Generation of correlated Rayleigh fading channels for accurate simulationof promising wireless communication systems

In this paper, a generalized method is proposed for the accurate simulation of equal/ unequal power correlated Rayleigh fading channels to overcome the shortcomings of existing methods. Spatial and spectral correlations are also considered in this technique for different transmission conditions. It employs successive coloring for the inphase and quadrature components of successive signals using real correlation vector of successive signal envelopes rather than complex covariance matrix of the Gaussian signals which is utilized in conventional methods. Any number of fading signals with any desired correlations of successive envelope pairs in the interval [0, 1] can be generated with high accuracy. Moreover, factorization of the desired covariance matrix is avoided to overcome the shortcomings and high computational complexity of conventional methods. Extensive simulations of different representative scenarios demonstrate the effectiveness of the proposedtechnique. The simplicity and accuracy of this method will help the researchers to study and simulate the impact of fading correlation on the performance evaluation of various multi-antenna and multicarrier communication systems. Moreover, it enables the engineers for efficient design and deployment of new schemes for feasible wireless application

Hardware Impairments in Large-scale MISO Systems: Energy Efficiency, Estimation, and Capacity Limits

The use of large-scale antenna arrays has the potential to bring substantial improvements in energy efficiency and/or spectral efficiency to future wireless systems, due to the greatly improved spatial beamforming resolution. Recent asymptotic results show that by increasing the number of antennas one can achieve a large array gain and at the same time naturally decorrelate the user channels; thus, the available energy can be focused very accurately at the intended destinations without causing much inter-user interference. Since these results rely on asymptotics, it is important to investigate whether the conventional system models are still reasonable in the asymptotic regimes. This paper analyzes the fundamental limits of large-scale multiple-input single-output (MISO) communication systems using a generalized system model that accounts for transceiver hardware impairments. As opposed to the case of ideal hardware, we show that these practical impairments create finite ceilings on the estimation accuracy and capacity of large-scale MISO systems. Surprisingly, the performance is only limited by the hardware at the single-antenna user terminal, while the impact of impairments at the large-scale array vanishes asymptotically. Furthermore, we show that an arbitrarily high energy efficiency can be achieved by reducing the power while increasing the number of antennas.Comment: Published at International Conference on Digital Signal Processing (DSP 2013), 6 pages, 5 figure

Maximum-Likelihood Sequence Detection of Multiple Antenna Systems over Dispersive Channels via Sphere Decoding

Multiple antenna systems are capable of providing high data rate transmissions over wireless channels. When the channels are dispersive, the signal at each receive antenna is a combination of both the current and past symbols sent from all transmit antennas corrupted by noise. The optimal receiver is a maximum-likelihood sequence detector and is often considered to be practically infeasible due to high computational complexity (exponential in number of antennas and channel memory). Therefore, in practice, one often settles for a less complex suboptimal receiver structure, typically with an equalizer meant to suppress both the intersymbol and interuser interference, followed by the decoder. We propose a sphere decoding for the sequence detection in multiple antenna communication systems over dispersive channels. The sphere decoding provides the maximum-likelihood estimate with computational complexity comparable to the standard space-time decision-feedback equalizing (DFE) algorithms. The performance and complexity of the sphere decoding are compared with the DFE algorithm by means of simulations