Interchannel Interference and Mitigation in Distributed MIMO RF Sensing.

The fifth generation cellular communication is all about high speed internet access with devices consuming less amount of power. Millimeter wave Multiple Input Multiple Output (MIMO) systems is a promising technology that offers high data rates due to a large bandwidth. These systems are compact with minimal antenna heights which allows us to deploy these systems more widely. The mm-Wave Massive MIMO systems enhances the data rates to a large extent. But due to the increasing number of antennas to support high data rates, the power consumption of the system goes high. In this paper, a limited channel state information (CSI) based precoding technique has been used to optimize the power consumption and to reduce the computational complexity of the system. The use of Hybrid Precoder architecture with sub array of precoding blocks helps in reducing the computational complexity by many folds. The conditional switching between two stages minimizes the number of computations required. The estimation of Angle of arrival (AoA) enables the precoder to steer the beam at the transmitter for higher throughput. The smart antenna selection algorithm optimizes the power consumption of the system. The two – staged precoding algorithm proposed employs all these techniques to optimize the performance of mm-Wave MIMO systems.

The concept of generalized spatial modulation (GenSM) aided millimeter wave (mm-wave) multiple-input multiple-output (MIMO) has recently attracted substantial research interest, as it benefits from the large bandwidth of mm-wave MIMO, while maintaining a reduced number of radio frequency (RF) chains. However, due to the lack of precoding design, the preceding GenSM-aided mm-wave MIMO schemes suffered from severe performance loss. Inspired by the reduced-RF-chain structure making implementation cost low while maintaining the benefit of GenSM, in this paper, we incorporate the technique of hybrid precoding into GenSM-aided mm-wave MIMO, where a hybrid digital and analog precoding regime is proposed to enhance the system's achievable rate. Moreover, a closed-form expression is proposed to provide an accurate approximation to the spectral efficiency (SE) achieved by the proposed scheme. The proposed expression is further simplified in the region of high signal-to-noise ratio (SNR). By exploiting the proposed SE expressions as low-complexity cost functions, two algorithms, i.e. the gradient ascent algorithm and the high-SNR approximation algorithm, are exploited to optimize the hybrid precoders in terms of SE maximization. Finally, numerical simulations are provided to substantiate the superior SE performance achieved by the proposed scheme over other GenSM-aided mm-wave MIMO schemes as well as the state-of-the-art mm-wave MIMO 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.

Recently, technological developments have enhanced, the use of Millimeter-wave (mm-Wave) Multiple Input Multiple Output (MIMO) system in various communication applications and wireless sensor networks as channel estimation efficiency can be immensely improved with the help of this technological developments in Millimeter-wave MIMO system and wireless sensor network as well. Moreover, they can improve quality of communication services to a great extent. However, cell interference in Millimeter-wave (mm-Wave) MIMO system can produce a massive impact on spectral efficiency. Therefore, a Routing Enabled Channel Estimation (RECE) technique is presented in this article to minimize interference between cells. The proposed Channel Estimation technique improves channel capacity as well as spectral efficiency. Moreover, Normalized Mean Square Error (NMSE) is minimized heavily using proposed RECE technique. Here, main aim of this article is to reduce cell interference and channel estimation inside a cell by using route selection, beam selection, and spatial frequency estimation. Here, different scenarios and parameters are considered to evaluate performance efficiency of proposed RECE technique in terms of spectral efficiency, NMSE and SNR and compared against varied traditional channel estimation techniques. Moreover, it is clearly evident from performance results that the proposed channel estimation technique performs better than the other two methods.

Multiple Input Multiple Output (MIMO) low complexity receiver which utilizes the compressive sensing detection for Spatial Modulation in large scale MIMO system in order to reduce the system complexity. In conventional MIMO system; huge amount of antennas is used at both ends to exploit the multipath propagation. This system maximizes the throughput performance and data rates are increased but only at the cost of high hardware complexity and increased power-consumption. Spatial Modulation Matching Pursuit (SMMP) is the proposed enhanced CS technique used for the improvement of detection performance. Hence, this paper reviews recent research findings concerning normalized Compressive Sensing (CS) detection algorithm, used for Spatial Modulation (SM) in massive MIMO, to lowers the signal processing complexity, which in result improves the energy efficiency of system against that of conventional MIMO system.This strategy is achieved by involving additional structures and sparsity in which a single transmitter antenna or a subset of it is turned on at each case to transmit a certain data. The subset of antenna which is turned on for transmission depends on approaching data bits. Therefore, the total increase in the spectral efficiency of the system is given as base-two logarithm of whole antennas at the transmitter. It reduces the signal processing load at base station and doesn’t depend upon any synchronization between transmitters. The Spatial Modulation Matching Pursuit used prevents the Inter Channel Interference (ICI) of the system which in result improves the Bit Error Rate (BER) performance than the typical MIMO system.

Millimeter Wave (mmWave) wireless communica­tions is considered an enabling technology to allow fifth generation (5G) cellular networks to achieve extremely higher data rates for future mobile applications. Compared to conventional multiple- input-multiple-output (MIMO) systems, in mmWave MIMO sys­tems, precoding cannot be performed only at baseband due to high cost and power consumption of signal mixers and analog-to-digital converters (ADCs). Alternatively, the hybrid analog-digital archi­tecture at transceiver is considered as a cost-effective solution. In this paper, a maximum likelihood (ML) fixed weight beamforming technique is added to the precoder, by approximating the signal's direction of departure in the presence of random noise. Moreover, a low-cost Maximum Likelihood Orthogonality Based Matching Pursuit MLOBMP algorithm is then designed, with predefined layered radio frequency (RF) orthogonal codebook as a candidate matrix, which approximates the BS beamforming vectors in more accurate transmit directions. Simulation results show an accepta­ble performance of the reduced complexity proposed hybrid pre- coding compared with optimal full-digital precoder.

In conventional ways of fast responding and high performing model predictive control using Field Programmable Gate Arrays (FPGA), the approaches developed are applicable only to Single Input Single Output System (SISO). Many real time systems are Multiple Input Multiple Output (MIMO) systems and loop interaction is a major concern and has to be reduced in control of MIMO systems. Also, direct digital control with ability to solve the Quadratic Programming (QP) problem online, which can be applied to complex systems with fast response time and/or embedded applications and with limited computational resource for supervisory control approach, is required in many complex industrial MIMO systems. A novel direct digital control of Model predictive controller with FPGA for a MIMO system which considers and reduces loop interaction and which can also be implemented with very less investment is proposed here. The system was modeled from first principles and then simulated in Labview environment for MIMO system. Implementation of the system and response was verified for a stirred tank system. The proposed system has good performance in terms of reduction in loop interaction which is a major concern in implementation of controller for MIMO systems. Also, direct digital control of MIMO system instead of supervisory control was accomplished.

The main limitation of using a multiple antenna architecture is the complexity and high cost of the hardware in the radio frequency (RF) section, which rise with an increase in the number of antennas. In addition, RF circuit mismatches [1-3] and coupling [4] grow with an increase in the number of antennas; consequently, these factors also limit the use of high numbers of antennas at the transceiver.One solution to compensate for the extra hardware cost and RF circuit imperfection is the utilization of a single RF front-end in a multiple input multiple output (MIMO) system, where a single RF path is used instead of multiple parallel RF paths. This results in an RF section that has lower complexity and cost, a simpler RF design, a compact size, and lower power consumption.A conventional MIMO receiver with N antennas at the receiver is shown in Figure 10.1. The receiver uses multiple parallel RF front-ends, where the number of RF front-ends is equal to the number of antennas. In this architecture, the baseband processing section decodes N received baseband paths to recover the signal and obtain the diversity or spatial multiplexing gain. To realize a single RF frontend path, orthogonal transmission of multiple RF streams over a single front-end must be recognized. In the following subsections, the different techniques to realize a single RF front-end in a MIMO system are discussed.KeywordsRadio FrequencyAntenna SelectionRadio Frequency CircuitRadio Frequency TransceiverSingle Radio FrequencyThese keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.

Consider a multiple input multiple output (MIMO) system with pulse amplitude modulation (PAM), where each transmit antenna is allowed to choose a different transmission delay. (We refer to this system as a MIMO system with asynchronous PAM.) Such MIMO systems have been studied before for increasing diversity and avoiding unintentional beamforming. We quantify the capacity/outage capacity improvement due to asynchronism and the corresponding spectral leakage (if any) for MIMO point-to-point and MIMO broadcast channels. We show that under commonly used transmitter spectral mask constraints, the channel has unbounded degrees of freedom. Synchronous PAM exploits only a finite number of these degrees of freedom, and asynchronism helps to improve capacity. For most cases of interest, two distinct shifts attain most of the capacity improvement. These gains are robust to interference from adjacent carriers. Analysis of the spectrum of the asynchronous PAM suggests that optimal water-filling based schemes are useful for improving capacity only in directional links due to possible spectral leakage, while uniform power allocation leads to capacity improvement without any extra spectral leakage.

Millimeter-wave (mm-wave) technology has been observed as an active part in 5th generation (5G) systems because of its potential for low-latency, multi-gigabit wireless links. The challenging fact in mm-wave technology is higher propagation losses at advanced carrier frequencies and also the increased complexity of hardware required. Multiple-input multiple-output (MIMO) is a key technology for increasing the capacity of 5G networks and the capability of supporting a large number of users. mm-Wave MIMO is considered to be the significant enabler for 5G wireless networks, which causes the maximum growth in the network capacity. The massive antennas connection and multiple radio frequency (RF) chains in wireless communication system cause excessive power consumption. In MIMO system, spatial modulation (SM) technique enables less complexity and low power consumption by reducing RF chain counts. This chapter elaborates simulation and analyses the result of SM technique that can be effectively implemented in mm-wave MIMO system to reduce power consumption.

The research of closed form expressions for the pdf of the lth ordered eigenvalue of a Wishart matrix has received a great attention in the past years owing to its applications in the performance analysis of multiple input multiple output (MIMO) systems in fading environments. Although several closed form expressions for this pdf were obtained in the past years, to the authors' knowledge, no one was very friendly for further analysis. We propose a methodology to obtain the pdf for the lth largest eigenvalue whose expression is given as a sum of terms xβe-xδ. This expression is easily usable to obtain closed form results for the performance of many MIMO systems, such as, for instance, MIMO beamforming, and MIMO with Singular Value Decomposition (SVD). The methodology is valid for both uncorrelated and correlated central Wishart, allowing the investigation of MIMO systems with uncorrelated and correlated Rayleigh fading. © 2008 IEEE.

Millimeter wave (mmWave) multiple-input multiple-output (MIMO) systems have recently been proposed to meet the needs of the future wireless communication standards. The efficient use of low resolution digital-to-analog converters (DACs) and hybrid architecture could significantly reduce the high power consumption associated with the mmWave MIMO system components. This paper designs an energy efficient transmitter with low resolution DACs for mmWave massive MIMO systems. An optimization problem is formulated and solved to find the optimal number of radio-frequency (RF) chains to be used at the transmitter to minimize the power consumption. This problem is constrained by the information loss which introduces the reduction of the number of the RF chains, expressed in terms of the system capacity. Rate and energy performance are compared with different beamforming techniques and architectures for various DAC resolutions.

Millimeter wave (mmWave) communication with advanced lens antenna arrays is a promising technology for achieving cost-effective 5G wireless systems. This paper studies a multi-user mmWave single-sided lens multiple-input multiple-output (MIMO) system in the uplink communication, where the base station (BS) is equipped with a full-dimensional (FD) lens antenna array with both elevation and azimuth angle resolution capabilities, and each mobile station (MS) has the conventional uniform planar array (UPA). With limited radio frequency (RF) chains at the BS and one single RF chain at each MS, we propose a low-complexity path division multiple access (PDMA) scheme to enable virtually interference-free multiuser communications, by exploiting the angle-dependent energy focusing property of the lens antenna array at the BS as well as the multi-path sparsity of mmWave channels. Besides, a new technique called path delay compensation is proposed at the BS to effectively transform the frequency-selective MIMO channel to parallel frequency-flat small-size MIMO channels, for each of which the low-complexity single-carrier (SC) transmission can be applied. Numerical results show significant sum-rate gain with the proposed design over benchmark systems.

Wireless Sensor Network (WSN) is used in various applications. Sensors acquire samples of physical data and send them to a central node in different topologies to process the data and makes decisions. A main performance factor for WSN is the battery life that depends on energy consumption on the sensor. To reduce the energy consumption, an energy efficient transmission technique is required. Multiple Input Multiple Output (MIMO) systems showed good utilization of channel characteristics. This leads to enhance the transmission and hence reduce energy consumed by the sensor. In MIMO systems multiple signals are combined at the transmitter and transmitted using multiple antennas. This provides each receiver the whole combined signal and hence, array processing techniques helps in getting better performance. To further enhance the transmission of data, a Low Density Parity Check (LDPC) Coded MIMO wireless sensor network is proposed. The system implements space diversity through Multiple Antennas and temporal diversity through LDPC code and uses a clustering procedure to optimize the forming of the MIMO system. Results showed that, if the number of sensors is greater than the number of receiving antennas, time or frequency multiplexing is possible to keep good performance for the devised system. And by controlling the encoder we can create a temporal and spatial code among the transmitted signals enhancing the BER results in longer battery life at sensor nodes.

Indoor communication systems operating at millimeter wave frequencies are getting increasing attention due to their ability of having wider bandwidth and as a result conveying information at higher bit rates. At these systems, the capacity of channel is very important especially where there is no line of sight due to an obstacle. Here we examine the use of a multiple-input multiple-output (MIMO) system in a 2D room with an obstacle at 60 GHz. We estimate the channel by using the closed-form Green's function of the room and show the ability of MIMO to increase the available capacity of the system in the room chiefly behind the obstacle.