Complex-Valued Phase Transmittance RBF Neural Networks for Massive MIMO-OFDM Receivers

Multi-input multi-output (MIMO) transmission schemes have become the techniques of choice for increasing spectral efficiency in bandwidth-congested areas. However, the design of cost-effective receivers for MIMO channels remains a challenging task. The maximum likelihood detector can achieve excellent performance—usually, the best performance—but its computational complexity is a limiting factor in practical implementation. In the present work, a novel MIMO scheme using a practically feasible decoding algorithm based on the phase transmittance radial basis function (PTRBF) neural network is proposed. For some practical scenarios, the proposed scheme achieves improved receiver performance with lower computational complexity relative to the maximum likelihood decoding, thus substantially increasing the applicability of the algorithm. Simulation results are presented for MIMO-OFDM under 5G wireless Rayleigh channels so that a fair performance comparison with other reference techniques can be established.

Channel Prediction for Mobile MIMO Wireless Communication Systems

<p>Temporal variation and frequency selectivity of wireless channels constitute a major drawback to the attainment of high gains in capacity and reliability offered by multiple antennas at the transmitter and receiver of a mobile communication system. Limited feedback and adaptive transmission schemes such as adaptive modulation and coding, antenna selection, power allocation and scheduling have the potential to provide the platform of attaining the high transmission rate, capacity and QoS requirements in current and future wireless communication systems. Theses schemes require both the transmitter and receiver to have accurate knowledge of Channel State Information (CSI). In Time Division Duplex (TDD) systems, CSI at the transmitter can be obtained using channel reciprocity. In Frequency Division Duplex (FDD) systems, however, CSI is typically estimated at the receiver and fed back to the transmitter via a low-rate feedback link. Due to the inherent time delays in estimation, processing and feedback, the CSI obtained from the receiver may become outdated before its actual usage at the transmitter. This results in significant performance loss, especially in high mobility environments. There is therefore a need to extrapolate the varying channel into the future, far enough to account for the delay and mitigate the performance degradation. The research in this thesis investigates parametric modeling and prediction of mobile MIMO channels for both narrowband and wideband systems. The focus is on schemes that utilize the additional spatial information offered by multiple sampling of the wave-field in multi-antenna systems to aid channel prediction. The research has led to the development of several algorithms which can be used for long range extrapolation of time-varyingchannels. Based on spatial channel modeling approaches, simple and efficient methods for the extrapolation of narrowband MIMO channels are proposed. Various extensions were also developed. These include methods for wideband channels, transmission using polarized antenna arrays, and mobile-to-mobile systems. Performance bounds on the estimation and prediction error are vital when evaluating channel estimation and prediction schemes. For this purpose, analytical expressions for bound on the estimation and prediction of polarized and non-polarized MIMO channels are derived. Using the vector formulation of the Cramer Rao bound for function of parameters, readily interpretable closed-form expressions for the prediction error bounds were found for cases with Uniform Linear Array (ULA) and Uniform Planar Array (UPA). The derived performance bounds are very simple and so provide insight into system design. The performance of the proposed algorithms was evaluated using standardized channel models. The effects of the temporal variation of multipath parameters on prediction is studied and methods for jointly tracking the channel parameters are developed. The algorithms presented can be utilized to enhance the performance of limited feedback and adaptive MIMO transmission schemes.</p>

Channel Prediction for Mobile MIMO Wireless Communication Systems

<p>Temporal variation and frequency selectivity of wireless channels constitute a major drawback to the attainment of high gains in capacity and reliability offered by multiple antennas at the transmitter and receiver of a mobile communication system. Limited feedback and adaptive transmission schemes such as adaptive modulation and coding, antenna selection, power allocation and scheduling have the potential to provide the platform of attaining the high transmission rate, capacity and QoS requirements in current and future wireless communication systems. Theses schemes require both the transmitter and receiver to have accurate knowledge of Channel State Information (CSI). In Time Division Duplex (TDD) systems, CSI at the transmitter can be obtained using channel reciprocity. In Frequency Division Duplex (FDD) systems, however, CSI is typically estimated at the receiver and fed back to the transmitter via a low-rate feedback link. Due to the inherent time delays in estimation, processing and feedback, the CSI obtained from the receiver may become outdated before its actual usage at the transmitter. This results in significant performance loss, especially in high mobility environments. There is therefore a need to extrapolate the varying channel into the future, far enough to account for the delay and mitigate the performance degradation. The research in this thesis investigates parametric modeling and prediction of mobile MIMO channels for both narrowband and wideband systems. The focus is on schemes that utilize the additional spatial information offered by multiple sampling of the wave-field in multi-antenna systems to aid channel prediction. The research has led to the development of several algorithms which can be used for long range extrapolation of time-varyingchannels. Based on spatial channel modeling approaches, simple and efficient methods for the extrapolation of narrowband MIMO channels are proposed. Various extensions were also developed. These include methods for wideband channels, transmission using polarized antenna arrays, and mobile-to-mobile systems. Performance bounds on the estimation and prediction error are vital when evaluating channel estimation and prediction schemes. For this purpose, analytical expressions for bound on the estimation and prediction of polarized and non-polarized MIMO channels are derived. Using the vector formulation of the Cramer Rao bound for function of parameters, readily interpretable closed-form expressions for the prediction error bounds were found for cases with Uniform Linear Array (ULA) and Uniform Planar Array (UPA). The derived performance bounds are very simple and so provide insight into system design. The performance of the proposed algorithms was evaluated using standardized channel models. The effects of the temporal variation of multipath parameters on prediction is studied and methods for jointly tracking the channel parameters are developed. The algorithms presented can be utilized to enhance the performance of limited feedback and adaptive MIMO transmission schemes.</p>

A new Transmission and Reception Algorithms for Improving the Performance of SISO/MIMO- OFDM Wireless Communication System

The future wireless communication requires a reliable transmission at high data rates, so the transmission over frequency-selective fading Multiple-Input–Multiple-Output MIMO channels become interesting since the capacity of "MIMO" channels expressions enormous gains above that of their essential single-input–single-output "SISO" channels. This paper examines the performance of the Low Complexity Zero Forcing "LCZF" equalizer for both systems single-input–single-output-Orthogonal Frequency Division Multiplexing" SISO-OFDM" and spatially multiplexed-Multiple-Input–Multiple-Output "SM-MIMO-OFDM" with different "QAM" modulations. It is exploring a new algorithm to improve the performance of the "BER", spectral efficiency, and power efficiency and to reduce the complexity of the "RF" communication system under the effect of the Additive White Gaussian Noise "AWGN" and multipath fading channel. It is also improves an efficient channel by developing a Low Complexity Zero Forcing "LCZF" equalizer for both "SISO-OFDM" and "SM-MIMO-OFDM" wireless Communication systems. This is done by proposing a new algorithm at the receiver side to covert the Linear Convolution in to Cyclic Convolution by adding Zero Padding "ZP" to the channel impulse response in such a way to be the same length to the transmitted signal in the time domain which is of length N, where N is the length of "IFFT".

Guided MAC to Enhance Quality of Experience of Video Data Transmission over Massive MIMO

Massive MIMO (M-MIMO) system comprises of multiple number of antennas to achieve energy- efficiency and large gains in spectral-efficiency in comparison to existing MIMO technology. High speed and Quality of Experience (QoE) of video data over wireless communication has always been a challenge for the researchers due to scarcity of the bandwidth, fading and interference. The channels with high noise corrupt the transmitted video and results in poor QoE of at the receiver. Therefore, to maintain the quality of transmitted video, it is highly desirable to identify noisy channels and avoid transmission over them. This paper deals with QoE of the transmitted video over Massive MIMO channels. The channels are categorized into two categories: good and bad depending upon the value of Signal to Interference and Noise Ratio (SINR). A channel above the minimum acceptable value (threshold) of SINR is categorized as good channel otherwise bad channel. A Guided MAC layer (GMAC) protocol is designed to transmit the video data over good channels only and to discard the transmission over bad channels.

Hardware Implementation and Performance Analysis of Improved Sphere Decoder in Spatially Correlated Massive MIMO Channels

Detection for high-dimensional multiple-input multiple-output (MIMO) and massive MIMO (MMIMO) systems is an active field of research in wireless communications. While most works consider spatially uncorrelated channels, practical MMIMO channels are correlated. This paper investigates the impact of correlation on Sphere Decoder (SD), for both single-user (SU) and multi-user (MU) scenarios. The complexity of SD is mainly determined by the initial radius (IR) method and the number of visited nodes during detection. This paper employs an efficient IR and proposes a new metric constraint in the tree searching algorithm, that significantly decrease the number of visited nodes and render SD feasible for large-scale systems. In addition, an introduced hardware implementation featured with a one-node-per-cycle architecture, minimizes the latency of the detection process. Trade-offs between bit error rate (BER) performance and computational complexity are presented. The trade-offs are achieved by either modifying the backtracking mechanism or limiting the number of radius updates. Simulation results prove that the proposed optimizations are effective for both correlated and uncorrelated channels, regardless of the level of noise. The decoding gain of SD compared to the low-complexity linear detectors (LD) is higher in the presence of correlation than in the uncorrelated case. However, as expected, spatial correlation adversely affects the performance and the complexity of SD. Simulation results reported here also confirm that correlation at the side equipped with more antennas is less detrimental. Hardware implementation aspects are examined for both a Virtex-7 FPGA device and a 28-nm ASIC technology.<br>