Achievable Information Rates and Coding for MIMO Systems Over ISI Channels and Frequency-Selective Fading Channels

Zhang, Zheng; Duman, Tolga M.; Kurtas, E.

doi:10.1109/tcomm.2004.836449

Cited by 42 publications

(21 citation statements)

References 31 publications

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Section: Information Rate For Standard Modulationsmentioning

confidence: 99%

“…Currently, no single-letter expression of the information rate in (8) is available [7], [8]. In principle, we can obtain an estimate of the information rate as accurately as desired by means of the simulation-based algorithm described in [8]. This approach requires the simulation of a full-complexity BCJR receiver that processes a trellis with |X| NtL states, where |X| is the cardinality of the modulation alphabet.…”

Section: Information Rate For Standard Modulationsmentioning

confidence: 99%

“…Hence, we can evaluate I LB first by collecting the joint statistics of X n and Z n through long simulations of the channel and the receiver, and then by numerically computing the mutual information I(X n ; Z n ). The specific receiver adopted for the computation of the lower bound in (10) does not affect its validity, but does UB from [8] Provable LB from [8] Conjectured LB from [8] Proposed HD-LB Proposed SD-LB Fig. 4.…”

Section: Information Rate For Standard Modulationsmentioning

confidence: 99%

“…For this case, analytical results are available that provide upper and lower bounds on the achievable information rates [6] (see [7] for the extension of the bounds in [6] to MIMO systems). Another approach is introduced in [8], according to which we can obtain an unbiased estimate of the information rate by running long simulations of the channel and of the optimal maximum-a-posteriori detector, i.e., the Bahl-Cocke-Jelinek-Raviv (BCJR) algorithm. Unfortunately, this second approach is infeasible for an UWA environment since the complexity of the BCJR algorithm exponentially grows with the delay spread of the equivalent discrete-time channel model, which, in UWA channels, can be on the order of hundreds of symbols.…”

Section: Introductionmentioning

confidence: 99%

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Capacity of MIMO systems in shallow water acoustic channels

Radošević

Fertonani

Duman

et al. 2010

2010 Conference Record of the Forty Fourth Asilomar Conference on Signals, Systems and Computers

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Abstract-Underwater acoustic (UWA) channels are typically characterized by a multipath structure with large delay spread, where only a few propagation paths carry significant energy. Each path exhibits time variability, which, together with the transmitter and receiver motion, induces Doppler spreading and shifting of the signal. In this paper, we analyze the limits on the information rate achievable through multiple-input multipleoutput (MIMO) communications over UWA channels. Assuming full channel state information (CSI) at the receiver, we evaluate the ergodic capacity in two scenarios: one with partial CSI at the transmitter, and another with no CSI. Also, we consider the constrained capacity for practical modulations, e.g., BPSK and QPSK, and, exploiting the sparseness of the multipath structure, we provide new lower bounds on the achievable information rate. Statistical characterization and numerical examples are given based on the data collected in a recent experiment, conducted off the coast of Kauai, Hawaii, in June 2008.

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Section: Information Rate For Standard Modulationsmentioning

confidence: 99%

Section: Information Rate For Standard Modulationsmentioning

confidence: 99%

Section: Information Rate For Standard Modulationsmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

See 2 more Smart Citations

Capacity of MIMO systems in shallow water acoustic channels

Radošević

Fertonani

Duman

et al. 2010

2010 Conference Record of the Forty Fourth Asilomar Conference on Signals, Systems and Computers

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“…The computational load required to obtain a tight bound is high, since the number of dominant terms of the precursor ISI for the sparse channels with large delay spreads is potentially very large. These bounds can be easily extended to multiple-input multiple-output (MIMO) systems based on the results given in [9] and [10]. None of the existing bounds seem to be suitable for ISI channels with long delay spreads and a sparse multipath structure.…”

Section: Introductionmentioning

confidence: 99%

Bounds on the Information Rate for Sparse Channels with Long Memory and i.u.d. Inputs

Radošević

Fertonani

Duman

et al. 2011

IEEE Trans. Commun.

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Abstract-In this paper we propose new bounds on the achievable information rate for discrete-time Gaussian channels with intersymbol interference (ISI) and independent and uniformly distributed (i.u.d.) channel input symbols drawn from finite-order modulation alphabets. Specifically, we are interested in developing new bounds on the achievable rates for sparse channels with long memory. We obtain a lower bound which can be achieved by practical receivers, based on MMSE channel shortening and suboptimal symbol detection for a reduced-state channel. An upper bound is given in the form of a semi-analytical solution derived using basic information theoretic inequalities, by a grouping of the channel taps into several clusters resulting in a newly defined single-input multiple-output (SIMO) channel. We show that the so obtained time-dispersive SIMO channel can be represented by an equivalent single-input single-output (SISO) channel with a significantly shorter channel memory. The reduced computational complexity allows the use of the BCJR algorithm for the newly defined channel. The proposed bounds are illustrated through several sparse channel examples and i.u.d. input symbols, showing that the upper bound significantly outperforms existing bounds. Performance of our lower bound strongly depends on the channel structure, showing best results for minimum-phase and maximum-phase systems.

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Diversity and MIMO Techniques

Djordjevic

2022

Advanced Optical and Wireless Communications Systems

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Achievable Information Rates and Coding for MIMO Systems Over ISI Channels and Frequency-Selective Fading Channels

Cited by 42 publications

References 31 publications

Capacity of MIMO systems in shallow water acoustic channels

Capacity of MIMO systems in shallow water acoustic channels

Bounds on the Information Rate for Sparse Channels with Long Memory and i.u.d. Inputs

Diversity and MIMO Techniques

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