Implementation of User-Pairing Algorithm for OFDMA

Implementation of User-Pairing Algorithm for OFDMATable of Contents (Jump to)IntroductionLiterature Survey task FormulationReferencesCHAPTER 1INTRODUCTIONIntroductionOrthogonal absolute frequency division triune access (OFDMA) is an encouraging technology that supports high takeive information place contagious disease. The blend of OFDMA with the communicate technology has expanded a large get across of potential to develop the overall ne bothrk performance, and therefore has received a stool of intension in the recent years. Network resources that may include bandwidth and infection power ar limited hence how to efficiently and fairly allocate these resources to drug users with guaranteed quality of service is a key issue. The models utilize while dealing with the partnering task usually involve some form of orthogonality across the user pairs, so that the pairs can cooperate without causing interference to distributively other. OFDMA, has a lot of essential properties due to which it has gained a lot of acceptance and popularity in the recent years, and because of its orthogonal structure it is considered as a good candidate for realizing practical cooperation. As we already know that huge amount of research is d bingle on sub- lineage and power allotment schemes for OFDMA. Yet, encoding techniques, and resource apportioning for mutually conjunctive OFDMA systems, overhear non been investigated much until rather recently. For cooperative OFDMA systems containing only two users, achievable rates based on mutual cooperation across sub-channels were characterized in 3, and for such systems, optimal power allocation algorithmic programs are used.Relay-assisted cooperative chatRelay-assisted cooperative communication has turn out to be very effective in several radio systems 1. This communication system is capable to enhance the overall system performance that includes spectral efficiency, lucre lifetime and coverage area. Efficient wireless resource allocation is critical to fully realize these benefits in cooperative communication systems. Resource allocation in orthogonal frequency division multiplexing (OFDM) based pass along communication systems involve even more than technical challenges. Single-hop OFDM or OFDMA which are traditional systems when compared then we must carefully and accurately coordinate the power and subcarrier allocations across diverse hops resulting from multiple electrical relays. Compared with single-carrier relay systems, in this we are able to order multiple orthogonal subcarriers in both(prenominal) single hop, which not only gives more design freedoms but also typically higher design complexity or intricacy. In this paper, we will take a close look on the power control problem, joint relay selection, and subcarrier assignment for a cooperative two-hop multi-relay OFDM system using the protocol called amplify-and-forward. The important aim is to make the most of the transmission system rate subject to an indivi twofold power constraint of each transmit node.Recently, a number of results have been described on relay selection in two-hop multi-relay systems. A common selection strategy is to choose the relay with the best equivalent end-to-end channel gain. Similar strategy can be used in OFDM systems, where a relay is selected based on the channel condition of the whole OFDM symbol. However, such symbol based relay selection may not be efficient as the differences of channel conditions amongst diverse subcarriers are not completely utilized. The subcarrier-based relay selection, which selects one best relay for each subcarrier, was then visualised to effect both frequency diversity and node diversity. 2Two-way relay networkIn this type of network, two users or operators communicate with each other via one or multiple relays. There are trine two-way relaying protocols which differ in the number of required phases.The depression protocol is called as the simple quadruple phase protocol consisting two one-way relaying protocols.The scrap protocol is named as the time-division broadcast (TDBC) protocol which consists of three phases.The third protocol is the multiple-access broadcast (MABC) protocol which consists of two phases.The MABC protocol is more bandwidth efficient compared with the other two protocols. Orthogonal frequency division multiple access (OFDMA) is one of efficient techniques to mitigate the problems of frequency selective fading. In an OFDMA network, a complete get holdable bandwidth is separated into a number of orthogonal subcarriers and multiple users transmit their information simultaneously using the different subcarriers without inter-user interference. Generally, it is sour that the bandwidth of each subcarrier is much smaller than the coherence bandwidth of the channel, and so the channel of each subcarrier has a flat fading. In assenting, the OFDMA network uses the manner of adaptive resource allocat ion and thus delivers improved performance 4-5. In a two-way OFDM relay network having a single user pair and a single relay, the integrality capacity for both users over all subcarriers is maximized by power allocation and tone permutation. In resource allocation for a multiuser two-way OFDMA relay network is investigated to support two-way communication between the base station and each of multiple users. In several relay selection policies for a MABC DF two-way relay network are proposed. The subcarrier based relay selection usually assumes that signals received over one subcarrier is amplified (or decoded) and forwarded by a relay over the same subcarrier in the next hop. However this is not optimal in terms of system performance. An improved performance can be attained if subcarriers in the first and second hops are paired according to the conditions of their channel. Such a subcarrier pairing approach was proposed in 1.AF-based two-hop multi-relay OFDM systemAn AF-based two-h op multi-relay OFDM system in which we optimally and mutually assign the three types of resources subcarriers, relay nodes, and power. Such joint optimization hasnt been well thought of or considered in the literature as far as we know. We spring up it as a joint relay power allocation problem, subcarrier pairing, and selection with an objective of exploiting the transmission rate under specific power constraints. A dual nature can be used for solving the optimization problem in three phases. First, we find the optimal power allocation for any assumption strategy of subcarrier pairing and relay assignment. In the second phase, we determine the optimal relay assignment when subcarrier pairing is given. And in the last or third phase, we obtain the optimal subcarrier paring by means of the Hungarian method. The overall complexity of the optimal algorithm is polynomial in the number of subcarriers and relay nodes. Based on the intuition derived from the optimal algorithm, we further propose two suboptimal algorithms that have lower complexity but can achieve close to optimal performances.PRACTICAL SUBOPTIMAL pairing ALGORITHMSIn our model, the locations of the users, and their distances to each other are the major factors that affect their transmission rates. The impacts of Rayleigh fading and noise variances on the rates are negligible in proportion to path loss. This forces the power allocation and partner selection to be mostly dependent on the topology of the network, which means that a suboptimal but fast algorithm can be derived based only on user locations as an alternative to the maximum weighted matching algorithm. But then, the weights of the graph will not be shooted to match the users, and this will decrease the time consumed by the matching algorithm drastically. We will be dealing with 5 algorithms as succeeding(a)-Select Nea symmetricalness to Receiver algorithm-The two users nearest to the receiver get matched. These users are removed from t he pool, and the algorithm repeatedly matches the rest of users with the same method until every user is matched.Select Farthest from Receiver algorithm-The two users farthest from the receiver get matched. These users are removed from the pool, and the algorithm repeatedly matches the rest of users with the same method until every user is matched.Maximum Matching on warm Four to Receiver algorithm-The user nearest to the receiver is selected. Then, three users which are nearest to it are selected. Maximum weighted matching algorithm runs on those users and the users get matched. The algorithm repeatedly matches the rest of users with the same method until every user is matched.Maximum Matching on Farthest Four from Receiver algorithm-The user farthest from the receiver is selected. Then, three users which are nearest to it are selected. Maximum weighted matching algorithm runs on those users and the users get matched. The algorithm repeatedly matches the rest of users with the sam e method until every user is matched.Select Nearest and Farthest to Receiver algorithm-The user farthest to the receiver gets matched with the nearest to the receiver. These users are removed from the pool, and the algorithm repeatedly matches the rest of users with the same method until every user is matched. The performance comparisons of the above algorithms are presented in this section.CHAPTER 2LITERATURE SURVEYIn 2010, N. Balasubramanian, A. Carroll and G. Heiser et al, proposed that-A rich body of literature has been dedicated to step the power consumption of cellular and WiFi interfaces for mobile users. Although a variety of power consumption models have been proposed and studied, one general conclusion is that, in cattiness of comparable power consumption (typically around 1 W), WiFi is much more power efficient in sending/receiving the same amount of data because of the higher data rates (e.g., a few Mbps for 3G while ten or more Mbps for 802.11n) 67. Assuming that the wireless link is experiencing path loss as well as Rayleigh fading during the process is centerly unacceptable. Note that, the data rate of the wireless link varies for different distances as well as channel realizations.In 2005, L. Shao and S. Roy, T. Thanabalasingham, S. Hanly, L. Andrew et al, proposed-Resource allocation and interference management of multi-cell downlink OFDMA systems were presented. A key focus of these works is on interference management among multiple cells. Our general cookery includes the case where resource coordination leads to no interference among different cells/sectors/sites. In our model, this is achieved by dynamically partitioning the sub channels across the different cells/sectors/sites. In addition to being easier to implement, the interference free operation assumed in our model allows us to optimize over a large class of achievable rate regions for this problem. If the interference strength is of the order of the signal strength, as would be typical in the broadband wireless setting, then this partitioning approach could also be the better option in information theoretic sense 97.In 2004, A. Nosratinia, G. Tsoulos et al proposed that-A. Nosratinia, G. Tsoulos et al proposed MIMO systems because in recent years, MIMO systems 10 have been widely accepted as the ultimate approach to fulfilling the high performance demands of current and future generation of wireless systems. Using multiple antennas at the transmitter or/and receiver dramatically increases the spectral eciency and enables the system to achieve very high data rates. It is also widely accepted that the majority of multi-antenna spatial diversity techniques are primarily applicable in downlink transmissions due to the size and complexity constraints that limit their implementation in small mobile units 11. However, to achieve spatial diversity on the uplink without the need to have more than one antenna per mobile unit, cooperative transmission is the answer. In this case, the mobile units help each other to emulate a multiple transmit antenna system.In 2006, Yang and Belfiore proposed that-Yang and Belfiore present a near optimal AF scheme which in certain conditions is able to achieve the diversity multiplexing trade-off (DMT) upper berth bound introduced. In 13, cooperative diversity protocols which are based on DF relaying are developed. The relay nodes that can fully decode the received transmission relay to the destination using a space time code. The idea of cooperative diversity under asynchronous channel conditions was suggested. The authors in 12 proposed a simple DF relay technique in a Code Division Multiple Access (CDMA) system where the relay nodes detect and forward the transmission regardless of whether successful decryption has occurred or not. In 13 a 2 hop asynchronous cooperative diversity technique is introduced where the authors propose two different protocols to determine the participating nodes. In this techniqu e, the set of participating relays that receive the packet without errors is the only set of nodes involved in the relaying process.In 2004, Sendonaris et al proposed that-The second type of uplink cooperation, which will be the main focus of this chapter, is based on pairing each user with a neighboring user, a partner, to create a MIMO-like effect on the uplink transmission. This was first suggested by Sendonaris et al. 14 in a synchronous full-duplex CDMA system utilizing orthogonal spreading codes. The technique was for a two user system where at the first transmission instance both users transmit their symbol to the other user and the base station. The symbol is then received and processed by the other user and in the following transmission instance the users transmit a composite signal consisting of their own symbol and a detected visualize of their partners symbol, each spread with its users spreading code, to the base station.In 2003, A. J. Jahromi, et. Al proposed that-In this case at each transmitting instance, each user transmits a composite signal of both his new symbol along with a detected estimate of his partners previous symbol. In this method, to maintain the total transmit power constant, the joint transmit-power is manipulated such that at the base station, the average received power and the received power per user remains constant. In 15, the authors propose a new multiuser uplink pairing CDMA technique in which each user transmits its own signal to the base station and follows that by relaying a processed estimate of his partners information. At the receiving end, an algorithm is utilized to achieve near optimum ML performance with reduced complexity.CHAPTER 3PROBLEM FORMULATION3. Problem formulationScheduling and resource allocation are essential components of wireless data systems. Here by scheduling we refer the problem of determining which user will be prompt in a given time-slot resource allocation refers to the problem of allocatin g physical layer resources such as bandwidth and power among these active users. In modern wireless data systems, frequent channel quality feedback is available enabling both the scheduled users and the allocation of physical layer resources to be dynamically adapted based on the users channel conditions and quality of service (QoS) requirements. This has led to a great deal of interest both in exert and in the research community on various channel aware scheduling and resource allocation algorithms. Many of these algorithms can be viewed as gradient-based algorithms, which select the transmission rate vector that maximizes the projection onto the gradient of the systems total utility.REFERENCES1 A. Nosratinia, T. E. Hunter, and A. Hedayat, conjunct communication in wireless networks, IEEE Comm. Magazine, vol. 42, no. 10, pp. 7480, Oct. 2004.2 A. Bletsas, A. Khisti, D. P. Reed, and A. Lippman, A simple cooperative diversity method based on network path selection, IEEE Journal on S elected Areas in Comm., vol. 24, no. 3, pp. 659672, March 2006.3 S. Bakm and O. Kaya. reconciling Strategies and Achievable rate for Two User OFDMA Channels. IEEE Trans. Wireless Commun., 10(12) 40294034, Dec. 2011.4 C. Y. Wong, R. S. Cheng, K. B. Letaief, and R. D. Murch, Multiuser OFDM with adaptive subcarrier, bit, and power allocation, IEEE J. Sel. Areas Commun., vol. 17, no. 10, pp. 1747-1758, Oct. 1999.5 Z. Shen, J. G. Andrews, and B. L. 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