We develop and analyze low-complexity cooperative diversity protocols that combat fading induced by multipath propagation in wireless networks. The underlying techniques exploit space diversity available through cooperating terminals' relaying signals for one another. We outline several strategies employed by the cooperating radios, including fixed relaying schemes such as amplify-and-forward and decode-and-forward, selection relaying schemes that adapt based upon channel measurements between the cooperating terminals, and incremental relaying schemes that adapt based upon limited feedback from the destination terminal. We develop performance characterizations in terms of outage events and associated outage probabilities, which measure robustness of the transmissions to fading, focusing on the high signal-to-noise ratio (SNR) regime. Except for fixed decode-and-forward, all of our cooperative diversity protocols are efficient in the sense that they achieve full diversity (i.e., second-order diversity in the case of two terminals), and, moreover, are close to optimum (within 1.5 dB) in certain regimes. Thus, using distributed antennas, we can provide the powerful benefits of space diversity without need for physical arrays, though at a loss of spectral efficiency due to half-duplex operation and possibly at the cost of additional receive hardware. Applicable to any wireless setting, including cellular or ad hoc networks-wherever space constraints preclude the use of physical arrays-the performance characterizations reveal that large power or energy savings result from the use of these protocols.

Cooperative diversity in wireless networks: Efficient protocols and outage behavior

Mobile users' data rate and quality of service are limited by the fact that, within the duration of any given call, they experience severe variations in signal attenuation, thereby necessitating the use of some type of diversity. In this two-part paper, we propose a new form of spatial diversity, in which diversity gains are achieved via the cooperation of mobile users. Part I describes the user cooperation strategy, while Part II (see ibid., p.1939-48) focuses on implementation issues and performance analysis. Results show that, even though the interuser channel is noisy, cooperation leads not only to an increase in capacity for both users but also to a more robust system, where users' achievable rates are less susceptible to channel variations.

http://dns2.asia.edu.tw/~ysho/YSHO-English/2000%20Engineering/PDF/IEE%20Tra%20Com51,%201927.pdf

User cooperation diversity. Part I. System description

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Convex Analysisの二,三の進展について

A relay channel consists of an input x_{l} , a relay output y_{1} , a channel output y , and a relay sender x_{2} (whose transmission is allowed to depend on the past symbols y_{1} . The dependence of the received symbols upon the inputs is given by p(y,y_{1}|x_{1},x_{2}) . The channel is assumed to be memoryless. In this paper the following capacity theorems are proved. 1)If y is a degraded form of y_{1} , then C \: = \: \max \!_{p(x_{1},x_{2})} \min \,{I(X_{1},X_{2};Y), I(X_{1}; Y_{1}|X_{2})} . 2)If y_{1} is a degraded form of y , then C \: = \: \max \!_{p(x_{1})} \max_{x_{2}} I(X_{1};Y|x_{2}) . 3)If p(y,y_{1}|x_{1},x_{2}) is an arbitrary relay channel with feedback from (y,y_{1}) to both x_{1} \and x_{2} , then C\: = \: \max_{p(x_{1},x_{2})} \min \,{I(X_{1},X_{2};Y),I \,(X_{1};Y,Y_{1}|X_{2})} . 4)For a general relay channel, C \: \leq \: \max_{p(x_{1},x_{2})} \min \,{I \,(X_{1}, X_{2};Y),I(X_{1};Y,Y_{1}|X_{2}) . Superposition block Markov encoding is used to show achievability of C , and converses are established. The capacities of the Gaussian relay channel and certain discrete relay channels are evaluated. Finally, an achievable lower bound to the capacity of the general relay channel is established.

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Capacity theorems for the relay channel

A channel with output Y = X + S + Z is examined, The state S \sim N(0, QI) and the noise Z \sim N(0, NI) are multivariate Gaussian random variables ( I is the identity matrix.). The input X \in R^{n} satisfies the power constraint (l/n) \sum_{i=1}^{n}X_{i}^{2} \leq P . If S is unknown to both transmitter and receiver then the capacity is \frac{1}{2} \ln (1 + P/( N + Q)) nats per channel use. However, if the state S is known to the encoder, the capacity is shown to be C^{\ast} =\frac{1}{2} \ln (1 + P/N) , independent of Q . This is also the capacity of a standard Gaussian channel with signal-to-noise power ratio P/N . Therefore, the state S does not affect the capacity of the channel, even though S is unknown to the receiver. It is shown that the optimal transmitter adapts its signal to the state S rather than attempting to cancel it.

Writing on dirty paper (Corresp.)

The advances in the area of multi-way channels during the period 1961-1976 are described Shannon's two-way channel, the multiple-access channel, the interference channel, the broadcast channel, and the relay channel are treated successively Only channel coding aspects are discussed Thirty-nine open problems are mentioned

A survey of multi-way channels in information theory: 1961-1976

The capacity regions are determined for various communication situations in which one or both encoders for a multiple access channel crib from the other encoder and learn the channel input(s) (to be) emitted by this encoder. Most of the achievability proofs in this paper hinge upon the new concept of backward decoding. Also, the notion of Shannon strategies seems to be of crucial importance. It is demonstrated that in some situations parts of the total cooperation line are achievable. Moreover, it is proved that if the encoders and the decoder are allowed to be nondeterministic, the capacity regions are not increased.

The discrete memoryless multiple-access channel with cribbing encoders

A simple proof using random partitions and typicality is given for Marton's coding theorem for broadcast channels.

A proof of Marton's coding theorem for the discrete memoryless broadcast channel (Corresp.)

The problem of the nonparametric estimation of a probability distribution is considered from three viewpoints: the consistency in total variation, the consistency in information divergence, and consistency in reversed-order information divergence. These types of consistencies are relatively strong criteria of convergence, and a probability distribution cannot be consistently estimated in either type of convergence without any restrictions on the class of probability distributions allowed. Histogram-based estimators of distribution are presented which, under certain conditions, converge in total variation, in information divergence, and in reversed-order information divergence to the unknown probability distribution. Some a priori information about the true probability distribution is assumed in each case. As the concept of consistency in information divergence is stronger than that of convergence in total variation, additional assumptions are imposed in the cases of informational divergences. >

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Distribution estimation consistent in total variation and in two types of information divergence

Three different communication situations are considered for the general nondegraded discrete memoryless broadcast channel with two components. In the most general situation, common and separate information is sent to both receivers. In another situation, only separate information is sent, and in a third, one Common and one separate message is sent. For each communication situation a random coding inner bound on the capacity region is derived. An example is presented which Shows that in the most general situation the inner bound strictly dominates the family of rates obtained by time-sharing. The capacity region for the general situation is characterized by a limiting expression. The relationship with the degraded broadcast channel and the connection with other multiway channels, such as the channel with two senders and two receivers, is shown.

E.C. van der Meulen

Papers

A survey of multi-way channels in information theory: 1961-1976

The discrete memoryless multiple-access channel with cribbing encoders

A proof of Marton's coding theorem for the discrete memoryless broadcast channel (Corresp.)

Distribution estimation consistent in total variation and in two types of information divergence

Random coding theorems for the general discrete memoryless broadcast channel