21 research outputs found
Exact MIMO Zero-Forcing Detection Analysis for Transmit-Correlated Rician Fading
We analyze the performance of multiple input/multiple output (MIMO)
communications systems employing spatial multiplexing and zero-forcing
detection (ZF). The distribution of the ZF signal-to-noise ratio (SNR) is
characterized when either the intended stream or interfering streams experience
Rician fading, and when the fading may be correlated on the transmit side.
Previously, exact ZF analysis based on a well-known SNR expression has been
hindered by the noncentrality of the Wishart distribution involved. In
addition, approximation with a central-Wishart distribution has not proved
consistently accurate. In contrast, the following exact ZF study proceeds from
a lesser-known SNR expression that separates the intended and interfering
channel-gain vectors. By first conditioning on, and then averaging over the
interference, the ZF SNR distribution for Rician-Rayleigh fading is shown to be
an infinite linear combination of gamma distributions. On the other hand, for
Rayleigh-Rician fading, the ZF SNR is shown to be gamma-distributed. Based on
the SNR distribution, we derive new series expressions for the ZF average error
probability, outage probability, and ergodic capacity. Numerical results
confirm the accuracy of our new expressions, and reveal effects of interference
and channel statistics on performance.Comment: 14 pages, two-colum, 1 table, 10 figure
Exact ZF Analysis and Computer-Algebra-Aided Evaluation in Rank-1 LoS Rician Fading
We study zero-forcing detection (ZF) for multiple-input/multiple-output
(MIMO) spatial multiplexing under transmit-correlated Rician fading for an N_R
X N_T channel matrix with rank-1 line-of-sight (LoS) component. By using matrix
transformations and multivariate statistics, our exact analysis yields the
signal-to-noise ratio moment generating function (m.g.f.) as an infinite series
of gamma distribution m.g.f.'s and analogous series for ZF performance
measures, e.g., outage probability and ergodic capacity. However, their
numerical convergence is inherently problematic with increasing Rician
K-factor, N_R , and N_T. We circumvent this limitation as follows. First, we
derive differential equations satisfied by the performance measures with a
novel automated approach employing a computer-algebra tool which implements
Groebner basis computation and creative telescoping. These differential
equations are then solved with the holonomic gradient method (HGM) from initial
conditions computed with the infinite series. We demonstrate that HGM yields
more reliable performance evaluation than by infinite series alone and more
expeditious than by simulation, for realistic values of K , and even for N_R
and N_T relevant to large MIMO systems. We envision extending the proposed
approaches for exact analysis and reliable evaluation to more general Rician
fading and other transceiver methods.Comment: Accepted for publication by the IEEE Transactions on Wireless
Communications, on April 7th, 2016; this is the final revision before
publicatio
Effects of Channel Features on Parameters of Genetic Algorithm for MIMO Detection
Genetic algorithm (GA) is now an important tool in the field of wireless communications. For multiple-input/multiple-output (MIMO) wireless communications system employing spatial multiplexing transmission, we evaluate the effects of GA parameters value on channel parameters in fading channels. We assume transmit-correlated Rayleigh and Rician fading with realistic Laplacian power azimuth spectrum. Azimuth spread (AS) and Rician K-factor are selected according to the measurement-based WINNER II channel model for several scenarios. Herein we have shown the effects of GA parameters and channel parameters in different WINNER II scenarios (i.e., AS and K values) and rank of the deterministic components. We employ meta GA that suitably selects the population (P), generation (G) and mutation probability (p(m)) for the inner GA. Then we show the cumulative distribution function (CDF) obtain experimentally for the condition number C of the channel matrix H. It is found that, GA parameters depend on-the channel parameters, i.e., GA parameters are the functions of the channel parameters. It is also found that for the poorer channel conditions smaller GA parameter values are required for MIMO detection. This approach will help to achieve maximum performance in practical condition for the lower numerical complexity