design and modeling,” J. Med. Syst., vol. 37, no. 2, pp. 1–14, Feb. 2013. [3] A. S. Go et al., “Heart disease and stroke statistics—2013 update: A report from the American Heart Association,” Circulation, vol. 127, no. 1, pp. e6–e245, Jan. 2013. [4] X. Wang, Q. Gui, B. Liu, Z. Jin, and Y. Chen, “Enabling smart personalized healthcare: A hybrid mobile-cloud approach for ECG telemonitoring,” IEEE J. Biomed. Health Inform., vol. 18, no. 3, pp. 739–745, May 2014. [5] “Nearly half of American adults are smartphone owners,” Pew Research Center, Washington, DC, USA, Rep. Pew Internet & Amer. Life Proj., May 2012. [6] U. Varshney, “Pervasive healthcare,” IEEE Comput., vol. 36, no. 12, pp. 138–140, Dec. 2003. [7] J. Oresko et al., “A wearable smartphone-based platform for real-time cardiovascular disease detection via electrocardiogram processing,” IEEE Trans. Inf. Technol. Biomed., vol. 14, no. 3, pp. 734–740, May 2010. [8] A. Pantelopoulos and N. G. Bourbakis, “A survey on wearable sensorbased systems for health monitoring and prognosis,” IEEE Trans. Syst., Man, Cybern. C, Appl. Rev., vol. 40, no. 1, pp. 1–12, Jan. 2010. [9] X. Wang, Q. Gui, B. Liu, Y. Chen, and Z. Jin, “Leveraging mobile cloud for telemedicine: A performance study in medical monitoring,” in Proc. 39th Northeast Bioeng. Conf., vol. 40, no. 1, pp. 1–12, Jan. 2010. [10] H. T. Dinh, C. Lee, D. Niyato, and P. Wang, “A survey of mobile cloud computing: Architecture, applications, and approaches,” Wireless Commun. Mobile Comput., vol. 13, no. 18, pp. 1587–1611, Dec. 2013. [11] M. Shiraz, A. Gani, R. Hafeez Khokhar, and R. Buyya, “A review on distributed application processing frameworks in smart mobile devices for mobile cloud computing,” IEEE Commun. Surveys Tuts., vol. 15, no. 3, pp. 1294–1313, 3rd Quart. 2013. [12] S. Abolfazli, Z. Sanaei, A. Gani, and R. Buyya, “Cloud-based augmentation for mobile devices: Motivation, taxonomies, and open challenges,” IEEE Commun. Surveys & Tuts., vol. 16, no. 1, pp. 337–368, 1st Quart. 2014. [13] J. G. Andrews et al., “What will 5G be?” IEEE J. Sel. Areas Commun., vol. 32, no. 6, pp. 1065–1082, Jun. 2014. [14] L. Lu, G. Y. Li, A. L. Swindlehurst, A. Ashikhmin, and R. Zhang, “An overview of massive MIMO: Benefits and challenges,” IEEE J. Sel. Topics Signal Process., vol. 8, no. 5, pp. 742–758, Oct. 2014. [15] R. Roy, A. Paulraj, and T. Kailath, “ESPRIT—A subspace rotation approach to estimation of parameters of cisoids in noise,” IEEE Trans. Acoust., Speech, Signal Process., vol. 34, no. 5, pp. 1340–1342, Oct. 1986. [16] A. Hu, T. Lv, H. Gao, Z. Zhang, and S. Yang, “An ESPRIT-based approach for 2-D localization of incoherently distributed sources in massive MIMO systems,” IEEE J. Sel. Topics Signal Process., vol. 8, no. 5, pp. 996–1011, Oct. 2014. [17] T. J. Shan, M. Wax, and T. Kailath, “On spatial smoothing for directionof-arrival estimation of coherent signals,” IEEE Trans. Acoust., Speech, Signal Process., vol. 33, no. 4, pp. 806–811, Apr. 1985. [18] Z. Ye, Y. Zhang, X. Xu, and C. Liu, “Direction of arrival estimation for uncorrelated and coherent signals with uniform linear array,” IET Radar Sonar Navigat., vol. 3, no. 2, pp. 144–154, Apr. 2009.

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[19] Y. Zhang, Z. Ye, and C. Liu, “An efficient DOA estimation method in multipath environment,” Signal Process., vol. 90, no. 2, pp. 707–713, Feb. 2010. [20] F. Liu, J. Wang, C. Sun, and R. Du, “Spatial differencing method for DOA estimation under the coexistence of both uncorrelated and coherent signals,” IEEE Trans. Antennas Propag., vol. 60, no. 4, pp. 2052–2062, Apr. 2012. [21] P. Li, J. Xu, and B. Yu, “An efficient method in array processing for eliminating the signal cancellation phenomena of the differencing method,” Signal Process., vol. 56, no. 3, pp. 305–312, Feb. 1997. [22] X. Xu, Z. Ye, and J. Peng, “Method of direction-of-arrival estimation for uncorrelated, partially correlated and coherent sources,” IET Microw. Antennas Propag., vol. 1, no. 4, pp. 949–954, Aug. 2007. [23] X. Xu, Z. Ye, and Y. Zhang, “DOA estimation for mixed signals in the presence of mutual coupling,” IEEE Trans. Signal Process., vol. 57, no. 9, pp. 3523–3532, Sep. 2009. [24] H. Tao, J. Xin, J. Wang, N. Zheng, and A. Sano, “Two-dimensional direction estimation for a mixture of noncoherent and coherent signals,” IEEE Trans. Signal Process., vol. 63, no. 2, pp. 318–333, Jan. 2015. [25] B. Friedlander and A. J. Weiss, “Direction finding in the presence of mutual coupling,” IEEE Trans. Antennas Propag., vol. 39, no. 3, pp. 273–284, Mar. 1991. [26] F. Sellone and A. Serra, “A novel online mutual coupling compensation algorithm for uniform and linear arrays,” IEEE Trans. Signal Process., vol. 55, no. 2, pp. 560–573, Feb. 2007. [27] Z. Ye, J. Dai, X. Xu and X. Wu, “DOA estimation for uniform linear array with mutual coupling,” IEEE Trans. Aerosp. Electron. Syst., vol. 45, no. 1, pp. 280–288, Jan. 2009. [28] J. Dai and Z. Ye, “Spatial smoothing for direction of arrival estimation of coherent signals in the presence of unknown mutual coupling,” IET Signal Process., vol. 5, no. 4, pp. 418–425, Jul. 2011. [29] M. Chen, S. Gonzalez, A. Vasilakos, H. Cao, and V. C. M. Leung, “Body area networks: A survey,” Mobile Netw. Appl., vol. 16, no. 2, pp. 171–193, Apr. 2011. [30] V. Oleshchuk and R. Fensli, “Remote patient monitoring within a future 5G infrastructure,” Wireless Pers. Commun., vol. 57, no. 3, pp. 431–439, Apr. 2011. [31] C. M. Bishop, Pattern Recognition and Machine Learning. Berlin, Germany: Springer-Verlag, Aug. 2006. [32] Z. Jin, Y. Sun, and A. C. Cheng, “Predicting cardiovascular disease from real-time electrocardiographic monitoring: An adaptive machine learning approach on a cell phone,” in Proc. Int. Conf. IEEE Eng. Med. Biol. Soc., 2009, pp. 6889–6892. [33] N. Fernando, S. Loke, and W. Rahayu, “Mobile cloud computing: A survey,” Future Generation Comput. Syst., vol. 29, no. 1, pp. 84–106, Jan. 2013. [34] E. Lee, E.-K. Lee, M. Gerla, and S. Oh, “Vehicular cloud networking: Architecture and design principles,” IEEE Commun. Mag., vol. 52, no. 2, pp. 148–155, Feb 2014. [35] J. Wan et al., “Cloud-enabled wireless body area networks for pervasive healthcare,” IEEE Netw., vol. 27, no. 5, pp. 56–61, Sep. 2013. [36] C. O. Rolim et al., “A cloud computing solution for patient’s data collection in health care institutions,” in Proc. 2nd eHealth, Telemed., Soc. Med. Conf., St. Maarten, The Netherlands, Feb. 2010, pp. 95–99. [37] Z. Ye and C. Liu, “On the resiliency of MUSIC direction finding against antenna sensor coupling,” IEEE Trans. Antennas Propagat., vol. 56, no. 2, pp. 371–380, Feb. 2008. [38] Z. Ye, Y. Zhang, and X. Xu, “Two-dimensional direction of arrival estimation in the presence of uncorrelated and coherent signals,” IET Signal Process., vol. 3, no. 5, pp. 416–429, Sep. 2009. [39] L. Gan and X. Luo, “Direction-of-arrival estimation for uncorrelated and coherent signals in the presence of multipath propagation,” IET Microw. Antennas Propag., vol. 7, no. 9, pp. 746–753, Jun. 2013. [40] Z. Liu, Z. Huang, F. Wang, and Y. Zhou, “DOA estimation with uniform linear arrays in the presence of mutual coupling via blind calibration,” Signal Process., vol. 89, no. 7, pp. 1446–1456, Jul. 2009. [41] M. M. Hyder and K. Mahata, “Direction-of-arrival estimation using a mixed l2,0 norm approximation,” IEEE Trans. Signal Process., vol. 58, no. 9, pp. 4646–4655, Sep. 2010. [42] J. A. Tropp and A. C. Gilbert, “Signal recovery from random measurements via orthogonal matching pursuit,” IEEE Trans. Inf. Theory, vol. 53, no. 12, pp. 4655–4666, Dec. 2007. [43] D. Malioutov, M. Cetin, and A. S. Willsky, “A sparse signal reconstruction perspective for source location with sensors arrays,” IEEE Trans. Signal Process., vol. 53, no. 8, pp. 3010–3022, Aug. 2005.

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Liangtian Wan received the B.S. degree and the Ph.D. degree in the College of Information and Communication Engineering from Harbin Engineering University, Harbin, China, in 2011 and 2015, respectively. He is currently a Visiting Scholar with Hohai University, Changzhou, China. His research interests include array signal processing and compressed sensing and its applications. Mr. Wan has served as a reviewer of over ten journals.

Guangjie Han (S’03–M’05) received the Ph.D. degree from Northeastern University, Shenyang, China, in 2004. From 2004 to 2006, he was a Product Manager with ZTE Company. In February 2008, he finished his work as a Postdoctoral Researcher with the Department of Computer Science, Chonnam National University, Gwangju, Korea. From October 2010 to 2011, he was a Visiting Research Scholar with Osaka University, Suita, Japan. He is currently a Professor with the Department of Information and Communication System, Hohai University, Changzhou, China. He has authored over 130 papers published in related international conference proceedings and journals and is the holder of 55 patents. His current research interests include sensor networks, computer communications, mobile cloud computing, and multimedia communication and security. Dr. Han is a member of the Association for Computing Machinery. He has served as a Cochair for over 20 international conferences/workshops and as a Technical Program Committee Member of over 70 conferences. He has served on the editorial boards of up to 16 international journals, including the International Journal of Ad Hoc and Ubiquitous Computing, the Journal of Internet Technology, and the KSII Transactions on Internet and Information Systems. He has served as a reviewer of over 50 journals. He had been awarded the ComManTel 2014, ComComAP 2014 and Chinacom 2014 Best Paper Awards.

Lei Shu (M’07) received the Ph.D. degree from the National University of Ireland, Galway, Ireland, in 2010. Until March 2012, he was a Specially Assigned Researcher with the Department of Multimedia Engineering, Graduate School of Information Science and Technology, Osaka University, Suita, Japan. Since October 2012, he has been a Full Professor with Guangdong University of Petrochemical Technology, Maoming, China, where he has been also the Vice Director of the Guangdong Provincial Key Laboratory of Petrochemical Equipment Fault Diagnosis. He is the Founder of the Industrial Security and Wireless Sensor Networks Laboratory. Since 2013, he has been also a Ph.D. Supervisor with Dalian University of Technology, Dalian, China, and a Master Supervisor with Beijing University of Posts and Telecommunications, Beijing, China. He has authored over 200 papers published in related conference proceedings, journals, and books. His current H-index is 18. His research interests include wireless sensor networks, multimedia communication, middleware, security, and fault diagnosis. Dr. Shu is a member of the IEEE Communications Society, the European Alliance for Innovation, and the Association for Computing Machinery. He has served as a Cochair for over 50 various international conferences/workshops, e.g., the IEEE International Wireless Communications and Mobile Computing Conference (IWCMC); the IEEE International Conference on Communications (ICC); the IEEE Symposium on Computers and Communications (ISCC), the IEEE International Conference on Computing, Networking and Communication; and the International Conference on Communications and Networking in China (Chinacom). He also served/will serve as a Symposium Cochair for IWCMC 2012 and ICC 2012; as a General Chair for Chinacom 2014 and the 2015 International Conference on Heterogeneous Networking for Quality, Reliability, Security, and Robustness; as a Steering Chair for the 2015 International Conference on Industrial Networks and Intelligent Systems; and as a Technical Program Committee member of over 150 conferences, including the IEEE International Conference on Distributed Computing in Sensor Systems, the IEEE International Conference on Mobile Ad hoc and Sensor Systems, ICC, Globecom, the IEEE International Conference on Computer Communications and Networks, the IEEE Wireless Communications and Networking Conference, and ISCC. He currently serves as the Editorin-Chief of the European Alliance for Innovation Endorsed Transactions on Industrial Networks and Intelligent Systems and the Associate Editor of a number of renowned international journals. He was a recipient of the 2010 IEEE Global Communications Conference and 2013 IEEE International Conference on Communications Best Paper Awards.

Naixing Feng received the B.S. degree in electronic science and technology and the M.S. degree in micro-electronics and solid-state electronics from Tianjin Polytechnic University, Tianjin, China, in 2010 and 2013, respectively. He is currently working toward the Ph.D. degree in radio physics at Xiamen University, Xiamen, China. His current research interests include computational electromagnetics and acoustics.