Mode Switching for Energy-Efficient Device-to-Device ... - IEEE Xplore

IEEE TRANSACTIONS ON WIRELESS COMMUNICATIONS, VOL. 14, NO. 12, DECEMBER 2015

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Mode Switching for Energy-Efficient Device-to-Device Communications in Cellular Networks Daquan Feng, Guanding Yu, Senior Member, IEEE, Cong Xiong, Yi Yuan-Wu, Geoffrey Ye Li, Fellow, IEEE, Gang Feng, Senior Member, IEEE, and Shaoqian Li, Senior Member, IEEE

Abstract—This paper investigates energy-efficient device-todevice (D2D) communications in cellular networks. We aim to maximize the overall energy-efficiency (EE) of D2D users and regular cellular users (RCUs) while considering the circuit power consumption and the quality-of-service (QoS) requirements for both types of users as well as power constraints. Three transmission modes, namely, dedicated mode, reusing mode, and cellular mode, are considered for D2D users to share spectrum with RCUs. Parametric Dinkelbach method and concave-convex procedure (CCCP) are adopted to transform the original optimization problems into more tractable forms through sequential convex approximations. Then, interior point method is exploited to obtain the optimal solution. Simulation results show that system EE can be improved significantly with the proposed mode switching algorithm compared with the single mode transmission. Besides, it is also shown that the reusing mode is more preferred in the EE based mode switching while it is the dedicated mode in the spectrum-efficiency (SE) based mode switching in most situations. Index Terms—Device-to-Device (D2D) communications, energyefficiency (EE), spectrum sharing, mode switch, concave-convex procedure (CCCP).

I. I NTRODUCTION

T

O satisfy the explosive growth of high-data-rate local mobile traffic and provide better user experience, device-todevice (D2D) communications have been considered as one of Manuscript received September 30, 2014; revised June 1, 2015; accepted July 23, 2015. Date of publication July 31, 2015; date of current version December 8, 2015. This work was supported in part by the National Science Foundation (NSF) under Grants 1247545 and 1405116, the National Basic Research Program of China under Grant 2012CB316003, the National HighTech R&D Program of China under 2014AA01A703, and the NSFC under Grant 61471089. This paper was presented in part at the IEEE GlobalSIP 2014, Atlanta, GA, USA. The associate editor coordinating the review of this paper and approving it for publication was N. C. Sagias. D. Feng is with the State Radio Monitoring Center (SRMC), Beijing 100037, China (e-mail: [email protected]). G. Yu is with the Institute of Information and Communication Engineering, Zhejiang University, Hangzhou 310027, China, and also with Zhejiang Provincial Key Laboratory of Data Storage and Transmission Technology, Hangzhou 310018, China (e-mail: [email protected]). C. Xiong is with the Mediatek USA Inc., Irvine, CA 92606 USA (e-mail: [email protected]). Y. Yuan-Wu is with the Department of Wireless Technology Evolution, Orange Lab Network, Paris, France (e-mail: [email protected]). G. Y. Li is with the School of ECE, Georgia Institute of Technology, Atlanta, GA 30332 USA (e-mail: [email protected]). G. Feng and S. Li are with the National Key Lab on Communications, UESTC, Chengdu 610054, China (e-mail: [email protected]; lsq@uestc. edu.cn). Color versions of one or more of the figures in this paper are available online at http://ieeexplore.ieee.org. Digital Object Identifier 10.1109/TWC.2015.2463280

Fig. 1. Three transmission modes of D2D communications.

the key techniques in the Third Generation Partnership Project (3GPP) Long Term Evolution Advanced (LTE-Advanced) systems [1], [2]. With D2D communications, two proximity users can transmit signal directly without going through the basestation (BS). Thus, many benefits, such as proximity gain, reusing gain, and hop gain, can be obtained to improve system performance [3]. Besides, D2D communications can bring new freedom since potential D2D users may have two additional transmission mode, dedicated mode and reusing mode, to be operated in addition to the traditional cellular mode [4], [5]. Fig. 1 illustrates the three transmission modes for D2D users. Particularly, in the dedicated mode, D2D users transmit data through the D2D direct link by the orthogonal resource to that of regular cellular users (RCUs) and no interference is generated. In the reusing mode, D2D users transmit data through the D2D direct link by reusing the resources of RCUs and thus interference is inevitable; however, the system spectrumefficiency (SE) and user access rate may be increased due to frequency reuse. In the cellular mode, D2D users are treated as RCUs and the standard BS-relaying link is adopted to transmit data. Proper D2D mode selection can improve system performance. In [6], a practical mode switching scheme based on a D2D direct link gain threshold has been suggested to increase system throughput. In [7], user location based mode switching with pathloss compensation power control has been proposed. In [8], mode switch with optimal power control to maximize

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sum-rate has been investigated. In [9], joint mode selection and resource allocation has been investigated, demonstrating that more throughput gains can be achieved if more communication modes are available. Similarly, joint mode selection and spectrum partitioning through dynamic Stackelberg game has been studied in [10]. However, the above work mainly focuses on the system SE while ignoring the energy-efficiency (EE). Due to the increasing power consumption of information and communication technology (ICT) industry and the slow progress in battery technology of user terminals, energyefficient design has become more and more imperative in wireless communications [11]. Energy-efficient D2D communications through auction, non-cooperative and cooperative game based approaches have been investigated in [12]–[15]. In particular, an auction-based resource allocation algorithm has been proposed in [12] to maximize the battery life of D2D terminals while in [13] a non-cooperative game based method has been designed to maximize each user’s EE for both D2D pairs and RCUs. In [14], a reverse iterative combinatorial auction based method has been proposed to improve network throughput and reduce the transmit power consumption of user terminals for D2D downlink resource sharing while in [15], coalition game based method has been presented to maximize the system EE for D2D uplink resource sharing. However, mode switching has not been considered in the aforementioned work. Energy-efficient mode switching has been studied in [16], [17] to reduce the power consumption of user terminals, where exhaustive search and coalitional game based approaches are proposed, respectively. The results in [16], [17] have shown that by taking multiple D2D transmission modes into account, the EE performance of D2D communications can be significantly improved. Nevertheless, the circuit power consumption has been ignored in [16], [17]. According to the result in [18], there exists a tradeoff between circuit power consumption and transmit power consumption for the overall EE. Thus, it is necessary to consider both the circuit power and transmit power when designing energy efficient wireless systems. In brief, most existing work on D2D mode switching focuses on SE while the work on EE of the D2D communications has not considered the mode switching or ignored the circuit power consumption of user terminals. In this paper, we study energy-efficient mode switching to reduce energy consumption of user terminals by considering both transmit and circuit powers. In addition, we guarantee the quality-of-service (QoS) requirements for both D2D pairs and RCUs. We develop novel frameworks to solve the EE optimization problems in the three transmission modes. For EE optimization problem, the objective function is usually non-convex due to the nonlinear fractional form (the ratio of rate to power) [19]. Therefore, we adopt parametric Dinkelbach method [20] to remove the fractional form of the original nonlinear fractional optimization problems for better tractability. After the parametric Dinkelbach transformation, we show that the corresponding subproblems in the dedicate mode and the cellular mode are standard convex optimization problems. The subproblem in the reusing mode is still difficult to solve due to the mutual interference between D2D pairs and RCUs. To deal with this issue, we modify the non-convex problem as difference-of-convex (D.C.) prob-

lem [21]. Then, concave-convex procedure (CCCP)1 [23], [24] along with the classical interior point method is applied to solve the D.C. problem. The main contribution of this paper can be summarized as follows: • We propose a centralized EE optimization framework for D2D communications by joint mode selection, power allocation, and spectrum partitioning, which can serve as a benchmark for other algorithms (e.g., distributed algorithms). • The proposed optimization algorithm in the reusing mode is one of the early attempts to solve the EE optimization problems in interference channels by the combination of Dinkelbach and CCCP method. Beside, we prove the convergence of the proposed EE optimization algorithms in the three different transmission modes. • Through numerical simulation, we validate the effectiveness of the proposed optimal D2D mode switching algorithm. In addition, we compare the performance of EE- and SE-based mode switching schemes and get insightful results: the reusing mode is the preferred mode when EE is concerned while it is the dedicated mode when SE is concerned; less reusing mode is selected for both the optimal EE- and SE-based mode switching when the D2D cluster radius increases. This is helpful to design low computation complexity and low signalling overhead transmission schemes for D2D users in practical systems. The rest of the paper is organized as follows. In Section II, we describe the system model and formulate energy-efficient mode switching optimization problem. In Section III, parametric programming and CCCP are proposed to solve the EE optimization problems in the three transmission modes. Then, we present simulation results in Section IV. Finally, conclusions are drawn in Section V. II. S YSTEM M ODEL AND P ROBLEM F ORMULATION In this section, we first introduce the system model and then formulate the optimization problems. Considering the uplink resource sharing for D2D communications as in Fig. 1, the total spectrum bandwidth for sharing is W Hz. We assume the BS has perfect channel state information (CSI) of all links and can schedule proper resource for users, including power, spectrum, and D2D mode selection according to CSI and the QoS requirements of different users. In a practical system, the CSI of the UE-BS uplink and the BS-UE downlink channels can be obtained at the BS by classical channel estimation methods with the help of training sequences. The CSI of the D2D channel can be estimated when potential D2D transmitters send discovery beacons to the neighbor nodes and the BS can get the CSI when D2D connection request is sent. As suggested in [25], D2D users can estimate the CSI of the interference channels from RCUs to D2D users by measuring the power 1 CCCP is based on the majorization-minimization method [22] and is widely used in statistic and machine learning [23].

FENG et al.: MODE SWITCHING FOR ENERGY-EFFICIENT D2D COMMUNICATIONS IN CELLULAR NETWORKS

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level of the received uplink pilot from the RCUs and then feed back to the BS by the control channels. As in [7], [8], we also consider a basic D2D mode switch scenario where one D2D pair sharing resource with one RCU. Moreover, we take the overall EE of D2D pairs and RCUs, instead of the summation of individual EE as our optimization objective since overall EE can better reflect the energy consumption of all user terminals. Finally, as in [26], we also consider the weighted data rate for the RCU and the D2D pair to prioritize different classes of services.

C. EE Maximization Problem in the Cellular Mode

A. EE Maximization Problem in the Dedicated Mode

Rd min{RdU , RdD }, (3) P hd,B where RdU = βαd W log2 1 + βαdd NW and RdD = (1 − PB gB,d β)αd W log2 1 + (1−β)αd NW denote the achievable data rate of the D2D pair at the uplink and downlink, respectively. Note that in this paper, we mainly concern the battery lifetime of user terminals, thus the power consumption of the BS in the downlink transmission is not taken into account. Thus, the EE optimization problem in the cellular mode can be expressed as

In the dedicated mode, there is no interference between the RCU and the D2D pair. Let gc,B and gd,d denote the channel power gains of the link between RCU and BS and between the D2D pair, respectively, Rc and Rd denote the transmission rates of the RCU and the D2D pair, respectively, Pc and Pd denote the transmit powers of the RCU and the D2D pair, respectively, αc and αd denote the percentage of allocated spectrum resource for RCU and D2D pair, respectively, and ωc and ωd denote the predetermined positive weight for the RCU and the D2D pair, respectively. Mathematically, the EE maximization problem in the dedicated mode can be written as max

αc ,αd ,Pc ,Pd

s.t.

ωc Rc + ωd Rd EED , Pc + Pd + 2P0 αc + αd ≤ 1, Pc gc,B ≥ Rmin Rc αc W log2 1 + c , α NW c Pd gd,d ≥ Rmin Rd αd W log2 1 + d , αd NW max Pc ≤ Pmax c , Pd ≤ Pd ,

In the cellular mode, the D2D pair use the orthogonal resource as in the dedicated mode and thus there is no interference between the RCU and the D2D pair. However, the resource for the D2D pair is divided into two parts since the transmission between the D2D pair is relayed by the BS. Let β and (1 − β) denote the D2D resource partition for the uplink and downlink transmission, respectively, PB denote the transmit power at the BS for the downlink transmission, and gB,d denote the channel power gain between the BS and the D2D receiver. Thus, the end-to-end data rate of the D2D pair can be expressed as

max

αc ,αd ,β,Pc ,Pd ,PB

(1) (1a) (1b) (1c) (1d)

and Rmin denote the minimum data rates for the where Rmin c d and Pmax denote RCU and the D2D pair, respectively, Pmax c d the peak power of the RCU and the D2D pair, respectively, N denotes the power spectrum density of noise, and P0 denotes the circuit power consumption of user terminals.

s.t.

ωc Rc + ωd min{RdU , RdD } , Pc + Pd + 2P0 αc + αd ≤ 1, β < 1 Pc gc,B ≥ Rmin Rc αc W log2 1 + c , αc NW Rd min{RdU , RdD } ≥ Rmin d , max Pc ≤ Pmax , P ≤ P . d c d

EEC

(4) (4a) (4b) (4c) (4d)

Let ηD , ηR , and ηC denote the maximum EE in the dedicated mode, reusing mode and cellular mode, respectively. Thus, the optimal transmission mode for the D2D pair, M ∗ , can be selected as M ∗ = arg

max

{ηD , ηR , ηC }.

M∈{D,R,C}

(5)

In the next section, we will develop frameworks to find ηD , ηR , and ηC .

B. EE Maximization Problem in the Reusing Mode In the reusing mode, D2D pair reuse the spectrum of the RCU. In this case, interference is generated; however, the system SE and user access rate may be increased according to [27]. Let gc,d and gd,B denote the channel power gains of the interference links from the RCU to the D2D receiver and from the D2D transmitter to the BS, respectively. The EE optimization problem in the reusing mode can be expressed as max

Pc ,Pd

s.t.

ωc Rc + ωd Rd , Pc + Pd + 2P0 Pc gc,B Rc W log2 1 + ≥ Rmin c , P g + NW d d,B Pd gd,d Rd W log2 1 + ≥ Rmin d , Pc gc,d + NW max Pc ≤ Pmax c , Pd ≤ Pd .

EER

(2) (2a) (2b) (2c)

III. EE O PTIMIZATIONS IN T HREE T RANSMISSION M ODES In this section, we develop frameworks to solve the EE optimization problems in the three D2D transmission modes. We first introduce parametric programming to make the original fractional EE optimization problems more tractable and then derive the solutions to the transformed subproblems in the three modes one by one. A. Parametric Transformation for Nonlinear Fractional Programming Consider a generalized EE optimization problem for the RCU and the D2D pair to share resource as below maxEE V∈S

ωc Rc + ωd Rd , Pc + Pd + 2P0

(6)

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TABLE I PDA FOR EE O PTIMIZATION P ROGRAMMING

TABLE II EE O PTIMIZATION IN D EDICATED M ODE

where V and S denote the variable vector and the constraint set, respectively. Then, according to [20], the above problem is equivalent to the following problem: max ωc Rc + ωd Rd − λ∗ (Pc + Pd + 2 ∗ P0 ) = 0, (7) V∈S

where λ∗ = maxEE. V∈S

A simple and constructive proof of the above conclusion can be found in [20], where parametric Dinkelbach algorithm (PDA) has also been proposed to obtain the optimal value of V in (6), V ∗ arg maxEE, as shown in Table I.

in the dedicated mode can be obtained. The whole procedure is illustrated in Table II. C. EE Optimization in the Reusing Mode

V∈S

The convergence of the PDA for EE Optimization Algorithm (PEOA) is guaranteed by the fact that λ(k+1) ≥ λ(k) and ee(λ(k+1) ) ≤ ee(λ(k) ) for all k = 0, 1, · · · , and the detailed proof is referred to [20]. From PEOA, it is easy to see the most critical step to solve the EE optimization problem in (6) is to find the optimal value of ee(λ) max {ωc Rc + ωd Rd − λ(Pc + Pd + 2 ∗ P0 )} , V∈S

(8)

for a fixed λ. In the following subsections, we will derive solutions of the subproblem (8) in the three D2D transmission modes. B. EE Optimization in the Dedicated Mode In the dedicated mode, the subproblem in (8) is given by max

αc ,αd ,Pc ,Pd

s.t.

ωc Rc + ωd Rd − λ(Pc + Pd + 2P0 ), (1a), (1b), (1c), (1d).

(9)

Denote VD = [αc , αd , Pc , Pd ] and SD the variable vector and feasible set in (9), respectively. To obtain the optimal solution, we have the following theorem and the proof is given in Appendix A. Theorem: The object function in (9) is concave in VD and the constraint set SD is a convex set. From Theorem 1, the problem in (9) is a standard form of convex optimization problem and the global optimal point can be easily obtained by the classical interior-point method with barrier function. Thus, with the inner loop of interior point method and the outer loop of the PEOA method, the optimal EE

In the reusing mode, the objective function in (2) has less variables than that in (1) since in the reusing mode, both the RCU and the D2D pair use the whole spectrum. However, the power allocation become more complicated due to interference. As in the dedicated mode, we use the parametric method to make (2) more tractable. The transformed subproblem is now given by max

Pc ,Pd

{ωc Rc + ωd Rd − λ(Pc + Pd + 2 ∗ P0 )} ,

s.t. (2a), (2b), (2c).

(10)

In the following, we denote VR = [Pc , Pd ] and SR as the variable vector and the feasible set in (10), respectively. The problem in (10) is NP-hard since its objective function comprises the sum-rate maximization of users in interference channel that has been proved NP-hard in [28]. Thus it is difficult to get the optimum value directly. However, after rearranging the elements in the objective function, we can find that it has a D.C. structure, which can be exploited to derive efficient algorithms [21], [23], [24]. Let f (VR ) denote the objective function in (10), which can be rewritten as f (VR ) = fcave (VR ) + fvex (VR ),

(11)

where fcave (VR ) ωc W log2 (Pc gc,B + Pd gd,B + NW) + ωd W log2 (Pd gd,d + Pc gc,d + NW) and fvex (VR ) −ωc W log2 (Pd gd,B +NW)−ωd W log2 (Pc gc,d +NW)− λ(Pc + Pd + 2 ∗ P0 ). Evidently, fcave (VR ) is a strictly concave function and fvex (VR ) is a strictly convex function on VR , thus (11) is a D.C.


TABLE III CCCP FOR D.C. O PTIMIZATION

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TABLE IV EE O PTIMIZATION IN R EUSING M ODE

function. We can also conclude that SR is a convex set as the constraints in (2a) and (2b) can be reshaped as linear form min Rc W Pc gc,B − 2 − 1 (Pd hd,B + NW) ≥0, (12a) Pd gd,d − 2

Rmin d W

− 1 (Pc hd,c + NW) ≥0.

(12b)

Therefore, the problem (10) can be transformed as maximizing a D.C. objective function under a convex constraint set max { fcave (VR ) + fvex (VR )} .

VR ∈SR

(13)

It can be solved by an optimization approach called D.C. algorithm (DCA) based on a primal-dual conjugate subdifferential method [21]. In [23], [24], DCA is further reduced to CCCP when fvex (VR ) is differentiable through the majorizationminimization method [22]. It is easy to verify that fvex (VR ) in (11) is differentiable and thus CCCP can be used. The main idea of CCCP is to iteratively linearise the convex part of the D.C. objective function by the first order Taylor expansion around the current point. Thus, (13) can be solved by the following sequential convex programming

(k+1) (k) = arg max fcave (VR ) + ∇fvex VR ∗ VR T , (14) VR VR ∈SR

In summary, the EE optimization problem in the reusing mode can be solved with triple-nested loop. The outer loop is the POEA to remove the fractional form of the original nonlinear fractional problem for better tractability, the middle loop is CCCP for sequential convex approximation, and the inner loop is the interior point method for convex optimization. The whole procedure is shown in Table IV. D. EE Optimization in the Cellular Mode Observe that in the objective function of (4), when RdU > RdD ≥ Rmin d , decreasing Pd can always increase EEC without violating the constraints until RdU = RdD . Thus, when the optimal EEC is achieved, it must have Rd = RdU ≤ RdD , which implies that RdD can be removed from (4). Therefore, after the parametric Dinkelbach procedure, the transformed subproblem in the cellular mode can be expressed as max

αc ,αd ,β,Pc ,Pd

s.t.

ωc Rc + ωd Rd − λ(Pc + Pd + 2P0 ), (4a), (4b), (4d),

where VR T denotes the transpose of V R and ∇fvex (VR(k) ) −ωd Wgc,d −ωc Wgd,B −λ, −λ denotes the gradient (k) (k)

Pd hd,B Rd βαd W log2 1 + ≥ Rmin d . βαd NW (15)

(k) of fvex (VR ) at VR(k) = [P(k) c , Pd ]. As (14) is a convex optimization problem and thus can be solved efficiently by the interior point method. The above CCCP can be summarized as in Table III. For the convergence of the CCCP algorithm, we have the following two theorems. Their proofs are given in Appendices B and C, respectively. Theorem 2: The objective function in (13) is strictly mono(k) tonically increase on the generated sequence {VR } by the CCCP Algorithm unless VR(k+1) = VR(k) . (k) Theorem 3: Suppose SR = ∅, then the sequence {VR } converges to the limit point VR(∞) , which is also a stationary point in (13) satisfying the Karush-Kuhn-Tucker (KKT) conditions. The above two theorems imply that at least a local optimal can be achieved by the CCCP.

By introducing an auxiliary variable, αd = αd β, we can see the problem in (15) is just the same as the EE optimization problem in the dedicated mode (9) by substituting αd with αd

and without considering the constraint of β. It means for a given D2D resource partition, β, the optimal EE in the cellular mode can be found by the algorithm designed for the dedicated mode. Actually, β is related to the transmit power from the BS to the D2D receiver, PB , which is determined by many factors. Thus, β is also hard to be determined. Therefore, we consider equivalent resource partition (β = 0.5) as in [15], [17], [29] for the uplink and downlink transmission and the BS can coordinate its transmit power to guarantee RdD ≥ RdU . Thus, the EE optimization in the cellular mode can be solved by the algorithm proposed for the dedicated mode by simply replacing αd with 12 αd for Rd in (1). Here, we omit the detailed procedures.

ln 2(Pc gc,d +NW)

ln 2(Pd gd,B +NW)

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After the optimal values of the EE maximization problems in the three D2D transmission modes are obtained, the best mode can be easily selected and will be the one has the highest EE value. Therefore, the optimization problem of mode switching to maximize the overall EE of the RCU and the D2D pair in (5) can be derived.

TABLE V S IMULATION PARAMETERS

E. Mode Switching for Multiple D2D Pairs and RCUs In the above, we have developed frameworks to solve the EE optimization problem in the three D2D transmission modes for a single RCU and D2D pair scenario. In this subsection, we discuss the possibility of the derived frameworks to solve the EE optimization problem in the multiple D2D pairs and RCUs scenario. It is easy to verify that when the D2D pairs are in the dedicated and cellular modes, the algorithms for the single RCU and D2D pair can be directly adopted. However, in the reusing mode, the optimization problem becomes much more complicated since interference is hard to be controlled. In this case, the jointly RCUs and D2D pairs matching to reuse the spectrum and power allocation is needed, which may cause significant signaling overhead. To reduce the signaling overhead of D2D communications, it is suggested in [30] that the BS reserves dedicated resources for D2D users in a long time scale and then executes micro-adjustment in a short time scale. Thus, the proposed algorithms in the above can be adopted: At the first, the optimization framework in the dedicated mode can be used to allocate the reserved resource for the D2D pairs, then the micro-adjustment like one D2D pair sharing the same resource of a RCU can be solved by the optimization framework in the reusing mode. Note that in case of multiple D2D pairs sharing the same resource [31], the proposed algorithm in the reusing mode can also be adopted as the problem is still the D. C. form, and finally the optimal user matching can be obtained by the Hungarian algorithm as in [27]. IV. N UMERICAL R ESULTS In this section, we evaluate the performance of the proposed optimal EE-based D2D mode switching algorithms. For comparison, we take the optimal SE-based D2D mode switching algorithms [32] for a reference. The SE optimization problem can be formulated in the same way of the EE optimization problem by simply replacing the “power terms” in the denominators of EE metric with the “spectrum terms”. Thus, optimal SE can be obtained in a similar way of the proposed optimal EE algorithms. In fact, SE optimization is much easier than EE optimization. First, in the dedicated and cellular modes, there is no interference between the RCU and the D2D pair and the optimal SE is achieved at their peak power, thus we only need to find the optimal spectrum allocation. Secondly, in the reusing mode, both the RCU and the D2D pair use the whole spectral resource and the denominator in the optimization problem becomes a constant, thus parametric Dinkelbach transformation is not necessary and only CCCP is enough to solve the problem. Therefore, we omit the detailed procedures of optimal SE-based mode switching.

Fig. 2. Average EE versus D2D cluster radius for optimally- and randomlyselected modes as well as three single transmission modes when ωc = ωd = 1.

In our simulations, we consider a single cell scenario. The RCU is uniformly distributed in the cell with radius of R while the D2D pair is uniformly distributed in a randomly located cluster with radius r. Let L denote the distance between the cluster center and the BS, then L2 , is randomly distributed in (0, R2 ). Both fast fading and shadowing for the channels are considered, and the channel power gain between transmitter i −n , where c0 and n are and receiver j is calculated as c0 ζi,j βi,j di,j the pathloss constant and exponent, respectively, ζi,j and βi,j are the fast fading gain with exponential distribution and slow fading gain with log-normal distribution, respectively, and di,j is the distance between the transmitter i and the receiver j. The simulation parameters are summarized in Table V. Note that in the following figures, to have a fair comparison for different cases, both EE and throughput are the actual overall EE and throughput of RCU and D2D pair without considering their weights, however, the optimal mode selection is based on the weighted EE or SE metrics. Fig. 2 compares the EE performance of the proposed optimal mode switching scheme (denoted as “Optimal mode”) with the three single mode transmission schemes and the random mode


Fig. 3. Average EE and throughput versus D2D cluster radius under optimal EE-based and SE-based mode switching when ωc = 1.

switching scheme (denoted as “Random mode”) where the BS randomly selects one of the three modes for the D2D pair and RCU to transmit signal. From the figure, the EE performance of the optimal mode is better than that of any single mode and the random mode. The reason is twofold. Firstly, mode switching can bring more communication freedom as we have indicated in the introduction part. Secondly, in the optimal mode, the BS always selects the mode that achieves the highest EE while it cannot be guaranteed in the random mode. It is also shown that the EE performance of the random mode is better than that of the dedicated mode and the cellular mode but worse than that of the reusing mode. It implies that we can always choose the reuse mode as the default transmission scheme to achieve a relatively high EE performance if we want to reduce the computation complexity and the signalling overhead of mode switching. Note that when the D2D cluster radius becomes bigger, the performance gap between the reusing mode and the optimal mode also becomes bigger since the channel gain of D2D link declines and thus more interference is generated to the RCU. Fig. 3 illustrates the overall EE and throughput performance of the optimal EE-based and SE-based mode switchings for D2D clusters with different radii as well as user priority weights. From Fig. 3(a), the EE performance of the EE-based mode switching is much better than that of optimal SE-based mode switching as we can imagine. The SE performance of the EE-based mode switching is not so good as that of the SE-based

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Fig. 4. Percentage of the selected optimal mode versus D2D cluster radius under optimal EE-based and SE-based mode switching when ωc = ωd = 1: (a) L random located and L2 ∼ U(0, 5002 ), (b) L = 100 m.

mode switching, but they are very close. In general, there exists a tradeoff between EE and SE as in [33]. It is also seen that both the EE and SE decrease with the increase of the radius of the D2D cluster. That is because the channel gain of D2D link declines when the cluster radius increases. Since the communication link of the D2D pair is usually better than that of the RCU due to the short communication distance, D2D pair has a stronger influence on the overall performance. Thus, from Fig. 3 when the weight of D2D pair is lower (ωd = 0.25), both EE and SE performance is also lower. There is also a trend that the impact of the weight of D2D pair becomes bigger as the D2D cluster radius increases. It is because when the D2D cluster radius is small, the channel gain of D2D link becomes better and resource is already favour on the D2D pair. Thus, there is litter difference on the performance. Besides, from Fig. 3(a), the impact of the D2D pair weight on EE performance for the EE-based mode switching is bigger than that for the SE-based mode switching. Similar phenomenon is also shown in Fig. 3(b) for the two mode switching methods on SE performance. It is easy to understand as the optimal mode selection in EE-based mode switching and SE-based mode switching is based on the weighted EE and SE metrics, respectively. Fig. 4 demonstrates the percentage of the three D2D transmission modes being selected as the optimal mode under

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often been selected since we only consider the power consumption of user terminals and ignore the power consumption of BS for downlink relay transmission. Comparing Fig. 4(a) and Fig. 4(b), the dedicated and cellular modes are more often selected for the SE-based mode switching when the D2D pair is close to the BS since the channel power gain between the D2D users and the BS becomes better. In Fig. 5, the effect of the peak power constraints for the D2D pair on the proposed algorithms is illustrated. From the figure, compared with the SE-based mode switching, the EE-based mode switching is less sensitive to the change of the peak power since the power allocation in EE optimization is much more conservative. From the figure, better SE and EE are achieved for both the SE-based and EE-based mode switching when the peak power of the D2D pair becomes higher because bigger peak power increases the feasibility area in the optimization problems. Note that the EE performance of the SE-based mode switching becomes worse as the peak power increases since the peak power is always used in the dedicated and cellular modes for the SE-based mode switching. V. C ONCLUSION

Fig. 5. Average EE and throughput for different locations of D2D cluster center under optimal EE-based and SE-based mode switching when ωc = ωd = 1.

optimal EE-based and optimal SE-based mode switching for D2D clusters with different radii. The D2D cluster center in Fig. 4(a) is randomly distributed in the cell while the D2D cluster center in Fig. 4(b) is fixed at a circle with a distance, 100 m, from the BS. From the figure, the most preferred mode in the optimal EE-based mode switching is the reusing mode while it is the dedicated mode in the optimal SE-based mode switching. The reason is that, power allocation for users with the optimal EE-based mode switching has already limited interference among users and thus the reusing mode turns out to be more energy-efficient in most situations while the dedicated mode is preferred for users with the optimal SE-based mode switching since no interference is generated and peak power is always used by the RCU and the D2D pair to achieve the highest throughput. It is also shown that as the D2D cluster radius increases, less reusing mode is selected for both the optimal EEbased and SE-based mode switching because the channel gain of D2D link declines when the cluster radius increases and more transmit power is required for the D2D pair to guarantee its QoS requirement which causes more interference to the RCU. Note that in the optimal SE-based mode switching, the cellular mode is rarely selected since the D2D transmission in this mode is relayed and thus consumed more spectrum resource and is less spectral efficient; however, for users with the optimal EE-based mode switching, the cellular mode is much more

In this paper, we have investigated energy-efficient D2D mode switching to reduce the energy consumption of user terminals and developed frameworks to solve the optimization problems in the three D2D transmission modes. In the dedicated and cellular modes, we first transfer the optimization problem into sequential convex subproblems by parametric Dinkelbach method and then use interior point method to solve the convex subproblems. In reusing model, the optimization problem is more complicated due to interference between the RCU and the D2D pair, thus we have adopted CCCP through iteratively linearization of the non-convex part of the objective function to solve D.C. subproblems after the parametric Dinkelbach transformation. It is shown by simulation that EE of user terminals with the proposed energy-efficient mode switching can be improved significantly. The dedicated mode is preferred when user capacity is concerned while the reusing mode is preferred for EE. Besides, for both the EE and SE mode switching, more dedicated mode will be selected when the radius of D2D pair increases. A PPENDIX A P ROOF OF T HEOREM 1 Proof: Defining f (x) log2 (1 + ax), it is easy to verify that f (x) is a concave function in x when a ≥ 0, x ≥ 0. Thus, the perspective function of f (x), g(t, x) tf (x/t) = t log2 1 + axt , is concave in [t, x] when a ≥ 0, t ≥ 0, x ≥ 0 since perspective operation preserves convexity [34]. Both Rc and Rd are with the same type of g(t, x), thus they are concave in VD . Since the objective function in (9) is a positive weighted sum of Rc and Rd plus an affine function, the convexity is not changed. Therefore, the objective function in (9) is a concave function in VD . Observe that the constraints in (1a) and (1d) are linear and thus convex. Besides, the constraints in (1b) and (1c) are min the Rmin c -superlevel set and Rd -superlevel set of Rc and Rd ,


respectively. As a superlevel set of a concave function is convex [34], the constraints in (1b) and (1c) are convex. Therefore, we can conclude that the constraint set, SD , is a convex set. A PPENDIX B P ROOF OF T HEOREM 2 (k+1)

(k)

= VR , we have Proof: When VR f VR(k+1) fcave VR(k+1) + fvex VR(k+1) T > fcave VR(k+1) +fvex VR(k) +∇fvex VR(k) VR(k+1)−VR(k) T ≥ fcave VR(k) + ∇fvex VR(k) VR(k) − VR(k+1) T + fvex VR(k) + ∇fvex VR(k) VR(k+1) − VR(k) (k) (k) (k) = fcave VR + fvex VR f VR , where the first strict inequality is from the fact that the firstorder Taylor approximation of a convex function is always a global underestimation of the function (k+1) (k) (k) (k+1) (k) T > fvex VR + ∇fvex VR VR fvex VR − VR ; and the first inequality is from

VR(k+1) = arg max fcave (VR ) + ∇fvex VR(k) VR T . VR ∈SR

Thus, Theorem 2 is proved. A PPENDIX C P ROOF OF T HEOREM 3 Proof: As all the constraints in (13) are linear inequality, it is easy to verify that the feasible set SR is closed and bounded as long as SR = ∅, which implies SR is compact when SR = ∅. Then, following Remark 7 and Theorem 3 in [24], we can conclude that the sequence {VR(k) } converges to the limit point (∞) (k+1) − VRk = 0. VR , as limk→∞ VR From [34], the Slater’s constraint qualification is reduced to feasibility when all the constraints are linear. Thus, when SR = ∅, the strong duality holds for (14) and its Lagrange dual problem. Therefore, the KKT conditions are satisfied for the convex optimization problem in (14). Besides, we have also proved that the limit point, VR(∞) , is a fixed point. Thus, when applying the KKT conditions for (14) at the limit point and (k+1) (k) (∞) replacing VR and VR with VR , the KKT conditions for (13) also hold at VR(∞) . Therefore, we can conclude that VR(∞) is a stationary point of the D.C. problem in (13). R EFERENCES [1] 3GPP, “3rd Generation Partnership Project; Technical Specification Group Services and System Aspects; Study on Architecture Enhancements to Support Proximity-Based Services (ProSe),” 3rd Generation Partnership Project, Sophia Antipolis, France, Tech. Rep. TR23.703 V0.4.1, Release 12, Dec. 2013. [Online]. Available: http://www.3gpp.org/DynaReport/ 23703.htm [2] A. Asadi, Q. Wang, and V. Mancuso, “A survey on device-to-device communication in cellular networks,” IEEE Commun. Surveys Tuts., vol. 16, no. 4, pp. 1801–1819, 4th Quart. 2014.

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[3] G. Fodor and N. Reider, “A distributed power control scheme for cellular network assisted d2d communications,” in Proc. IEEE GLOBECOM, Dec. 2011, pp. 1–6. [4] K. Doppler, C.-H. Yu, C. Ribeiro, and P. Jänis, “Mode selection for device-to-device communication underlaying an LTE-advanced network,” in Proc. IEEE WCNC, Apr. 2010, pp. 1–6. [5] D.-Q. Feng et al., “Device-to-device communications in cellular networks,” IEEE Commun. Mag., vol. 52, no. 4, pp. 49–55, Apr. 2014. [6] S. Hakola, T. Chen, J. Lehtomaki, and T. Koskela, “Device-to-Device (D2D) communication in cellular network-performance analysis of optimum and practical communication mode selection,” in Proc. IEEE WCNC, Apr. 2010, pp. 1–6. [7] Z. Liu, T. Peng, S. Xiang, and W. Wang, “Mode selection for Deviceto-Device (D2D) communication under lte-advanced networks,” in Proc. IEEE ICC, Jun. 2012, pp. 5563–5567. [8] C.-H. Yu, K. Doppler, C. Ribeiro, and O. Tirkkonen, “Resource sharing optimization for device-to-device communication underlaying cellular networks,” IEEE Trans. Wireless Commun., vol. 10, no. 8, pp. 2752–2763, Aug. 2011. [9] G.-D. Yu et al., “Joint mode selection and resource allocation for deviceto-device communications,” IEEE Trans. Commun., vol. 62, no. 11, pp. 3814–3824, Nov. 2014. [10] K. Zhu and E. Hossain, “Joint mode selection and spectrum partitioning for device-to-device communication: A dynamic stackelberg game,” IEEE Trans. Wireless Commun., vol. 14, no. 3, pp. 1406–1420, Mar. 2015. [11] D.-Q. Feng et al., “A survey of energy-efficient wireless communications,” IEEE Commun. Surveys Tuts., vol. 15, no. 1, pp. 167–178, 1st Quart. 2013. [12] F. Wang et al., “Energy-aware resource allocation for device-todevice underlay communication,” in Proc. IEEE ICC, Jun. 2013, pp. 6076–6080. [13] Z. Zhou, M. Dong, K. Ota, J. Wu, and T. Sato, “Distributed interferenceaware energy-efficient resource allocation for device-to-device communications underlaying cellular networks,” in Proc. IEEE GLOBECOM, Dec. 2014, pp. 1–6. [14] C. Xu et al., “Efficiency resource allocation for device-to-device underlay communication systems: A reverse iterative combinatorial auction based approach,” IEEE J. Sel. Areas Commun., vol. 31, no. 9, pp. 348–358, Sep. 2013. [15] D. Wu, J. Wang, R. Hu, Y. Cai, and L. Zhou, “Energy-efficient resource sharing for mobile device-to-device multimedia communications,” IEEE Trans. Veh. Technol., vol. 63, no. 5, pp. 2093–2103, Jun. 2014. [16] M. Jung, K. Hwang, and S. Choi, “Joint mode selection and power allocation scheme for power-efficient Device-to-Device (D2D) communication,” in Proc. IEEE 75th VTC Spring, May 2012, pp. 1–5. [17] K. Akkarajitsakul, P. Phunchongharn, E. Hossain, and V. K. Bhargava, “Mode selection for energy-efficient D2D communications in LTEadvanced networks: A coalitional game approach,” in Proc. IEEE ICCS, Nov. 2012, pp. 488–492. [18] G. Miao, N. Himayat, Y. Li, and A. Swami, “Cross-layer optimization for energy-efficient wireless communications: A survey,” Wireless Commun. Mobile Comput., vol. 9, no. 4, pp. 529–542, Apr. 2009. [19] C. Isheden, Z. Chong, E. Jorswieck, and G. Fettweis, “Framework for link-level energy efficiency optimization with informed transmitter,” IEEE Trans. Wireless Commun., vol. 11, no. 8, pp. 2946–2957, Aug. 2012. [20] W. Dinkelbach, “On nonlinear fractional programming,” Manage. Sci., vol. 13, no. 7, pp. 492–498, 1967. [21] P. D. Tao and L. T. H. An, “The DC (difference of convex functions) programming and DCA revisited with DC models of real world nonconvex optimization problems,” Ann. Oper. Res., vol. 133, no. 1–4, pp. 23–46, Jan. 2005. [22] D. R. Hunter and K. Lange, “A tutorial on MM algorithms,” Amer. Statist., vol. 58, no. 1, pp. 30–37, 2004. [23] A. L. Yuille and A. Rangarajan, “The Concave-Convex Procedure (CCCP),” in Proc. Adv. Neural Inf. Process. Syst., 2001, pp. 1033–1040. [24] G. R. Lanckriet and B. K. Sriperumbudur, “On the convergence of the concave-convex procedure,” in Proc. Adv. Neural Inf. Process. Syst., 2009, pp. 1759–1767. [25] P. Jänis et al., “Interference-aware resource allocation for device-to-device radio underlaying cellular networks,” in Proc. IEEE 69th VTC Spring, Apr. 2009, pp. 1–5. [26] C. Xiong, L. Lu, and G. Y. Li, “Energy-efficient spectrum access in cognitive radios,” IEEE J. Sel. Areas Commun., vol. 32, no. 3, pp. 550–562, Mar. 2014.

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[27] D.-Q. Feng et al., “Device-to-device communications underlaying cellular networks,” IEEE Trans. Commun., vol. 61, no. 8, pp. 3541–3551, Aug. 2013. [28] Z.-Q. Luo and S. Zhang, “Dynamic spectrum management: Complexity and duality,” IEEE J. Sel. Topics Signal Process., vol. 2, no. 1, pp. 57–73, Feb. 2008. [29] C.-P. Chien, Y.-C. Chen, and H.-Y. Hsieh, “Exploiting spatial reuse gain through joint mode selection and resource allocation for underlay deviceto-device communications,” in Proc. 15th Int. Symp. WPMC, Sep. 2012, pp. 80–84. [30] G. Fodor et al., “Design aspects of network assisted device-to-device communications,” IEEE Commun. Mag., vol. 50, no. 3, pp. 170–177, Mar. 2012. [31] R. Yin, G. Yu, H. Zhang, Z. Zhang, and G. Y. Li, “Pricing-based interference coordination for d2d communications in cellular networks,” IEEE Trans. Wireless Commun., vol. 14, no. 3, pp. 1519–1532, Mar. 2015. [32] D.-Q. Feng et al., “Mode switching for device-to-device communications in cellular networks,” in Proc. IEEE GlobalSIP, Dec. 2014, pp. 1291–1295. [33] C. Xiong, G. Li, S. Zhang, Y. Chen, and S. Xu, “Energy- and spectralefficiency tradeoff in downlink OFDMA networks,” IEEE Trans. Wireless Commun., vol. 10, no. 11, pp. 3874–3886, Nov. 2011. [34] S. P. Boyd and L. Vandenberghe, Convex Optimization. Cambridge, U.K.: Cambridge Univ. Press, 2004.

Daquan Feng received the B.S.E degree from Henan University, Kaifeng, China, in 2008 and the Ph.D. degree from the National Key Laboratory of Science and Technology on Communications, UESTC, Chengdu, China in 2015. He had been a visiting student in the School of Electrical and Computer Engineering, Georgia Institute of Technology, Atlanta, GA, USA, from 2011 to 2014. After graduation, he joined the State Radio Monitoring Center (SRMC), Beijing, China. His research interests include deviceto-device communications, full-duplex communications, energy-efficient wireless network design, and heterogeneous network.

Guanding Yu (S’05–M’07–SM’13) received the B.E. and Ph.D. degrees in communication engineering from Zhejiang University, Hangzhou, China, in 2001 and 2006, respectively. After that, he joined Zhejiang University, where he is an Associate Professor in the Department of Information and Electronic Engineering. From 2013 to 2015, he was also a Visiting Professor with the School of Electrical and Computer Engineering, Georgia Institute of Technology, Atlanta, GA, USA. His research interests include energy-efficient wireless communication system design, device-to-device communications, and small cell technique. Dr. Yu served as the General Co-chair for the CRNet 2010 Workshop, a TPC Co-chair for the Green Communications and Computing Symposium of Chinacom 2013, and a TPC member for various academic conferences. He serves as a guest editor of IEEE Communications Magazine special issue on full-duplex communications and an editor of IEEE J OURNAL ON S ELECTED A REAS IN C OMMUNICATIONS Series on Green Communications and Networking.

Cong Xiong received the B.S.E. degree from the School of Telecommunication Engineering and the M.S.E. degree from the School of Information and Communication Engineering, Beijing University of Posts and Telecommunications, Beijing, China, in 2007 and 2010, respectively, and the Ph.D. degree from the School of Electrical and Computer Engineering, Georgia Institute of Technology, Atlanta, GA, USA, in 2014. He joined Mediatek USA Inc. as Member of Technical Staff in 2014 and then became Staff Engineer in 2015. His research interests include MIMO, cooperative communications, energy-efficient wireless network design, and energy-efficient cross-layer optimization.

Yi Yuan-Wu received the B.Eng. degree in electronic from Huazhong University, Wuhan, China, in 1982, the M.Sc. degree in 1983, and the Ph.D. in 1987 in signal processing and telecommunication from Rennes University, Rennes, France. Between 1983 and 1987, she conducted Ph.D. research on the systems of digital diffusion to mobile (DAB) at CCETT of Rennes. Between 1989 and 1991, she worked at Thomson-LGT on video diffusion networks. In 1992, she joined FranceTelecom R&D (today’s Orange Labs), Issy-les-Moulineaux France. Her working domains are the signal detection, channel estimation, broadcast channel and D2D communications underlaying cellular network for the mobile systems. Between 1992 and 1996, she was in charge of studying and specifying a CDMA Modem with variable flows. Between 1996 and 2000, she studied DECT and PHS radio link systems for the 64 kbits/s and 2 Mbits/s data transmission. Between 2000 and 2003, she was in charge of studying the UMTS-TDD physical layer performance. Between 2003 and 2005, she worked on the MC-CDMA system within the European Matrice and 4More projects. Now, she is working for the European Metis and Sharing projects on multiuser MIMO and the D2D subjects. She is a Senior Research Expert of Orange Labs since 2004.

Geoffrey Ye Li (S’93–M’95–SM’97–F’06) received the B.S.E. and M.S.E. degrees from the Department of Wireless Engineering, Nanjing Institute of Technology, Nanjing, China, in 1983 and 1986, respectively, and the Ph.D. degree in 1994 from the Department of Electrical Engineering, Auburn University, AL, USA. He was a Teaching Assistant and then a Lecturer with Southeast University, Nanjing, China, from 1986 to 1991, a Research and Teaching Assistant with Auburn University, from 1991 to 1994, and a Post-Doctoral Research Associate with the University of Maryland, College Park, MD, USA, from 1994 to 1996. He was with AT&T Labs—Research, Red Bank, NJ, USA, as a Senior and then a Principal Technical Staff Member from 1996 to 2000. Since 2000, he has been with the School of Electrical and Computer Engineering, Georgia Institute of Technology as an Associate Professor and then a Full Professor. He is also holding the Cheung Kong Scholar title at the University of Electronic Science and Technology of China since March 2006. His general research interests include statistical signal processing and communications, with emphasis on cross-layer optimization for spectral- and energy-efficient networks, cognitive radios and opportunistic spectrum access, and practical issues in LTE systems. In these areas, he has published over 300 refereed journal and conference papers in addition to 26 granted patents. His publications have been cited over 21,000 times and he has been recognized as the World’s Most Influential Scientific Mind, also known as a Highly-Cited Researcher, by Thomson Reuters. He has been involved in editorial activities for about 20 technical journals for the IEEE Communications and Signal Processing Societies. He organized and chaired many international conferences, including technical program vice-chair of IEEE ICC’03, technical program cochair of IEEE SPAWC’11, general chair of IEEE GlobalSIP’14 and technical program co-chair of IEEE VTC’16 (Spring). He has been awarded IEEE Fellow for his contributions to signal processing for wireless communications since 2006. He won 2010 Stephen O. Rice Prize Paper Award and 2013 WTC Wireless Recognition Award from the IEEE Communications Society. He also received 2013 James Evans Avant Garde Award and 2014 Jack Neubauer Memorial Award from the IEEE Vehicular Technology Society. Recently, he won 2015 Distinguished Faculty Achievement Award from the School of Electrical and Computer Engineering, Georgia Tech.


Gang Feng (M’01–SM’06) received the B.Eng. and M.Eng. degrees in electronic engineering from the University of Electronic Science and Technology of China (UESTC), Chengdu, China, in 1986 and 1989, respectively, and the Ph.D. degree in information engineering from The Chinese University of Hong Kong, Shating, Hong Kong, in 1998. He joined the School of Electric and Electronic Engineering, Nanyang Technological University in December 2000 as an Assistant Professor and was promoted to Associate Professor in October 2005. At present, he is a Professor with the National Laboratory of Communications, University of Electronic Science and Technology of China. Dr. Feng has extensive research experience and has published widely in computer networking and wireless networking research. His research interests include resource management in wireless networks, wireless network coding, energy efficient wireless networking, etc.

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Shaoqian Li (AM’04–SM’12) received the M.E. degree in information and communication systems from the University of Electronic Science and Technology of China (UESTC), Chengdu, China, in 1984 and the B.Eng. degree in wireless engineering from Northwest Institute of Telecommunication Engineering (currently Xidian University), Xi’an, China, in 1981. He joined the University of Electronic Science and Technology of China in 1984. Currently, he is Professor at UESTC and Director of the National Key Laboratory of Science and Technology on Communications (formerly the National Key Lab of Communications), UESTC. His research interest include mobile and wireless communications, anti-jamming techniques for wireless communications, frequency-hopping techniques, cognitive radio and spectrum sharing technologies. He has co-authored two books and published more than 40 referred journal papers and 200 conference papers. He is inventor of more than 20 issued patents and more than 50 filed Chinese Patents. He has received the 2nd class National Award for Technological Invention of China in 2008 and the 2nd class National Award for Science and Technology Progress of China in 2007. He received the Innovation and Excellent Award for contribution to National Information Industrial, by Ministry of Industry and Information Technology of P. R. China and Excellent Award for personal contribution to National HighTech Development Program (863) of P. R. China from 2001–2005. Since the 1990s, he has served many domestic academic conferences as Senior member of the China Institute of Communications (CIC) and the Chinese Institute of Electronics (CIE). And since 2001, he has served the International Conference on Communications, Circuits and Systems (ICCCAS), in 2007, 2008, 2009, 2010, and 2012, as Chair of Steering Committee, and/or General (co-) Chair, respectively. He also served as consultant Member of the Board of Communications and Information Systems of Academic Degrees committee of the State Council, P. R. China, and Member of the expert group of Key Project on Next-Generation Mobile Broadband Wireless Communications Systems towards 2020, Ministry of Industry and Information Technology of P. R. China.