International Journal of Interactive Mobile Technologies (iJIM) - Volume 3, Special Issue 2: "Technical Basics", October 2009 DYNAMIC RESOURCE MANAGEMENT IN MC-CDMA BASED CELLULAR WIRELESS NETWORKS Dynamic Resource Management in MC-CDMA Based Cellular Wireless Networks doi:10.3991/ijim.v3s2.890 Dhananjay Kumar, C. Chellappan, B. Bala Jeevitha Vani Department of Information Technology, Anna University, Chennai, India Abstract—Most of the multimedia and Internet services today are asymmetric in nature and require high data rate support. Allocating equal band width in both uplink and downlink is not a prudent solution, as most of the time user requirements are either greater in uplink or downlink. The Multi Carrier Code Division Multiple Access (MC-CDMA) system with Time Division Duplex (TDD) mode can easily meet this requirement by dynamically declaring traffic direction in the TDD slot and adaptively allocating the sub channels. In this paper, we propose an adaptive slot and sub carrier allocation algorithm that can be independently im- plemented in each cell of a mobile communication network. Our analytical model is the generalization of two cell con- cept to represent a multi cell model. Based on the two cell concept, four cases of interference pattern have been con- sidered and simulated separately in presence of Additive White Gaussian Noise (AWGN) and Rayleigh Channel. The simulated result suggests the requirement of approximately 9dB of Signal to Noise Ratio (SNR) to maintain Bit Error Rate (BER) below 10-3. We also analyze the average delay incurred by the proposed algorithm in allocating resources. Index Terms—BER, TDD, MC-CDMA, SNR, Delay I. INTRODUCTION The explosive growth in multimedia and Internet ap- plications necessitates the development of next genera- tion mobile networks. The multi-carrier communication systems, because of their inherent property, have become a natural choice for Fourth Generation (4G) systems [1- 2]. The conventional code-division multiple-access (CDMA) technique used in third generation system faces serious limitations by fading problem in channel, causing Inter Symbol Interference (ISI), and it requires advanced signal processing algorithms to contain it [3]. The MC- CDMA employing multiple stream of data channel can combat channel dispersion, hence ISI, thereby increasing system capability to accommodate higher number of users and different multimedia services. The efficiency in the multiple access techniques be- comes an important issue as the demand for high data rate to support Internet applications continue to grow. Many applications demand more bandwidth either in forward channel or in reverse channel, and popular applications like web browsing are biased towards downlink. Allocat- ing equal resources in both uplink and downlink becomes a bottleneck for the system as uplink remains underuti- lized while downlink gets strained. The TDD mode not only allows efficient utilization of spectrum but also flexible resource allocation that can easily support this asymmetric traffic. Further the MC-CDMA shows higher efficiency by adopting adaptive modulation techniques like M-ary Quadrature Amplitude Modulation (M-QAM) [4-5]. By dynamically allocating subcarriers and an adap- tive slot management the system can meet the large dy- namic resource requirements of a real-time multimedia application in the Internet. Due to multiple reflections from various objects, the electromagnetic waves reach the receiver along different paths of varying lengths. The interaction between these waves causes multipath fading at a specific location, and also decreases the strength of the radio wave with respect to the increase in distance from transmitter. Even when line-of-sight exists, random phenomenon, such as, reflec- tion, refraction, diffraction, and scattering weaken the signal and result in multiple reception of the signal with different delays and strength (Fig.1). When the mobile station is stationary it may receive signals with constant strength, but when it moves the received signal fluctuates. In other words, due to multipath phenomenon the channel frequency response becomes irregular as shown in Fig.2 Single carrier systems are simple but can’t support high data rates efficiently in fading channels. The multi carrier system can not only improve performance in bad channel conditions, but also support high data rates while maximizing the capacity of the system. The other advan- tages of multi carrier system include flexible resource allocation to support different bit rate services. Figure 1. Multipath propagation Figure 2. Multipath channel frequency response iJIM – Volume 3, Special Issue 2: Technical Basics, October 2009 51 http://dx.doi.org/10.3991/ijim.v3s2.890� DYNAMIC RESOURCE MANAGEMENT IN MC-CDMA BASED CELLULAR WIRELESS NETWORKS Figure 3. MC-CDMA Transmitter Figure 4. MC-CDMA Receiver The CDMA based systems are inherently interference limited. The presence of every user affects the SNR, and hence data supported on a particular channel. The chan- nel state information [6-7] can be estimated periodically with the help of a pilot carrier, and based on the estimated value the order of modulation M can be decided, thus optimizing the system capacity. A simplified block diagram of MC-CDMA is pre- sented in Fig.3 & 4. The serial input data is converted to parallel before spreading by a CDMA code. Then the spread data is modulated using IFFT. The output of IFFT is converted back to the serial to be appended by a cyclic prefix. The cyclic prefix is added to mitigate the inter symbol interference. The Digital to Analog Converter (DAC) converts the cyclic prefix appended input data into analog, which further gets modulated by RF modula- tor. The reverse procedure is carried out at the receiver to decode the signal as shown in Fig.4. Considering two channel: Pilot and data channel, the MC-CDMA M-QAM (M=2) signal transmitted by the kth user can be given by[8], where Here A & B are the signal amplitude of the pilot and data channel respectively. is the user data, h(t) is the impulse response of the chip wave shaping filter, and are the carrier frequency and carrier phase of the lth subcarrier respectively and L is the no. of subcarriers. Assuming that each sub channel experiences an inde- pendent flat Rayleigh fading, and a perfect average power control is employed, the received signal can be given by where, K is the total no. of users. is the relative time delay of user k. and are the fading ampli- tude & phase respectively of the lth path for the kth user and is zero mean white Gaussian noise with two sided spectral density . In this paper the proposed algorithm tries to optimize the number of subcarrier and slot utilization for MC- CDMA employing TDD technique. Every cell can have its own slot allocation method based on the traffic load. The SNR depends mainly on the direction of traffic (up- link / downlink) in home and interfering cells. In multi- cell environment, based on prevailing conditions many cases of interference pattern may arise. The system repre- sentation could be the generalization of two cell model for multi-cell. Further, the analysis presented here in- cludes worst case scenario to evaluate system perform- ance. The simulation carried out is the extension of the 52 http://www.i-jim.org DYNAMIC RESOURCE MANAGEMENT IN MC-CDMA BASED CELLULAR WIRELESS NETWORKS two cell model to represent MC-CDMA in time duplex mode. The remaining part of this paper is organized as fol- lows. In Section2, a comprehensive literature survey is presented. System model plays a vital role in defining the objective and conducting simulation. Section3 represents multi-cell as well as two cell model for SNR estimation. The development of the proposed protocol is discussed in section4. The simulated result and analysis are shown in section5. Finally this paper concludes in section6. II. RELATED WORK The performance of adaptive resource allocation algo- rithm for MC-CDMA has been studied by many re- searcher under different scenario [5,7,9]. Mamoun Guenach, and Heidi Steendam [3] investigated the sensi- tivity of downlink MC-CDMA performance considering the optimum number of carriers and guard interval. They derived SNR and tried to show that the load of the MC-CDMA system only has small influence on the opti- mization of the parameters. U. O. Ibom [7] has proposed an adaptive MC-CDMA for next generation network. He has observed the effect of bit allocation & BER in pres- ence of white noise, and a noise with exponential spec- trum. The performance study of MC-CDMA in cross- layer resource allocation by Virginia Corvino et al.[9] demonstrates user outage rate depending on scheduling and traffic load. In [10-11], BER performance evaluation under differ- ent channel model has been carried out. Çetin Kurnaz and Hulya Gokalp [10] have investigated the performance of the downlink in MC-CDMA systems using a channel model from in-the-field propagation measurements. They have studied the effect of transmission bandwidth, num- ber of users and number of sub-carriers on system per- formance. Throughput was the focus of attention in ob- servation made by L. Guerrero et al. [11] in Nakagami-m fading channels. The BER Comparison of OFDM and MC-CDMA sys- tem has been carried out by [12] Muhammad Talha Ah- med et al. [12]. Their observation in Rayleigh fading channel in presence of additive white Gaussian noise shows that MC-CDMA outperforms OFDMA. S. Chatter- jee et al.[4] have analyzed BER performance of adaptive modulation based MC-CDMA systems for 4G wireless systems. They have considered different modulation schemes for simulation. Although many research papers have been presented on performance of MC-CDMA, the effect of dynamic slot management using adaptive modulation in time divi- sion duplex mode has not been studied. This paper pre- sents an algorithm that optimally manages the resources to maximize the system performance. III. SYSTEM MODEL In MC-CDMA because of traffic asymmetry and hence different slot allocation from cell to cell, the system suf- fers from both intra-cell and inter-cell interference (Fig.5). For example, mobiles in cell1 may use uplink slots and at the same time the base station of an adjacent cell ie.cell2 may use downlink slot to transmit signals. In this situation, the uplink (downlink) channel in a cell can be interfered by the downlink (uplink) of the adjacent cell and, in turn, this results in capacity degradation. Figure 5. Interference in uplink and downlink because of cross-slot Figure 6. Different slot allocation in a adjacent cell Although a different slot allocation within a cell is also possible, practically it is not implemented as it will cause heavy interference in both uplink and downlink. Here, we assume that slot allocation within a cell is same and per- fectly synchronized between base station (BS) and mo- bile station (MS). Cross slot allocation between two cells is the case for traffic asymmetry (Fig.6). The number of cross slot allocations can be governed by the base station controller (BSC) in a location area consisting of multiple cells. Since cross slot allocation normally causes more interference, it will be a major factor in supporting qual- ity of service and also capacity of the system. A. SNR in Multi-cell TDD Environment In MC-CDMA with TDD, each slot will carry many user data on different channels separated / identified by Walsh-Hadamard code. Considering intra-cell and inter- cell interference separately in a multi-cell environment, the signal to noise ratio for each user’s ith subcarrier can be modeled as Where Pr is the received power, SF is the spreading factor, Iint is the internal noise within the cell, Iext is exter- nal noise coming from other cells, No is the noise power spectral density, and W is the total transmission band- width. To approximate link gain, the received power Pr at BS, can be related to the transmit power Pt of the MS as Where d is the distance between BS and MS and λ is a constant. In mobile communication, the loss of power in propagation is inversely proportional to the 4th power (i.e. v = 4) of the distance between transmitter and receiver [14]. UL DL TDD Frame DL DL DL DL DL UL UL UL UL DL DL DL DL UL UL UL DL UL Cross Slot Allocation Cell 2 Cell 1 Cell1 Cell2 iJIM – Volume 3, Special Issue 2: Technical Basics, October 2009 53 DYNAMIC RESOURCE MANAGEMENT IN MC-CDMA BASED CELLULAR WIRELESS NETWORKS In MC-CDMA a channel is represented by a no. of sub-channels/carriers. The no. of subcarriers needed to support an application is dictated mainly by the band- width requirement. Further, the sub-carrier selection is based on their current SNR. System model for MC-CDMA in multi-cell environ- ment can be built by generalizing the two cell models. Considering the two cell approach, four cases arise: i. cell1 uplink cell2 uplink, ii. Cell1 uplink cell2 downlink, iii. Cell1 downlink cell2 uplink, and iv. Cell1 downlink cell2 downlink. Here, cell1 represents a home cell for tagged mobile and cell2 represents a cell in first tier inter- fering cells. 1) Cell1 Uplink Cell2 Downlink Suppose mk is the number of MS served by a channel, where k = 1,2,3,…..K. Let Pik denote the transmit power of ith subcarrier, and Hik the channel gain between ith MS and its BS. The internal interference Iint in uplink for ith subcarrier carrying part of kth MS data may be given by (6) The external interference Iext is computed as follows. The BS present in neighboring cell J will cause interfer- ence to the uplink signal of tagged mobile. Now the Iext can be represented as (7) 2) Cell1 Downlink Cell2 Uplink Most commonly data in downlink channels (for exam- ple in W-CDMA) are transmitted with orthogonal codes. Assuming perfect time synchronization between MS and BS, and if the channel is of flat fading type, then or- thogonality is preserved during downlink slot and hence the internal noise Iint is absent. But the multipath propaga- tion damages the orthogonality. An orthogonality factor (α), which is the percentage of downlink orthogonality remaining at the mobile receiver, is introduced to com- pute Iint. α is 1 for a signal without multipath, and near zero in Rayleigh fading environment. The internal interference Iint arising due to non- orthogonality of the received signals is given by (8) where, Pi is the transmit power of the BS, Hi the channel gain, and αi the corresponding orthogonality factor for the ith subcarrier. To compute external interference Iext , it is assumed that same sub-carriers are used in neighboring cell. Let mj be the number of MS served in a jth cell, where j = 1,2,…..J represents J neighbor cells. Now Iext may be expressed as (9) where Hij,k the link gain between ith MS in neighbor cell and the tagged BS, and Pij,k the transmit power of ith MS to support its QoS requirement in its cell. 3) Cell1 Uplink Cell2 Uplink In this case, the Iint is same as in section 3.1.1 given by (6). The external interference Iext , is given by (9). 4) Cell1 Downlink Cell2 Downlink Here the internal interference Iint and external interfer- ence Iext are given by (8) and (7) respectively. B. Delay Estimation The total must be upper bounded as per the QoS re- quirement of real time services. Here, we first talk about different components of end to end delay, but our main focus is on processing delay due to resource allocation algorithm at base station. The major components of delay are discussed as follows [15]. 1) Packetization Delay The packetization delay is incurred by the Codec to encapsulate raw data into packets. It is given by where is the payload size and is codec band- width in kbits/s. 2) Decompression delay It is on an average 10% of the compression delay. So, the decompression delay can be defined as Where, decompression delay in ms, N is is no. of the voice blocks in the packet, and is coder delay in ms. 3) Delay in Transmission Network This delay depends upon the link band width and pay- load and header size, and is given by where, is the line speed in kbit/s, and is the header length in bit. 4) Processing delay This delay happens at the base station of the MC- CDMA system. It has the following components: (i) serial to parallel conversion time, (ii) spreading delay i.e. application of modulo-2 operation on each bit, (iii) com- putation of IFFT, (iv) time spent on running resource allocation algorithm, and (v) delay incurred in cyclic prefix and DAC device. Almost the same amount of de- lay occurs in the receiver side except fourth component i.e. resource allocation. 54 http://www.i-jim.org DYNAMIC RESOURCE MANAGEMENT IN MC-CDMA BASED CELLULAR WIRELESS NETWORKS C. Channel Model There are many models available in literatures to char- acterize a fading channel. Rayleigh channel model as- sumes a direct / dominant path and many reflected path. The probability density function of a Rayleigh fading channel is defined as follows [16]. where, p is instantaneous power and is related to sig- nal amplitude ρ as D. Noise Model We consider an Additive White Gaussian Noise (AWGN) in presence of Rayleigh channel to simulate BER given by where W is the total bandwidth, N0 is the noise spec- tral density, N is total subcarriers. E. BER Calculation The BER for the ith Sub-Carrier corresponding to M- QAM is given by [18] where SNRi is the signal to noise ratio for ith subcarrier, and M is the constellation points in M-QAM. IV. SUBCARRIER AND SLOT ALLOCATION ALGORITHM The proposed algorithm manages the subcarrier and slots to meet the quality of service requirement of an application. The selection of subcarriers is carried out based on its current SNR to support a minimum BER. The slot management algorithm decides whether an out- going slot is to be declared as uplink/downlink based on the existing capacity of the present slot. First, the SNR is calculated using the formulae shown in section 3, that falls under any one of the four cases considered there. Since the CDMA based systems are inherently interfer- ence limited, the SNR is recomputed every time based on the new call arrival rate, and hence the addition of a new subcarrier in a slot. The new call includes handoff user too. The BER is computed using (16) based on the SNR and a high modulation order (M=8). If BER > 10-3, then order of modulation M is reduced and BER is computed again, and this process is repeated till BER falls below 10-3 and the corresponding M value is retained to be used for order of modulation in M-QAM. If BER does not fall below 10-3 and M = 2, then the next slot is declared as same status (e.g. uplink if the current slot is uplink) and new calls are accommodated in new slots. Based on the existing SNR and application’s bandwidth requirement, the no. of subcarriers are allocated to these new calls. If the accumulated bandwidth (BWc) i.e. no of subcar- riers are just enough to meet the requirements (BWr), the resource allocation completes for an user and the algo- rithm takes next call to be processed. Following are the steps of the proposed algorithm. Step1: Pick up an unallocated sub carrier and compute the SNR Step2: Estimate BER for the Modulation order (M) Step3: If BER is below 10-3 , retain the value of M and allocate this subcarrier. Else decrement M by one and if M>2 means go to Step2 .Otherwise declare next slot in same direction and allocate the subcarrier in that slot. Go to step1. Step4: Check whether these allocated carrier meets the requirements. Step5: If BWc >=BWr means resource allocation for this particular user is over. Step6: See if any more call is in waiting queue. If so, go to step1 Step7: If all calls are accommodated, the resource allocation procedure ends. V. RESULTS AND DISCUSSION Simulations were carried out for the four cases of up- link and downlink scenario of MC-CDMA system. First we simulated the BER performance of our proposed algo- rithm in presence of Rayleigh channel. Different Walsh codes were used for spreading user data on each subcar- rier. Fig.7 shows the traffic distribution in terms of sub- carrier for different slots used in driving the simulation. The major simulation parameters are listed in table-I. 1 2 3 4 5 6 7 8 0 5 10 15 20 25 30 35 40 45 50 Slot Number S ub -C ar rie r pe r sl ot Figure 7. Input traffic distribution for simulation. TABLE I. SIMULATION PARAMETERS Bandwidth 10 MHz Spreading Factor 128 FFT Length 1024 Modulation (M-QAM) 2 - 8 Rayleigh Channel Ts=0.01s , ∆f=10 Hz Number of users 15-30 Length of Walsh Code 8 iJIM – Volume 3, Special Issue 2: Technical Basics, October 2009 55 DYNAMIC RESOURCE MANAGEMENT IN MC-CDMA BASED CELLULAR WIRELESS NETWORKS 4 5 6 7 8 9 10 10 -4 10 -3 10 -2 10 -1 SNR in dB B E R case1(UL,UL) case2 (UL,DL) Figure 8. BER performance in case1 and case2. TABLE II. SIMULATION SCENARIO Case Cell1 Cell2 Case1 Uplink (UL) Uplink (UL) Case2 Uplink (UL) Downlink (DL) Case3 Downlink (DL) Downlink (DL) Case4 Downlink (DL) Uplink (UL) The four cases considered here represent different sce- narios as listed in Table-II. Each case represents a traffic direction, and the interference pattern changes accord- ingly. The BER in case1 as shown in Fig.8 follows a higher path because the uplink base station suffers from both intra cell and inter cell interference from a large no. of users. As discussed earlier, in downlink the intra cell interfer- ence is caused by orthogonality factor. Fig.9 shows the BER performance with respect to variation in orthogonal- ity factor. In case of cross slot, i.e. case4, there could be heavy interference, as interfering user in neighboring cell may be near to the tagged mobile. Case3 shows a slight better performance, as interference is caused by a base station only as the neighboring cell is in downlink. A curve fitting function is used to plot BER here. The distance ratio considered in Fig.10 represents the ratio of distance between tagged mobile and base station to the mean distance of interfering mobile in the same cell. As indicated in (5), the received power inversely varies approximately with fourth power of distance in wireless communication, a small change in distance causes large changes in interference pattern and hence SNR. 0.45 0.5 0.55 0.6 0.65 0.7 0.75 0.8 0.85 0.9 0.95 3 4 5 6 7 8 9 10 11 12 x 10 -3 Alpha B E R case3(DL,DL) case4(DL,UL) Figure 9. BER with respect to orthoganility factor in internal interference 0.2 0.4 0.6 0.8 1 1.2 1.4 1.6 1.8 2 4 5 6 7 8 9 10 Distance Ratio S N R case1(UL,UL) case2(UL,DL) Figure 10. SNR vs distance ratio in internal interference 0.2 0.3 0.4 0.5 0.6 0.7 0.8 0.9 1 7 7.5 8 8.5 9 9.5 10 10.5 Distance Ratio S N R case1(UL,UL) case2(UL,DL) case3(DL,DL) case4(DL,UL) Figure 11. SNR with respect to distance ratio for external interference. 1 2 3 4 5 6 7 8 0 0.5 1 1.5 2 2.5 Slot Number C ap ac ity ( pe r S lo t) in b ps /H z fo r su b- ca rr ie r Figure 12. Capacity (b/s per Hz) per subcarrier The capacity (b/s per Hz) of each subcarrier in a given slot is computed during simulation of the proposed algo- rithm and is plotted in Fig.12.. In the given scenario, since the maximum order of modulation is only eight, the achievable capacity will be 3 b/s per Hz at most, as pre- dicted by the Shannon’s capacity formula. Fig.13 shows the delay in processing new calls. The new calls may originate from a new user or due to a hand off call. The delay observed here is the total processing delay. Once the resource is allocated to a particular call, this delay will be constant for a particular service class. 56 http://www.i-jim.org DYNAMIC RESOURCE MANAGEMENT IN MC-CDMA BASED CELLULAR WIRELESS NETWORKS 2 3 4 5 6 7 8 9 10 0 10 20 30 40 50 60 no. of users D el ay ( m s) Figure 13. Delay with respect to no. of users VI. CONCLUSION To analyze the performance of the proposed algorithm in MC-CDMA system the interference pattern corre- sponding to different scenario (four cases) was presented. The multi-cell environment was modeled and simulated by generalizing the two-cell model. The interference analysis has two major components: the intra cell and inter cell. The BER performance for different cases pro- vides an overview of system analysis in presence of AWGN noise and Rayleigh channel. The capacity shown here is low because of using lower order of modulation. The delay analysis was carried out with respect to proc- essing of new calls. The system performance of MC-CDMA under TDD mode accounts for many parameters and it requires fur- ther analysis. The BER, delay and system capacity alone were analyzed here, and other system parameters like throughput need to be analyzed. Future work could also include the performance analysis of MC-CDMA based on efficient codes like orthogonal variable spreading factor code. REFERENCES [1] S. Plass, S. Kaisar, "MC-CDMA versus OFDMA in Cellular Environments", EUSIPCO 2005, Turkey, September 6, 2006. [2] Filippo Giannetti, Vincenzo Lottici, and Ivan Stupia, “Iterative Multi-User Data Predistortion for MC-CDMA Communications”, IEEE Transaction on wireless communication, Vol .7, No.10, Oc- tober 2008. [3] Mamoun Guenach, and Heidi Steendam, “Performance Evaluation and Parameter Optimization of MC-CDMA”, IEEE Transactions on Vehicular Technology, vol. 56, no. 3, May 2007. (doi:10.1109/TVT.2007.895605) [4] S. Chatterjee1, W.A.C. Fernando2, M.K. Wasantha1,"Adaptive Modulation based MC-CDMA Systems for 4G Wireless Con- sumer Applications", IEEE, June 2003. [5] G.K.D. 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Aghvami, “Throughput of Distributed Cyclic Delay Diversity MC-CDMA Relaying Over Nakagami-M Fading Channels”, IEEE WCNC 2008. [12] Muhammad Talha Ahmed, Junaid Khalid, Khalida Noori, Sami Ahmed Haider , “Bit Error Rate Comparison of OFDM and MCCDMA Systems” , International Conference on Computer Science and Information Technology, 2008. [13] Klein S Gilhousen et. al., “On the Capacity of a Cellular CDMA System”, IEEE Transaction on Vechicular Technology, Vol 40, No2, May1991. [14] W. C.Y. Lee, Mobile Cellular Telecommunications, 2nd ed. NewYork: McGraw-Hill, 1995. [15] Miroslav Vozňák – František, “Analytic model of a delay varia- tion valid for the RTP”, Technical Report 16/2007. Available at http://homel.vsb.cz/~voz29/files/TR2007.pdf. [16] JPL's Wireless Communication Reference Website http://www.wireless.per.nl/reference/chaptr03/ricepdf/ricepdf.htm [17] T. S. Rappaport, “Wireless Communications Principles and Prac- tice”, Pearson Education, Inc., 2004. [18] A. J. Goldsmith and S.-G. Chua, “Variable-rate variable-power MQAM for fading channels”, IEEE Trans. Commun., vol. 45, pp. 1218–1230, Oct. 1997. (doi:10.1109/26.634685) AUTHORS Dhananjay Kumar is with Anna University Chennai, India. He is working as a senior lecturer in the depart- ment of Information Technology (e-mail: dhanan- jay@annauniv.edu). Dr. Chellappan C is with Anna University, Chennai, India. He is working as a professor and head in the de- partment of computer science & engineering (e-mail: drcc@annauniv.edu). Bala Jeevitha Vani B is with Anna University, Chen- nai, India. She has just completed her Master of Technol- ogy in Information Technology (e-mail: jee_vaani@yahoo.co.in). Submitted 1st April 2009. Published as resubmitted by the authors on 9 October 2009. iJIM – Volume 3, Special Issue 2: Technical Basics, October 2009 57 http://dx.doi.org/10.1109/TVT.2007.895605� http://www.wireless.per.nl/reference/chaptr03/ricepdf/ricepdf.htm� http://dx.doi.org/10.1109/26.634685�