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CHAPTER 1: INTRODUCTION
Introduction
Orthogonal frequency division multiple access (OFDMA) is an encouraging technology that supports high data rate transmission. The blend of OFDMA with the relay technology has expanded a large extend of potential to develop the overall network performance, and therefore has received a lot of intension in the recent years. Network resources that may include bandwidth and transmission power are limited; hence how to efficiently and fairly allocate these resources to users with guaranteed quality of service is a key issue. The models used while dealing with the partnering problem usually involve some form of orthogonality across the user pairs, so that the pairs can cooperate without causing interference to each 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 done on sub-channel and power allocation schemes for OFDMA. Yet, encoding techniques, and resource allocation for mutually cooperative OFDMA systems, have not 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 algorithms are used.
Relay-assisted cooperative communication
Relay-assisted cooperative communication has turn out to be very effective in several wireless systems [1]. This communication system is capable to enhance the overall system performance that includes spectral efficiency, network 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 relay communication systems involve even more 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 different hops resulting from multiple relays. Compared with single-carrier relay systems, in this we are able to assign multiple orthogonal subcarriers in every 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 main aim is to make the most of the transmission rate subject to an individual power constraint of each transmit node.
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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 proposed to exploit both frequency diversity and node diversity. [2]
Two-way relay network
In this type of network, two users or operators communicate with each other via one or multiple relays. There are three two-way relaying protocols which differ in the number of required phases.
- The first protocol is called as the simple four phase protocol consisting two one-way relaying protocols.
- The second 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 obtainable 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 assumed 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 addition, the OFDMA network uses the method of adaptive resource allocation and thus delivers improved performance [4]-[5]. In a two-way OFDM relay network having a single user pair and a single relay, the sum 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 system
An AF-based two-hop multi-relay OFDM system in which we optimally and mutually assign the three types of resources: subcarriers, relay nodes, and power. Such joint optimization hasn’t been well thought of or considered in the literature as far as we know. We formulate 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 given 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 ALGORITHMS
In 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 comparison 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 needed to match the users, and this will decrease the time consumed by the matching algorithm drastically. We will be dealing with 5 algorithms as following:-
- Select Nearest to Receiver algorithm:-
The two users nearest to 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.
- 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 Nearest 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 same 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 2: LITERATURE SURVEY
In 2010, N. Balasubramanian, A. Carroll and G. Heiser et al, proposed that:-
A rich body of literature has been dedicated to measuring 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 spite 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) [6]–[7]. Assuming that the wireless link is experiencing path loss as well as Rayleigh fading during the process is totally 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 formulation 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 [9][7].
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 efficiency 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 mainly 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 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 decoding 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 technique, 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 estimate of their partner’s symbol, each spread with its user’s 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 partner’s 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 partner’s information. At the receiving end, an algorithm is utilized to achieve near optimum ML performance with reduced complexity.
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CHAPTER 3: PROBLEM FORMULATION
3. Problem formulation
Scheduling and resource allocation are essential components of wireless data systems. Here by scheduling we refer the problem of determining which user will be active in a given time-slot; resource allocation refers to the problem of allocating 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 practice 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 system’s total utility.
REFERENCES
[1] A. Nosratinia, T. E. Hunter, and A. Hedayat, “Cooperative communication in wireless networks,” IEEE Comm. Magazine, vol. 42, no. 10, pp. 74–80, 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 Selected Areas in Comm., vol. 24, no. 3, pp. 659–672, March 2006.
[3] S. Bakım and O. Kaya. “Cooperative Strategies and Achievable Rates for Two User OFDMA Channels.” IEEE Trans. Wireless Commun., 10(12): 4029–4034, 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. Evans, “Adaptive resource allocation in multiuser OFDM systems with proportional rate constraints,” IEEE Trans. Wireless Commun., vol. 4, no. 6, pp. 2726-2737, Nov. 2005.
[6] A. Carroll and G. Heiser, “An analysis of power consumption in a smartphone,” in Proc. USENIX, June 2010.
[7] N. Balasubramanian, A. Balasubramanian, and A. Venkataramani, “Energy consumption in mobile phones: a measurement study and implications for network applications,” in Proc. IMC, Nov. 2010.
[8] L. Shao and S. Roy, Downlink multicell MIMO-OFDM: an architecture for next generation wireless networks,” IEEE WCNC, vol. 2, pp. 1120 { 1125 Vol. 2, Feb 2005.
[9] T. Thanabalasingham, S. Hanly, L. Andrew, and J. Papandriopoulos, Joint allocation of subcarriers and transmit powers in a multiuser OFDM cellular network,” IEEE ICC, vol. 1, pp. 269 { 274, Jun 2006.
[10] G. Tsoulos, 2006. MIMO System Technology for Wireless Communications. Boca Raton: Taylor & Francis Group
[11] A. Nosratinia, T. E. Hunter and A. Hedayat, “Cooperative communication in wireless networks “, IEEE Commun. Magazine, vol. 42, no. 10, pp. 74—80, Oct. 2004
[12] K. Vardhe and D. Reynolds, “The Performance of Multi-User Cooperative Diversity in an Asynchronous CDMA Uplink”, IEEE Trans. Wireless Commun., vol. 7, no. 5, pp. 1930—1940, May 2008.
[13] S. Wei, D. L. Goeckel and M. C. Valenti, “Asynchronous Cooperative Diversity”, IEEE Trans. Wireless Commun., vol. 5, no. 6, pp. 1930—1940, Jun. 2006.
[14] A. Sendonaris, E. Erkip, and B. Aazhang, “User cooperation diversity — Part I: System description”, IEEE Trans. Commun., vol. 51, pp. 1927 — 1938, Nov. 2003.
[15] A. J. Jahromi, et. al., “On multi-user detection in CDMA based cooperative networks”, IEEE Sarnoff Symposium, 2009, SARNOFF ’09, 30 Mar. – 1 Apr. 2009
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