We study the design of receivers in extra-large scale MIMO (XL-MIMO) systems, i.e. systems in which the base station is equipped with an antenna array of extremely large dimensions. While XL-MIMO can significantly increase the system's spectral efficiency, they present two important challenges. One is the increased computational cost of the multi-antenna processing. The second is the variations of user energy distribution over the antenna elements and therefore spatial non-stationarities in these energy distributions. Such non-stationarities limit the performance of the system. In this paper, we propose a distributed receiver for such an XL-MIMO system that can address both challenges. Based on variational message passing (VMP), We propose a set of receiver options providing a range of complexity-performance characteristics to adapt to different requirements. Furthermore, we distribute the processing into local processing units (LPU), that can perform most of the complex processing in parallel, before sharing their outcome with a central processing unit (CPU). Our designs are specifically tailored to exploit the spatial non-stationarities and require lower computations than linear receivers such as zero-forcing. Our simulation study, performed with a channel model accounting for the special characteristics of XL-MIMO channels, confirms the superior performance of our proposals compared to the state of the art methods.
In this letter, we propose three linear receivers including Bussgang-based Maximal Ratio Combining (BMRC), Bussgang-based Zero-Forcing (BZF), and Bussgang-based Minimum Mean Squared Error (BMMSE) for massive MIMO systems with one-bit analog-to-digital converters (ADCs). Closed-form expressions of the proposed receivers are obtained by using the Bussgang decomposition to cope with the non-linear effect of the one-bit ADCs. Simulation results show significantly lower bit error rate floors obtained by the proposed receivers than those of conventional linear receivers.
We proposed and demonstrated a hybrid blind source separation system which can switch between multiple-input and multi-output mode and free space optical communication mode depends on different situation to get best condition for separation.
We consider a massive MIMO system where the array at the access point reaches a dimension that is much larger than the array in current systems. Transitioning to an extremely large dimension and hence large number of antennas implies a need to scale up the multi-antenna processing while maintaining a reasonable computational complexity. In this paper, we study the receiver of such an extra-large scale MIMO (XL-MIMO) system. We propose to base the reception on Variational Message Passing (VMP). The motivation is that the complexity of VMP scales (almost) linearly with the number of antennas and…
Multiple-input multiple-output (MIMO) systems exploit spatial diversity to facilitate multi-user communications with high spectral efficiency by beamforming. As MIMO systems utilize multiple antennas and radio frequency (RF) chains, they are typically costly to implement and consume high power. A common method to reduce the cost of MIMO receivers is utilizing less RF chains than antennas by employing hybrid analog/digital beamforming (HBF). However, the added analog circuitry involves active components whose consumed power may surpass that saved in RF chain reduction. An additional method to r…
Accurate channel estimation is critical to the performance of orthogonal frequency-division multiplexing (OFDM) underwater acoustic (UWA) communications, especially under multiple-input multiple-output (MIMO) scenarios. In this paper, we explore Vector Approximate Message Passing (VAMP) coupled with Expected Maximum (EM) to obtain channel estimation (CE) for MIMO OFDM UWA communications. The EM-VAMP-CE scheme is developed by employing a Bernoulli-Gaussian (BG) prior distribution for the channel impulse response, and hyperparameters of the BG prior distribution are learned via the EM algorithm.…
Multi-input multi-output orthogonal frequency division multiplexing (MIMO OFDM) is a key technology for mobile communication systems. However, due to the issue of high peak-to-average power ratio (PAPR), the OFDM symbols may suffer from nonlinear distortions of the power amplifier (PA) at the transmitters, which degrades the channel estimation and detection performances of the receivers. To mitigate the clipping distortions at the receivers end, we leverage deep learning (DL) and devise a DL based receiver which is aided by the traditional least square (LS) channel estimation and the zero-forc…
Sub-terahertz (sub-THz) antenna array architectures significantly impact power usage and communications capacity in multi-user multiple-input multiple-output (MU-MIMO) systems. In this work, we compare the energy efficiency and spectral efficiency of three MU-MIMO capable array architectures for base station receivers. We provide a sub-THz circuits power analysis, based on our review of state-of-the-art D-band and G-band components, and compare communications capabilities through wideband simulations. Our analysis reveals that digital arrays can provide the highest spectral efficiency and ener…
Receivers with one-bit analog-to-digital converters (ADCs) are promising for high bandwidth millimeter wave (mmWave) systems as they consume less power than their full resolution counterparts. The extreme quantization in one-bit receivers and the use of large antenna arrays at mmWave make channel estimation challenging. In this paper, we develop channel estimation algorithms that exploit the low-rank property of mmWave channels. We also propose a novel training solution that results in a low complexity implementation of our algorithms. Simulation results indicate that the proposed methods achi…
In a massive MIMO system with large arrays, the channel becomes spatially non-stationary. We study the impact of spatial non-stationarity characterized by visibility regions (VRs) where the channel energy is significant on a portion of the array. Relying on a channel model based on VRs, we provide expressions of the signal-to-interference-plus-noise ratio (SINR) of conjugate beamforming (CB) and zero-forcing (ZF) precoders. We also provide an approximate deterministic equivalent of the SINR of ZF precoders. We identify favorable and unfavorable multi-user configurations of the VRs and compare …
The use of one-bit analog-to-digital converters (ADCs) is a practical solution for reducing cost and power consumption in massive Multiple-Input-Multiple-Output (MIMO) systems. However, the distortion caused by one-bit ADCs makes the data detection task much more challenging. In this paper, we propose a two-stage detection method for massive MIMO systems with one-bit ADCs. In the first stage, we propose several linear receivers based on the Bussgang decomposition, that show significant performance gain over existing linear receivers. Next, we reformulate the maximum-likelihood (ML) detection p…
Low-resolution analog-to-digital converters (ADCs) are promising for reducing energy consumption and costs of multiuser multiple-input multiple-output (MIMO) systems with many antennas. We propose low-resolution multiuser MIMO receivers where the signals are simultaneously processed by 1-bit ADCs and a comparator network, which can be interpreted as additional virtual channels with binary outputs. We distinguish the proposed comparator networks in fully and partially connected. For such receivers, we develop the low-resolution aware linear minimum mean-squared error (LRA-LMMSE) channel estimat…
Short range channels commonly arise in millimeter wave (mmWave) wearable settings, where the length of the antenna arrays can be comparable to the distance between the radios. Conventional mmWave MIMO channel estimation techniques based on the far field assumption may perform poorly in short range settings due to the large angular spread and, hence, high available rank. We propose a geometry-aided message passing algorithm that exploits structure in short range line-of-sight (LoS) channels for spatial sub-Nyquist channel estimation. Our approach parametrizes the channel using angle-of-arrivals…
In this paper, we propose a new algorithm of iterative least squared (LS) channel estimation for 64 antennas Massive Multiple Input, Multiple Output (MIMO) turbo-receiver. The algorithm employs log-likelihood ratios (LLR) of low-density parity-check (LDPC) decoder and minimum mean square error (MMSE) estimator to achieve soft data symbols. These soft data symbols are further MMSE-weighted again and combined with pilot symbols to achieve a modified LS channel estimate. The modified LS estimate is employed by the same channel estimation unit to enhance turbo-receiver performance via channel re-e…
This letter investigates a multiple-input multiple-output (MIMO) wireless power transfer (WPT) system under practical non-liner energy harvesting (EH) models. We propose a new generic energy receiver (ER) architecture consisting of $N$ receive antennas and $L$ rectifiers, for which one power splitter is inserted after each antenna to adaptively split the received radio frequency (RF) signals among the $L$ rectifiers for efficient non-linear RF-to-direct current (DC) conversion. With the proposed architecture, we maximize the total harvested DC power at the ER, by jointly optimizing the transmi…
Decentralized baseband processing (DBP) architecture, which partitions the base station antennas into multiple antenna clusters, has been recently proposed to alleviate the excessively high interconnect bandwidth, chip input/output data rates, and detection complexity for massive multi-user multiple-input multiple-output (MU-MIMO) systems. In this paper, we develop a novel decentralized Gaussian message passing (GMP) detection for the DBP architecture. By projecting the discrete probability distribution into a complex Gaussian function, the local means and variances iteratively calculated in e…
This work concerns receiver design for light-emitting diode (LED) multiple input multiple output (MIMO) communications where the LED nonlinearity can severely degrade the performance of communications. In this paper, we propose an extreme learning machine (ELM) based receiver to jointly handle the LED nonlinearity and cross-LED interference, and a circulant input weight matrix is employed, which significantly reduces the complexity of the receiver with the fast Fourier transform (FFT). It is demonstrated that the proposed receiver can efficiently handle the LED nonlinearity and cross-LED inter…
Score-Based Conditional Flow Models for MIMO Receiver Design with Superimposed Pilots
Beijing University of Posts and Telecommunications（北京邮电大学）；China Mobile Research Institute（中国移动研究院）；The Hong Kong University of Science and Technology (Guangzhou)（香港科技大学（广州））；University of Kent（肯特大学）
Recent years have witnessed growing interest in reduced cost radar systems operating with low power. Multiple-input multiple-output (MIMO) radar technology is known to achieve high performance sensing by probing with multiple orthogonal waveforms. However, implementing a low cost low power MIMO radar is challenging. One of the reasons for this difficulty stems from the increased cost and power consumption required by analog-to-digital convertors (ADCs) in acquiring the multiple waveforms at the radar receiver. In this work we study reduced cost MIMO radar receivers restricted to operate with l…
Filter bank-based multicarrier (FBMC) systems have attracted increasing attention recently in view of their many advantages over the classical cyclic prefix (CP)-based orthogonal frequency division multiplexing (CP-OFDM) modulation. However, their more advanced structure (resulting in, for example, self interference) complicates signal processing tasks at the receiver, including synchronization, channel estimation and equalization. In a multiple-input multiple-output (MIMO) configuration, the multi-antenna interference has also to be taken into account. (Semi-) blind receivers, of increasing i…
This paper considers the application of widely linear (WL) receivers in an uplink multi-user system using real-valued modulation schemes, where the cellular base station (BS) with multiple antennas provides connectivity for randomly deployed single-antenna users. The targeted use case is massive machine type communication (mMTC) with grant-free access in the uplink, where the network is required to host a large number of low data rate devices transmitting in an uncoordinated fashion. Four types of WL receivers are investigated, namely the WL zero-forcing (ZF) and the WL minimum meansquared err…
Next-generation communication systems with wide bandwidths need to operate in interference-limited networks. A discrete-time delay (TD) technique in a baseband receiver array is proposed for canceling wide modulated bandwidth spatial interference and reducing the ADC dynamic range requirements. The proposed discrete TD technique first aligns the interference using non-uniform sampled phases followed by uniform cancellation using a Truncated Hadamard Transform implemented with antipodal binary coefficients. A digital timeinterleaver with 5 ps resolution spanning 15 ns implements a scalable disc…
We investigate a turbo soft detector based on the expectation propagation (EP) algorithm for large-scale multiple-input multiple-output (MIMO) systems. Optimal detection in MIMO systems becomes computationally unfeasible for high-order modulations and/or large number of antennas. In this situation, the linear minimum mean square error (LMMSE) exhibits a low-complexity with a good performance, however far from optimal. To improve the performance, the EP algorithm can be used. In this paper, we review previous EP-based detectors and enhance their estimation in terms of complexity and performance…
The stringent requirements on reliability and processing delay in the fifth-generation ($5$G) cellular networks introduce considerable challenges in the design of massive multiple-input-multiple-output (M-MIMO) receivers. The two main components of an M-MIMO receiver are a detector and a decoder. To improve the trade-off between reliability and complexity, a Bayesian concept has been considered as a promising approach that enhances classical detectors, e.g. minimum-mean-square-error detector. This work proposes an iterative M-MIMO detector based on a Bayesian framework, a parallel interference…
Much stringent reliability and processing latency requirements in ultra-reliable-low-latency-communication (URLLC) traffic make the design of linear massive multiple-input-multiple-output (M-MIMO) receivers becomes very challenging. Recently, Bayesian concept has been used to increase the detection reliability in minimum-mean-square-error (MMSE) linear receivers. However, the latency processing time is a major concern due to the exponential complexity of matrix inversion operations in MMSE schemes. This paper proposes an iterative M-MIMO receiver that is developed by using a Bayesian concept a…
Multiple-input multiple-output (MIMO) systems are required to communicate reliably at high spectral bands using a large number of antennas, while operating under strict power and cost constraints. In order to meet these constraints, future MIMO receivers are expected to operate with low resolution quantizers, namely, utilize a limited number of bits for representing their observed measurements, inherently distorting the digital representation of the acquired signals. The fact that MIMO receivers use their measurements for some task, such as symbol detection and channel estimation, other than r…
In this letter, we extend the sparse Kronecker-product (SKP) coding scheme, originally designed for the additive white Gaussian noise (AWGN) channel, to multiple input multiple output (MIMO) unsourced random access (URA). With the SKP coding adopted for MIMO transmission, we develop an efficient Bayesian iterative receiver design to solve the intended challenging trilinear factorization problem. Numerical results show that the proposed design outperforms the existing counterparts, and that it performs well in all simulated settings with various antenna sizes and active-user numbers.
DOA estimation for massive multiple-input multiple-output (MIMO) system can provide ultra-high-resolution angle estimation. However, due to the high computational complexity and cost of all digital MIMO systems, a hybrid analog digital (HAD) structure MIMO was proposed. In this paper, a fast ambiguous phase elimination method is proposed to solve the problem of direction-finding ambiguity caused by the HAD MIMO. Only two-data-blocks are used to realize DOA estimation. Simulation results show that the proposed method can greatly reduce the estimation delay with a slight performance loss.
Nokia Bell Labs（诺基亚贝尔实验室）；Nokia Mobile Networks（诺基亚移动网络）
Recently, deep learning has been proposed as a potential technique for improving the physical layer performance of radio receivers. Despite the large amount of encouraging results, most works have not considered spatial multiplexing in the context of multiple-input and multiple-output (MIMO) receivers. In this paper, we present a deep learning-based MIMO receiver architecture that consists of a ResNet-based convolutional neural network, also known as DeepRx, combined with a so-called transformation layer, all trained together. We propose two novel alternatives for the transformation layer: a m…
Efficient massive/ultra-massive multiple-input multiple-output (MIMO) detection algorithms with satisfactory performance and low complexity are critical to meet the high throughput and ultra-low latency requirements in 5G and beyond communications, given the extremely large number of antennas. In this paper, we propose a low-complexity graph neural network (GNN) enhanced approximate message passing (AMP) algorithm, AMP-GNN, for massive/ultra-massive MIMO detection. The structure of the neural network is customized by unfolding the AMP algorithm and introducing the GNN module for multiuser inte…
When a quantizer input signal is the sum of the desired signal and input white noise, the quantization error is a function of total input signal. Our new equivalent model splits the quantization error into two components: a non-linear distortion (NLD) that is a function of only the desired part of input signal (without noise), and an equivalent out-put white noise. This separation is important because these two terms affect MIMO system performance differently. This paper introduces our model, and applies it to determine the minimal Analog-to-Digital Converter (ADC) resolution necessary to oper…
Massive multiple-input-multiple-output (M-MIMO) features a capability for spatial multiplexing of large number of users. This number becomes even more extreme in extra-large (XL-MIMO), a variant of M-MIMO where the antenna array is of very large size. Yet, the problem of signal processing complexity in M-MIMO is further exacerbated by the XL size of the array. The basic processing problem boils down to a sparse system of linear equations that can be addressed by the randomized Kaczmarz (RK) algorithm. This algorithm has recently been applied to devise low-complexity M-MIMO receivers; however, …
Hangzhou Institute of Technology, Xidian University（西安电子科技大学杭州研究院）；Xidian University（西安电子科技大学）；University of Wollongong（伍伦贡大学）；Weizmann Institute of Science（魏茨曼科学研究所）；University of Sydney（悉尼大学）
In this paper, we investigate signal detection in multiple-input-multiple-output (MIMO) communication systems with hardware impairments, such as power amplifier nonlinearity and in-phase/quadrature imbalance. To deal with the complex combined effects of hardware imperfections, neural network (NN) techniques, in particular deep neural networks (DNNs), have been studied to directly compensate for the impact of hardware impairments. However, it is difficult to train a DNN with limited pilot signals, hindering its practical applications. In this work, we investigate how to achieve efficient Bayesi…
Based on a new equivalent model of quantizer with noisy input recently presented in [23], we propose a new low complexity receiver that takes into account the nonlinear distortion (NLD) generated by Analog to Digital converter (ADC) with insufficient resolution. The strength of new model is that it presents the NLD as a function of only the desired part of input signal (without noise). Therefore it can easily be used in a variety of NLD mitigation techniques. Here, as an illustration of this, we use a pseudo-ML approach to detect the original QAM modulation based on the equivalent transfer fun…
The combination of orthogonal frequency modulation (OFDM) and multiple-input multiple-output (MIMO) systems plays an important role in modern communication systems. In order to meet the growing throughput demands, future MIMO-OFDM receivers are expected to utilize a massive number of antennas, operate in dynamic environments, and explore high frequency bands, while satisfying strict constraints in terms of cost, power, and size. An emerging technology to realize massive MIMO receivers of reduced cost and power consumption is based on dynamic metasurface antennas (DMAs), which inherently implem…
We develop a direction of arrival (DoA) and channel estimation algorithm for the one-bit quantized millimeter-wave (mmWave) massive multiple-input multiple-output (MIMO) system. By formulating the estimation problem as a noisy one-bit compressed sensing problem, we propose a computationally efficient gridless solution based on the expectation-maximization generalized approximate message passing (EMGAMP) approach. The proposed algorithm does not need the prior knowledge about the number of DoAs and outperforms the existing methods in distinguishing extremely close DoAs for the case of one-bit q…
Despite the extensive use of a centralized approach to design receivers at the base station for massive multiple-input multiple-output (M-MIMO) systems, their actual implementation is a major challenge due to several bottlenecks imposed by the large number of antennas. One way to deal with this problem is by fully decentralizing the classic zero-forcing receiver across multiple processing nodes based on the gradient descent method. In this paper, we first explicitly relate this decentralized receiver to a distributed version of the Kaczmarz algorithm and to the use of the successive interferen…
In this work, we propose the joint optimization of the automatic gain control (AGC), which works in the remote radio heads (RHHs), and a low-resolution aware (LRA) linear receive filter based on the minimum mean square error (MMSE), which works on the baseband unit (BBU) pool, for large-scale multi-user multiple-input multiple-output (MU-MIMO) systems with coarsely quantized signals in cloud radio access networks. We develop successive interference cancellation receivers based on the proposed joint AGC and LRA-MMSE (AGC-LRA-MMSE) approach. An analysis of the achievable sum rates is also carrie…
Orthogonal time-frequency space (OTFS) scheme, which transforms a time and frequency selective channel into an almost non-selective channel in the delay-Doppler domain, establishes reliable wireless communication for high-speed moving devices. This work designs and analyzes low-complexity zero-forcing (LZ) and minimum mean square error (LM) receivers for multiple-input multiple-output (MIMO)-OTFS systems with perfect and imperfect receive channel state information (CSI). The proposed receivers provide exactly the same solution as that of the conventional counterparts, and reduce the complexity…
The Gaussian multiple-input multiple-output (MIMO) multi-receiver wiretap channel is studied in this paper. The base station broadcasts confidential messages to K intended users while keeping the messages secret from an eavesdropper. The capacity of this channel has already been characterized by applying dirty-paper coding and stochastic encoding. However, K factorial encoding orders may need to be enumerated for that, which makes the problem intractable. We prove that there exists one optimal encoding order and reduced the K factorial times to a one-time encoding. The optimal encoding order i…
