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C O O R D I N AT E D A N D D I S T R I B U T E D MIMO

COOPERATIVE COMMUNICATIONS BASED ON RATELESS NETWORK CODING IN DISTRIBUTED MIMO SYSTEMS XIANGMING LI, BEIJING INSTITUTE OF TECHNOLOGY TAO JIANG, HUAZHONG UNIVERSITY OF SCIENCE AND TECHNOLOGY SHUGUANG CUI, TEXAS A&M UNIVERSITY JIANPING AN, BEIJING INSTITUTE OF TECHNOLOGY QIAN ZHANG, HONG KONG UNIVERSITY OF SCIENCE AND TECHNOLOGY

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The authors discuss how to construct cooperative communication schemes based on rateless network coding in distributed MIMO systems. They propose two cooperation strategies: singlesource cooperation and multisource cooperation. 60

In cellular distributed antenna systems, cooperation among base stations or remote antenna units to exploit multiple-input multiple-output gains could mitigate interference as well as provide more spatial diversity, which could significantly improve overall spectrum efficiency. However, most existing cooperation schemes require perfect synchronization, which is difficult and even impossible in practical distributed multi-user systems. When synchronization is not perfect, cooperative communication performance severely degrades. Orthogonal channel assignment among collaborating stations with rateless network coding allows synchronization to be performed independently, and provides a coding scheme with close-to-capacity performance. In this article we discuss how to construct cooperative communication schemes based on rateless network coding in distributed MIMO systems. Specifically, we propose two cooperation strategies: single-source cooperation based on rateless coding and multisource cooperation based on rateless network coding. The proposed cooperative strategies are applicable to both uplink cooperation among users and downlink cooperation among BSs or RAUs in cellular DASs, providing a parameter-flexible, encodingsimple, and bandwidth-efficient cooperative solution.

INTRODUCTION Recently, distributed multiple-input multipleoutput (MIMO) or distributed antenna systems (DASs), which allow the exploration of various MIMO gains over spatially collaborative nodes, have been shown to be capable of significantly increasing system capacity [1–4]. Specifically, in distributed MIMO systems some neighboring nodes such as base stations (BSs), remote anten-

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na units (RAUs), or users in a particular geographic area can share their antennas in a cooperative manner and generate a virtual MIMO system [5–8]. When the cooperative nodes are BSs, RAUs, and users, the cooperation is referred to as BS cooperation, RAU cooperation, and user cooperation, respectively. In such virtual MIMO systems, transmissions of cooperatively coded signals from different locations lead to spatial diversity, which effectively combats channel fading and shadowing. In particular, when it is hard to obtain diversity with a single BS or RAU (e.g., channels vary slowly such that time diversity is not available), cooperative spatial diversity is critical. Moreover, when multiple BSs or RAUs transmit signals in a cooperative way, the co-channel interference is effectively mitigated. As a result, the overall system spectrum efficiency could be greatly improved via virtual MIMO systems. Obviously, both uplink user and downlink BS or RAU cooperation in distributed MIMO systems can increase spectrum efficiency by providing more spatial diversity and better interference mitigation. However, most existing schemes assume that the signals transmitted from different nodes arrive at the same destination synchronously. For example, perfect synchronization is explicitly assumed for downlink BS cooperation in multicell multiuser MIMO networks in [9], coded cooperation at distributed multi-user wireless networks in [10], and amplify-and-forward and decode-and-forward user cooperation in [5]. However, for cooperative communications with multiple transmitters and a single destination, it is hard for the multiple transmitters to align their transmissions such that signals intended for the single destination arrive at the same time. For multiple sources to multiple destinations, it is even more challenging to align all the signals even if perfect cooperation is assumed

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among the transmitting nodes. Therefore, in practical communication systems it is only practical to assume quasi-synchronization for distributed multiuser networks. Nevertheless, when synchronization is not perfect, cooperative communication performance severely degrades [6]. On the other hand, high-accuracy synchronization requires complex control mechanisms and extra control messages, such that both system complexity and bandwidth demands are high. To improve the overall spectrum efficiency, frequency reuse or spatial multiplexing is often deployed in practical wireless communication systems with various channel assignment strategies. In the cellular system, each cell is usually assigned a group of radio channels that are completely different from those in its neighbor cells, and BS transmission is controlled to achieve the desired coverage within the particular cell. Therefore, if we could limit the coverage area to be within a finite area, the same group of channels may be used to cover different cells that are separated far enough from each other such that the co-channel interference levels are within tolerable limits [11]. Within a resource group, the channels can be allocated in an orthogonal way: when multiple user equipment (UE) units are active, they are assigned orthogonal channels with different frequency bands, different codes, different time slots, or different spatial streams for uplink and downlink transmissions. Such a scheme is referred to as the orthogonal channel assignment strategy, where each UE performs synchronization and channel estimation independently, without regarding its neighboring UE. Since the orthogonal channel assignment strategy is of practical significance, cooperative communications based on such a channel assignment scheme is addressed in this article. In cooperative communications for general ad hoc networks, the channels between nodes are usually non-ideal. For example, when the source node broadcasts signals, each partner in a cooperative group only receives a channel-corrupted signal. As a result, the cooperative communication performance heavily depends on the diversified internode signal-to-interference-plusnoise ratios (SINRs). However, in certain practical wireless communication networks, it is possible that reliable connections are available within a cooperative group. For example, in cellular networks the collaborating BSs are connected through a central office via a high-speed optical fiber network, or the collaborating UE within a small geographic area may be connected with reliable short-range links such as a highspeed wireless local area network (WLAN). When a group of nodes are connected reliably, these nodes build a virtual DAS; thus, they could work conveniently as a team to communicate cooperatively with other nodes outside the group. In addition, if each node in a cooperative group is assigned one or more orthogonal channels as discussed earlier, cooperation could be implemented with low complexity, since each transmission could perform synchronization and channel estimation independently. In a practical wireless system, such as the cellular system, each node participating in cooperation not only helps other nodes but also


transmits its own data. Therefore, it is desirable for each node to contribute only part of channel resources to cooperate. In other words, only partial cooperation is performed at each node. For example, let us consider a team consisting of three nodes, A, B, and C, to perform cooperative communications. Each node may contribute a part of its channels to participate in the transmissions from a particular source node, say A. Due to the time-varying nature of the underlying system, the percentage a node contributes to help A may change from time to time. As an example, during cooperation period 1, A may contribute 40 percent of its total resource, B may contribute 30 percent of its total resource, and C may contribute 35 percent of its total resource, for the data transmission from A. During cooperation period 2, the percentages A, B, and C contribute to cooperation may change to 55, 35, and 45 percent, respectively. Since the total resource for the transmission may change from time to time with no a prior information, a predesigned fixed-rate channel code is difficult to apply to such a rate varying application. Therefore, to achieve efficient time-varying partial cooperation among multiple networked nodes, it is natural and useful to combine rateless coding and networked transmissions for cooperative communications. With rateless codes, a sequence of K input symbols are encoded to a potentially infinitely long stream of parity-check symbols. The transmission of a rateless codeword is terminated once the receiver sends an acknowledgment (ACK) to the transmitter via a feedback channel, when the source messages are reconstructed correctly. As suggested by its name, a rateless code does not have a fixed rate, but rather has the rate determined on the fly by the time when the receiver decodes the message correctly. As such, rateless codes have the merits of fully exploiting every transmitted symbol to approach the channel capacity. Recently, some projects on rateless codes have found that a class of rateless codes known as extended irregular repeat-accumulate (eIRA) codes not only have low encoding and decoding complexity, but also have capacity-achieving or capacity-approaching performance over certain channels, such as additive white Gaussian noise (AWGN) channels and Rayleigh fading channels [12, 13]. The eIRA code can be constructed as follows. Let m = [m1, m2, …, mK] be the binary source message. Then the codeword v = [v 1 , v 2 , …, v N ] can be generated as v 1 = m 1, v j = v j–1 + m s1 + m s2 + … + m s d for j > 1, where the additions are j modulo 2. The symbols ms1, ms2, …, msdj are dj distinct source symbols, which can be generated uniformly at random, where d j is a non-zero integer standing for the encoding degree of the symbol vj. The encoding degree dj can be a fixed integer or a random integer chosen from any given probability distribution. It has been observed that such codes are efficiently encodable in a flexible way, which makes them highly suitable for the time-varying partial cooperation scenario discussed above. In addition, eIRA codes are a class of low-density parity-check (LDPC) codes, which can be decoded in low complexity with belief-propagation (BP) or sum-

In a practical wireless system, such as the cellular system, each node participating in cooperation not only helps other nodes but also transmits its own data. Therefore, it is desirable for each node to contribute only part of channel resources to cooperate.

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RAU RAU BS UE

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Figure 1. Downlink cooperation model for the cellular and distributed antenna systems: a) base station cooperation; b) distributed antenna system. product algorithms. Moreover, when we assume that each node in a cooperative group perfectly knows what information the other nodes have in the same group, which is possible when the cooperating nodes are connected with highspeed links, these nodes could deploy network coding [14, 15] for more efficient transmissions. In this article we study the partial cooperation problem with rateless network coding, particularly for the UE, BS, or RAU cooperation in cellular and DAS systems. The rest of the article is organized as follows. In the next section we build the system model with partial cooperation based on rateless network coding. In the following section simulation results are conducted to verify the performance of the proposed system model, followed by the conclusions in the final section.

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cessing tasks including RAU control, power control, channel estimation, coding and decoding, modulation and demodulation, and so on. Moreover, RAUs are assumed to be connected to a CU through fiber or wired links of reliable high capacity, such that data transmissions between RAUs and the CU experience negligible time delays and bit errors. For uplink cooperation, it is also possible to establish reliable short-range communications among collaborating UE. For example, such reliable short-range communications could be realized via WLAN, free space optics (FSO), or even wired LAN, where Figs. 2a and 2b show two examples of team-based UE cooperation for uplink transmissions in a cellular system. Within the team, UE units first construct a local network via wireless or wired links of high capacity and high reliability; they could then select a server UE to handle the central processing tasks necessary for performing external communications with one or more BSs via cooperative schemes. In such a way, the team-based uplink transmission can be modeled as a virtual DAS where the common UE units and the server UE act as the virtual RAU and virtual CU, respectively, as shown in Fig. 2c. With the above setup, we expect to increase the overall data rate of the uplink transmission by cooperative communications with the virtual DAS. For convenience of description, from now on we call a UE unit, a BS, or an RAU a station for both uplink and downlink cooperation. A group of stations constitute a team to perform cooperative communications with another particular set of stations. Assume that the orthogonal channel assignment strategy is used, and the transmission resource assigned to a particular station in a cooperative group is represented by a given number of symbols supported during a transmission (a transmission means a certain time duration of data transmission). Two cooperation strategies, single-source partial cooperation with rateless coding and multisource cooperation with rateless network coding, are discussed next.

COOPERATIVE COMMUNICATIONS WITH RATELESS NETWORK CODING

PROPOSED SINGLE-SOURCE COOPERATION WITH RATELESS CODING

In Fig. 1 we show two possible network topologies for downlink cooperation in a cellular DAS. In the BS cooperation, as shown in Fig. 1a, all the BSs exchange information through the backbone network of high capacity. For simplicity, the backbone network is not shown here. Furthermore, we assume that the data exchange between any two BSs is error-free and delayfree. In the DAS, as shown in Fig. 1b, a set of spatially separated antenna nodes (i.e., RAUs) are connected to a common entity, a central unit (CU), which manipulates the overall wireless service within a particular geographic area. Specifically, an RAU consists of the transmitting and receiving radio frequency (RF) components and the minimum processing unit, acting as an RF transreceiver, as well as undertaking some of the baseband signal processing tasks such as constellation mapping and data packetizing. A CU is the central control and signal processing unit, undertaking most of the control and signal pro-

Figure 3 depicts the proposed configuration of single-source partial cooperation with rateless coding. In each transmissio a single source node in the cooperative group wants to transmit K source symbols to a destination outside the group. Suppose a total of T stations (including the source station) in the cooperative group could participate in the cooperative communication, and Ni (i = 1, 2, …, T) denotes the number of transmission symbols contributed by the ith cooperative station. We have the total number of coded symbols as N = ∑Ti=1 Ni, and we assume N > K, where the K-symbol source information is encoded into an N-symbol codeword via a particular channel coding scheme, and thus the code rate is R = K/N. Note that Ni may change from time to time as discussed earlier. Therefore, the overall codeword length N is time varying, and the code rate R is not fixed. As such, if we deploy the conventional adaptive coding technique with fixed-rate coding schemes such as general LDPC


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or turbo codes (TCs), for every rare code R, a new codebook must be constructed, which means that a lot of codebooks should be designed and stored at the transmitters and receivers in advance. Obviously, this is rather impractical since the supportable rate in a particular cooperation system may take an arbitrary value in the range (0, 1) at each time instant. On the other hand, rateless codes such as eIRA codes [13] that have capacity-achieving or capacity-approaching performance over various channels with low encoding and decoding complexity, are naturally good solutions for the partial cooperation problem considered here. As shown in Fig. 3, one or more stations may dynamically and continuously adjust their contribution according to the channel state condition, until they receive the termination signal from the destination, which indicates that the source information could now be decoded correctly. Moreover, eIRA codes are a class of irregular LDPC codes. Thus even if no feedback channels are available for termination signals, rateless codes can still be good forward error correction (FEC) codes flexibly providing various required code rates. Therefore, the single-source partial cooperation at each time instant could be performed as follows: • Each station involved in the cooperation determines the contribution factor Ni • The source station collects Ni, i = 1, 2, …, T, and calculates the code rate R = K/N = K/∑Ti=1 Ni • The source station generates N encoded symbols using a rateless code and assigns these encoded symbols to collaborating stations according to the values of Ni’s. Equivalently, the encoding operations could also be performed in a distributional way: The ith station generates Ni encoded symbols that it transmits, which could be easily done sequentially if the (i – 1)th station sends its encoding status to the ith station after it generates its Ni–1 encoded symbols.

PROPOSED MULTISOURCE COOPERATION WITH RATELESS NETWORK Figure 4 shows a typical communication system deploying the proposed multisource cooperation scheme with rateless network coding, in which the ith station transmits Ki source symbols to a particular destination outside the group during each transmission period, and its total transmission capacity is denoted as M i , i = 1, 2, … , T, with T the total number of stations in the group. Let K = ∑Ti=1 Ki, M = ∑Ti=1 Mi, and suppose M > K. Here, M i is not necessarily constant over time just as N i was in the previous section. Unlike single-source partial cooperation with rateless coding, where each station contributes only part of its transmission capacity for cooperation, in multisource cooperation we assume that each station contributes all its transmission capacity to the collaborative pool. Obviously, the code rate could be set as R = K/M, which means that K 1, K 2, … , K T symbols from the T stations in the cooperative group are concatenated in series to form a K-symbol source message and then encoded into M symbols. The encoded sym-


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Figure 2. Uplink cooperative communications based on virtual DAS: a) wiredLAN-based UE cooperation; b) WLAN-based UE cooperation; c) UE forming a virtual DAS. bols are then assigned to the T stations for collaborative transmissions according to their transmission capacity Mis. With a similar argument to that for the single-source cooperation case, rateless coding could be deployed to achieve cooperation flexibility as well as low encoding and decoding complexity. At the ith destination, the M symbols from the T stations are combined and then decoded using the belief propagation (BP) algorithm, where the corresponding K i source symbols are extracted. Destination i sends a termination signal when it successfully decodes its own data, while all the transmitters stop transmissions when all the T termination signals are received. Accordingly, multisource cooperation with rateless network coding operates as follows: 1. Select a station, called a virtual server, to undertake the necessary central processing tasks such as common data collection and control, where the virtual server could be any of the stations in the team, selected by either willingness or user designation.

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The procedures of the multi-source cooperation are similar to that of the single-source cooperation, except that the multi-source cooperation needs a virtual server, which is not a major issue when we assume that the cooperative nodes are connected via high-speed links.

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Figure 3. Partial cooperation with rateless coding.

2. The virtual server collects the source messages, source parameters, K = ∑Ti=1 Ki and M = ∑Ti=1 Mi, and calculates the code rate R = K/M = K/∑Ti=1 Mi. 3. The virtual server generates M encoded symbols using a rateless code and assigns these encoded symbols to collaborating stations according to the values of Mis. Equivalently, the encoding operations in Step 3 could be performed in a distributional way. We see that the procedures of multisource cooperation are similar to those of single-source cooperation, except that multisource cooperation needs a virtual server, which is not a major issue when we assume that the cooperative nodes are connected via high-speed links. Obviously, multisource cooperation with rateless network coding is suitable for both multistation-to-single-station and multistation-to-multistation communications. In addition, for the uplink cooperative communications based on virtual DAS, shown in Fig. 2, all the UE units want to communicate with the BS. Since the BS needs to decode all the data streams, naturally multisource rateless network coding [14, 15] could be applied. Similarly, in the BS cooperation case shown in Fig. 1, if each BS wants to transmit an independent data stream to a common UE, the BSs may combine the messages and jointly encode them with rateless network coding. Moreover, multisource cooperation performance can be further improved by allocating different transmitting power levels among the transmitting stations according to their individual channel conditions. However, to implement power allocation in an optimal way, the

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transmitters need to know all the channel state information, which is not covered in this article. Note that in the two proposed cooperation strategies, we have a few stations cooperating to transmit messages with the orthogonal channel assignment. That is, a rateless codeword is divided into a few parts and assigned to several orthogonal channels. In such a way it is expected that synchronization could be implemented with low complexity since each orthogonal channel corresponding to an independent stream could execute synchronization independently.

SIMULATION RESULTS To verify the performance of the proposed cooperation schemes with rateless coding in terms of frame error rate (FER), simulation results are presented in this section. For fair comparisons, we consider a coded baseline system with the same overall rate of 1/2 for all cases: noncooperative, two-station cooperation, three-station cooperation, four-station cooperation, eight-station cooperation, 12-station cooperation, 20-station cooperation, and ∞station cooperation. ∞-station cooperation stands for the case in which a sufficiently large number of stations perform cooperative communications. In the conducted computer simulations, the encoding is performed over the finite field GF(4) with quadrature phase shift keying (QPSK) modulation, K = 500, N = 1000, and M = 1000. Therefore, the bandwidth efficiency is 1.0 b/s/Hz for all cases. The wireless channel is assumed to be quasi-static Rayleigh fading; thus the path gains are constant complex Gaussian random


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In the proposed two cooperation strategies, we have a few stations cooperating to transmit messages with the orthogonal channel assignment. That is, a rateless codeword is divided into a few parts and assigned to several orthogonal channels.

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Figure 4. Multi-user cooperation with rateless network coding. variables over a transmission period and vary from one frame to another. For ∞-station cooperation, it is equivalent to transmissions over a fast fading channel, where the channel varies from symbol to symbol. In other words, when a large number of stations participate in cooperation, the spatial diversity mechanism is equivalent to the time diversity in fast fading channels. The path gains are modeled as independent samples of a complex Gaussian random variable with zero mean and variance 0.5 per real dimension. The rateless codes adopted are eIRA codes since they could be encoded efficiently and decoded with low complexity using the BP decoding algorithm [12, 13]. Moreover, eIRA codes could provide uniformly good performance over fading channels at various rates and code lengths. To avoid symbol conversion from binary to non-binary at the transmitter and soft-information conversion from non-binary to binary for BP decoding at the receiver, non-binary rateless codes are used in the conducted simulations. In particular, the original rateless codes studied in [12, 13] are binary codes, while in this article we modify the non-binary rateless codes to be over GF(q) for cooperative communications with high bandwidth efficiency. Specifically, let m = [m1, m 2 , … , m K ] be the source information vector, where mi is defined over GF(q) for i = 1, 2, …, K. Let v = [v1, v2, …, vN] denote the codeword. The encoding symbol vj is generated as v1 = m1, vj = vj–1 + mj for 1 < j ≤ K, and vj = vj–1 + c1 ⋅ ms1 + c2 ⋅ ms2 + … + cd ⋅ msd for j > K, where the operations are over GF(q), d is the encoding degree, and c1, c2, …, cd are d non-zero encoding coeffi-


cients from the finite field GF(q), which are chosen uniformly at random. The column degree distribution of the first K columns of the parity-check matrix for the eIRA code is uniform. In our simulations we set q = 4 and d = 3. Figure 5 shows different results of frame error rate (FER) vs. signal-to-noise ratio (SNR) for the proposed rateless-coding-based cooperative communications. The performance results apply to both single-source partial cooperation and multisource cooperation with the following setup. For single-source cooperation with rateless coding, T stations form a team to perform cooperative communication. Each station participating in the cooperative transmission equally contributes Ni = N/T encoded symbols during a transmission period. For multisource cooperation, T stations work together as a team to perform cooperative communication, and each station has K/T source symbols to transmit during a transmission period. Moreover, each station transmits M/T encoded symbols during a transmission period. It is obvious from Fig. 5 that cooperation schemes outperform non-cooperation schemes, and the FER improves over the number of cooperative stations.

CONCLUSIONS In this article we study cooperative communication schemes with rateless coding to exploit MIMO diversity gains. Two cooperation strategies, single-source partial cooperation with rateless coding and multi-user cooperation with rateless network coding, were proposed. Simulation results verified that the proposed coopera-

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Figure 5. FER vs. SNR without cooperation and with rateless network-codingbased cooperation, respectively. tion strategies provide a parameter-flexible, encoding-simple, and bandwidth-efficient solution for distributed MIMO systems.

ACKNOWLEDGMENTS The work presented in this article was supported in part by the National Science Foundation (NSF) of China with Grants 60872008, 60972017, and 60972018; the Program for New Century Excellent Talents in the University of China under Grant NCET-08-0217; the National High Technology Development 863 Program of China under Grant 2009AA011803; the Research Fund for the Doctoral Program of Higher Education of the Ministry of Education (MOE) of China under Grants 200804871142, 20070007019, and 20091101110019; the Excellent Young Teachers Program of MOE, PRC with grant 20091101120028; research grants from RGC under Contracts CERG 622407, 622508, RPC0607.EG05, and N HKUST60907; the NSFC Oversea Young Investigator Grant under Grant 60629203; the U.S. NSFwith Grants CNS0721935 and CCF-0726740; and the U.S. Department of Defense with Grants HDTRA-07-1-0037 and HDTRA-08-1-0010.

REFERENCES [1] A. Sanderovich, S. Shamai, and Y. Steinberg, “Distributed MIMO Receiver-Achievable Rates and Upper Bounds,” IEEE Trans. Info. Theory, vol. 55, no. 10, Oct. 2009, pp. 4419–38. [2] Z. Xiong et al., “A GMD-Based Precoding Scheme for Downlink Multiuser Multistream MIMO Channels,” 42nd Asilomar Conf. Sig., Sys., Comp., Pacific Grove, CA, Oct. 2008, pp. 311–15. [3] W. Choi and J. G. Andrews, “Downlink Performance and Capacity of Distributed Antenna Systems in a Multicell Environment,” IEEE Trans. Wireless Commun., vol. 6, no. 1, Jan. 2007, pp. 69–73. [4] J. Park, E. Song, and W. Sung, “Capacity Analysis for Distributed Antenna Systems Using Cooperative Transmission Schemes in Fading Channels,” IEEE Trans. Wireless Commun., vol. 8, no. 2, Feb. 2009, pp. 586–92. [5] A. Nosratinia, T. E. Hunter, and A. Hedayat, “Cooperative Communications in Wireless Networks,” IEEE Commun. Mag., vol. 42, no. 10, Oct. 2004, pp. 74–80.

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[6] H. Zhang et al., “Asynchronous Interference Mitigation in Cooperative Base Station Systems,” IEEE Trans. Wireless Commun., vol. 7, no. 1, Jan. 2008, pp. 155–65. [7] T. Mayer, H. Jenkac, and J. Hagenauer, “Turbo Base-Station Cooperation for Intercell Interference Cancellation,” IEEE ICC, Istanbul, June 2006, pp. 4977–82. [8] P. Liu et al., “Cooperative Wireless Communications: A Cross-Layer Approach,” IEEE Wireless Commun., vol. 13, no. 4, Aug. 2006, pp. 84–92. [9] H. Zhang and H. Dai, “Cochannel Interference Mitigation and Cooperative Processing in Downlink Multicell Multiuser MIMO Networks,” Euro. J. Wireless Commun. Net., vol. 2004, 4th qtr., 2004, pp. 222–35. [10] T. E. Hunter and A. Nosratinia, “Distributed Protocols for User Cooperation in Multi-User Wireless Networks,” IEEE GLOBECOM, Dallas, TX, Dec. 2004, pp. 3788–92. [11] T. S. Rappaport, Wireless Communications: Principles and Practice, 2nd ed., Prentice Hall, 2002. [12] J. Castura and Y. Mao, “Rateless Coding over Fading Channels,” IEEE Commun. Letters, vol. 10, no. 1, Jan. 2006, pp. 46–48. [13] M. Yang, W. E. Ryan, and Y. Li, “Design of Efficiently Encodable Moderate-Length High-Rate Irregular LDPC Codes,” IEEE Trans. Commun., vol. 52, no. 4, Apr. 2004, pp. 564–71. [14] X. Li et al., “Binary Linear Multicast Network Coding on Acyclic Networks: Principles and Applications in Wireless Communication Networks,” IEEE JSAC, vol. 27, no. 5, June 2007, pp. 738–48. [15] Y. Wu et al., “Network Planning in Wireless Ad Hoc Networks: A Cross-Layer Approach,” IEEE JSAC, Special Issue on Wireless Ad Hoc Networks, vol. 23, no. 1, Jan. 2005, pp. 136–50.

BIOGRAPHIES XIANGMING LI [M‘06] (xiangming [email protected]) received his Ph.D. degree in communication and information engineering from Beijing University of Posts and Telecommunications, China, in 2000. From August 2000 to January 2002 he worked at Agilent Technologies as a software engineer. From January 2002 to June 2003 he was a postdoctoral fellow at the Department of Electrical and Computer Engineering, Concordia University, Canada, and the Department of Electronic Engineering, City University of Hong Kong. From June 2003 to December 2005 he was an associate professor in the School of Communication and Information Engineering, Chongqing University of Posts and Telecommunications, China. From January 2006 to February 2008 he worked at NTT DOCOMO Beijing Communications Laboratories Co. Ltd. as a researcher and research manager. In February 2008 he joined the School of Information and Electronics Engineering, Beijing Institute of Technology as an associate professor. His current research interests include wireless and mobile communications, MIMOOFDM, channel coding, and applications of coding theory to wireless communication systems. T AO J IANG [M‘06] ([email protected]) is currently a full professor at Wuhan National Laboratory for Optoelectronics, Department of Electronics and Information Engineering, Huazhong University of Science and Technology, Wuhan, China. He received his B.S. and M.S. degrees in applied geophysics from China University of Geosciences, Wuhan, in 1997 and 2000, respectively, and his Ph.D. degree in information and communication engineering from Huazhong University of Science and Technology in April 2004. From August 2004 to December 200, he worked at Brunel University, United Kingdom, and the University of Michigan, among others. He has authored or co-authored over 60 technical papers in major journals and conferences, and five books/chapters in the areas of communications. His current research interests include the areas of wireless communications and corresponding signal processing, especially for cognitive wireless access, vehicular technology, OFDM, UWB, and MIMO, cooperative networks, nano networks and wireless sensor networks. He has served or is serving as a symposium technical program committee member for many major IEEE conferences, including INFOCOM, VTC, ICC, GLOBECOM, and WCNC. He is invited to serve as TPC Symposium Chair for the International Wireless Communications and Mobile Computing Conference 2010. He has served or is serving as Associate Editor of some technical journals in communications, including Wiley’s Wireless Communications and Mobile Computing Journal and Wiley’s International Journal of Communication Systems.


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SHUGUANG CUI [S‘99, M‘06] ([email protected]) received his Ph.D. in electrical engineering from Stanford University, California, in 2005, his M.Eng. in electrical engineering from McMaster University, Hamilton, Canada, in 2000, and his B.Eng. in radio engineering with highest distinction from Beijing University of Posts and Telecommunications in 1997. He is now working as an assistant professor in the Electrical and Computer Engineering Department, Texas A&M University, College Station. From 1997 to 1998 he worked at Hewlett-Packard, Beijing, P. R. China, as a system engineer. In the summer of 2003 he worked at National Semiconductor, Santa Clara, California, on the ZigBee project. From 2005 to 2007 he worked as an assistant professor in the Department of Electrical and Computer Engineering, University of Arizona, Tucson. His current research interests include resource allocation for constrained networks, network information theory, statistical signal processing, and general communication theories. He was a recipient of the NSERC graduate fellowship from the National Science and Engineering Research Council of Canada and the Canadian Wireless Telecommunications Association (CWTA) graduate scholarship. He has servied as TPC Co-Chair for the 2007 IEEE Communication Theory Workshop, the ICC ‘08 Communication Theory Symposium, and the GLOBECOM ‘10 Communication Theory Symposium. He has also served as Associate Editor for IEEE Communication Letters and IEEE Transactions on Vehicular Technology, and is an elected member of the IEEE Signal Processing Society SPCOM Technical Committee. JIANPING AN ([email protected]) received his Ph.D. degree from Beijing Institute of Technology in 2005. He joined the School of Information and Electronics Engineering, Beijing Institute of Technology in 1996, where he currently is a professor. His research interests are in the fields of software radio, cognitive radio, wireless networks, and communications.


Q IAN Z HANG [M’00, SM‘04] ([email protected]) received her B.S., M.S., and Ph.D. degrees from Wuhan University in 1994, 1996, and 1999, respectively, all in computer science. She joined Hong Kong University of Science and Technology in September 2005 as an associate professor. Before that, she was in Microsoft Research, Asia, Beijing, China, from July 1999, where she was the research manager of the Wireless and Networking Group. She has published about 200 refereed papers in international leading journals and key conferences in the areas of wireless/Internet multimedia networking, wireless communications and networking, and overlay networking. She is the inventor of about 30 pending patents. Her current research interests are in the areas of wireless communications, IP networking, multimedia, P2P overlay, and wireless security. She is an Associate Editor for IEEE Transactions on Mobile Computing, IEEE Transactions on Multimedia, IEEE Transactions on Vehicular Technology, Computer Networks, and Computer Communications. She has also served as Guest Editor for IEEE Journal on Selected Areas in Communications, IEEE Wireless Communications, Computer Networks, and ACM/Kluwer Mobile Networks and Applications. She has been involved in the organizing committees for many important IEEE conferences, including ICC, GLOBECOM, WCNC, and INFOCOM. She has received TR 100 (MIT Technology Review) World’s Top Young Innovator Award. She also received the Best Asia Pacific (AP) Young Researcher Award from the IEEE Communications Society in 2004. She received the Best Paper Award from the Multimedia Technical Committee (MMTC) of the IEEE Communications Society and the Best Paper Award at QShine 2006, IEEE GLOBECOM 2007, and IEEE ICDCS 2008. She received the Oversea Young Investigator Award from the National Natural Science Foundation of China (NSFC) in 2006. She is Chair of the Multimedia Communication Technical Committee of the IEEE Commºunications Society.

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