Search

Recently, lattice-reduction-aided detectors have been proposed for multiinput multioutput (MIMO) systems to achieve performance with full diversity like the maximum likelihood receiver. However, these lattice-reduction-aided detectors are based on the traditional Lenstra-Lenstra-Lovász (LLL) reduction algorithm that was originally introduced for reducing real lattice bases, in spite of the fact that the channel matrices are inherently complex-valued. In this paper, we introduce the complex LLL algorithm for direct application to reducing the basis of a complex lattice which is naturally defined by a complex-valued channel matrix. We derive an upper bound on proximity factors, which not only show the full diversity of complex LLL reduction-aided detectors, but also characterize the performance gap relative to the lattice decoder. Our analysis reveals that the complex LLL algorithm can reduce the complexity by nearly 50% compared to the traditional LLL algorithm, and this is confirmed by simulation. Interestingly, our simulation results suggest that the complex LLL algorithm has practically the same bit-error-rate performance as the traditional LLL algorithm, in spite of its lower complexity. [ABSTRACT FROM AUTHOR]

Abstract-Frames and oversampled filter banks have been extensively studied over the past few years due to their increased design freedom and improved error resilience. In frame expansions, the least square signal reconstruction operator is called the dual frame, which can be obtained by choosing the synthesis filter bank as the para-pseudoinverse of the analysis bank. In this paper, we study the computation of the dual frame by exploiting the Greyule formula, which was originally derived in 1960 to compute the pseudoinverse of a matrix when a new row is appended. Here, we first develop the backward Greville formula to handle the case of row deletion. Based on the forward Greville formula, we then study the computation of para-pseudoinverse for extended filter banks and Laplacian pyramids. Through the backward Greville formula, we investigate the frame-based error resilient transmission over erasure channels. The necessary and sufficient condition for an oversampled filter bank to be robust to one erasure channel is derived. A postfiltering structure is also presented to implement the para-pseudoinverse when the transform coefficients in one subband are completely lost. [ABSTRACT FROM AUTHOR]

Recently, Gallager's bounding techniques have been used to derive tight performance bounds for coded systems in fading channels. Most works in this field have thus far dealt with coherent decoding. This paper develops Gallager bounds for noncoherent systems in fading channels. Unlike coherent decoding, the exact error probability of a noncoherent decoder/detector conditioned on the fading coefficients does not admit a closed-form expression. This difficulty is overcome in this paper by employing the Chernoff technique. Although it weakens the bounds to some extent, the Chernoff technique enables the derivations of the limit-before-average (LBA) bound and Gallager bounds in closed form for noncoherent fading channels. Numerical examples show that the proposed bounds are convergent and are tighter than the conventional union bound. [ABSTRACT FROM AUTHOR]

Gallager's second bounding technique, also known as the generalized union bound, is employed to derive a new upper bound on the error probability of space-time codes (STCs) with maximum-likelihood (ML) decoding on quasi-static Rayleigh fading channels. The new bound is distinguished by two characteristics: unlike the classical union bound, the new bound is rapidly convergent and is only a few decibels away from simulation results; and compared with Gallager's first bound, it has better computational efficiency and numerical stability. Hence, the new bound is a useful tool for performance analysis and computer search of good STCs. Moreover, the correlation between fading coefficients is easily accommodated by the new bound. The application of the new bound to convolutional coding on block-fading channels is also demonstrated, and an improved version is derived for the bit-error probability of maximum a posteriori probability decoding. [ABSTRACT FROM AUTHOR]

Approximate maximum-likelihood noncoherent sequence detection (NSD) for differential space-time modulation (DSTM) in time-selective fading channels is proposed. The starting point is the optimum multiple-symbol differential detection for DSTM that is characterized by exponential complexity. By truncating the memory of the incremental metric, a finite-state trellis is obtained so that a Viterbi algorithm can be implemented to perform sequence detection. Compared to existing linear predictive receivers, a distinguished feature of NSD is that it can accommodate nondiagonal constellations in continuous fading. Error analysis demonstrates that significant improvement in performance is achievable over linear prediction receivers. By incorporating the reduced-state sequence detection techniques, performance and complexity tradeoffs can be controlled by the branch memory and trellis size. Numerical results show that most of the performance gain can be achieved by using an L-state trellis, where L is the size of the DSTM constellation. [ABSTRACT FROM AUTHOR]

Novel decision-feedback (DF) linear prediction (LP) receivers, which process multiple samples per symbol interval in conjunction with optimal sample combining, are proposed for differential space-time modulation (DSTM) over Rayleigh fast-fading channels. Performance analysis demonstrates that multisampling DF-LP receivers outperform their symbol-rate sampling counterpart in fast fading substantially. In addition, an asymptotically tight upper bound on the pairwise error probability is derived. In view of this bound, the design criterion of DSTM for fast fading is the same as that for block-wise static fading. To avoid the estimation of the second-order statistics of the channel, a polynomial-model-based DF-LP receiver is proposed. It can approach the performance of the optimum DF-LP receiver at high signal-to noise ratios, provided fading is moderate. [ABSTRACT FROM AUTHOR]

Presents information on a study which focused on a computationally efficient method for chaotic signal estimation. Performance characteristics; Conclusion and discussion.

Deals with a family of frequency hopping (FH) sequences generated by chaotic communication systems. Background of FH communications systems; Theorem on the design of FH sequences; Hardware implementation of the sequence generator.

We study the problem of recovering an s-sparse signal x* ∊ ℂn from corrupted measurementsy = Ax* + z* +w, where z* ∊ ℂm is a k-sparse corruption vector whose nonzero entries may be arbitrarily large and w ∊ ℂm is a dense noise with bounded energy. The aim is to exactly and stably recover the sparse signal with tractable optimization programs. In this paper, we prove the uniform recovery guarantee of this problem for two classes of structured sensing matrices. The first class can be expressed as the product of a unit-norm tight frame (UTF), a random diagonal matrix, and a bounded columnwise orthonormal matrix (e.g., partial random circulant matrix). When the UTF is bounded (i.e. μ(U) ~ 1/ √ m), we prove that with high probability, one can recover an s-sparse signal exactly and stably by l1 minimization programs even if the measurements are corrupted by a sparse vector, provided m = O(s log2 s log2 n) and the sparsity level k of the corruption is a constant fraction of the total number of measurements. The second class considers a randomly subsampled orthonormal matrix (e.g., random Fourier matrix). We prove the uniform recovery guarantee provided that the corruption is sparse on certain sparsifying domain. Numerous simulation results are also presented to verify and complement the theoretical results. [ABSTRACT FROM AUTHOR]

In this paper, we propose a new upper bound on the error performance of binary linear codes over block-fading channels by employing Gallager's first- and second-bounding techniques. As the proposed bound is numerically intensive in its general form, we consider two special cases, namely, the spherical bound and the DS2-exponential bound, which are found to be tight in nonergodic and near-ergodic block-fading channels, respectively. The tightness of the proposed bounds is demonstrated for turbo codes. Many existing bounds for quasistatic or fully interleaved fading channels can be viewed as special cases of the proposed Gallager bound. [ABSTRACT FROM AUTHOR]

User selection plays a crucial role in multiple-access channels (e.g., uplink channels of cellular systems) to exploit the multiuser diversity. Although the achievable rate can be adopted for a performance indicator in user selection, it may not be proper if a suboptimal detector or decoder is employed. In particular, for multiuser multiple-input–multiple-output (MIMO) systems, a low-complexity suboptimal MIMO detector can be used instead of optimal MIMO detectors, which require prohibitively high computational complexity. Under this practical circumstance, it may be desirable to derive user selection criteria based on the error probability for a given low-complexity MIMO detector. In this paper, we propose a low-complexity greedy user selection scheme with an iterative lattice reduction (LR) updating algorithm when an LR-based MIMO detector is used. We also analyze the diversity gain for combinatorial user selection approaches with various MIMO detectors. Based on the simulation results, we can confirm that the proposed greedy user selection approach can provide a comparable performance with the combinatorial approaches with much lower complexity. [ABSTRACT FROM PUBLISHER]