Multiple antennas can be used for increasing the amount of diversity or the number of degrees of freedom in wireless communication systems. In this paper, we propose the point of view that both types of gains can be simultaneously obtained for a given multiple antenna channel, but there is a fundamental tradeo# between how much of each any coding scheme can get. For the richly scattered Rayleigh fading channel, we give a simple characterization of the optimal tradeo# curve and use it to evaluate the performance of existing multiple antenna schemes.

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In a point-to-point wireless fading channel, multiple transmit and receive antennas can be used to improve the reliability of reception (diversity gain) or increase the rate of communication for a fixed reliability level (multiplexing gain). In a multiple access situation, multiple receive antennas can also be used to spatially separate signals from different users (multiple access gain). Recent work has characterized the fundamental tradeoff between diversity and multiplexing gains in the point-to-point scenario. In this paper, we extend the results to a multiple access fading channel. Our results characterize the fundamental tradeoff between the three types of gain and provide insights on the capabilities of multiple antennas in a network context. 1

In multiaccess wireless systems, dynamic allocation of resources such as transmit power, bandwidths, and rates is an important means to deal with the time-varying nature of the environment. In this two-part paper, we consider the problem of optimal resource allocation from an information-theoretic point of view. We focus on the multiaccess fading channel with Gaussian noise, and define two notions of capacity depending on whether the traffic is delay-sensitive or not. In Part I, we have analyzed the throughput capacity region which characterizes the long-term achievable rates through the time-varying channel. However, the delay experienced depends on how fast the channel varies. In the present paper, Part II, we introduce a notion of delay-limited capacity which is the maximum rate achievable with delay independent of how slow the fading is. We characterize the delay-limited capacity region of the multiaccess fading channel and the associated optimal resource allocation schemes. We show that successive decoding is optimal, and the optimal decoding order and power allocation can be found explicitly as a function of the fading states; this is a consequence of an underlying polymatroid structure that we exploit.

We use statistical multiplexing and power control to define a notion of delay limited capacity, valid for traffic such as voice and video that cannot tolerate delay. Our capacity bounds the performance of schemes that are robust to changes in the rate of the fading processes. 1 Introduction Fading poses severe problems in mobile, wireless communications. An important technique currently used to mitigate fading is error control coding. Shannon theory enables us to calculate the maximum possible rate in bits/sec that can be reliably sent on the channel. It is not clear, however, that such Shannon capacities are always useful measures if the traffic is delay limited. With block lengths constrained by the delay requirements of the traffic, it is not clear that the fading will always be sufficiently fast to allow averaging over the block length. In the present paper, we show that a notion of "delay limited capacity" can be defined. We first consider a white Gaussian noise model in which us...

Power loading algorithms improve the data rates of OFDM systems. However, they require the transmitter to have perfect channel state information, which is impossible in most wireless systems. We investigate the effects of imperfect (and thus partial) channel feedback on the throughput of OFDM systems. Two channel uncertainty models are studied: i) the ergodic model, where average rate is the figure of merit; and ii) the quasi-static model, where outage rate is relevant. Rate-power allocation algorithms are developed. The throughput achieved by these algorithms and the effects of channel multipath are investigated analytically and with simulations.

The throughput performance of incremental redundomcy (INR) schemes, based on short constraint length convolutional codes, is evaluated for the block-fading Gaussian collision chomnel. Results based on simulations omd union bound computations are compared to estimates of the achievable throughput per- formomce with random binary and Gaussian coding in the limit of laxge block lengths, obtained through information outage considerations. For low channel loads, it is observed that INR schemes with binary convolutional codes and limited block length may provide throughput close to the achievable performance for binary random coding. However, for these low loads, compared to binaxy tomdom coding, Gaussian tomdom coding may provide significantly better throughput performance, which prompts to the use of larger modulation constellations. For high channel loads, a relatively large gap in throughput performance between binary convolutional codes and binary tomdom codes indicates a potential for extensive perfor- mance improvement by alternative coding strategies. Only small improvements of the throughput have been observed by increasing the complexity through increased state convolutional coding.

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We build on Zheng and Tse’s elegant formulation of diversity-multiplexing tradeoff to provide a better understanding of the asymptotic interplay between transmission rate, error probability and signal-to-noise ratio in blockfading MIMO channels. In particular, we identify the limitation imposed by the notion of multiplexing gain and develop a new formulation called the throughput-reliability tradeoff, that avoids this limitation. The new characterization is then used to elucidate the asymptotic trends exhibited by the outage probability curves of block-fading MIMO channels.

In multi-access wireless systems, dynamic allocation of resources such as transmit power, bandwidths and rates is an important means to deal with the time-varying nature of the environment. In this two-part paper, we consider the problem of optimal resource allocation from an information theoretic point of view. We focus on the multi-access fading channel with Gaussian noise, and define two notions of capacity depending on whether the traffic is delay-sensitive or not. In part I, we have analyzed the throughput capacity region which characterizes the long-term achievable rates through the time-varying channel. However, the delay experienced depends on how fast the channel varies. In the present paper, part II, we introduce a notion of delay-limited capacity which is the maximum rate achievable with delay independent of how slow the fading is. We characterize the delay-limited capacity region of the multi-access fading channel and the associated optimal resource allocation schemes. We...