Iterative decoding for MIMO channels via modified sphere decoding

In recent years, soft iterative decoding techniques have been shown to greatly improve the bit error rate performance of various communication systems. For multiantenna systems employing space-time codes, however, it is not clear what is the best way to obtain the soft information required of the iterative scheme with low complexity. In this paper, we propose a modification of the Fincke-Pohst (sphere decoding) algorithm to estimate the maximum a posteriori probability of the received symbol sequence. The new algorithm solves a nonlinear integer least squares problem and, over a wide range of rates and signal-to-noise ratios, has polynomial-time complexity. Performance of the algorithm, combined with convolutional, turbo, and low-density parity check codes, is demonstrated on several multiantenna channels. The results for systems that employ space-time modulation schemes seem to indicate that the best performing schemes are those that support the highest mutual information between the transmitted and received signals, rather than the best diversity gain

On linear H∞ equalization of communication channels

As an alternative to existing techniques and algorithms, we investigate the merit of the H∞ approach to the linear equalization of communication channels. We first give the formulation of all causal H∞ equalizers using the results of and then look at the finite delay case. We compare the risk-sensitive H∞ equalizer with the MMSE equalizer with respect to both the average and the worst-case BER performances and illustrate the improvement due to the use of the H∞ equalizer

Blind channel identification based on second-order statistics: a frequency-domain approach

In this communication, necessary and sufficient conditions are presented for the unique blind identification of possibly nonminimum phase channels driven by cyclostationary processes. Using a frequency domain formulation, it is first shown that a channel can be identified by the second-order statistics of the observation if and only if the channel transfer function does not have special uniformly spaced zeros. This condition leads to several necessary and sufficient conditions on the observation spectra and the channel impulse response. Based on the frequency-domain formulation, a new identification algorithm is proposed

How much does transmit correlation affect the sum-rate scaling of MIMO Gaussian broadcast channels?

This paper considers the effect of spatial correlation between transmit antennas on the sum-rate capacity of the MIMO Gaussian broadcast channel (i.e., downlink of a cellular system). Specifically, for a system with a large number of users n, we analyze the scaling laws of the sum-rate for the dirty paper coding and for different types of beamforming transmission schemes. When the channel is i.i.d., it has been shown that for large n, the sum rate is equal to M log log n + M log P/M + o(1) where M is the number of transmit antennas, P is the average signal to noise ratio, and o(1) refers to terms that go to zero as n → ∞. When the channel exhibits some spatial correlation with a covariance matrix R (non-singular with tr(R) = M), we prove that the sum rate of dirty paper coding is M log log n + M log P/M + log det(R) + o(1). We further show that the sum-rate of various beamforming schemes achieves M log log n + M log P/M + M log c + o(1) where c ≤ 1 depends on the type of beamforming. We can in fact compute c for random beamforming proposed in and more generally, for random beamforming with preceding in which beams are pre-multiplied by a fixed matrix. Simulation results are presented at the end of the paper

Mixed H^2/H^∞ Estimation: Preliminary Analytic Characterization And A Numerical Solution

We introduce and motivate the problem of mixed H^2/H∞ estimation by studying the stochastic and deterministic approaches of H^2 and H^∞ estimation. Mixed H^2/H^∞ estimators have the property that they have the best average performance over all estimators that achieve a certain worst-case performance bound. They thus allow a tradeoff between average and worst-case performances. In the finite horizon case, we obtain a numerical solution (based on convex optimization methods) for the optimal mixed H^2/H^∞ estimator. We also give some analytic characterizations, both on this optimal solution, and on the set of all estimators achieving a guaranteed worst-case bound. A numerical example is also provided

Design of optimal mixed H_2/H_∞ static state feedback controllers

Despite advances in robust control theory, the robust performance problem formulated in the mixed H_2/H_∞ framework largely remains an open problem. In this approach, one seeks a controller that minimizes the H_2 norm of a closed-loop map over all admissible controllers while satisfying an H_∞ constraint on another closed-loop map. Unlike the optimal H_2 problem or the γ-level sub-optimal H_∞ problem, the mixed H_2/H_∞ problem does not have a readily computable solution. In the paper we restrict consideration to static state feedback controllers and propose an efficient iterative algorithm for computing the optimal H_2/H_∞ solution

H^∞ equalization of communication channels

As an alternative to existing techniques and algorithms, we investigate the merit of the H-infinity approach to the equalization of communication channels. We first look at causal H-infinity equalization problem and then look at the improvement due to finite delay. By introducing the risk sensitive property, we compare the average performance of the central H-infinity equalizer with the MMSE equalizer in equalizing minimum phase channels

FIR H∞ equalization

We approach FIR equalization problem from an H∞ perspective. First, we formulate the calculation of the optimal H∞ performance for a given equalization setting as a semidefinite programming (SDP) problem. H∞ criterion provides a set of FIR equalizers with different optimality properties. Among these, we formulate the calculation of risk sensitive or minimum entropy FIR filter as the constrained analytic centring problem and mixed H2/H" problem as another SDP. We provide an example to il- lustrate the procedures we described

On H^∞ optimal signal reconstruction in noisy filter banks

We study the design of synthesis filters in noisy filter bank systems using an H^∞ point of view. For unitary analysis polyphase matrices we obtain an explicit expression for the minimum achievable disturbance attenuation. Numerical examples and comparisons with existing methods are also included

On robust signal reconstruction in noisy filter banks

We study the design of synthesis filters in noisy filter bank systems using an H∞ estimation point of view. The H∞ approach is most promising in situations where the statistical properties of the disturbances (arising from quantization, compression, etc.) in each subband of the filter bank is unknown, or is too difficult to model and analyze. For the important special case of unitary analysis polyphase matrices we obtain an explicit expression for the minimum achievable disturbance attenuation. For arbitrary analysis polyphase matrices, standard state-space H∞ techniques can be employed to obtain numerical solutions. When the synthesis filters are restricted to being FIR, as is often the case in practice, the design can be cast as a finite-dimensional semi-definite program. In this case, we can effectively exploit the inherent non-uniqueness of the H∞ solution to optimize for an additional criteria. By optimizing for average performance in addition to the H∞ criteria, we obtain mixed H^2/H∞ optimal FIR synthesis filters. Alternatively, if the additional criteria is concerned with penalizing occasional occurrence of large values of reconstruction errors more than frequent occurrence of small to moderate ones, we obtain risk-sensitive FIR synthesis filters. Numerical examples and comparisons with existing methods are also included