Improve The Channel Performance Of Wireless ... - IEEE Xplore

Improve The Channel Performance Of Wireless Multimedia Sensor Network Using MIMO Properties Arjav A. Bavarva

Dr. Preetida V. Jani

Department of Electronics & Communication RK University Rjakot, India [email protected]

Department of Electronics & Telecommunication Sardar Patel Institute of Technology Mumbai, India [email protected]

Abstract—Wireless Multimedia Sensor Networks (WMSN) are designed to transmit audio and video streams, still images and scalar data. Multimedia transmission over wireless sensor network has many killer applications like, video surveillance system, object tracking, telemedicine, theft control system and traffic monitoring. Researchers are facing many challenges such as higher data rate, lower energy consumption, reliability, signal detection and estimation, uncertainty in network topology, Quality of Service and security related issues to accomplish various applications of WMSN. Multiple Node Multiple Input Multiple Output (MNMIMO) properties have been used to improve system performance in terms of data rate, energy consumption and channel capacity. In this paper, mathematical model is presented to calculate and analyze various parameters of the network like, SNR, channel capacity and data rate. Simulation results demonstrate the effect of various channel models on output in deep fading environment and proposed channel model performs better for WMSN compare to non-adaptive system in terms of Bit Error Rate. Keywords—Multi-node MIMO, Wireless multimedia sensor networks, Bit Error Rate performance

I.

INTRODUCTION

Wireless Multimedia Sensor Network (WMSN) has been introduced to provide multimedia services such as audio and video streams along with scalar information such as temperature, pressure and humidity. Typical Wireless Sensor Network (WSN) is made up of tiny sensor nodes which consume very low power. Sensor nodes can be configured as end device (sensors are connected with it) and router. Network coordinator gathers information coming from all sensor nodes. WMSN has a capability not only to retrieve multimedia real time data but also store and process it which is originated from heterogeneous sources. Advance research trend in CMOS and embedded system design can construct multimedia sensor nodes with effective features like small size nodes with multiple antennas, lower energy

c 978-1-4799-8792-4/15/$31.00 2015 IEEE

consumption and high processing speed. Crossbow and Intel have developed stargate and Imote2 respectively that supports wireless multimedia applications. Typical multimedia sensor node consists of CPU, memory, power unit, sensors and communication system [1]. WMSN architecture can be designed with various methods: (a) Single-tier flat, homogeneous sensors, distributed processing, centralized storage; (b) Single-tier clustered, heterogeneous sensors, centralized processing, centralized storage and (c) Multitier, heterogeneous sensors, distributed processing, distributed storage [2]. Heterogeneous sensors mean cluster contains various types of sensors for instance, scalar and multimedia sensors. Single-tier represents cluster structure while multitier corresponds to cluster under another cluster head. High bandwidth requirement, energy efficiency, delivery of real time data, delay tolerance and rate of frame loss are main challenges that influence the design of WMSN. WMSN requires adaptive energy-efficient MAC protocol [3] for better quality of service. Multiple Input and Multiple Output (MIMO) is one of the effective wireless techniques that reduces transmission power consumption and increases reliability. A typical MIMO system has multiple antennas at transmitter and/or receiver end. MIMO based scheduling algorithm has been presented in [4] that adopts diverse transmission strategies based on channel condition and node type. Multi node MIMO system improves ergodic capacity of WMSN [9]. To implement multiple antennas on mote is challenging task. However, MIMO technology can be applied in WMSN by organizing multiple sensor nodes into a MIMO array called cooperative MIMO (CMIMO). It has been demonstrated that CMIMO significantly improves energy efficiency [5] and channel capacity in WSN. CMIMO achieves energy efficiency by adaptation of the antenna elements and powers in the inter cluster communication phase, proper selection of master cluster head (MCHs) and slave cluster head (SCHs), and using a crosslayer MIMO aware route selection algorithm for multi-hop

277

୩

communication [6]. CMIMO increases end to end delay thus not preferred in live streaming multimedia applications.

୲୭୲ୟ୪ ൌ ෍ ୩ ୩ ൅ ୱ୩

(2)

୩ୀଵ

In this paper, multi-node MIMO system model for single-tier clustered, homogeneous sensors, centralized processing, centralized storage WMSN has been proposed. Mathematical model demonstrate noise factor analysis, pre coding weight calculation and channel capacity of the system. Simulation and result analysis has been done by varying different parameters of system model. II.

SYSTEM MODEL

Centralized processing, single-tire clustered base WMSN is considered with homogeneous sensors. Cluster head is equipped with R antennas. Each sensor node has T antennas where T൑ R. Figure 1 shows system model of the cluster where s1, s2, . . . , sk are sensor nodes within the cluster. h1, h2, . . . , hk are channel matrices and ns1, ns2, . . . , nsk are noise added in channels. Wireless channel is accessed by multiple sensor nodes and it has to be taken care by Medium Access Control (MAC) layer protocols. Here system model represented in correspondence with physical layer. ys1, ys2, . . . , ysk are received components at cluster head, transmitted by sensor nodes s1, s2, . . . , sk respectively. xk is information transmitted by sensor node sk. ୩ is channel matrix.

III.

CHANNEL MATRIX AND NOISE ANALYSIS

System model contains homogeneous sensor nodes. Fig. 2 gives detail idea about various channel components and channel matrix. In this figure k = 2, T = 2 and R = 4 has been chosen for th th the simplicity. ୱ୩ ୧୨ is channel component of i receiver and j ୱ୩ transmitter antenna sensor node sk. ୧୨ represents weight factor of ith receiver and jth transmitter antenna for sensor node sk. Using system model in figure 1 and 2, generalized channel matrix can be expressed as ୱ୩ ‫ ۍ‬ଵଵ ୱ୩ ୩ ൌ ‫ێ‬ଶଵ ‫ڭ ێ‬ ‫ۏ‬ୱ୩ ୰ଵ

ୱ୩ ଵଶ ୱ୩ ଶଶ ‫ڭ‬ ୱ୩ ୰ଶ

ୱ୩ ‫ ڮ‬ଵ୲ ‫ې‬ ୱ୩ ‫ ڮ‬ଶ୲ ‫ۑ‬ ‫ڰ‬ ‫ۑ ڭ‬ ‫ ڮ‬ୱ୩ ୰୲ ‫ے‬

(3)

Now, ୩

୲୭୲ୟ୪ ൌ ෍ ୱ୩ ୩ୀଵ

ୱ୩ ‫ ۍ‬ଵୱ୩ ‫ې‬ ‫ێ‬ଶ ‫ۑ‬ ‫ ێ‬ୱ୩ ‫ۑ‬ ୱ୩ = ‫ێ‬ଷ ‫ۑ‬ ‫ ێ‬Ǥ ‫ۑ‬ ‫ ێ‬Ǥ ‫ۑ‬ ‫ۏ‬ୱ୩ ୰ ‫ے‬

(4)

Where, k = 1, 2, . . . , k Now, error estimation can be represented as ୱ୩‫כ‬ E (|nrsk|2) = ıଶୱ୩ and ൫ୱ୩ ୰ Ǥ ୲ ൯ ൌ Ͳ

ıǤ୲୭୲ୟ୪ at receiver antenna is Gaussian white noise as no noise correlation at different receiver antennas and can be written as,

Fig. 1. System model

ୱ୩ ൌ ୩ ୩ ൅ ୱ୩ Where, k = 1, 2, . . . , k

ୱଵ + ୱଶ + . . . . + ୱ୩ = ୲୭୲ୟ୪ ୱଵ + ୱଶ + . . . . + ୱ୩ = ୲୭୲ୟ୪

278

(5)

(1)

E (୧ Ǥ ୨‫ = ) כ‬0 ଵୱ୩ ‫ۍ‬ଶୱ୩ ‫ې‬ ୱ୩‫כ‬ ‫ ێ‬Ǥ ‫ ۑ‬ൣଵୱ୩‫ כ‬ୱ୩‫כ‬ ൫തୱ୩ Ǥ തୌ ଶ ǥ୲ ൧ ୱ୩ ൯ ൌ ‫ ێ‬Ǥ ‫ۑ‬ ‫ ۏ‬୰ୱ୩ ‫ے‬ ൌ ɐଶୱ୩

2015 International Conference on Advances in Computing, Communications and Informatics (ICACCI)

(6)

Here, I is identity matrix. It is spatio temporally white noise because noise is uncorrelated across antennas and time. In channel matrix shown in (3), numbers of rows are more compare to numbers of columns because T൑ R. This is called thin matrix and hence more equations are available than unknowns. Because of this, there might not be an exact solution. From (1), unwanted portion of the received signal is defined as, ԡୱ୩ െ ୩ ୩ ԡ2 ൌ ሺതୱ୩ െ ୩ ത୩ ሻ୘ ሺതୱ୩ െ ୩ ത୩ ሻ

In (7), T represents transpose matrix of mentioned bracket not transmitting antennas. Differentiate this equation with respect to x. Noise is uniformly distributed so, estimated signal can be written as ഥ ୱ୩ ො ൌ ሺ୩୘ ୩ ሻିଵ ୩୘

(8)

(7)

Fig. 2. Channel components of MIMO system for two nodes

Equation (8) can be applied as T൑ R. This is the approximate solution that minimizes the least square error. If ୩ ՜ Ͳ, from (8), noise becomes infinite. This is the disadvantage of Zero Forcing Receiver (ZFR) technique. To remove the disadvantage of zero forcing technique, MIMO-MMSE (Minimum Mean Squared Error) with Bayesian approach can be used. In practical scenario, transmit symbol vectors are random and hence received symbol vectors are also random in nature. Linear estimator, ො ൌ ୘ തୱ୩

(9)

Here, ത is Linear Minimum Mean Squares Error (LMMSE) estimator and it is a function of co-variance matrix of ୱ୩ and cross covariance matrix of ୩ and ୱ୩ .

LMMSE estimator for kth sensor node,

ො୩ ൌ

ୢ୩ ‫כ‬ തୱ୩ ɐଶ୬୩

(10)

ୢ୩ is transmitted power of kth sensor node. MIMO-MMSE does not result in noise enhancement. Thus, it is superior compare to ZFR. At high SNR, ୢ୩ ൒ ɐଶ୬୩ and MIMO-LMMSE estimator reduces to ZFR. At low SNR, ୢ୩ ൑ ɐଶ୬୩ and MIMO-LMMSE estimator reduces to matched filter. Eigen values are valid only for separated matrix while Singular Value Decomposition (SVD) does not have such restrictions. So,


279

channel matrix Hk will be manipulated in form of ρɂ ୌ matrix (T൑ R).

particular BER, value of energy per bit to noise (Eb/N0) will increase with .

As shown in fig. 2, there are total T x k x R parallel channels which creates interference to each other. By using proper pre coding method, transmitting symbol should be pre coded such that noise interference effect can be reduced. This is called decoupling of MIMO channel or Spatial multiplexing. Total channel capacity, ୩

୲୭୲ୟ୪ ൌ ෍ ୱ୩ ୩ୀଵ

Here, ୘

ୱଵ ൌ ෍ ଶ ቆͳ ൅ ୧ୀ଴ ୩

୧ ɐଶ୧ ቇ ɐଶ୬

୘

୲୭୲ୟ୪ ൌ ෍ ෍ ଶ ቆͳ ൅ ୩ୀଵ ୧ୀ଴

୧ ɐଶ୧ ቇ ɐଶ୬

(11)

E ሼԡୱ୩ െ ୩ ୩ ԡଶ ሽ

IV.

PERFORMANCE EVALUATION

Performance of WMSN has been performed on the basis of data rate and energy consumption via simulation. Here, MIMO adopts transmission mode and power on per – bit basis. We consider 40 nodes that are randomly deployed in a square of 500 m with operating frequency 2.4 GHz. In this type of environment, neighbor nodes produce inter symbol interference and it is considered in simulation work. We simulate the model with Rayleigh fading channel as hardly ever nodes have direct line of site in WMSN. System model includes Quadrature Phase Shift Keying Orthogonal Space Time Block Code with pi phase offset modulation and demodulation. In simulation, ୱ୩ is simulated according to (4) as a Gaussian noise and it is strictly related to noise variance parameter ( ). is a function of symbol period, sample time, signal power and channel environment. In proposed model, imperfect channel state information is detected by receiver. A. Simulation results and analysis Variation in , changes the value of Bit Error Rate (BER) which is shown in fig. 3 and 4 for 3x3 and 2x4 MN-MIMO model, respectively. Value of BER should be decided on the basis of application and quality index. In most of the multimedia applications, BER value varies from 10-3 to 10-6. Our target is to achieve 10-4 BER. Simulation results have been obtained using 3x3 and 2x4 MIMO system. In both the cases, to achieve

280

Fig. 3. BER performance for 3x3 MN-MIMO system

For = 1, 3x3 MIMO requires 20.5 dB and 2x4 requires only 16 dB which is better compare to [13]. Fig. 5 gives an idea about channel condition during the simulation. It could be helpful to do detail analysis of the channel characteristics e.g. delay spread, time offset and magnitude.

Fig. 4. BER performance for 2x4 MN-MIMO system


Fig. 5. Rayleigh fading channel scenario

Change in Modulation Order (MO) impacts on energy consumption. MO represents numbers of bits per symbol and it varies with modulation scheme. As shown in fig. 6, higher value of MO improves bit error rate in 3x3 and 2x4 MN-MIMO models. Here, 2 bits per symbol and 4 bits per symbol have been chosen as MO 1 and MO 2, respectively.

Fig. 6. Effect of Modulation Order (MO) on 2x4 and 3x3 MN-MIMO model

V. CONCLUSION AND FUTURE WORK

Fig. 7. Adaptive and Non adaptive MN-MIMO models

To get better result, simulation has been made adaptive for 3x3 and 2x4 MN-MIMO. According to channel condition, MO parameter has been set. Here, for adaptive simulation, 2, 4, 8 and 16 bits per symbol has been taken. Simulation result shown in fig. 7 demonstrates that adaptive 2x4 MN-MIMO performs better compare to non adaptive 3x3 and 2x4 MN-MIMO.

WMSNs have many attractive applications and it comes along with many challenges. Multimedia information transmission over wireless channel with better Quality of Service (QoS) is one of the major challenges as battery is major constrained in WMSN. In WMSN, video streams are encoded and extremely susceptible to loss of quality due to bit errors hence, BER is very sensitive. This paper proposed 2x4 and 3x3 MN-MIMO wireless channel designs. Mathematical model is derived to do the detail analysis of channel components and noise. Simulation results indicate that 2x4 performs better than 3x3 MN-MIMO model in terms of energy consumption. 10-4 BER is targeted for better QoS. For the same fading environment and noise variance, 2x4 MN-MIMO system saves Eb/N0 of 4.5 dB compare to 3x3 MN-MIMO system. Energy consumption is directly proportional to noise variance. Result has been improved by increasing modulation order. Adaptive 2x4 MN-MIMO performs well and achieves the target at 8 dB which is even 3 dB less energy consumption compare to non adaptive 2x4 MN-MIMO. In future, various modulation techniques can be compared and need to find out the most suitable with MN-MIMO. Modulation matrix (M) can be correlated with (1) mathematically.

Acknowledgment This research work was supported by RK University. I would like to express my thanks to Maharshi Randani for his support.

References [1]

Akyildiz, T. Melodia and K. R. Chowdury, “Wireless Multimedia Sensor Networks: Applications and Testbeds”, Proceeding of IEEE, Vol. 96, Issue 10, pp. 1588-1605, October 2008


281

[2]

I. Akyildiz, T. Melodia and K. R. Chowdury, “Wireless multimedia sensor networks: a survey”, Wireless sensor networking, IEEE Wireless Communications, pp. 32-39, December 2007 [3] N. Saxena, A. Roy and J. Shin, “Dynamic duty cycle and adaptive contention window based QoS-MAC protocol for wireless multimedia sensor networks”, Computer networks, Vol. 52, pp. 2532-2542, 2008 [4] S. Chu, X. Wang and Y. Yang, “Adaptive Scheduling in MIMO-Based Heterogeneous Ad Hoc Networks”, IEEE Transactions on mobile computing, May 2014, Vol. 13, No. 5, pp. 964-978 [5] S.K. Jayaweera, “Virtual MIMO-based cooperative communication for energy constrained wireless sensor networks”, IEEE Transactions on Wireless Communications, pp. 984–989, 2006 [6] M. Krunz, M. Z. Siam and D. N. Nguyen, “Clustering and power management for virtual MIMO communications in wireless sensor networks”, Ad Hoc Networks, Elsevier, Vol. 11, pp. 1571-1587, 2013 [7] T. Ma, M. Hempel and D. Peng, “A Survey of Energy-Efficient Compression and Communication Techniques for Multimedia in Resource Constrained Systems”, IEEE Communications survey & tutorials, Vol. 15, No. 3, pp. 963-972, Third quarter 2013 [8] M. Bocus, J. Coon, C. Canagarajah, J. McGeehan, Armour and A. Doufexi, “Resource Allocation for OFDMA-Based Cognitive Radio Networks with Application to H.264 Scalable Video Transmission”, EURASIP Journals on Wireless Communications and Networking, 2011, Online, available on < http://jwcn.eurasipjournals.com/content/2011/1/245673 > [9] D. Gong, M. Zhao and Y. Yang, “A multi-channel cooperative MIMO MAC protocol for clustered wireless sensor networks”, Journal of Parallel and Distributed Computing , vol.74, no. 11, pp. 3098-3114, 2014. [10] H. Soy, O. Ozdermir, M. Bayrak, R. Hamila and N. Al-Dhahir, “Decentralized multiuser diversity with opportunistic packet transmission in MIMO wireless snesor networks”, International Journal of Electronics and Communication, Elsevier, Vol. 67, pp. 910-925, 2013 [11] B. Ahmad, E. Dutkiewicz and X. Huang, “BER-delay characteristics analysis of IEEE 802.15.4 wireless sensor networks with cooperative MIMO”, 2007 IEEE Asia-Pacific Conference on Applied Electromagnetics, APACE 2007, Malaysia, 2007.

282
