Optimal precoding for orthogonalized spatial multiplexing in closed-loop MIMO systems

This paper proposes a new precoding algorithm for orthogonalized spatial multiplexing (OSM) systems over flat-fading multiple-input multiple-output (MIMO) channels. The OSM scheme was recently introduced for closed-loop MIMO systems which allows single symbol decodability for maximum likelihood detection. To further improve the performance in OSM systems, we propose a new precoding method. For identifying the parameters of a precoder, we introduce a partitioning approach on the minimum Euclidean distance between constellation points in the effective channel. Also, it is shown that two real value parameters and one bit are required for feedback information in 4-QAM systems. Simulation results demonstrate that our precoding algorithm allows us to significantly improve the system performance with small increase of feedback values. We also confirm through simulations that the performance of the proposed scheme is the same as the optimum closed-loop MIMO systems.

We introduce a vision transformer (ViT)-based deep joint source and channel coding (DeepJSCC) scheme for wireless image transmission over multiple-input multiple-output (MIMO) channels, called DeepJSCC-MIMO. We employ DeepJSCC-MIMO in both open-loop and closed-loop MIMO systems. The novel DeepJSCC-MIMO architecture surpasses the classical separation-based benchmarks, while exhibiting robustness to channel estimation errors and flexibility in adapting to diverse channel conditions and antenna configurations without requiring retraining. Specifically, by harnessing the self-attention mechanism of the ViT, DeepJSCC-MIMO intelligently learns feature mapping and power allocation strategies tailored to the unique characteristics of the source image and prevailing channel conditions. Extensive numerical experiments validate the significant improvements in both distortion quality and perceptual quality achieved by DeepJSCC-MIMO for both open-loop and closed-loop MIMO systems across a wide range of scenarios. Moreover, DeepJSCC-MIMO exhibits robustness to varying channel conditions, channel estimation errors, and different antenna numbers, making it an appealing technology for emerging semantic communication systems.

Recent advances in multiple-input multiple-output (MIMO) systems have renewed the interests of researchers to further explore this area for addressing various dynamic challenges of emerging radio communication networks. Various measurement campaigns reported recently in the literature show that physical multipath MIMO channels exhibit sparse impulse response structure in various outdoor radio propagation environments. Therefore, a comprehensive physical description of sparse multipath MIMO channels is presented in first part of this paper. Superimposing a training sequence (low power, periodic) over the information sequence offers an improvement in the spectral efficiency by avoiding the use of dedicated time/frequency slots for the training sequence, which is unlike the traditional schemes. The main contribution of this paper includes three superimposed training (SiT) sequence based channel estimation techniques for sparse multipath MIMO channels. The proposed techniques exploit the compressed sensing theory and prior available knowledge of channel's sparsity. The proposed sparse MIMO channel estimation techniques are named as, SiT based compressed channel sensing (SiT-CCS), SiT based hardlimit thresholding with CCS (SiT-ThCCS), and SiT training based match pursuit (SiT-MP). Bit error rate (BER) and normalized channel mean square error are used as metrics for the simulation analysis to gauge the performance of proposed techniques. A comparison of the proposed schemes with a notable first order statistics based SiT least squares (SiT-LS) estimation technique is presented to establish the improvements achieved by the proposed schemes. For sparse multipath time-invariant MIMO communication channels, it is observed that SiT-CCS, SiT-MP, and SiT-ThCCS can provide an improvement up to 2, 3.5, and 5.2 dB in the MSE at signal to noise ratio (SNR) of 12 dB when compared to SiT-LS, respectively. Moreover, for $$\mathrm {BER}=10^{-1.9}$$BER=10-1.9, the proposed SiT-CCS, SiT-MP, and SiT-ThCCS, compared to SiT-LS, can offer a gain of about 1, 2.5, and 3.5 dB in the SNR, respectively. The performance gain in MSE and BER is observed to improve with an increase in the channel sparsity.

Throughputs of 2 × 2 long term evolution (LTE) multiple-input multiple-output (MIMO) systems in both open- and closed-loop configurations have been studied previously. However, in the previous work, the study of the closed-loop MIMO system is limited to receive semi-correlation (i.e., correlations exist only at the receive side only), resulting in inaccurate modeling of the closed-loop throughput with large transmit antenna correlations and incomplete conclusion about the effectiveness of codebook based precoding based on 2-bit feedback. In this work, the effect of the transmit antenna correlation on the closed-loop throughput is examined. Unlike in the previous work, an inversely weighted mean square error (MSE) matrix is proposed as the precoding selection criterion, resulting in good agreements between the simulations and the RC measurements of a closed-loop LTE system with various correlations. It is found that the closed-loop MIMO system is superior to its open-loop counterpart in the presence of large transmit antenna correlation in the low and medium signal-to-noise (SNR) regimes.

The bit error rate (BER) performance of orthogonal space-time block codes (STBC) in correlated multiple input multiple output (MIMO) channels is studied. We derive closed-form exact BER equations for pulse amplitude modulation (PAM), quadrature amplitude modulation (QAM), and phase shift keying (PSK) constellations in a correlated Rayleigh MIMO channel. The BER expressions can be easily evaluated without any numerical methods.

This paper introduces a divide and conquer approach to the design of transmit and receive filters for communication over a Multiple Input Multiple Output (MIMO) Gaussian channel subject to an average power constraint. It involves conversion to a set of parallel scalar channels, possibly with very different gains, followed by coding per sub-channel (i.e. over time) rather than coding across sub-channels (i.e. over time and space). The loss in performance is negligible at high signal-to-noise ratio (SNR) and not significant at medium SNR. The advantages are reduction in signal processing complexity and greater insight into the SNR thresholds at which a channel is first allocated power. This insight is a consequence of formulating the optimal power allocation in terms of an upper bound on error rate that is determined by parameters of the input lattice such as the minimum distance and kissing number. The resulting thresholds are given explicitly in terms of these lattice parameters. By contrast, when the optimization problem is phrased in terms of maximizing mutual information, the solution is mercury waterfilling, and the thresholds are implicit.

In this paper, linear precoding (LP) algorithm has been studied to improve the bit error rate (BER) performance of large scale multiple input multiple output (MIMO) system. A new technique of enhancing the BER performance of linear precoding algorithm without complexity in hardware and mathematical calculations is proposed and it is used on a traditional LP algorithms like a Maximum Ratio Transmission (MRT), a Zero-Forcing (ZF), a Regularized Zero-Forcing (RZF), and a Minimum Mean Square Error (MMSE) for Millimeter wave MU-MIMO communication system. A new technique is also used to mitigate the BER gab problem between (ZF, MMSE) and (RZF, MMSE) linear precoding algorithms. The result shows that with using the new technique, the BER performance of modified LP significantly outperform their traditional LP by 10−5 when using 4 element of BS antenna, 10−6 when using 16 element of BS antenna and mitigate the BER gab problem at different signal to noise ratio (SNR).

We present a new hybrid multiple input multiple output (MIMO) system combining spatial multiplexing and transmit diversity at different levels. The inner core of the transmit processing is responsible for space-time coding with variable rate-diversity control. The outer core is composed of two (coarse) precoding matrices acting as transmit antenna and mode selection matrices. Exploiting the structure of these selection matrices, a capacity maximizing selection criterion is proposed to optimize the hybrid transmission mode jointly with the transmit antenna mapping. Simulation results show that the proposed hybrid system offers significant capacity gains over its open-loop counterpart and outperforms pure antenna selection and pure mode selection based systems.

It has been generally assumed that binary data from a maximum-length pseudo-random binary source can be grouped into m-ary symbols that will uniformly cover constellation points in a modulation scheme due to the central limit theorem. This is not the case. Sources based on the Galois field GF(16) are introduced and compared to binary, GF(2), ones in terms of the mean and variability of the resulting bit error rate (BER) estimate using a single carrier quadrature amplitude modulation (16-QAM) system as an example. The use of data sources over GF(16) for Monte Carlo simulation of the example system eliminates BER estimate variability with certain channels. Such variability is important when the channel is such that some of the constellation points cause higher probabilities of error than others. The approach is then extended to orthogonal frequency division multiplex (OFDM) m-ary systems. It is found that it is important to map separate Galois field data sources to each sub-carrier in the case of OFDM systems. There is found to be some residual BER estimate variability. The extension of the approach to systems employing forward error correction coding and interleaving is discussed.

We consider a wireless transmission scheme based on randomized space-time spreading (STS) systems over massive multiple-input multiple-output (MIMO) channels. In the systems, the signals are spread by spreading matrices over time and antenna domains at the transmitter. We propose that the spreading matrices are generated from binary pseudo-noise (PN) and mutual quasi-orthogonal sequences, by partitioning each of the sequences into subsequences treated as real and imagination parts of rows or columns of a spreading matrix. We give a likelihood ascent search (LAS) detector for the STS systems and analyze its computational complexity. The number of multiplication and division operations for the LAS detector does not depend on its iterative process and the number of chips in the bit period. We use Monte Carlo method to solve the bit error rates (BERs) of the STS systems and to count numbers of evaluating bits in the LAS detectors. We choose the system parameters, such as the number of chips in the bit period and the transmission bit rates, according to BER performance and/or the numbers of evaluating bits attained from Monte-Carlo method. With the spreading matrices, the detection, and the suitable parameters, the STS systems achieve BERs near to their single-bit performance, which is the BER of a STS system with a single bit spread by orthonormal sequences at each transmitting antenna. We also compare our systems with V-BLAST-like spatial multiplexing systems with BPSK signals to show the improvement of the BER performance.

In this paper, we propose a new spatial multiplexing scheme for transmission over flat-fading multiple-input multiple-output (MIMO) channels, which allows a simple maximum-likelihood decoding at the receiver with small feedback information. We begin with a real-valued representation of the complex-valued system model and show that we can achieve orthogonality between transmitted signals by applying a proper rotation to transmitted symbols. Based on the minimum Euclidean distance between received vectors, we also present a simple antenna selection metric for the proposed spatial multiplexing systems. Simulation results demonstrate that our spatial multiplexing system performs close to the optimum closed loop system with much reduced complexity and feedback overhead.

Modern communications systems utilize effective encoding plans, multiple-input multiple-output (MIMO) and high-order QAM-constellations for improves spectral efficiency. However, as the dimensions of the system grow, the design of efficient and low-complexity MIMO receivers possesses technical difficulties. Here in this paper, we have demonstrated data reduction method using Principal Component Analysis (PCA) to fuse the data at sensor level at the transmitter and regress the same at the receiver for improvising the channel capacity of the system. We have fused 45 signals sensor data having a bandwidth from 100Hz - 3000Hz which is sampled at 6000 Hz each. The regression is measured using the RMSE of the transmitted and received signals. Also, the communication system has demonstrated the performance of the 2, 4, 8QAM modulation technique with and without LDPC decoder(log-domain) for 2x2, 3x3, 4x4 Multiple Input Multiple Output(MIMO) channel for Bit Error Rate (BER). The performance parameters for data regression and communication error is evaluated over AWGN channel ranging from -15dB to 40dB SNR. The proposed system useful for the next generation low bandwidth communication system such as wireless systems developments, i.e., WiMAX and 4G, 5G, 6G.

Multiple input multiple output (MIMO) communication systems have emerged as a key technol-ogy to enhance spectral efficiency and reliability in wireless communications. In recent years, deep neural network (DNN)-based approaches have shown promise in addressing the challenges of MIMO signal detection. Among these approaches, the Transformer architecture, known for its effectiveness in capturing long-range dependencies in sequential data, has gained significant attention. Therefore, this paper proposes a revolutionary DNN-based MIMO signal detection scheme using the Transformer-based architecture. This novel scheme leverages the multi-head self-attention mechanism inherent in Transformer architectures, which enables the model to capture both spatial and temporal dependencies in MIMO channels, thereby improving symbol detection accuracy and robustness under varying channel conditions. The proposed scheme's bit error rate (BER) performance is compared with traditional methods through simulations. The results show that the proposed method achieves a signal-to-noise ratio (SNR) gain of nearly 1.5 ​dB against the traditional detection methods, with the optimal maximum likelihood detector (MLD) only outperforming it ​by ​< ​0.5 ​dB. • Transformer-based MIMO signal detection. • Deep learning for wireless communications. • Open access journals on signal processing. • Next-generation wireless communication systems.

The spatial and intertap correlation matrices of fading channels are first estimated for underwater multiple input multiple output (MIMO) systems using real experimental data. We confirm that underwater MIMO channels exhibit strong spatial and temporal correlation. The bit error rate (BER) performance of the MIMO system at different correlation scenarios are also evaluated with computer simulation. At BER=0.01%, the required SNR of highly correlated channel is 5 dB higher than the i.i.d. channel. BER results of the experimental data also quantitatively show the performance degradation caused by the correlation.

A 2-D shooting-and-bouncing (SBR) ray-tracing method considering ray reflect, transmission and diffraction is used to analyze antenna spacing effect on capacity complementary cumulative distribution functions(CCDFS) of indoor non-line-of-sight (NLOS) MIMO channel and indoor multiple-input multiple-output (MIMO) channel with line-of-sight (LOS) path. The results show that the capacities of MIMO channel with LOS path are less than NLOS MIMO channel capacities in general. But whether in indoor MIMO channel with LOS path or in NLOS indoor MIMO channel, capacities CCDFS of indoor MIMO systems with antenna spacing of 5/spl times/ can reach the theoretical independent and identically distribution (i.i.d) Rayleigh capacities nearly fully. With the decrease of antenna spacing, the capacities dropped also. And the less antenna spacing, the more difference between indoor NLOS MIMO channel capacities and capacities of MIMO channel with LOS path.