Single Channel Blind Source Separation for MISO Communication Systems

Expressions are derived for the coverage probability and average rate of both multi-user multiple input multiple output (MU-MIMO) and single input multiple output (SIMO) systems in the context of a fractional frequency reuse (FFR) scheme. In particular, given a reuse region of $\frac{1}{3}$ (FR3) and a reuse region of 1 (FR1) as well as a signal-to-interference-plus-noise-ratio (SINR) threshold $S_{th}$ , which decides the user assignment to either the FR1 or FR3 regions, we theoretically show that: $1)$ the optimal choice of $S_{th}$ which maximizes the coverage probability is $S_{th} = T$ , where $T$ is the target SINR required for ensuring adequate coverage, and $2)$ the optimal choice of $S_{th}$ which maximizes the average rate is given by $S_{th}= T^{\prime}$ , where $T^{\prime}$ is a function of the path loss exponent, the number of antennas and of the fading parameters. The impact of frequency domain correlation amongst the OFDM sub-bands allocated to the FR1 and FR3 cell-regions is analysed and it is shown that the presence of correlation reduces both the coverage probability and the average throughput of the FFR network. Furthermore, the performance of our FFR-aided MU-MIMO and SIMO systems is compared. Our analysis shows that the (2 $\times$ 2) MU-MIMO system achieves 22.5% higher rate than the (1 $\times$ 3) SIMO system and for lower target SINRs, the coverage probability of a (2 $\times$ 2) MU-MIMO system is comparable to a (1 $\times$ 3) SIMO system. Hence the former one may be preferred over the latter. Our simulation results closely match the analytical results.

Single channel blind source separation (SCBSS) is an important method to separate source signals only from one received signal without any prior information. This paper proposes a SCBSS algorithm based on dual-tree complex wavelet transform (DTCWT) and ensemble empirical mode decomposition (EEMD). First, DTCWT is used to decompose the received signal into several decomposed signals. Then some of them with larger energy are decomposed by EEMD into the intrinsic mode functions, which are selected to constitute virtual signals with the received signal. Finally, the source signals are estimated by Fast-ICA from these virtual multichannel signals. The experimental results show that the performance of the proposed algorithm is significantly improved compared with the SCBSS algorithms based on EEMD and DTCWT respectively.

Interference caused by other users in a multi-user environment degrades the system performance significantly. Conventionally, user separation in a multi-user environment is achieved by signal separation in time, frequency or code. The use of multiple elements antenna (MEA) systems can apply separation of the users in space by targeting the transmitted power to the user of interest. Inevitably in that case, the separation is not ideal and there is interference on the other users in the systems. Moreover in a wideband transmission, frequency selective fading causing inter symbol interference (ISI) is a big challenge for point to point wireless communications. In this paper, we study the feasibility of applying the time reversal technique (TR) in multiple input single output (MISO) systems to alleviate the effect of ISI. The ability of TR to focus the signal on the receiver of interest in a multi-user environment is also considered. The studies are supported by measured complex channel impulse responses with 10 MHz bandwidth centered at 2.14 GHz. For an 8/spl times/1 MISO system, using TR the root mean square delay spread is reduced by a factor of 2. To evaluate the capability of the TR in reducing the ISI, simulation of the bit error rate (BER) was made. The irreducible BER of the TR-MISO system was shown to be lower than that of the SISO system by at least an order of magnitude. By using TR and assuming that two users are communicating simultaneously with the same BS the two signals have 17 dB of isolation.

MISO (Multiple-Input Single-Output) similar architecture of most terrestrial wireless network networks instead of more and more scientists striving to apply MISO technology to satellite technology, it can be used to achieve a lower rate of application bit error and risk of complications, while managing increased power boost technology. In mitigating the phenomenon increasingly crowded networks, sites, and increasingly strained sources and frequency of orbital interacting. That channel electricity, bit error rate, and single-band dual-polarized probabilities of MISO outage communication systems are evaluated in the paper. In the first step, when the XPD (Cross-Polarization Discrimination) antenna in a certain organism is greater than 1. With the XPD change, the channel throughput increases in scale linearly. Second, under BPSK modulation, it analyses the bit error rate BER of the specification. The design incorporates a sufficiently low (BER) when the SNR is substantial; analysis of the frequency of outage of a distributed MISO system demonstrated when the signal to noise ratio (SNR) is massive, the handset would maintain the prospect of an outage low enough. Assessment of the spectral range of satellite communication, in the analysis process, the criteria of the rice channel are applied to the channel, review excluding certain traits of the dual-polarized satellite MISO. The public would provide great feedback for the productivity of the future of MISO satellite technology. In a previous paper, the author designed a channel modeling dual-band for satellite communication using the MIMO technique. Using this technique, the system cannot achieve greater performance and at the same time using a dual-band will decrease the system's capability. To decrease the above issues, the paper introduces a new method called the MISO system. Using the MISO technique, the system can gain high performance and the system will get greater bandwidth, BER, and SNR. The advantage of using single-band dual-polarized is, uplink and a downlink frequency of the satellite can be calculated very easily and accurately.

Joint Beamforming and power allocation schemes are considered for cognitive radio (CR) multiple input single output (MISO) system in this paper. We considered the robust downlink beamforming and power allocation problem with per-antenna power constraints which is more realistic in practice, instead of sum-power constraint, under the imperfect channel state information (CSI). Since the given optimization problem is a non-convex problem which is difficult to handle, we transformed it into second order cone program (SOCP) problem, which can be solved efficiently. Simulation shows that the proposed scheme indeed has a better performance in CR system.

A family of new MMSE blind channel equalization algorithms based on second order statistics are proposed. Instead of estimating the channel impulse response, we directly estimate the cross-correlation function needed in Wiener-Hopf filters. We develop several different schemes to estimate the cross-correlation vector, with which different Wiener filters are derived according to minimum mean square error (MMSE). Unlike many known subspace methods, these equalization algorithms do not rely on signal and noise subspace separation and are consequently more robust to channel order estimation errors. Their implementation requires no adjustment for either single or multiple user systems. They can effectively equalize single-input multiple-output (SIMO) systems and can reduce the multiple-input multiple-output (MIMO) systems into a memoryless signal mixing system for source separation. The implementations of these algorithms on SIMO system are given and simulation examples are provided to demonstrate their superior performance over some existing algorithms.

This paper presents an experimental power angle profile measurement procedure, as well as theoretical capacity results of a single-input-multiple-output (SIMO) system which operates in the millimeter wave band and especially at 60 GHz. Power angle profiles (PAPs) were measured in different indoor locations while detailed angle-of-arrival (AoA) shape factors describing the spatial properties of the millimeter wave channel were derived. The measurement results were related to the site-specific environments and showed correlation between the propagation environments and the multipath channel structure. The 10% outage capacity was found theoretically between 5.7 and 6.2 b/s/Hz for a 1 × 4 SIMO system, and in the range of 4.3 and 4.6 b/s/Hz for a 1 × 2 SIMO system.

Motivated by the demand for energy-efficient communication solutions in the next generation cellular network, a mixed-ADC receiver architecture for massive multiple input multiple output (MIMO) systems is proposed, which differs from previous works in that herein one-bit analog-to-digital converters (ADCs) partially replace the conventionally assumed high-resolution ADCs. The information-theoretic tool of generalized mutual information (GMI) is exploited to analyze the achievable data rates of the proposed system architecture and an array of analytical results of engineering interest are obtained. For deterministic single input multiple output (SIMO) channels, a closed-form expression of the GMI is derived, based on which the linear combiner is optimized. Then, the asymptotic behaviors of the GMI in both low and high SNR regimes are explored, and the analytical results suggest a plausible ADC assignment scheme. Finally, the analytical framework is applied to the multi-user access scenario, and the corresponding numerical results demonstrate that the mixed system architecture with a relatively small number of high-resolution ADCs is able to achieve a large fraction of the channel capacity without output quantization.

Outage capacity analysis of the massive MIMO diversity channel

In this paper, we consider Single Input Multiple Output (SIMO) systems with Maximum Ratio Combining(MRC) and Equal Gain Combining (EGC). We assume spatially independent and timely correlated Rayleigh flat fading channels, modeled by AR2 processes. We consider a frame of N+2 symbols, where Channel State Information at Receiver (CSIR) is available in the beginning for two consecutive symbols, also called as partial CSIR. Using this CSIR, we predict CSIR for the remaining N symbols and detect them. For this partially coherent systems, we have derived closed-form expressions of BER for BPSK constellation. We have also presented simulation results and verified our analytical expressions. We have shown the effects of frame size N, time varying condition of channel by doppler frequency ω <sub xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">d</sub> on BER. We have compared performance of the SIMO systems with SISO system and shown significant improvement in BER over SISO systems.

This paper considers the problem of massive multiple-input-multiple-output (MIMO) wireless communication systems with quasi-orthogonal space-time block code (QOSTBC) transmission in the presence of spatial correlation effect (SCE) and mutual coupling effect (MCE). Conventional MIMO channels with QOSTBC transmission suffer from several drawbacks. The number of transmit antennas is restricted to a few in order to achieve full transmission rate. Therefore, it is difficult to make a quasi-orthogonal space time block coded massive MIMO system to achieve full transmission rate. Moreover, MIMO channel with QOSTBC transmission is usually considered for the case where the number of user equipments (UEs) is one. We present a joint beamforming and spatial precoding method to deal with the above drawbacks. The proposed method incorporates a beamforming scheme and spatial precoding to formulate an appropriate optimization process to effectively alleviate the considered problems. The resulting optimization problem will be solved by using a cooperative coevolutionary particle swarm optimization algorithm under two proposed fitness functions. During the optimization process, the proposed method finds the optimal beamforming coefficients of the precoding matrix, the optimal normalized positions of transmit and receive antenna elements for the case of using linear antenna arrays, and the optimal angle differences of transmit and receive antenna elements for the case of using circular arrays. Based on the proposed method, we are able to cure the performance degradation of a quasi-orthogonal space time block coded massive MIMO system due to the SCE and MCE. Moreover, the proposed method makes QOSTBC MIMO communications with full transmission rate for any number of transmit antennas achievable. Several simulation examples are presented to show the superior bit error rate (BER) performances of QOSTBC wireless MIMO scenarios with linear as well as circular antenna arrays by using the proposed method as compared to the existing methods.

Signal estimation from functional magnetic resonance imaging data (fMRI) is a difficult and challenging task that involves carefully chosen models that can be validated by domain experts. Blind source separation (BSS) techniques such as principal component analysis (PCA), independent component analysis (ICA), and tensor decomposition have been proposed for estimating the task-fMRI signals. However, few studies have investigated their efficacy for classifying different tasks. This paper proposes yet another novel method to validate the signal estimation using predictive performance of the methods. Three commonly used BSS techniques are used for estimating signals (spatio-temporal maps) from task-fMRI. The dataset (consisting of 4 tasks) is taken from the Human Connectome Project (HCP). The extracted temporal signals are used to train a classifier that classifies two different tasks. For a binary classification, a Constrained-PARAFAC tensor decomposition achieves 92% accuracy for differentiating gambling vs. relational tasks. In addition, results for other binary task classifications are also presented. Overall, the proposed Constrained-PARAFAC performs better in prediction performance compared to PCA and ICA.

Recently, energy detection (ED) has been investigated in massive single-input multiple-output (SIMO) systems, where transmit symbols can be decoded by averaging the received power across all receive antennas. In this paper, we concentrate on the performance of non-coherent massive SIMO in the presence of antenna correlation. Specifically, closed-form expressions of symbol error rate (SER) and achievable rate are derived. Furthermore, asymptotic behaviors of SER and achievable rate in regimes of a large number of receive antennas, high antenna correlation and large signal-to-noise ratio (SNR) are investigated. Interestingly, the results show that antenna correlation poses a great impact to SER, but has little effect on the achievable rate. Numerical results are presented to verify our analytical results.

Blind single-channel source separation is a long-standing machine learning and signal processing problem. Traditional blind source separation (BSS) algorithms were proposed to solve this task utilizing multiple signal constraints. Generative adversarial network (GAN) are free from statistical constraints and samples, but the role of adversarial training in the task of BSS has not been fully demonstrated. Therefore, a new separation network model that enables to learn the known separated signal distribution from the stepwise fine estimation of the unknown mixture distribution was presented in this paper, and a self-attention mechanism was introduced to solve the problem of the blurring details of the generated image by the generator which preserves image details in the process of image separation. Compared with the existing single-channel blind source separation algorithm based on generative adversarial network-neural egg separation (NES), the detailed information of this new separation algorithm is more prominent, and the source signal in the mixed image has been separated more effectively, and has better separation performance than the classic blind source separation algorithms.

Massive multiple-input multiple-output (MIMO) will be employed in the 5th generation mobile communications (5G). To implement the massive MIMO, hybrid beamforming (HB) is one of the most promising techniques, because HB is composed of the analog beamforming (AB) and the digital beamforming (DB) and thus can decrease the number of baseband and radio frequency (RF) circuits significantly. Furthermore, HB can reduce a much larger amount of cost and power consumption than the full DB (FDB) configuration, which requires one baseband and RF circuit for each antenna element. This paper proposes a scheme to estimate both phases for phase rotators in AB and a precoding matrix for DB so that HB can approximate the block diagonalization (BD), which can nullify interference from the undesired users and form efficient transmit beams. For time division duplex (TDD) multiuser MIMO communications, the proposed scheme estimates the FDB channel matrix in the uplink (UL) and the phase for the phase rotators of AB in the downlink (DL). After obtaining an equivalent channel matrix from both the estimated FDB channel matrix and the AB precoding matrix, the scheme estimates the DB precoding matrix in DL from the equivalent channel matrix so that HB can approximate BD in DL. Computer simulations demonstrate that HB employing the proposed scheme can achieve DL average bit error rate (BER) performances that are little inferior to those of FDB.