Wireless MIMO communications — waves, scattering, and system design

The multiple input multiple output (MIMO) technique provides the higher bit rate and the better reliability in wireless systems. These advantages are achieved by designing appropriate apace-time codes that provide diversity improvement, spatial multiplexing gain, or a trade-off between diversity order and spatial multiplexing. This chapter provides an overoview on MIMO wireless system concept and its performance. Moreover, the MIMO channel models are discussed.

Hybrid precoding design for secure smart-grid enabled MIMO wireless communications in Industry 5.0

Multiple-input–multiple-output (MIMO) technology is one of the most widely used communication technologies. However, with the increasing number of antennas, the complexity of the MIMO wireless communication receiver becomes higher and higher. On the other hand, the complex communication channels also raise up a great challenge to the reliability of the communication receiver system. With the rapid development and wide application of deep learning, it has been applied in the field of communication to solve some problems that are difficult to solve by the traditional methods, and thereby, improves the reliability of communication systems. Inspired by this idea, this article reviewed the signal processing process of the MIMO receiver system from the perspective of system reliability. Based on deep learning, the signal processing modules of the receiver system are jointly optimized, which changes the information recovery process of the traditional receiver and proposes the intelligent receiver for MIMO communication. In order to verify the system reliability of the intelligent receiver, this article analyzes it from the aspects of antenna numbers and channel conditions. The influence of different implementation methods of the intelligent receiver on the system reliability is also analyzed. Simulation results show that the proposed intelligent receiver for the MIMO wireless communication can recover information with a lower bit error rate and higher reliability compared with the traditional receiver under different conditions and antenna configurations.

Multiple-Input Multiple-Output (MIMO) wireless communication system, which is also called Multiple-Antenna system, is well known as one of the most important technologies and widely studied nowadays (Winters, 1987; Foschini & Gans, 1998; Marzetta & Hochwald, 1999; Raleigh & Cioffi, 1998). The main idea of MIMO wireless communication is to utilize the spatial degree of freedom of the wireless multi-path channel by adopting multiple antennas at both transmit and receive ends to improve spectrum efficiency and transmission quality of the wireless communication systems. MIMO technology is able to extremely improve the transmission data rates and alleviate the conflict between the increasing demand of wireless services and the scarce of electromagnetic spectrum. Two famous techniques of the MIMO systems are spatial multiplexing (SM) and transmit diversity (TD) (Nabar et al, 2002). In the scheme of SM, multiple data pipes between transmit and receive ends provide multiplexing gain to dramatically increase the channel capacity linearly with the number of antennas (Telatar, 1999; Bolcskei et al, 2002). The TD technologies, such as space-time coding, are adopted to improve the link reliability of wireless communication, especially in the multi-path fading channels (Marzetta & Hochwald, 1999; Tarokh et al, 1998; Bolcskei et al, 2001). The channel knowledge is not required in the transmit end for TD technologies. MIMO is the key technology for future wireless communication systems, such as 3GPP LTE, WiMAX 802.16, IEEE 802.20, IMT-Advanced and so on.

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.

Massive MIMO is an extension of MIMO (Multiple-input Multiple-output), which essentially groups together several number of transmitting antennas and receiving antennas in order to provide better spectrum efficiency and better throughput. Massive MIMO technology will almost be a key component of the super-fast 5G networks of the future generation. However, the Multiple User Interference (MUL) is the major problem that needs to be address to detect each user appropriately at the base station. Hence, detection of encoded signals is one of the crucial receiver functions in MIMO wireless communication. The well-known detection algorithms like Zero forcing detector (ZF) and MMSE provides less complex design at the cost of reduced performance. Maximum Likelihood detector (ML) gives the optimal performance, but its computational complexity limits its usage in practical systems. Thus, to balance the trade-off between complexity and performance, the suboptimal Likelihood Ascent Search (LAS) algorithm has been proposed. However, the LAS algorithm detection performance depends on selection of initial solution. So, we propose Neighborhood search algorithm to select the initial vector and this healthy initial vector can be used for LAS algorithm to find solution. This algorithm is iterative in nature and it searches for that vector which minimizes the maximum likelihood (ML) cost in the neighborhood. The proposed algorithm is expected to balance the tradeoff between performance and complexity of design.

In recent years, the research on multiple-input multiple-output (MIMO) wireless communications has attracted much interest. This study investigates precoding techniques for multi-user MIMO communications. By applying QR decomposition to augmented channel matrix based on minimum mean squared error (MMSE) approach plus Tomlinson-Harashima precoding (THP) and equal-rate power allocation, we show that the proposed equal-rate QR-MMSE-THP precoder has better performance than the one based on zero-forcing (ZF) channel matrix, called equal-rate QR-ZF-THP precoder, and the conventional MMSE precoder. In addition, sorting strategies can be adopted to enhance performance with more freedom for QR decomposition compared to block diagonalized decompositions such as block diagonalized-geometric mean decomposition (BD-GMD) and BD-GMD-MMSE schemes while complexity can still be kept practical. Also, the sorting strategies such as full sorting (FS), per-layer sorting (PLS) and per-user sorting (PUS) are discussed and compared in this paper. Simulation results show that for large MIMO systems, the proposed equal-rate QR-MMSE-THP with PLS strategy outperforms the equal-rate BD-GMD-MMSE-THP precoder with PUS.

A self-decoupled technique is described that enables the radiating elements in the antenna array to be densely packed for multiple-input multiple-output (MIMO) wireless communications systems. High isolation between the adjacent antenna elements is obtained by fixing the radiating elements in an orthogonal configuration with respects to each other. Current from the adjacent ports cancels their impact which results in low mutual coupling. The additional benefit of this configuration is realizing a densely packed array. The ground plane of each radiating element on the array board itself are isolated to mitigate surface wave propagations to suppress mutual coupling between the antenna elements. The radiating elements are based on a modified edge-fed circular patch antenna that includes a curved slot line and open-circuited stub to widen the array’s impedance bandwidth with no impact on the antenna’s footprint size. The proposed technique was verified with the design of an antenna array of matrix size 4 × 4 centered at 3.5 GHz. The array had a measured impedance bandwidth of 4 GHz from 1.5 GHz to 5.5 GHz, which corresponds to a fractional bandwidth of 114%, peak gain of 3 dBi and radiation efficiency of 84%. Its average diversity gain and envelope correlation coefficient (ECC) over its operating band are 9.6 dB and <0.016, respectively. The minimum isolation achieved between the radiating elements is better than 15 dB. The dimensions of the array are 0.4 × 0.4 × 0.039λ_g^3. The proposed array has characteristics suitable for sub-6 GHz wireless communication systems

The demand for higher data rate and better quality of service (QoS) in wireless communications was growing fast in the past few years. Obtaining these requirements becomes challenging for wireless communication systems due to the problems of channel multi-path fading, higher power and bandwidth limitations. One of the most promising solutions to this problem is Multiple Input Multiple Output (MIMO) system. This paper proposed a combined spatial multiplexing MIMO scheme with beamforming for high data rate wireless communication. The proposed transmission scheme combines the benefits of both techniques and the system was able to transmit parallel data streams as well as providing beamforming gain. Actually, these diverse techniques, share the same requirement of multiple antenna elements, but differ in the antenna element spacing necessary for the different schemes to work. Thus, smart antenna array was proposed as a possible solution and was adopted at the both transmitter and the receiver. The hybrid technique provides higher spectral efficiency and improve better Bit error rate (BER) of the system than the conventional MIMO, spatial multiplexing and beamforming techniques under the same simulation environment.

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

In the present world, the spectrum efficiency along with the capacity and coverage of wireless networks are being improved by wireless specialists. Wireless link performance could be enhanced and improved by using multiple antenna technology called Space time wireless technology. There exists some advancement in the present MIMO techniques which could further improve the overall wireless Local Area Network (LANs) and Wide Area Network (WANs). In Multipath fading performance gain is very important which could be achieved by using an efficient Space-Time Coding (STC) technique. This Space Time coding along with Alamouti Scheme is a very vibrant research topic. In the forthcoming generation of mobile communication there will be a need of high data rate, reliable communication link and vast multimedia capabilities, STBC is the one solution for these. This paper presents the Space-Time Block Codes (STBC) for wireless networks using Alamouti Scheme in which multiple antennas are being used at both transmitter and receiver. The simulations have been done in MATLAB.

Multiple-input multiple-output (MIMO) wireless communications systems have attracted a lot of attentions, because of their potential for high gain in channel capacity. Recently, attention has shifted to multiuser MIMO (MU-MIMO) systems. In the schemes for downlink MU-MIMO system, dirty paper coding (DPC) and block diagonalization (BD) algorithms can provide higher sum rate capacity of MU-MIMO systems; however, exhaustive user scheduling for them may cause prohibitive computation complexity. The greedy scheduling algorithms were proposed for DPC and for BD; and proportionally fair (PF) scheduling for MU-MIMO was also discussed in the previous works. However, the discussions in where mainly based on the system model with equal average signal to noise power ratio (SNR) users. In this paper, we focus on the user scheduling for the downlink of MU-MIMO systems with DPC transmission scheme, where each user is assumed to have only one receive antenna, which will simplify the analysis and is expected to give more insight of PF scheduling. We also assume each individual user has different average transmit SNR in the system. We modify the conventional user scheduling algorithm for DPC to modified Gram-Schmidt orthogonalization (M-GSO) based algorithm and extend it into PF scheduling. We also propose a modified PF metric corresponding to the unequal SNR users system model, that increases the sum rate capacity of MU-MIMO system.

Multiple-input multiple-output (MIMO) wireless communication provides a number of potential advantages over traditional single-input single-output (SISO) approaches, including increased data rates for a given total transmit power and improved robustness to interference. In this paper, the details of a variety of receivers are incorporated into the effects of the channel so that information-theoretic performance bounds can be exploited to evaluate receiver approaches. Two classes of environments are considered: first, channel complexity, characterized by the distribution of the narrowband channel-matrix singular values and second, external interference. Receiver approaches discussed in this paper include minimum-mean-squared error, minimum-interference, as well as an iterative receiver that incorporates estimation subtraction, denoted here as multichannel multiuser detection (MCMUD). Various assumptions are made about the limitations on channel and interference estimation. Receiver performance implications are also demonstrated using experimental data.

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.

Since the first communication systems were developed, the scientific community has been witnessing attempts to increase the amount of information that can be transmitted. In the last 10--15 years there has been a tremendous amount of research towards developing multi-antenna systems which would hopefully provide high-data-rate transmissions. However, increasing the overall amount of transmitted information increases the complexity of the necessary signal processing. A large portion of this thesis deals with several important issues in signal processing of multi-antenna systems. In almost every particular case the goal is to develop a technique/algorithm so that the overall complexity of the signal processing is significantly decreased. In the first part of the thesis a very important problem of signal detection in MIMO (multiple-input multiple-output) systems is considered. The problem is analyzed in two different scenarios: when the transmission medium (channel) 1) is known and 2) is unknown at the receiver. The former case is often called coherent and the later non-coherent MIMO detection. Both cases usually amount to solving highly complex NP-hard combinatorial optimization problems. For the coherent case we develop a significant improvement of the traditional sphere decoder algorithm commonly used for this type of detection. An interesting connection between the new improved algorithm and the H-infinity estimation theory is established, and the performance improvement over the standard sphere decoder is demonstrated. For the non-coherent case we develop a counterpart to the standard sphere decoder, the so-called out-sphere decoder. The complexity of the algorithm is viewed as a random variable; its expected value is analyzed and shown to be significantly smaller than the one of the overall exhaustive search. In the non-coherent case, in addition to the complexity analysis of the exact out-sphere decoder, we analyze the performance loss of a suboptimal technique. We show that only a moderate loss of a few dbs in power required at the transmitter will occur if a polynomial algorithm based on the semi-definite relaxation is used in place of any exact technique (which of course is not known to be polynomial). In the second part of the thesis we consider a few problems that arise in wireless broadcast channels. Namely, we consider the problem of the information symbol vector design at the transmitter. A polynomial linear precoding technique is constructed. It enables achieving data rates very close to the ones achieved with DPC (dirty paper coding) technique. Additionally, for another suboptimal polynomial scheme (the so-called nulling and cancelling), we show that it asymptotically achieves the same data rate as the optimal, exponentially complex, DPC. In the last part of the thesis we consider a quantum counterpart of the signal detection from classical communication. In quantum systems the signals are quantum states and the quantum detection problem amounts to designing measurement operators which have to satisfy certain quantum mechanics laws. A specific type of quantum detection called unambiguous detection, which has numerous applications including quantum filtering, has recently attracted a lot of attention in the research community. We develop a general framework for numerically solving this problem using the tools from the convex optimization theory. Furthermore, in the special case where the two quantum states are of rank 2, we construct an explicit analytical solution for the measurement operators. At the end we would like to emphasize that the contribution of this thesis goes beyond the specific problems mentioned here. Most algorithmic optimization techniques developed in this paper are generally applicable. While it is a fact that our results were originally motivated by wireless and quantum communications applications, we believe that the developed techniques will find applications in many different areas where similar optimization problems appear.