The Impact of CSI and Power Allocation on Relay Channel Capacity and Cooperation Strategies

Capacity gains from transmitter and receiver cooperation are compared in a relay network where the cooperating nodes are close together. Under quasi-static phase fading, when all nodes have equal average transmit power along with full channel state information (CSI), it is shown that transmitter cooperation outperforms receiver cooperation, whereas the opposite is true when power is optimally allocated among the cooperating nodes but only CSI at the receiver (CSIR) is available. When the nodes have equal power with CSIR only, cooperative schemes are shown to offer no capacity improvement over non-cooperation under the same network power constraint. When the system is under optimal power allocation with full CSI, the decode-and-forward transmitter cooperation rate is close to its cut-set capacity upper bound, and outperforms compress-and-forward receiver cooperation. Under fast Rayleigh fading in the high SNR regime, similar conclusions follow. Cooperative systems provide resilience to fading in channel magnitudes; however, capacity becomes more sensitive to power allocation, and the cooperating nodes need to be closer together for the decode-and-forward scheme to be capacity-achieving. Moreover, to realize capacity improvement, full CSI is necessary in transmitter cooperation, while in receiver cooperation optimal power allocation is essential.Comment: Accepted for publication in IEEE Transactions on Wireless Communication

Multi-Antenna Cooperative Wireless Systems: A Diversity-Multiplexing Tradeoff Perspective

We consider a general multiple antenna network with multiple sources, multiple destinations and multiple relays in terms of the diversity-multiplexing tradeoff (DMT). We examine several subcases of this most general problem taking into account the processing capability of the relays (half-duplex or full-duplex), and the network geometry (clustered or non-clustered). We first study the multiple antenna relay channel with a full-duplex relay to understand the effect of increased degrees of freedom in the direct link. We find DMT upper bounds and investigate the achievable performance of decode-and-forward (DF), and compress-and-forward (CF) protocols. Our results suggest that while DF is DMT optimal when all terminals have one antenna each, it may not maintain its good performance when the degrees of freedom in the direct link is increased, whereas CF continues to perform optimally. We also study the multiple antenna relay channel with a half-duplex relay. We show that the half-duplex DMT behavior can significantly be different from the full-duplex case. We find that CF is DMT optimal for half-duplex relaying as well, and is the first protocol known to achieve the half-duplex relay DMT. We next study the multiple-access relay channel (MARC) DMT. Finally, we investigate a system with a single source-destination pair and multiple relays, each node with a single antenna, and show that even under the idealistic assumption of full-duplex relays and a clustered network, this virtual multi-input multi-output (MIMO) system can never fully mimic a real MIMO DMT. For cooperative systems with multiple sources and multiple destinations the same limitation remains to be in effect.Comment: version 1: 58 pages, 15 figures, Submitted to IEEE Transactions on Information Theory, version 2: Final version, to appear IEEE IT, title changed, extra figures adde

Collaborative techniques for achieving spatial diversity in wireless networks

Recently, there has been lots of interest in techniques that achieve spatial diversity to improve the reliability and/or the rate of the transmission in wireless networks. To achieve spatial diversity the transmitters need to be equipped with more than one antenna. To obtain maximum diversity gain, the fading among these antennas should be uncorrelated and hence the antennas should be well separated. This is usually not possible due to the cost and the small size of the wireless devices. In this case, the only way to achieve spatial diversity is a new technique which is known as cooperative diversity. In this technique, diversity is achieved through collaboration between the transmitting nodes in the network In this thesis, we develop a collaboration protocol and a practical coding strategy for the collaborative communication in a three node network with no knowledge of channel state information (CSI) at the transmitter side. Unlike most other works, we have used a variable time-fraction scheme as the basis of our protocol and will show that this protocol achieves full diversity while providing a noticeable coding gain. Then, assuming the availability of the channel state information at the transmitters via a simple feedback from the destination to the relay or to both the source and the relay, we collaboration protocols for both feedback scenarios. The performance of all of these protocols has been obtained using Monte Carlo simulations and has been compared with the outage probability of a single transmitter scenario. We have also obtained constant gain contours that demonstrate the loci of the relay that guarantees a minimum gain for all three scenarios

Cooperative communications in wireless networks.

Zhang Jun.Thesis (M.Phil.)--Chinese University of Hong Kong, 2006.Includes bibliographical references (leaves 82-92).Abstracts in English and Chinese.Chapter 1 --- Introduction --- p.1Chapter 1.1 --- Multipath Fading Channels --- p.1Chapter 1.2 --- Diversity --- p.3Chapter 1.3 --- Outline of the Thesis --- p.6Chapter 2 --- Background and Related Work --- p.8Chapter 2.1 --- Cooperative Diversity --- p.8Chapter 2.1.1 --- User Cooperation --- p.9Chapter 2.1.2 --- Cooperative Diversity --- p.10Chapter 2.1.3 --- Coded Cooperation --- p.12Chapter 2.2 --- Information-Theoretic Studies --- p.13Chapter 2.3 --- Multihop Cellular Networks --- p.15Chapter 2.3.1 --- MCN: Multihop Cellular Network --- p.15Chapter 2.3.2 --- iCAR: Integrated Cellular and Ad Hoc Relaying Systems --- p.17Chapter 2.3.3 --- UCAN: Unified Cellular and Ad Hoc Network Architecture --- p.17Chapter 2.4 --- Wireless Ad Hoc Networks --- p.18Chapter 2.5 --- Space-Time Processing --- p.20Chapter 3 --- Single-Source Multiple-Relay Cooperation System --- p.23Chapter 3.1 --- System Model --- p.23Chapter 3.2 --- Fixed Decode-and-Forward Cooperation System --- p.26Chapter 3.2.1 --- BER for system with errors at the relay --- p.28Chapter 3.2.2 --- General BER formula for single-source nr-relay cooperation system --- p.30Chapter 3.2.3 --- Discussion of Interuser Channels --- p.31Chapter 3.3 --- Relay Selection Protocol --- p.33Chapter 3.3.1 --- Transmission Protocol --- p.34Chapter 3.3.2 --- BER Analysis for Relay Selection Protocol --- p.34Chapter 4 --- Multiple-Source Multiple-Relay Cooperation System --- p.40Chapter 4.1 --- Transmission Protocol --- p.41Chapter 4.2 --- Fixed Cooperative Coding System --- p.43Chapter 4.2.1 --- Performance Analysis --- p.43Chapter 4.2.2 --- Numerical Results and Discussion --- p.48Chapter 4.3 --- Adaptive Cooperative Coding --- p.49Chapter 4.3.1 --- Performance Analysis of Adaptive Cooperative Coding System --- p.50Chapter 4.3.2 --- Analysis of p2(2) --- p.52Chapter 4.3.3 --- Numerical Results and Discussion --- p.53Chapter 5 --- Cooperative Multihop Transmission --- p.56Chapter 5.1 --- System Model --- p.57Chapter 5.1.1 --- Conventional Multihop Transmission --- p.58Chapter 5.1.2 --- Cooperative Multihop Transmission --- p.59Chapter 5.2 --- Performance Evaluation --- p.59Chapter 5.2.1 --- Conventional Multihop Transmission --- p.60Chapter 5.2.2 --- Cooperative Multihop Transmission --- p.60Chapter 5.2.3 --- Numerical Results --- p.64Chapter 5.3 --- Discussion --- p.64Chapter 5.3.1 --- Cooperative Range --- p.64Chapter 5.3.2 --- Relay Node Distribution --- p.67Chapter 5.3.3 --- Power Allocation and Distance Distribution (2-hop Case) --- p.68Chapter 5.4 --- Cooperation in General Wireless Ad Hoc Networks --- p.70Chapter 5.4.1 --- Cooperation Using Linear Network Codes --- p.71Chapter 5.4.2 --- Single-Source Single-Destination Systems --- p.74Chapter 5.4.3 --- Multiple-Source Single-Destination Systems --- p.75Chapter 6 --- Conclusion --- p.80Bibliography --- p.82Chapter A --- Proof of Proposition 1-4 --- p.93Chapter A.1 --- Proof of Proposition 1 --- p.93Chapter A.2 --- Proof of Proposition 2 --- p.95Chapter A.3 --- Proof of Proposition 3 --- p.95Chapter A.4 --- Proof of Proposition 4 --- p.9

Cognitive Radio Systems

Cognitive radio is a hot research area for future wireless communications in the recent years. In order to increase the spectrum utilization, cognitive radio makes it possible for unlicensed users to access the spectrum unoccupied by licensed users. Cognitive radio let the equipments more intelligent to communicate with each other in a spectrum-aware manner and provide a new approach for the co-existence of multiple wireless systems. The goal of this book is to provide highlights of the current research topics in the field of cognitive radio systems. The book consists of 17 chapters, addressing various problems in cognitive radio systems

Receiver

Abstract — Capacity gain from transmitter and receiver cooperation are compared in a relay network where the cooperating Relay nodes are close together. When all nodes havePSfrag equal replacements average transmit power along with full channel state information (CSI), it is proved that transmitter cooperation outperforms receiver cooperation, whereas the opposite is true when power is optimally allocated among the nodes but only receiver phase CSI is available. In addition, when the nodes have equal average powe

Capacity and Power Allocation for Transmitter and Receiver Cooperation in Fading Channels

Abstract — Capacity gain from transmitter and receiver cooperation under channel fading are compared in a relay network where the cooperating nodes are close together. We assume a Rayleigh flat-fading environment in the high signal-to-noise ratio (SNR) regime where the transmitters only have channel distribution information (CDI) but not channel state information (CSI). When all nodes have equal average transmit power, we show that the decode-and-forward transmitter cooperation strategy is capacity-achieving and is superior to receiver cooperation. However, the compress-and-forward receiver cooperation strategy is shown to outperform transmitter cooperation when power is optimally allocated among the nodes. Furthermore, we show that cooperative systems provide resilience to channel fading. However, in a fading channel, capacity becomes more sensitive to power allocation, and the cooperating nodes need to be closer together. With respect to limits on cooperation, it is shownthatinalargeclusterofM cooperating nodes, transmitter cooperation without CSI at the transmitter (CSIT), or receiver cooperation under equal power allocation, provides no capacity gain in a static channel, and at most a constant capacity gain that fails to grow with M in a fading channel. I