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270
How Much Training is Needed in MultipleAntenna Wireless Links?
 IEEE Trans. Inform. Theory
, 2000
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Iterative Waterfilling for Gaussian Vector Multiple Access Channels
 IEEE TRANSACTIONS ON INFORMATION THEORY
, 2001
"... This paper characterizes the capacity region of a Gaussian multiple access channel with vector inputs and a vector output with or without intersymbol interference. The problem of finding the optimal input distribution is shown to be a convex programming problem, and an efficient numerical algorithm ..."
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Cited by 309 (12 self)
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This paper characterizes the capacity region of a Gaussian multiple access channel with vector inputs and a vector output with or without intersymbol interference. The problem of finding the optimal input distribution is shown to be a convex programming problem, and an efficient numerical algorithm is developed to evaluate the optimal transmit spectrum under the maximum sum data rate criterion. The numerical algorithm has an iterative waterfilling int#j pret#4968 . It converges from any starting point and it reaches with in s per output dimension per transmission from the Kuser multiple access sum capacity af t#j just one it#4 at#49 . These results are also applicable to vector multiple access fading channels.
Bandwidth Scaling for Fading Multipath Channels
, 1999
"... We show that very large bandwidths on fading multipath channels cannot be effectively utilized by spread spectrum systems that (in a particular sense) spread the available power uniformly over both time and frequency. The approach is to express the input process as an expansion in an orthonormal set ..."
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Cited by 108 (12 self)
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We show that very large bandwidths on fading multipath channels cannot be effectively utilized by spread spectrum systems that (in a particular sense) spread the available power uniformly over both time and frequency. The approach is to express the input process as an expansion in an orthonormal set of functions each localized in time and frequency. The fourth moment of each coefficient in this expansion is then uniformly constrained. We show that such a constraint forces the mutual information to 0 inversely with increasing bandwidth. Simply constraining the second moment of these coefficients does not achieve this effect. The results suggest strongly that conventional direct sequence CDMA systems do not scale well to extremely large bandwidths. To illustrate how the interplay between channel estimation and symbol detection affects capacity, we present results for a specific channel and CDMA signaling scheme.
Capacity and power allocation for fading MIMO channels with channel estimation error
 IEEE Transactions on Information Theory
, 2006
"... Abstract—In this correspondence, we investigate the effect of channel estimation error on the capacity of multipleinput–multipleoutput (MIMO) fading channels. We study lower and upper bounds of mutual information under channel estimation error, and show that the two bounds are tight for Gaussian i ..."
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Cited by 103 (0 self)
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Abstract—In this correspondence, we investigate the effect of channel estimation error on the capacity of multipleinput–multipleoutput (MIMO) fading channels. We study lower and upper bounds of mutual information under channel estimation error, and show that the two bounds are tight for Gaussian inputs. Assuming Gaussian inputs we also derive tight lower bounds of ergodic and outage capacities and optimal transmitter power allocation strategies that achieve the bounds under perfect feedback. For the ergodic capacity, the optimal strategy is a modified waterfilling over the spatial (antenna) and temporal (fading) domains. This strategy is close to optimum under small feedback delays, but when the delay is large, equal powers should be allocated across spatial dimensions. For the outage capacity, the optimal scheme is a spatial waterfilling and temporal truncated channel inversion. Numerical results show that some capacity gain is obtained by spatial power allocation. Temporal power adaptation, on the other hand, gives negligible gain in terms of ergodic capacity, but greatly enhances outage performance. Index Terms—Capacity, channel estimation error, feedback delay, multipleinput–multipleoutput (MIMO), mutual information, outage capacity, power allocation, waterfilling. I.
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 70 (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.
Pilot Assisted Wireless Transmissions
 IEEE Signal Processing Mag
, 2004
"... The design of pilot assisted wireless transmissions is considered from signal processing and information theoretical perspectives. A general pilot placement model is presented and related figures of merit discussed. A survey of recent pilot assisted transmission theory and techniques is provided. ..."
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Cited by 63 (2 self)
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The design of pilot assisted wireless transmissions is considered from signal processing and information theoretical perspectives. A general pilot placement model is presented and related figures of merit discussed. A survey of recent pilot assisted transmission theory and techniques is provided.
Optimal Insertion of Pilot Symbols for Transmissions over TimeVarying Flat Fading Channels
 IEEE Trans. Signal Processing
, 2003
"... Two major training techniques for wireless channels are time division multiplexed (TDM) training, and superimposed training. For the TDM schemes with regular periodic placements (RPP), the closedform expression for the steadystate minimum mean square error (MMSE) of channel estimate is obtained ..."
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Cited by 59 (4 self)
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Two major training techniques for wireless channels are time division multiplexed (TDM) training, and superimposed training. For the TDM schemes with regular periodic placements (RPP), the closedform expression for the steadystate minimum mean square error (MMSE) of channel estimate is obtained as a function of placement for GaussMarkov flat fading channels. We then show that, among all periodic placements, the single pilot RPP scheme (RPP1) minimizes the maximum steadystate channel MMSE.
Downlink scheduling and resource allocation for OFDM systems
 IN CISS
, 2006
"... Abstract—We consider scheduling and resource allocation for the downlink of a cellular OFDM system, with various practical considerations including integer carrier allocations, different subchannelization schemes, a maximum SNR constraint per tone, and “selfnoise ” due to channel estimation errors ..."
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Cited by 59 (15 self)
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Abstract—We consider scheduling and resource allocation for the downlink of a cellular OFDM system, with various practical considerations including integer carrier allocations, different subchannelization schemes, a maximum SNR constraint per tone, and “selfnoise ” due to channel estimation errors and phase noise. During each timeslot a subset of users must be scheduled for transmission, and the available tones and transmission power must be allocated among the selected users. Employing a gradientbased scheduling scheme presented in earlier papers reduces this to an optimization problem to be solved in each timeslot. Using dual decomposition techniques, we give an optimal algorithm for this problem when multiple users can timeshare each carrier. We then give several low complexity heuristics that enforce an integer constraint on the carrier allocation. Simulations show that the algorithms presented all achieve similar performance under a wide range of scenarios, and that the performance gap between the optimal and suboptimal algorithms widens when per user SNR constraints or channel estimation errors are considered. I.
How Accurate Channel Prediction needs to be for TransmitBeamforming with Adaptive Modulation over Rayleigh MIMO Channels?
, 2004
"... Adaptive modulation improves the system throughput considerably by matching transmitter parameters to timevarying wireless fading channels. Crucial to adaptive modulation is the quality of channel state information (CSI) at the transmitter. In this paper, we first present a channel predictor based o ..."
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Cited by 55 (3 self)
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Adaptive modulation improves the system throughput considerably by matching transmitter parameters to timevarying wireless fading channels. Crucial to adaptive modulation is the quality of channel state information (CSI) at the transmitter. In this paper, we first present a channel predictor based on pilot symbol assisted modulation (PSAM) for multiinput multioutput (MIMO) Rayleigh fading channels. We then analyze the impact of the channel prediction error on the bit error rate (BER) performance of a transmitbeamformer with adaptive modulation that treats the predicted channels as perfect. Our numerical results reveal the critical value of the normalized prediction error, below which the predicted channels can be treated as perfect by the adaptive modulator; otherwise, explicit consideration of the channel imperfection must be accounted for at the transmitter.