Cooperative diversity is a transmission technique where multiple terminals pool their resources to form a virtual antenna array that realizes spatial diversity gain in a distributed fashion. In this paper, we examine the basic building block of cooperative diversity systems, a simple fading relay channel where the source, destination and relay terminals are each equipped with single antenna transceivers. We consider three different TDMA-based cooperative protocols that vary the degree of broadcasting and receive collision. The relay terminal operates in either the amplify-and-forward (AF) or decode-and-forward (DF) modes. For each protocol, we study the ergodic and outage capacity behavior (assuming Gaussian code books) under the AF and DF modes of relaying. We analyze the spatial diversity performance of the various protocols and find that full spatial diversity (second-order in this case) is achieved by certain protocols provided that appropriate power control is employed. Our analysis unifies previous results reported in the literature and establishes the superiority (both from a capacity as well as a diversity point-of-view) of a new protocol proposed in this paper. The second part of the paper is devoted to (distributed) space-time code design for fading relay channels operating in the AF mode. We show that the corresponding code design criteria consist of the traditional rank and determinant criteria for the case of co-located antennas as well as appropriate power control rules. Consequently space-time codes designed for the case of co-located multi-antenna channels can be used to realize cooperative diversity provided that appropriate power control is employed.

We provide an overview of the extensive recent results on the Shannon capacity of single-user and multiuser multiple-input multiple-output (MIMO) channels. Although enormous capacity gains have been predicted for such channels, these predictions are based on somewhat unrealistic assumptions about the underlying time-varying channel model and how well it can be tracked at the receiver, as well as at the transmitter. More realistic assumptions can dramatically impact the potential capacity gains of MIMO techniques. For time-varying MIMO channels there are multiple Shannon theoretic capacity definitions and, for each definition, different correlation models and channel information assumptions that we consider. We first provide a comprehensive summary of ergodic and capacity versus outage results for single-user MIMO channels. These results indicate that the capacity gain obtained from multiple antennas heavily depends

Much of the performance analysis on multiuser receivers for direct-sequence code-division multiple-access (CDMA) systems is focused on worst case near--far scenarios. The user capacity of power-controlled networks with multiuser receivers are less well-understood. In [1], it was shown that under some conditions, the user capacity of an uplink power-controlled CDMA cell for several important linear receivers can be very simply characterized via a notion of effective bandwidth. In the present paper, we show that these results extend to the case of antenna arrays. We consider a CDMA system consisting of users transmitting to an antenna array with a multiuser receiver, and obtain the limiting signal-to-interference (SIR) performance in a large system using random spreading sequences. Using this result, we show that the SIR requirements of all the users can be met if and only if the sum of the effective bandwidths of the users is less than the total number of degrees of freedom in the system. The effective bandwidth of a user depends only on its own requirement. Our results show that the total number of degrees of freedom of the whole system is the product of the spreading gain and the number of antennas. In the case when the fading distributions to the antennas are identical, we show that a curious phenomenon of "resource pooling" arises: the multiantenna system behaves like a system with only one antenna but with the processing gain the product of the processing gain of the original system and the number of antennas, and the received power of each user the sum of the received powers at the individual antennas.

In this paper, the effect of spatial diversity on the throughput and reliability of wireless networks is examined. Spatial diversity is realized through multiple independently fading transmit/receive antenna paths in single-user communication and through independently fading links in multiuser communication. Adopting spatial diversity as a central theme, we start by studying its information-theoretic foundations, then we illustrate its benefits across the physical (signal transmission/coding and receiver signal processing) and networking (resource allocation, routing, and applications) layers. Throughout the paper, we discuss engineering intuition and tradeoffs, emphasizing the strong interactions between the various network functionalities.

We consider direct sequence code division multiple access (DS-CDMA), modeling interference from users communicating with neighboring base stations by additive colored noise. We consider two types of receiver structures: first we consider the information-theoretically optimal receiver and use the sum capacity of the channel as our performance measure. Second, we consider the linear minimum mean square error (LMMSE) receiver and use the signal-to-interference ratio (SIR) of the estimate of the symbol transmitted as our performance measure. Our main result is a constructive characterization of the possible performance in both these scenarios. A central contribution of this characterization is the derivation of a qualitative feature of the optimal performance measure in both the scenarios studied. We show that the sum capacity is a saddle function:itisconvex in the additive noise covariances and concave in the user received powers. In the linear receiver case, we show that the minimum average power required to meet a set of target performance requirements of the users is a saddle function: it is convex in the additive noise covariances and concave in the set of performance requirements.

Suppression of Multi-User Interference (MUI) and mitigation of time- and frequency-selective (doubly-selective) channel effects constitute major challenges in the design of thirdgeneration wireless mobile systems. Relying on a Basis Expansion Model (BEM) for doubly-selective channels, we develop a channel-independent block spreading scheme that preserves mutual orthogonality among single-cell users at the receiver. This alleviates the need for complex multi-user detection, and enables separation of the desired user by a simple code-matched channelindependent block despreading scheme that is Maximum Likelihood (ML)-optimal under the BEM plus white Gaussian noise assumption on the channel. In addition, each user achieves the maximum delay-Doppler diversity for Gaussian distributed BEM coefficients. Issues like links with existing multi-user transceivers, existence, user efficiency, special cases, backward compatibility with DS-CDMA, and error control coding, are briefly discussed. Index Terms--- Delay Diversity, Doppler Diversity, Multiple Access, Multi-User Detection, Time- and Frequency-Selective Channels.

Abstract—Sensor network with mobile access (SENMA) is an architecture in which randomly deployed low-power sensors are orchestrated by a few powerful mobile access points (APs). This paper considers SENMA from energy-efficiency and informationtheoretic perspectives. By allowing sensors to propagate data directly to mobile APs over multiaccess channels, and relieving sensors from energy-consuming network functions, SENMA has the potential of offering orders of magnitude of improvement in energy efficiency over the multihop ad hoc architecture, as demonstrated by our analysis on scalability. Optimization configurations of SENMA such as the altitude, the trajectory, and the coverage of APs are considered next, using the sum-rate as the performance metric. Optimal strategies for single and multiple APs are determined. For multiple APs, the possibility of and the gain due to cooperation (i.e., joint decoding of signals received at different APs) are investigated. Index Terms—Capacity, energy efficiency, mobile access points (APs), multiple access, sensor networks. I.

A simple multicell uplink communication model is suggested and analyzed for optimally coded randomly spread direct sequence code-division multiple access (DS-CDMA). The model adheres to Wyner's (1994) infinite linear cell-array model, according to which only adjacent-cell interference is present, and characterized by a single parameter 0

The Gaussian multiple--access channel with weighted energy constraint ~ Esum = P k Ek=jdk j 2 models the situation in cellular multiuser communication more accurately than the standard approach with dk = 1; 8k. The minimum weighted sum energy ~ Esum is shown to be only achievable at a unique energy tupel (E1 ; : : : ; EK ), if all weights differ in amplitude. Moreover, the theory is generalized to a multiple receiver system describing cellular communication systems with optimum multiple cell--site processing. While for single-receiver systems the optimum energy tupel is a vertex of the allowed energy region, i.e. successive cancellation is possible, for a multi-receiver system this does not hold in general. I. Introduction Recently, many publications deal with the Shannon capacity of cellular mobile radio systems, e.g. [1, 2, 3] and references therein. These models are more or less accurate, as they have to involve a trade--off between analytical tractability and a precise ...

Abstract—This paper investigates a one-dimensional model of a cellular Gaussian multiple-access channel based on the model introduced by Wyner. A closed form expression is given for the sumrate constraint with a finite number of cells. It is demonstrated that optimal joint decoding can be accomplished using a variation of the forward-backward algorithm that operates on the posterior codeword probabilities. Index Terms — interference channel, cellular communications, multiuser decoding, forward-backward algorithm I.