We study the problem of semi-blind channel estimation and symbol detection in the uplink of multi-cell massive MIMO systems with spatially correlated time-varying channels. An algorithm based on expectation propagation (EP) is developed to iteratively approximate the joint a posteriori distribution of the unknown channel matrix and the transmitted data symbols with a distribution from an exponential family. This distribution is then used for direct estimation of the channel matrix and detection of data symbols. A modified version of the popular Kalman filtering algorithm referred to as KF-M emerges from our EP derivation and it is used to initialize the EP-based algorithm. Performance of the Kalman smoothing algorithm followed by KF-M is also examined. Simulation results demonstrate that channel estimation error and the symbol error rate (SER) of the semi-blind KF-M, KS-M, and EP-based algorithms improve with the increase in the number of base station antennas and the length of the transmitted frame. It is shown that the EP-based algorithm significantly outperforms KF-M and KS-M algorithms in channel estimation and symbol detection. Finally, our results show that when applied to time-varying channels, these algorithms outperform the algorithms that are developed for block-fading channel models.
We provide the optimal receive combining strategy for federated learning in multiple-input multiple-output (MIMO) systems. Our proposed algorithm allows the clients to perform individual gradient sparsification which greatly improves performance in scenarios with heterogeneous (non i.i.d.) training data. The proposed method beats the benchmark by a wide margin.
We propose blind adaptive multi-input multi-output (MIMO) linear receivers for DS-CDMA systems using multiple transmit antennas and space-time block codes (STBC) in multipath channels. A space-time code-constrained constant modulus (CCM) design criterion based on constrained optimization techniques is considered and recursive least squares (RLS) adaptive algorithms are developed for estimating the parameters of the linear receivers. A blind space-time channel estimation method for MIMO DS-CDMA systems with STBC based on a subspace approach is also proposed along with an efficient RLS algorithm…
Intelligent reflecting surface (IRS) is a promising technology for beyond 5th Generation of the wireless communications. In fully passive IRS-assisted systems, channel estimation is challenging and should be carried out only at the base station or at the terminals since the elements of the IRS are incapable of processing signals. In this letter, we formulate a tensor-based semi-blind receiver that solves the joint channel and symbol estimation problem in an IRS-assisted multi-user multiple-input multiple-output system. The proposed approach relies on a generalized PARATUCK tensor model of the …
Reliability is of paramount importance for the physical layer of wireless systems due to its decisive impact on end-to-end performance. However, the uncertainty of prevailing deep learning (DL)-based physical layer algorithms is hard to quantify due to the black-box nature of neural networks. This limitation is a major obstacle that hinders their practical deployment. In this paper, we attempt to quantify the uncertainty of an important category of DL-based channel estimators. An efficient statistical method is proposed to make blind predictions for the mean squared error of the DL-estimated c…
We introduce novel blind and semi-blind channel estimation methods for cellular time-division duplexing systems with a large number of antennas at each base station. The methods are based on the maximum a-posteriori principle given a prior for the distribution of the channel vectors and the received signals from the uplink training and data phases. Contrary to the state-of-the-art massive MIMO channel estimators which either perform linear estimation based on the pilot symbols or rely on a blind principle, the proposed semi-blind method efficiently suppresses most of the interference caused by…
In this paper, we study joint antenna activity detection, channel estimation, and multiuser detection for massive multiple-input multiple-output (MIMO) systems with general spatial modulation (GSM). We first establish a double-sparsity massive MIMO model by considering the channel sparsity of the massive MIMO channel and the signal sparsity of GSM. Based on the double-sparsity model, we formulate a blind detection problem. To solve the blind detection problem, we develop message-passing based blind channel-and-signal estimation (BCSE) algorithm. The BCSE algorithm basically follows the affine …
Massive MIMO has been regarded as a key enabling technique for 5G and beyond networks. Nevertheless, its performance is limited by the large overhead needed to obtain the high-dimensional channel information. To reduce the huge training overhead associated with conventional pilot-aided designs, we propose a novel blind data detection method by leveraging the channel sparsity and data concentration properties. Specifically, we propose a novel $\ell_3$-norm-based formulation to recover the data without channel estimation. We prove that the global optimal solution to the proposed formulation can …
Directional beamforming is a crucial component for realizing robust wireless communication systems using millimeter wave (mmWave) technology. Beam alignment using brute-force search of the space introduces time overhead while location aided blind beam alignment adds additional hardware requirements to the system. In this paper, we introduce a method for blind beam alignment based on the RF fingerprints of user equipment obtained by the base stations. The proposed system performs blind beam alignment on a multiple base station cellular environment with multiple mobile users using deep reinforce…
In this paper we present low-complexity blind multi-input multi-output (MIMO) adaptive linear multiuser receivers for direct sequence code division multiple access (DS-CDMA) systems using multiple transmit antennas and space-time block codes (STBC) in multipath channels. A space-time code-constrained constant modulus (CCM) design criterion based on constrained optimization techniques and low-complexity stochastic gradient (SG) adaptive algorithms are developed for estimating the parameters of the space-time linear receivers. The receivers are designed by exploiting the unique structure imposed…
We consider the massive MIMO downlink with time-division duplex (TDD) operation and conjugate beamforming transmission. To reliably decode the desired signals, the users need to know the effective channel gain. In this paper, we propose a blind channel estimation method which can be applied at the users and which does not require any downlink pilots. We show that our proposed scheme can substantially outperform the case where each user has only statistical channel knowledge, and that the difference in performance is particularly large in certain types of channel, most notably keyhole channels.…
This paper considers the transceiver design for uplink massive multiple-input multiple-output (MIMO) systems with channel sparsity in the angular domain. Recent progress has shown that sparsity-learning-based blind signal detection is able to retrieve the channel and data by using massage-passing based sparse matrix factorization methods. Short pilots sequences are inserted into user packets to eliminate the so-called phase and permutation ambiguities inherent in sparse matrix factorization. In this paper, to exploit the knowledge of these short pilot sequences more efficiently, we propose a s…
Intelligent reflecting surface (IRS) is a promising technology for the 6th generation of wireless systems, realizing the smart radio environment concept. In this paper, we present a novel tensor-based receiver for IRS-assisted multiple-input multiple-output communications capable of jointly estimating the channels and the transmitted data streams in a semi-blind fashion. Assuming a fully passive IRS architecture and introducing a simple space-time coding scheme at the transmitter, the received signal model can be advantageously built using the PARATUCK tensor model, which can be seen as a hybr…
We propose a method for MIMO decoding when channel state information (CSI) is unknown to both the transmitter and receiver. The proposed method requires some structure in the transmitted signal for the decoding to be effective, in particular that the underlying sources are drawn from a hypercubic space. Our proposed technique fits a minimum volume parallelepiped to the received samples. This problem can be expressed as a non-convex optimization problem that can be solved with high probability by gradient descent. Our blind decoding algorithm can be used when communicating over unknown MIMO wir…
We provide a maximum likelihood formulation for the blind estimation of massive mmWave MIMO channels while taking into account their underlying sparse structure. The main advantage of this approach is the fact that the overhead due to pilot sequences can be reduced dramatically especially when operating at low SNR per antenna. Thereby, the sparsity in the angular domain is exploited as a key property to enable the unambiguous blind separation between user's channels. On the other hand, as only the sparsity is assumed, the proposed method is robust with respect to the statistical properties of …
This paper proposes a blind technique for MIMO cognitive radio Secondary Users (SU) to transmit in the same band simultaneously with a Primary User (PU) under a maximum interference constraint. In the proposed technique, the SU is able to meet the interference constraint of the PU without explicitly estimating the interference channel matrix to the PU and without burdening the PU with any interaction with the SU. The only condition required of the PU is that for a short time interval it uses a power control scheme such that its transmitted power is a monotonic function of the interference infl…
Reconfigurable intelligent surface (RIS) is envisioned to be a promising green technology to reduce the energy consumption and improve the coverage and spectral efficiency of massive multiple-input multiple-output (MIMO) wireless networks. In a RIS-aided MIMO system, the acquisition of channel state information (CSI) is important for achieving passive beamforming gains of the RIS, but is also challenging due to the cascaded property of the transmitter-RIS-receiver channel and the lack of signal processing capability of the passive RIS elements. The state-of-the-art approach for CSI acquisition…
The use of low-resolution analog-to-digital converters (ADCs) is considered to be an effective technique to reduce the power consumption and hardware complexity of wireless transceivers. However, in systems with low-resolution ADCs, obtaining channel state information (CSI) is difficult due to significant distortions in the received signals. The primary motivation of this paper is to show that learning techniques can mitigate the impact of CSI unavailability. We study the blind detection problem in multiple-input-multiple-output (MIMO) systems with low-resolution ADCs using learning approaches…
Blind Null Space Learning (BNSL) has recently been proposed for fast and accurate learning of the null-space associated with the channel matrix between a secondary transmitter and a primary receiver. In this paper we propose a channel tracking enhancement of the algorithm, namely the Blind Null Space Tracking (BNST) algorithm that allows transmission of information to the Secondary Receiver (SR) while simultaneously learning the null-space of the time-varying target channel. Specifically, the enhanced algorithm initially performs a BNSL sweep in order to acquire the null space. Then, it perfor…
Pilot contamination problem in massive MIMO networks operating in time-division duplex (TDD) mode can limit their expected capacity to a great extent. This paper addresses this problem in cosine modulated multitone (CMT) based massive MIMO networks; taking advantage of their so-called blind equalization property. We extend and apply the blind equalization technique from single antenna case to multi-cellular massive MIMO systems and show that it can remove the channel estimation errors (due to pilot contamination effect) without any need for cooperation between different cells or transmission o…
In practical massive MIMO systems, a substantial portion of system resources are consumed to acquire channel state information (CSI), leading to a drastically lower system capacity compared with the ideal case where perfect CSI is available. In this paper, we show that the overhead for CSI acquisition can be largely compensated by the potential gain due to the sparsity of the massive MIMO channel in a certain transformed domain. To this end, we propose a novel blind detection scheme that simultaneously estimates the channel and data by factorizing the received signal matrix. We show that by ex…
We study the maximum likelihood problem for the blind estimation of massive mmWave MIMO channels while taking into account their underlying sparse structure, the temporal shifts across antennas in the broadband regime, and ultimately one-bit quantization at the receiver. The sparsity in the angular domain is exploited as a key property to enable the unambiguous blind separation between user's channels. The main advantage of this approach is the fact that the overhead due to pilot sequences can be dramatically reduced especially when operating at low SNR per antenna. In addition, as sparsity is…
This paper concerns the maximum-likelihood channel estimation for MIMO systems with orthogonal space-time block codes when the finite alphabet constraint of the signal constellation is relaxed. We study the channel coefficients estimation subspace generated by this method. We provide an algebraic characterisation of this subspace which turns the optimization problem into a purely algebraic one and more importantly, leads to several interesting analytical proofs. We prove that with probability one, the dimension of the estimation subspace for the channel coefficients is deterministic and it dec…
This work presents a novel framework for random access in crowded scenarios of multiple-input multiple-output(MIMO) systems. A multi-antenna base station (BS) and multiple single-antenna users are considered in these systems. A huge portion of the system resources is dedicated as orthogonal pilots for accurate channel estimation which imposes a huge training overhead. This overhead can be highly mitigated by exploiting intrinsic angular domain sparsity of massive MIMO channels and the sporadic traffic of users, i.e., few number of users are active to sent or receive data in each coherence inte…
In this paper, we study blind channel-and-signal estimation by exploiting the burst-sparse structure of angular-domain propagation channels in massive MIMO systems. The state-of-the-art approach utilizes the structured channel sparsity by sampling the angular-domain channel representation with a uniform angle-sampling grid, a.k.a. virtual channel representation. However, this approach is only applicable to uniform linear arrays and may cause a substantial performance loss due to the mismatch between the virtual representation and the true angle information. To tackle these challenges, we propo…
We consider a channel separation approach to counter the pilot attack in a massive MIMO system, where malicious users (MUs) perform pilot spoofing and jamming attack (PSJA) in uplink by sending symbols to the basestation (BS) during the channel estimation (CE) phase of the legitimate users (LUs). More specifically, the PSJA strategies employed by the MUs may include (i) sending the random symbols according to arbitrary stationary or non-stationary distributions that are unknown to the BS; (ii) sending the jamming symbols that are correlative to those of the LUs. We analyze the empirical distri…
Multiple carrier-frequency offsets (CFO) arise in a distributed antenna system, where data are transmitted simultaneously from multiple antennas. In such systems the received signal contains multiple CFOs due to mismatch between the local oscillators of transmitters and receiver. This results in a time-varying rotation of the data constellation, which needs to be compensated for at the receiver before symbol recovery. This paper proposes a new approach for blind CFO estimation and symbol recovery. The received base-band signal is over-sampled, and its polyphase components are used to formulate…
In spatially distributed multiuser antenna systems, the received signal contains multiple carrier-frequency offsets (CFOs) arising from mismatch between the oscillators of transmitters and receivers. This results in a time-varying rotation of the data constellation, which needs to be compensated at the receiver before symbol recovery. In this paper, a new approach for blind CFO estimation and symbol recovery is proposed. The received base-band signal is over-sampled, and its polyphase components are used to formulate a virtual Multiple-Input Multiple-Output (MIMO) problem. By applying blind MI…
We study the Gaussian MIMOME wiretap channel where a transmitter wishes to communicate a confidential message to a legitimate receiver in the presence of eavesdroppers, while the eavesdroppers should not be able to decode the confidential message. Each node in the network is equipped with arbitrary number of antennas. Furthermore, channels are time varying, and there is no channel state information available at the transmitter (CSIT) with respect to eavesdroppers' channels; and transmitter only has access to delayed CSIT of the channel to the legitimate receiver. The secure degrees of freedom …
Blind algorithms for multiple-input multiple-output (MIMO) signals interception have recently received considerable attention because of their important applications in modern civil and military communication fields. One key step in the interception process is to blindly recognize the modulation type of the MIMO signals. This can be performed by employing a Modulation Classification (MC) algorithm, which can be feature-based or likelihood-based. To overcome the problems associated with the existing likelihood-based MC algorithms, a new algorithm is developed in this paper. We formulated the MC…
Institut National de la Recherche Scientifique (INRS-EMT)（加拿大魁北克大学国家科学研究院（电磁与电信研究所））
In this paper, we propose a multi-layer artificial neural network (ANN) that is trained with the Levenberg-Marquardt algorithm for use in signal detection over multiple-input multiple-output orthogonal frequency-division multiplexing (MIMO-OFDM) systems, particularly those with low-resolution analog-to-digital converters (LR-ADCs). We consider a blind detection scheme where data symbol estimation is carried out without knowing the channel state information at the receiver (CSIR)---in contrast to classical algorithms. The main power of the proposed ANN-based detector (ANND) lies in its versatil…
We consider the Massive Multiple-Input Multiple-Output (MIMO) downlink with maximum-ratio and zero-forcing processing and time-division duplex (TDD) operation. To decode, the terminals must know their instantaneous effective channel gain. Conventionally, it is assumed that by virtue of channel hardening, this instantaneous gain is close to its average and hence that terminals can rely on knowledge of that average (also known as statistical channel information). However, in some propagation environments, such as keyhole channels, channel hardening does not hold. We propose a blind algorithm to …
In this paper, adaptive hybrid beamforming methods are proposed for millimeter-wave range massive multiple-input-multiple-output (MIMO) systems considering single carrier wideband transmission in uplink data mode. A statistical analog beamformer is adaptively constructed in slow-time, while the channel is time-varying and erroneously estimated. A recursive filtering approach is proposed, which aims robustness against estimation errors for generalized eigen-beamformer (GEB). Approximated expressions are obtained for channel covariance matrices that decouple angular spread and center angle of mu…
This paper proposes a new channel estimation scheme for the multiuser massive multiple-input multiple-output (MIMO) systems in time-varying environment. We introduce a discrete Fourier transform (DFT) aided spatial-temporal basis expansion model (ST-BEM) to reduce the effective dimensions of uplink/downlink channels, such that training overhead and feedback cost could be greatly decreased. The newly proposed ST-BEM is suitable for both time division duplex (TDD) systems and frequency division duplex (FDD) systems thanks to the angle reciprocity, and can be efficiently deployed by fast Fourier …
Massive multiple-input multiple-output (MIMO) systems rely on channel state information (CSI) feedback to perform precoding and achieve performance gain in frequency division duplex (FDD) networks. However, the huge number of antennas poses a challenge to conventional CSI feedback reduction methods and leads to excessive feedback overhead. In this article, we develop a real-time CSI feedback architecture, called CsiNet-long short-term memory (LSTM), by extending a novel deep learning (DL)-based CSI sensing and recovery network. CsiNet-LSTM considerably enhances recovery quality and improves tr…
A modulation classification (MC) scheme based on Independent Component Analysis (ICA) in conjunction with either maximum likelihood (ML) or Support Vector Machines (SVM) is proposed for MIMO-OFDM signals over frequency selective, time varying channels. The method is blind in the sense that it is assumed that the receiver has no information about the channel and transmitted signals other than that the spatial streams of signals are statistically independent. The processing consists of separation of the MIMO streams followed by modulation classification of the separated signals. While in general…
In this work, a class of information theoretic secrecy problems is addressed where the eavesdropper channel states are completely unknown to the legitimate parties. In particular, MIMO wiretap channel models are considered where the channel of the eavesdropper is arbitrarily varying over time. Assuming that the number of antennas of the eavesdropper is limited, the secrecy rate of the MIMO wiretap channel in the sense of strong secrecy is derived, and shown to match with the converse in secure degrees of freedom. It is proved that there exists a universal coding scheme that secures the confide…
Multiple Input Multiple Output (MIMO) Orthogonal Frequency Division Multiplexing (OFDM) is a promising transmission scheme for high performance broadband wireless communications. However, this technique suffers from a major drawback which is the high Peak to Average Power Ratio (PAPR) of the output signals. In order to overcome this issue, several methods that require the transmission of explicit Side Information (SI) bits have been proposed. In fact, the transmitted bits must be channel-encoded as they are particularly critical to the performance of the considered OFDM system. This channel-en…
This article reviews existing related work and identifies the main challenges in the key 5G area at the intersection of waveform design and large-scale multiple antenna systems, also known as Massive MIMO. The property of self-equalization is introduced for Filter Bank Multicarrier (FBMC)-based Massive MIMO, which can reduce the number of subcarriers required by the system. It is also shown that the blind channel tracking property of FBMC can be used to address pilot contamination -- one of the main limiting factors of Massive MIMO systems. Our findings shed light into and motivate for an enti…
In this paper, we study two important metrics in multiple-input multiple-output (MIMO) time-varying Rayleigh flat fading channels. One is the eigen-mode, and the other is the instantaneous mutual information (IMI). Their second-order statistics, such as the correlation coefficient, level crossing rate (LCR), and average fade/outage duration, are investigated, assuming a general nonisotropic scattering environment. Exact closed-form expressions are derived and Monte Carlo simulations are provided to verify the accuracy of the analytical results. For the eigen-modes, we found they tend to be spa…
This paper considers a scenario for multi-input multi-output (MIMO) communication systems when perfect channel state information at the transmitter (CSIT) is given while the equivalent channel state information at the receiver (CSIR) is not available. Such an assumption is valid for the downlink multi-user MIMO systems with linear precoders that depend on channels to all receivers. We propose a concept called dual systems with zero-forcing designs based on the duality principle, originally proposed to relate Gaussian multi-access channels (MACs) and Gaussian broadcast channels (BCs). For the t…
This paper proposes a Bayesian downlink channel estimation algorithm for time-varying massive MIMO networks. In particular, the quantization effects at the receiver are considered. In order to fully exploit the sparsity and time correlations of channels, we formulate the time-varying massive MIMO channel as the simultaneously sparse signal model. Then, we propose a sparse Bayesian learning (SBL) framework to learn the model parameters of the sparse virtual channel. To reduce complexity, we employ the expectation maximization (EM) algorithm to achieve the approximated solution. Specifically, th…
Massive MIMO is, in general, severely affected by pilot contamination. As opposed to traditional detectors, we propose a group-blind detector that takes into account the presence of pilot contamination. While sticking to the traditional structure of the training phase, where orthogonal pilot sequences are reused, we use the excess antennas at each base station to partially remove interference during the uplink data transmission phase. We analytically derive the asymptotic SINR achievable with group-blind detection, and confirm our findings by simulations. We show, in particular, that in an int…
Multiple antenna (MIMO) devices are widely used to increase reliability and information bit rate. Optimal error rate performance (full diversity and large coding gain), for unknown channel state information at the transmitter and for maximal rate, can be achieved by approximately universal space-time codes, but comes at a price of large detection complexity, infeasible for most practical systems. We propose a new coded modulation paradigm: error-correction outer code with space-only but time-varying precoder (as inner code). We refer to the latter as Ergodic Mutual Information (EMI) code. The …
Transmission of a Gaussian source over a time-varying multiple-input multiple-output (MIMO) channel is studied under strict delay constraints. Availability of a correlated side information at the receiver is assumed, whose quality, i.e., correlation with the source signal, also varies over time. A block-fading model is considered for the states of the time-varying channel and the time-varying side information; and perfect state information at the receiver is assumed, while the transmitter knows only the statistics. The high SNR performance, characterized by the \textit{distortion exponent}, is…
Grant-free non-coherent index-modulation (NC-IM) has been recently considered as an efficient massive access scheme for enabling cost- and energy-limited Internet-of-Things (IoT) devices that transmit small data packets. This paper investigates the grant-free NC-IM scheme combined with orthogonal frequency division multiplexing for applicant to unmanned aerial vehicle (UAV)-based massive IoT access. Specifically, each device is assigned a unique non-orthogonal signature sequence codebook. Each active device transmits one of its signature sequences in the given time-frequency resources, by modu…
This work studies secure transmission in intelligent reflecting surfaces (IRS)-assisted MIMO systems when an active eavesdropper is available in the network. We consider a scenario in which the eavesdropper performs an active pilot attack to contaminate the channel estimation at the base station. Invoking the method of secure regularized zero forcing, we develop an algorithm that designs beamforming vectors, as well as phase-shifts at the IRS, such that the active attacker is blinded. Our numerical investigations confirm that the proposed algorithm can suppress the active eavesdropper effectiv…
In this paper, the problem of sequential beam construction and adaptive channel estimation based on reduced rank (RR) Kalman filtering for frequency-selective massive multiple-input multiple-output (MIMO) systems employing single-carrier (SC) in time division duplex (TDD) mode are considered. In two-stage beamforming, a new algorithm for statistical pre-beamformer design is proposed for spatially correlated time-varying wideband MIMO channels under the assumption that the channel is a stationary Gauss-Markov random process. The proposed algorithm yields a nearly optimal pre-beamformer whose be…
In recent release of the new cellular standard known as 5G New Radio (5G-NR), the physical downlink control channel (PDCCH) has adopted polar codes for error protection. Similar to 4G-LTE, each active user equipment (UE) must blindly detect its own PDCCH in the downlink search space. This work investigates new ways to improve the accuracy of PDCCH blind detection in 5G-NR. We develop a novel design of joint detection and decoding receiver for 5G multiple-input multiple-output (MIMO) transceivers. We aim to achieve robustness against practical obstacles including channel state information (CSI)…
In this paper, we consider the problem of joint user scheduling and dynamic pilot allocation in a Time-Division Duplex (TDD) based Massive MIMO network under varying traffic condition. One of the main problems with Massive MIMO systems is that the number of available orthogonal pilot signals is limited, and the dynamic allocation of these signals to different users is crucially needed to utilize the full benefit of these systems. In addition, pilot signals are radio resource control (RRC) configured in practice, and hence the frequent reconfiguration causes high signaling overhead and is costl…
