The outage probability of maximal-ratio combining (MRC) for a multiple-input multiple-output (MIMO) wireless communications system under Rician fading is given by the cumulative distribution function (CDF) for the largest eigenvalue of a complex noncentral Wishart matrix. This CDF has previously been expressed as a determinant whose elements are integrals of a confluent hypergeometric function. For the determinant elements, conventional evaluation approaches, e.g., truncation of infinite series ensuing from the hypergeometric function or numerical integration, can be unreliable and slow even for moderate antenna numbers and Rician $ K $-factor values. Therefore, herein, we derive by hand and by computer algebra also differential equations that are then solved from initial conditions computed by conventional approaches. This is the holonomic gradient method (HGM). Previous HGM-based evaluations of MIMO relied on differential equations that were not theoretically guaranteed to converge, and, thus, yielded reliable results only for few antennas or moderate $ K $. Herein, we reveal that gauge transformations can yield differential equations that are {\emph{stabile}}, i.e., guarantee HGM convergence. The ensuing HGM-based CDF evaluation is demonstrated reliable, accurate, and expeditious in computing the MRC outage probability even for very large antenna numbers and values of $ K $.
The conventional single-antenna receiver suffers in wireless fading channels from limitations that preclude deployment of envisioned wireless applications. By increasing complexity, improvements are possible using multi-branch receivers. In particular, smart antenna arrays employ maximal-ratio combining (MRC) or statistical beamforming (BF) to exploit diversity and array gain. However, varying azimuth spread creates unfavorable spatial correlation conditions that diminish these gains, while BF and MRC complexity remains constant. On the other hand, adaptive eigen-combining can yield near-optimum performance for more efficient resource usage. This motivates our study of maximal-ratio eigen-combining (MREC). We unravel the relationship between MREC, BF, and MRC performance, and evaluate their complexity. Outage and average error probability expressions are derived for MREC assuming perfectly and imperfectly known channel gains. These results are specialized to MRC and BF, as well as to well-accepted pilot-symbol-based channel estimation techniques. In the process, new performance analyses are provided. Numerical results for typical urban scenarios with variable correlation demonstrate MREC’s advantages. Existing criteria for optimum eigen-mode selection in MREC are reviewed, and a new adaptation approach that accounts for channel condition, algorithm complexity, resource availability, and intended performance level, is proposed and evaluated. These singleand multi-branch receivers are then evaluated on a field-programmable gate array (FPGA) in terms of symbol-detection performance and resource and power consumption.
Smart antennas may enhance performance by applying conventional algorithms such as maximal-ratio combining (MRC) or maximum average signal-to-noise-ratio beamforming, i.e., statistical beamforming (BF). However, MRC and BF yield advantages that offset their complexity only for extreme antenna correlation values, which seldom occur for space-limited base-station antenna arrays deployed in typical urban (TU) scenarios, with predominantly-small, random, azimuth spread (AS). Therefore, the principles of BF and MRC have been integrated to forge maximal-ratio eigencombining (MREC), which promises to reap the available array and diversity gains more effectively. Nonetheless, the relative performance and numerical complexity of MREC, BF, and MRC have not yet been investigated for channel estimated from received-signal-vector samples in a TU uplink scenario with realistic Laplacian base-station power azimuth spectrum and log-normally distributed AS with exponential temporal correlation. Therefore, herein, Yangpsilas effective and low-complexity deflation-based projection approximation subspace tracking (PASTd) algorithm is deployed to recursively update the channel eigenstructure required for MREC adapted to AS using the classical bias-variance tradeoff criterion (BVTC). Simulation results indicate that BVTC-based MREC can significantly outperform BF for much lower complexity than MRC.
For multiple-input/multiple-output (MIMO) wireless communications systems employing spatial multiplexing at the transmitter, we have recently been studying the advantages and disadvantages of genetic algorithm (GA)-based detection vs. the maximum-likelihood (ML) detection and linear detection, for various channel fading assumptions, e.g., Rayleigh and Rician fading, fixed and random azimuth spread (AS) and Rician K-factor, and various ranks of the channel matrix mean. In this paper, we step away from comparing their performance and complexity and focus instead on the selection of GA parameters, such as population size, P, generation number G, and mutation probability, p m , for the GA in MIMO detection. Thus, we employ a meta (or outer) GA to optimize P, G, and p m values for the inner GA employed for MIMO detection. The empirical distributions of the selected parameter values are then compared for various channel fading assumptions. It is found that the optimum GA parameter values for MIMO detection are directly affected by fading type, AS and K distribution, and especially by the rank of the channel matrix mean. The meta-GA approach helps reveal that the parameters of the inner GA should be tuned in order to achieve maximum performance for the lowest numerical complexity. Future work will seek efficient methods.
This paper proposes a new robust speech recognition method. Since the hidden Markov model (HMM) algorithm need a lot of training calculation, The dynamic time warping (DTW) algorithm based on median filter is used instead in our system. According to the short-term energy method, the non-speech segment can be removed. Recognition accuracy is thus improved. The cepstral mean subtraction (CMS), running spectrum filtering (RSF) and dynamic range adjustment (DRA) methods are also applied for noise reduction. Our results show that the RSF/DRA and CMS/DRA are good. The median filter improved the accuracy of DTW and The recognition accuracy almost are same and that one using HMM in the 10 dB and 20 dB white noise.
For multiple-input multiple-output (MIMO) wireless communications systems, we propose a new zero-forcing (ZF) detection approach that explicitly accounts for instantaneous channel state information (ICSI) estimation error and spatial correlation. For this ZF approach, we derive an average error probability (AEP) expression for transmit-correlated Rician fading. Our AEP derivation exploits the effective signal-to-noise ratio that results by compounding ICSI estimation error and receiver noise. The derived AEP expression is then applied to evaluate MIMO ZF performance in Rayleigh and Rician fading for samples from recently-measured lognormal azimuth spread (AS) and Rician K-factor distributions, for pilot-based ICSI estimation. Numerical results depict the dependence of the AEP averaged over the AS and K distributions on fading type, rank of the deterministic component of the channel matrix, and AS-K correlation, for realistic scenarios.
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