Tighter Receiver Performance Lower Bound for MIMO Wireless Communication Systems

Independent component analysis for multiple-input multiple-output wireless communication systems

This chapter is attempted to provide a survey of the advanced concepts and related issues involved in Multiple Input Multiple Output (MIMO) systems. MIMO system technology has been considered as a really significant foundation on which to build the next and future generations of wireless networks. The chapter addresses advanced MIMO techniques such as polarization diversity and antenna selection. We gradually provide an overview of the MIMO features from basic to more advanced topics. The first sections of this chapter start by introducing the key aspects of theMIMO theory. TheMIMO systemmodel is first presented in a genericway. Then, we proceed to describe diversity schemes used in MIMO systems. MIMO technology could exploit several diversity techniques beyond the spatial diversity. These techniques essentially cover frequency diversity, time diversity and polarization diversity. We further provide the reader with a geometrically based models for MIMO systems. The virtue of this channel modeling is to adopt realisticmethods for modeling the spatio-temporal channel statistics from a physical wave-propagation viewpoint. Two classes for MIMO channel modeling will be described. These models involve the Geometry-based Stochastic ChannelModels (GSCM) and the Stochastic channel models. Besides the listedMIMO channel models already described, we derive and discuss capacity formulas for transmission over MIMO systems. The achieved MIMO capacities highlight the potential of spatial diversity for improving the spectral efficiency of MIMO channels. When Channel State Information (CSI) is available at both ends of the transmission link, the MIMO system capacity is optimally derived by using adaptive power allocation based on water-filling technique. The chapter continues by examining the combining techniques for multiple antenna systems. Combining techniques are motivated for MIMO systems since they enable the signal to noise ratio (SNR) maximization at the combiner output. The fundamental combing techniques are the Maximal Ratio Combining (MRC), the Selection Combining (SC) and the Equal Gain Combining(EGC). Once the combining techniques are analyzed, the reader is introduced to the beamforming processing as an optimal strategy for combining. The use of multiple antennas significantly improves the channel spectral efficiency. Nevertheless, this induces higher system complexity of the communication system and the communication system performance is effected due to correlation between antennas that need to be deployed at the same terminal. As such, the antenna selection algorithm for MIMO systems is presented. To elaborate on this point, we introduce Space time coding techniques for MIMO systems and we evaluate by simulation the performance of the communication system. Next, we emphasis on multi polarization techniques for MIMO systems. As a background, we presume that the reader has a thorough understanding of antenna theory. We recall the basic antenna theory and concepts that are used throughout the rest of the chapter. We rigorously introduce the 3D channel model over the Non-Line of Sight (NLOS) propagation channel for MIMO system with polarized antennas. We treat the depolarization phenomena and we study its effect on MIMO system capacity. The last section of the chapter provides a scenario for collaborative sensor nodes performing distributed MIMO system model which is devoted to sensor node localization in Wireless Sensor Networks. The localization algorithm is based on beamforming processing and was tested by simulation. Our chapter provides the reader by simulation examples for almost all the topics that have been treated for MIMO system development and key issues affecting achieved performance.

Spatial correlation is a crucial impairment for practical Multiple Input Multiple Output (MIMO) wireless communication systems. It is important to consider spatial correlation between antennas in actual communication scenario at both sides, i.e. transmit and receive side. Furthermore, we have considered nonorthogonal full rate STBC in MIMO systems equipped with four transmit antennas and four receive antennas in a quasi-static Rayleigh fading channel. In this paper, Bit Error Rate (BER) performance of full rate non-orthogonal Space Time Block code (STBC) is obtained using simulations in MIMO wireless communication systems. Spatial correlation is assumed between antennas at both the transmitter and the receiver sides. For this analysis, we have considered various transmit antenna spacing values of d t = 0.1π, 0.2π, and 0.4π keeping fixed d r = 0.1π and various receive antenna spacing values of d r = 0.1π, 0.2π, and 0.4π keeping fixed d t = 0.1π. The obtained results show that the transmitting diversity is more serious than the receiving diversity in the spatially correlated antenna environment for the specified signal-to-noise ratio. This study will be useful for actual wireless communication implementation where spatial correlation antennas are present in the practical environment of the MIMO wireless communication system.

Accurate channel state information (CSI) is a prerequisite for diversity and multiplexing gains in multiple-input multiple-output (MIMO) wireless systems. More refined CSI can be attained by bootstrapping channel estimation techniques. We propose adaptive filtering-based iterative channel estimators with the incorporation of an iterative receiver over a flat-fading MIMO wireless link. The performance of three different algorithms with the aid of hard decision feedback is evaluated and compared through simulations. We show that the least-mean-square algorithm is a reasonable choice among the other considered algorithms in terms of performance and feasibility.

Nonlinearities play an imperative role in performance of wireless communication systems. In this paper, High Power Amplifiers (HPA) have been used to check the performance of the proposed system by analyzing its amplitude, phase and their joint distortions using Multiple-Input Multiple-Output (MIMO) wireless communication system. This paper takes-in account the overall effects of HPA that provokes nonlinearities at the transmitter's end in a communication system. Therefore, a novel method for the HPA nonlinearities mitigation has been proposed to improve the performance of amplitude, phase and joint distortions in MIMO communication systems. The simulation results show an improvement in the overall effect of the MIMO system and enhance the input response on the transmitter side. Furthermore, this method has been proposed to exploit the benefits of the MIMO system to improve the Symbol Error Rate (SER) versus Signal to Noise Ratio (SNR) curves for amplitude, phase and their joint distortions.

Traditional receiver algorithms developed for multiple-input multiple-output (MIMO) wireless communication systems are based on the assumption that the channel state information (CSI) is perfectly known at the receiver. However, in real environments the exact CSI is unavailable. In this paper, we address the problem of robustness of multiuser space-time block coded MIMO systems against imperfect CSI and develop a simple linear receiver which guarantees robustness against CSI errors with a certain selected probability. The proposed receiver is formulated via a probability-constrained optimization problem which is further relaxed using Chebyshev inequality to a much simpler second-order cone programming (SOCP) problem. Simulations demonstrate that the proposed receiver has a moderate performance degradation as compared to the exact solution to the original probability-constrained optimization problem, while offering much simpler implementation.

Multipath fading is one of the primary factors for degrading the performance in a wireless network. Information theoretic and past research suggest the use various diversity techniques to combat fading in wireless networks. Antenna diversity, a form of diversity technique, when incorporated in a wireless transceiver increases the system capacity and is one of the effective methods to combat fading in wireless systems. Also, recent research by Laneman et.al., Sendonaris et.al. suggests that cooperation among users in a wireless networks is an effective approach for a better signal reception in multipath fading environments. The diversity gains obtained by cooperation among the users of a wireless network is termed as cooperative diversity . Although, prior research in cooperative diversity considers users equipped with single antenna, in practical scenarios users may be able to accommodate multiple antennas due to the recent advanced research in semiconductor industry. Hence, the primary purpose of this thesis is to design, simulate and analyze an end-end performance of multi-antenna wireless systems employing cooperative multi antenna relay nodes so as to exploit the cooperative diversity and antenna diversity simultaneously in a wireless networks. Three main contributions to the area of cooperative multiple-input multiple-output (MIMO) wireless systems is presented in this thesis. First, we perform information theoretic analysis to study the impact of antenna arrays on cooperative wireless networks and propose the best possible distribution of antenna arrays among the three terminals of a simple three terminal cooperative relay network. Second, we design, simulate, and analyze a cooperative multiple-input multiple-output (MIMO) wireless systems employing orthogonal space-time block codes as proposed by Alamouti in 1998 with a decode-and-forward (DF) relay terminal. We implement a maximal ratio combining receiver that provides almost twice the diversity gain with respect to point-point multiple input multiple output link. Finally, we implement a practical receiver for cooperative reception using multiple antennas at all nodes based on Bell-Labs Layered Space Time architecture (BLAST). We incorporate a practical adaptive decode-and-forward (DF) relaying technique for reliable signal retransmission for both Alamouti space-time coding and the BLAST schemes. Results presented in terms of bit error rates and throughput show that remarkable performance gains are achievable by combining the concepts drawn from space-time coding, cooperative relaying and array processing.

Symbol decoding in multiple-input multiple-output (MIMO) wireless communication systems requires the deployment of fast, energy-efficient computing hardware deployable at the edge. The brute-force and exact maximum likelihood (ML) decoder, solved on conventional classical digital hardware to decode MIMO symbols, has exponential time complexity. Approximate classical solvers implemented on the same hardware have polynomial time complexity at the best. In this article, we design an alternative ring-oscillator-based coupled oscillator array (also known as oscillatory neural network (ONN)) to act as an oscillator Ising machine (OIM) and heuristically solve the ML-based MIMO detection problem. Complementary metal oxide semiconductor (CMOS) technology is used to design the ring oscillators, and ferroelectric field effect transistor (FeFET) technology is chosen as the non-volatile memory (NVM) coupling element (X) between the oscillators in this CMOS + X OIM design. For this purpose, we experimentally report high linear range of conductance variation (1 µS to 60 µS) with programming voltage pulses in a HfO 2 -based FeFET device fabricated at 28 nm high-K/ metal gate (HKMG) CMOS technology node. We incorporate the conductance modulation characteristic in SPICE simulation of the ring oscillators connected in an all-to-all fashion through a crossbar array of these FeFET devices. We show that the above range of conductance variation of FeFET is suitable to obtain best OIM performance, thereby making FeFET a suitable NVM device for this application. Our SPICE simulations show that there is no significant performance drop for symbol detection up to MIMO array sizes of 90 transmitting and 90 receiving antennas. Our simulations, combined with analytical treatment using Kuramoto model of oscillators, predict that this designed classical analog OIM, if implemented experimentally, will offer logarithmic scaling of computation time with MIMO size, thereby offering huge improvement (in terms of computation speed) over exact and approximate classical solvers run on conventional digital hardware.

Mobile communication companies at present are looking for a 4G technology that aims at better reliability. This aim can be achieved by using Multiple Inputs Multiple Outputs (MIMO) system which is difficult to implement for the mobile terminals due to the limitations of the complexity, size and cost. So, these companies have been looking for a system which emulates a MIMO system. The cooperative relaying system has been proposed as a solution to be used to take the advantages of MIMO. The cooperative relaying system enables single antenna mobiles in a multi user environment to share their antennas and generate a virtual multiple antenna transmitters. In this paper, we investigate a single relay system that aims for a reliable communication. The main goal of this paper is to study the feasibility of the proposed single relay cooperative transmission in the mobile environment, and evaluate it with respect to the direct link transmission. Both systems are presented and a comparison in terms of bit error rate (BER) reveals that the single relay system performs better. The proposed single relay system needs an efficient coding technique to improve the performance in terms of reliability. Therefore, STBC as a coding technique is used, and the results proved that this technique decreases the BER.

Link Adaptation can maximize system throughput while maintaining transmission reliability. With the growing demand for high-speed data transmission, multiple-input multiple-output (MIMO) and orthogonal frequency division multiplexing (OFDM) technologies have been widely used in wireless communication systems. However, performing link adaptation in MIMO systems is challenging due to the complexity of channel and coupling among equalization, precoding, spatial mode, modulation and coding scheme (MCS). In this paper, we present a link adaptation scheme in MIMO systems through machine learning algorithms to maximize spectral efficiency while maintaining transmission reliability. We propose to use autoencoder model to extract feature from channel state information (CSI), combined with logical regression algorithms to select modulation and coding scheme. Spatial mode can be chosen based on the objective of maximizing the spectral efficiency. Simulation results demonstrate the improved performance and validate the application of the proposed learning based framework in MIMO systems.

The sequence detection problem for fading channels in multiple-input multiple-output (MIMO) wireless systems is investigated. The sphere decoding (SD) algorithm provides an exact maximum-likelihood (ML) solution, while has expected worst-case complexity which is exponential in the number of symbols jointly detected. On the other hand, the M-algorithm (MA) is a suboptimal solution. Its complexity is linear in the product of the list number and the number of symbols jointly detected. We combined the sphere-constrained search strategy of SD with the M-constrained search principle of the MA. The resulting algorithms can achieve good performance in term of bit error rate (BER) while lower complexity

In this paper, the performance of phase hop diversity in multiple input multiple output (MIMO) wireless system is researched. We get the diversity order of the phase hop diversity (PHD) according to the definition of diversity order and discuss the diversity order in different cases. Although diversity order is an important parameter to evaluate the performance of a communication system using diversity, but the diversity order can only be the performance of the PHD when the SNR (signal-to-noise ratio) is higher, so we consider the performance of PHD when the SNR is lower too. Furthermore, we get the up bound for performance of the (PHD) when the channel state information (CSI) is known and not known at transmitter, respectively, by transforming the problem of cost function of the PEP (pairwise error probability) into a maximum the determinant of a matrix. At last, the problem is resolved by using the maximum trace with constraint.

We propose a precoder design to minimize the codeword error rate of multiple-input multiple-output (MIMO) systems in the presence of channel estimation errors. Our proposed scheme only requires knowledge of the second-order statistics of the channel, noise and the estimation errors; an estimate of the instantaneous channel state information (CSI) is not required. Compared to the instantaneous CSI requirement, our assumption is more practical since obtaining an accurate CSI is challenging if the channel fluctuates rapidly, while the channel statistics are likely to remain unchanged for a much longer period. Furthermore, the feedback overhead from the receiver to the transmitter is greatly reduced. When compared to the case without transmit preceding, our proposed design achieves a significant improvement in error performance: (i) for a real 2 × 2 MIMO system with 4-PAM modulation and at codeword error rate of 104, our proposed precoder design achieves coding gains of up to 6 dB and (ii) for a real 4 × 4 MIMO system with BPSK modulation and at codeword error rate of 104, coding gains of up to 6.5 dB can be achieved.

This paper reports on investigations into the effects of spatial correlation on channel estimation and capacity of a multiple input multiple output (MIMO) system. Minimum mean square error (MMSE) method is applied for channel estimation under a correlated channel scenario. Simulation results for a 4 times 4 MIMO system with the Jakes model describing a channel between a base station and a mobile station demonstrate a trade-off between the effects of spatial correlation, channel estimation and capacity. For low values of signal to noise ratio (SNR), spatial correlation decreases channel estimation errors and improves capacity. For high values of SNR, spatial correlation has a less pronounced effect on improving channel estimation and thus the MIMO capacity is adversely affected by the decreased channel matrix rank.

Tensor space–time (TST) coding for MIMO wireless communication systems