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221
Capacity Limits of MIMO Channels
 IEEE J. SELECT. AREAS COMMUN
, 2003
"... We provide an overview of the extensive recent results on the Shannon capacity of singleuser and multiuser multipleinput multipleoutput (MIMO) channels. Although enormous capacity gains have been predicted for such channels, these predictions are based on somewhat unrealistic assumptions about t ..."
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Cited by 216 (10 self)
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We provide an overview of the extensive recent results on the Shannon capacity of singleuser and multiuser multipleinput multipleoutput (MIMO) channels. Although enormous capacity gains have been predicted for such channels, these predictions are based on somewhat unrealistic assumptions about the underlying timevarying 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 timevarying 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 singleuser MIMO channels. These results indicate that the capacity gain obtained from multiple antennas heavily depends
Communication on the Grassmann Manifold: A Geometric Approach to the Noncoherent MultipleAntenna Channel
 IEEE Trans. Inform. Theory
, 2002
"... In this paper, we study the capacity of multipleantenna fading channels. We focus on the scenario where the fading coefficients vary quickly; thus an accurate estimation of the coefficients is generally not available to either the transmitter or the receiver. We use a noncoherent block fading model ..."
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Cited by 173 (5 self)
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In this paper, we study the capacity of multipleantenna fading channels. We focus on the scenario where the fading coefficients vary quickly; thus an accurate estimation of the coefficients is generally not available to either the transmitter or the receiver. We use a noncoherent block fading model proposed by Marzetta and Hochwald. The model does not assume any channel side information at the receiver or at the transmitter, but assumes that the coefficients remain constant for a coherence interval of length symbol periods. We compute the asymptotic capacity of this channel at high signaltonoise ratio (SNR) in terms of the coherence time , the number of transmit antennas , and the number of receive antennas . While the capacity gain of the coherent multiple antenna channel is min bits per second per hertz for every 3dB increase in SNR, the corresponding gain for the noncoherent channel turns out to be (1 ) bits per second per herz, where = min 2 . The capacity expression has a geometric interpretation as sphere packing in the Grassmann manifold.
Mutual information and minimum meansquare error in Gaussian channels
 IEEE Trans. Inform. Theory
, 2005
"... Abstract — This paper deals with arbitrarily distributed finitepower input signals observed through an additive Gaussian noise channel. It shows a new formula that connects the inputoutput mutual information and the minimum meansquare error (MMSE) achievable by optimal estimation of the input given ..."
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Cited by 135 (22 self)
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Abstract — This paper deals with arbitrarily distributed finitepower input signals observed through an additive Gaussian noise channel. It shows a new formula that connects the inputoutput mutual information and the minimum meansquare error (MMSE) achievable by optimal estimation of the input given the output. That is, the derivative of the mutual information (nats) with respect to the signaltonoise ratio (SNR) is equal to half the MMSE, regardless of the input statistics. This relationship holds for both scalar and vector signals, as well as for discretetime and continuoustime noncausal MMSE estimation. This fundamental informationtheoretic result has an unexpected consequence in continuoustime nonlinear estimation: For any input signal with finite power, the causal filtering MMSE achieved at SNR is equal to the average value of the noncausal smoothing MMSE achieved with a channel whose signaltonoise ratio is chosen uniformly distributed between 0 and SNR. Index Terms — Mutual information, Gaussian channel, minimum meansquare error (MMSE), Wiener process, optimal
Capacity bounds via duality with applications to multipleantenna systems on flatfading channels
 IEEE Trans. Inform. Theory
, 2003
"... A general technique is proposed for the derivation of upper bounds on channel capacity. The technique is based on a dual expression for channel capacity where the maximization (of mutual information) over distributions on the channel input alphabet is replaced with a minimization (of average relativ ..."
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Cited by 101 (36 self)
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A general technique is proposed for the derivation of upper bounds on channel capacity. The technique is based on a dual expression for channel capacity where the maximization (of mutual information) over distributions on the channel input alphabet is replaced with a minimization (of average relative entropy) over distributions on the channel output alphabet. Every choice of an output distribution — even if not the channel image of some input distribution — leads to an upper bound on mutual information. The proposed approach is used in order to study multiantenna flat fading channels with memory where the realization of the fading process is unknown at the transmitter and unknown (or only partially known) at the receiver. It is demonstrated that, for high signaltonoise ratio (SNR), the capacity of such channels typically grows only doublelogarithmically in the SNR. This is in stark contrast to the case with perfect receiver side information where capacity grows logarithmically in the SNR. To better understand this phenomenon
Energyconstrained modulation optimization
 IEEE Transactions on Wireless Communications
, 2005
"... Abstract — We consider wireless systems where the nodes operate on batteries so that energy consumption must be minimized while satisfying given throughput and delay requirements. In this context, we analyze the best modulation strategy to minimize the total energy consumption required to send a giv ..."
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Cited by 75 (9 self)
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Abstract — We consider wireless systems where the nodes operate on batteries so that energy consumption must be minimized while satisfying given throughput and delay requirements. In this context, we analyze the best modulation strategy to minimize the total energy consumption required to send a given number of bits. The total energy consumption includes both the transmission energy and the circuit energy consumption. For uncoded systems, by optimizing the transmission time and the modulation parameters we show that up to 80 % energy savings is achievable over nonoptimized systems. For coded systems, we show that the benefit of coding varies with the transmission distance and the underlying modulation schemes. Index Terms — Energy efficiency, modulation optimization, MQAM, MFSK.
Network Information Flow with Correlated Sources
 IEEE Trans. Inform. Theory
, 2006
"... Consider the following network communication setup, originating in a sensor networking application we refer to as the “sensor reachback ” problem. We have a directed graph G = (V, E), where V = {v0v1...vn} and E ⊆ V × V. If (vi, vj) ∈ E, then node i can send messages to node j over a discrete memor ..."
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Cited by 64 (9 self)
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Consider the following network communication setup, originating in a sensor networking application we refer to as the “sensor reachback ” problem. We have a directed graph G = (V, E), where V = {v0v1...vn} and E ⊆ V × V. If (vi, vj) ∈ E, then node i can send messages to node j over a discrete memoryless channel (Xij, pij(yx), Yij), of capacity Cij. The channels are independent. Each node vi gets to observe a source of information Ui (i = 0...M), with joint distribution p(U0U1...UM). Our goal is to solve an incast problem in G: nodes exchange messages with their neighbors, and after a finite number of communication rounds, one of the M + 1 nodes (v0 by convention) must have received enough information to reproduce the entire field of observations (U0U1...UM), with arbitrarily small probability of error. In this paper, we prove that such perfect reconstruction is possible if and only if H(USUSc) < i∈S,j∈Sc Cij, for all S ⊆ {0...M}, S � = ∅, 0 ∈ S c. Close examination of our achievability proof reveals that in this setup, Shannon information behaves as a classical network flow, identical in nature to the flow of water in pipes. This “information as flow ” view provides an algorithmic interpretation for our results, among which we
Optimal Power Control, Scheduling and Routing in UWB Networks
"... UltraWide Band (UWB) is an emerging wireless physical layer technology that uses a very large bandwidth. We are interested in finding the design objectives of the medium access (MAC, namely, power control and scheduling) and routing protocols of a multihop, besteffort, UWB network. Our objective ..."
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Cited by 61 (5 self)
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UltraWide Band (UWB) is an emerging wireless physical layer technology that uses a very large bandwidth. We are interested in finding the design objectives of the medium access (MAC, namely, power control and scheduling) and routing protocols of a multihop, besteffort, UWB network. Our objective is to maximize flow rates (more precisely, logutility of flow rates) given node power constraints. The specificity of UWB is expressed by the linear dependence between rate and signaltonoise ratio at the receiver. It is known that, in wireless networks, different routing strategies can imply differences in MAC protocol design. Hence we search for the jointly optimal routing, scheduling and power control.
Bounds on capacity and minimum energyperbit for AWGN relay channels
 IEEE TRANS. INF. THEORY
, 2006
"... Upper and lower bounds on the capacity and minimum energyperbit for general additive white Gaussian noise (AWGN) and frequencydivision AWGN (FDAWGN) relay channel models are established. First, the maxflow mincut bound and the generalized blockMarkov coding scheme are used to derive upper an ..."
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Cited by 54 (2 self)
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Upper and lower bounds on the capacity and minimum energyperbit for general additive white Gaussian noise (AWGN) and frequencydivision AWGN (FDAWGN) relay channel models are established. First, the maxflow mincut bound and the generalized blockMarkov coding scheme are used to derive upper and lower bounds on capacity. These bounds are never tight for the general AWGN model and are tight only under certain conditions for the FDAWGN model. Two coding schemes that do not require the relay to decode any part of the message are then investigated. First, it is shown that the “sideinformation coding scheme ” can outperform the blockMarkov coding scheme. It is also shown that the achievable rate of the sideinformation coding scheme can be improved via time sharing. In the second scheme, the relaying functions are restricted to be linear. The problem is reduced to a “singleletter ” nonconvex optimization problem for the FDAWGN model. The paper also establishes a relationship between the minimum energyperbit and capacity of the AWGN relay channel. This relationship together with the lower and upper bounds on capacity are used to establish corresponding lower and upper bounds on the minimum energyperbit that do not differ by more than a factor of 1 45 for the FDAWGN relay channel model and 1 7 for the general AWGN model.
Impact of antenna correlation on the capacity of multiantenna channels
 IEEE TRANS. INFORM. THEORY
, 2005
"... This paper applies random matrix theory to obtain analytical characterizations of the capacity of correlated multiantenna channels. The analysis is not restricted to the popular separable correlation model, but rather it embraces a more general representation that subsumes most of the channel model ..."
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Cited by 51 (2 self)
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This paper applies random matrix theory to obtain analytical characterizations of the capacity of correlated multiantenna channels. The analysis is not restricted to the popular separable correlation model, but rather it embraces a more general representation that subsumes most of the channel models that have been treated in the literature. For arbitrary signaltonoise ratios @ A, the characterization is conducted in the regime of large numbers of antennas. For the low and high regions, in turn, we uncover compact capacity expansions that are valid for arbitrary numbers of antennas and that shed insight on how antenna correlation impacts the tradeoffs among power, bandwidth, and rate.
On the power efficiency of sensory and ad hoc wireless networks
 in Proc. Asilomar Conf. Signals, Systems, and Computing
, 2002
"... Abstract—We consider the power efficiency of a communications channel, i.e., the maximum bit rate that can be achieved per unit power (energy rate). For additive white Gaussian noise (AWGN) channels, it is well known that power efficiency is attained in the low signaltonoise ratio (SNR) regime whe ..."
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Cited by 48 (3 self)
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Abstract—We consider the power efficiency of a communications channel, i.e., the maximum bit rate that can be achieved per unit power (energy rate). For additive white Gaussian noise (AWGN) channels, it is well known that power efficiency is attained in the low signaltonoise ratio (SNR) regime where capacity is proportional to the transmit power. In this paper, we first show that for a random sensory wireless network with users (nodes) placed in a domain of fixed area, with probability converging to one as grows, the power efficiency scales at least by a factor of. In other words, each user in a wireless channel with nodes can support the same communication rate as a singleuser system, but by expending only 1 times the energy. Then we look at a random ad hoc network with relay nodes and simultaneous transmitter/receiver pairs located in a domain of fixed area. We show that as long as, we can achieve a power efficiency that scales by a factor of. We also give a description of how to achieve these gains. Index Terms—Capacity, sensor networks, wireless communication systems and networks. I.