Rong Rong Chen

Joint noncoherent demodulation and decoding for the block fading channel: a practical framework for approaching Shannon capacity

The paper contains a systematic investigation of practical coding strategies for noncoherent communication over fading channels, guided by explicit comparisons with information-theoretic benchmarks. Noncoherent reception is interpreted as joint data and channel estimation, assuming that the channel is time varying and a priori unknown. We consider iterative decoding for a serial concatenation of a standard binary outer channel code with an inner modulation code amenable to noncoherent detection. For an information rate of about 1/2 bit per channel use, the proposed scheme, using a quaternary phase-shift keying (QPSK) alphabet, provides performance within 1.6-1.7 dB of Shannon capacity for the block fading channel, and is about 2.5-3 dB superior to standard differential demodulation in conjunction with an outer channel code. We also provide capacity computations for noncoherent communication using standard phase-shift keying (PSK) and quadrature amplitude modulation (QAM) alphabets; comparing these with the capacity with unconstrained input provides guidance as to the choice of constellation as a function of the signal-to-noise ratio. These results imply that QPSK suffices to approach the unconstrained capacity for the relatively low information and fading rates considered in our performance evaluations, but that QAM is superior to PSK for higher information or fading rates, motivating further research into efficient noncoherent coded modulation with QAM alphabets.

Value iteration and optimization of multiclass queueing networks

This paper considers in parallel the scheduling problem for multiclass queueing networks, and optimization of Markov decision processes. It is shown that the value iteration algorithm may perform poorly when the algorithm is not initialized properly. The most typical case where the initial value function is taken to be zero may be a particularly bad choice. In contrast, if the value iteration algorithm is initialized with a stochastic Lyapunov function, then the following hold: (i) a stochastic Lyapunov function exists for each intermediate policy, and hence each policy is regular (a strong stability condition), (ii) intermediate costs converge to the optimal cost, and (iii) any limiting policy is average cost optimal. It is argued that a natural choice for the initial value function is the value function for the associated deterministic control problem based upon a fluid model, or the approximate solution to Poisson’s equation obtained from the LP of Kumar and Meyn. Numerical studies show that either choice may lead to fast convergence to an optimal policy.

K-t ISD: Dynamic cardiac MR imaging using compressed sensing with iterative support detection

Compressed sensing (CS) has been used in dynamic cardiac MRI to reduce the data acquisition time. The sparseness of the dynamic image series in the spatial‐ and temporal‐frequency (x‐f) domain has been exploited in existing works. In this article, we propose a new k‐t iterative support detection (k‐t ISD) method to improve the CS reconstruction for dynamic cardiac MRI by incorporating additional information on the support of the dynamic image in x‐f space based on the theory of CS with partially known support. The proposed method uses an iterative procedure for alternating between image reconstruction and support detection in x‐f space. At each iteration, a truncated ℓ1 minimization is applied to obtain the reconstructed image in x‐f space using the support information from the previous iteration. Subsequently, by thresholding the reconstruction, we update the support information to be used in the next iteration. Experimental results demonstrate that the proposed k‐t ISD method improves the reconstruction quality of dynamic cardiac MRI over the basic CS method in which support information is not exploited. Magn Reson Med, 2012.

On performance of sphere decoding and Markov chain Monte Carlo detection methods

In a recent work, it has been found that the suboptimum detectors that are based on Markov chain Monte Carlo (MCMC) simulation techniques perform significantly better than their sphere decoding (SD) counterparts. In this letter, we explore the sources of this difference and show that a modification to existing sphere decoders can result in some improvement in their performance, even though they still fall short when compared with the MCMC detector. We also present a novel SD detector that is an exact realization of max-log-MAP detector. We call this exact max-log SD detector. Comparison of the results of this detector with those of the max-log version of the MCMC detector reveals that the latter is near optimal.

Approaching MIMO capacity using bitwise Markov Chain Monte Carlo detection

This paper examines near capacity performance of Markov Chain Monte Carlo (MCMC) detectors for multiple-input and multiple-output (MIMO) channels. The proposed MCMC detector (Log-MAP-tb b-MCMC) operates in a strictly bit-wise fashion and adopts Log-MAP algorithm with table look-up. When concatenated with an optimized low-density parity-check (LDPC) code, Log-MAP-tb b-MCMC can operate within 1.2-1.8 dB of the capacity of MIMO systems with 8 transmit/receive antennas at spectral efficiencies up to ¿ = 24 bits/channel use (b/ch). This result improves upon best performance achieved by turbo coded systems using list sphere decoding (LSD) detector by 2.3-3.8 dB, leading to nearly 50% reduction in the capacity gap. Detailed comparisons of the Log-MAP-tb b-MCMC with LSD based detectors demonstrate that MCMC detector is indeed the detector of choice for achieving channel capacity both in terms of performance and complexity.

Application of Nonbinary LDPC Cycle Codes to MIMO Channels

In this paper, we investigate the application of nonbinary low-density parity-check (LDPC) cycle codes over Galois field GF(q) to multiple-input multiple-output (MIMO) channels. Two types of LDPC coded systems that employ either joint or separate MIMO detection and channel decoding are considered, depending on the size of the Galois field and the modulation choice. We construct a special class of nonbinary LDPC cycle codes called the parallel sparse encodable (PSE) codes. The PSE code, consisting of a quasi-cyclic (QC) LDPC cycle code and a simple tree code, has the attractive feature that it is not only linearly encodable, but also allows parallel encoding which can reduce the encoding time significantly. We provide a systematic comparison between nonbinary coded systems and binary coded systems in both performance and complexity. Our results show that the proposed nonbinary system employing the PSE code outperforms not only the binary LDPC code specified in the 802.16e standard, but also the optimized binary LDPC code obtained using the EXIT chart methods. Through a detailed complexity analysis, we conclude that for the MIMO channel considered, the nonbinary coded systems achieve a superior performance at a receiver complexity that is comparable to that of the binary systems.

On fixed input distributions for noncoherent communication over high-SNR Rayleigh-fading channels

It is well known that independent and identically distributed Gaussian inputs, scaled appropriately based on the signal-to-noise ratio (SNR), achieve capacity on the additive white Gaussian noise (AWGN) channel at all values of SNR. In this correspondence, we consider the question of whether such good input distributions exist for frequency-nonselective Rayleigh-fading channels, assuming that neither the transmitter nor the receiver has a priori knowledge of the fading coefficients. In this noncoherent regime, for a Gauss-Markov model of the fading channel, we obtain explicit mutual information bounds for the Gaussian input distribution. The fact that Gaussian input generates bounded mutual information motivates the search for better choices of fixed input distributions for high-rate transmission over rapidly varying channels. Necessary and sufficient conditions are derived for characterizing such distributions for the worst case scenario of memoryless fading, using the criterion that the mutual information is unbounded as the SNR gets large. Examples of both discrete and continuous distributions that satisfy these conditions are given. A family of fixed input distributions with mutual information growth rate of O((loglogSNR)/sup 1-u/), u>0 are constructed. It is also proved that there does not exist a single fixed-input distribution that achieves the optimal mutual information growth rate of loglogSNR.

Random Access Protocols for Collaborative Spectrum Sensing in Multi-Band Cognitive Radio Networks

In this paper, collaborative sensing and distributed detection are addressed in the context of multi-band cognitive radios. In a cognitive radio network, all the nodes may sense the spectrum simultaneously. They should then exchange their sensing results in order to improve the reliability of the detection. This exchange of information has to be done effectively to improve the bandwidth efficiency of the network. We propose a generalized medium access control (MAC) signaling protocol based on random access and study its performance through a thorough theoretical analysis. We begin with a nonadaptive protocol with fixed parameters. The numerical results obtained from analysis reveals that the fixed parameter protocol is not robust to the variation of the network conditions which, in general, are unknown a priori. We thus extend the proposed protocol to an adaptive one. Analysis of this adaptive protocol reveals its much superior performance. Our analysis covers a wide range of network conditions, including the case where some spectral activities may be hidden from a few of cognitive nodes and the case when a cognitive node senses only a subset of spectral bands. All theoretical results are corroborated through computer simulations.

Markov Chain Monte Carlo Detectors for Channels With Intersymbol Interference

In this paper, we propose novel low-complexity soft-in soft-out (SISO) equalizers using the Markov chain Monte Carlo (MCMC) technique. We develop a bitwise MCMC equalizer (b-MCMC) that adopts a Gibbs sampler to update one bit at a time, as well as a group-wise MCMC (g-MCMC) equalizer where multiple symbols are updated simultaneously. The g-MCMC equalizer is shown to outperform both the b-MCMC and the linear minimum mean square error (MMSE) equalizer significantly for channels with severe amplitude distortion. Direct application of MCMC to channel equalization requires sequential processing which leads to long processing delay. We develop a parallel processing algorithm that reduces the processing delay by orders of magnitude. Numerical results show that both the sequential and parallel processing MCMC equalizers perform similarly well and achieve a performance that is only slightly worse than the optimum maximum a posteriori (MAP) equalizer. The MAP equalizer, on the other hand, has a complexity that grows exponentially with the size of the memory of the channel, while the complexity of the proposed MCMC equalizers grows linearly.

AN EXTENDED CLASS OF TIME-CONTINUOUS BRANCHING PROCESSES

This paper is devoted to studying an extended class of time-continuous branching processes, motivated by the study of stochastic control theory and interacting particle systems. The uniqueness, extinction, recurrence and positive recurrence criteria for the processes are presented. The main new point in our proofs is the use of several different comparison methods. The resulting picture shows that the methods are effective and hence should also be meaningful in other situations.