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The use of massive multiple-input multiple-output (MIMO) technology has enabled increased efficiency and capacity of wireless communication systems. When the downlink channel to user terminals (UTs) is known at the base station (BS), the BS can precode transmission to simplify detection at the UTs. In frequency division duplexed (FDD) systems, obtaining CSI at the transmitter to fully exploit the advantages of massive MIMO is complicated, since the number of channel coefficients to be trained and fed back from the UT is very large. This is exacerbated in the case where the channel is time varying, where frequent retraining and feedback is required. However, if the channel coefficients are spatially correlated, the amount of feedback required can be reduced significantly. In this paper, we consider the case where the channel coefficients from the BS to the UTs are spatially correlated, and show that efficient allocation of training power based on eigenvalues of the channel correlation matrix significantly boosts achievable rates. Further, we show that using Kalman filters to track the evolving channel coefficients reduces the need to retrain channels, and the reduced training requirement translates to higher data rates. Simulations confirm that optimal training and tracking channel modes enhances rates significantly. [ABSTRACT FROM AUTHOR]

Massive multiple-input multiple-output (MIMO) is one of the most promising techniques for next generation wireless communications due to its superior capability to provide high spectrum and energy efficiency. Considering the very large number of antennas employed at the base station, however, the pilot overhead for downlink channel estimation becomes unaffordable in frequency division duplex (FDD) multiuser massive MIMO systems. In this paper, we propose an information-theoretic metric to design the pilot for downlink channel estimation in FDD multiuser massive MIMO systems. By exploiting the low-rank nature of the channel covariance matrix, we first derive the minimum number of pilot symbols required to ensure perfect channel recovery, which is much less than the number of antennas at the base station. Further, under a general channel model that the channel vector of each user follows a Gaussian mixture distribution, the pilot symbols are designed by maximizing the weighted sum of the Shannon mutual information between the measurements of the users and their corresponding channel vectors on the complex Grassmannian manifold. Simulation results demonstrate the effectiveness of the proposed information-theoretic pilot design for the downlink channel estimation in FDD massive MIMO systems. [ABSTRACT FROM AUTHOR]

This paper studies the security aspect of a recently introduced “cell-free massive MIMO” network under a pilot spoofing attack. First, a simple method to recognize the presence of this type of an active eavesdropping attack to a particular user is shown. In order to deal with this attack, we consider the problem of maximizing the achievable data rate of the attacked user or its achievable secrecy rate. The corresponding problems of minimizing the power consumption subject to security constraints are also considered in parallel. Path-following algorithms are developed to solve the posed optimization problems under different power allocation to access points (APs). Under equip-power allocation to APs, these optimization problems admit closed-form solutions. Numerical results show their efficiency. [ABSTRACT FROM AUTHOR]

In this paper, we explore low-complexity transmission in physically-constrained massive multiple-input multiple-output (MIMO) systems by means of channel state information (CSI) relaxation. In particular, we propose a strategy to take advantage of the correlation experienced by the channels of neighbour antennas when deployed in tightly packed antenna arrays. The proposed scheme is based on collecting CSI for only a subset of antennas during the pilot training stage and, subsequently, using averages of the acquired CSI for the remaining closely-spaced antennas. By doing this, the total number of radio frequency (RF) chains, for both CSI acquisition and data transmission, and the baseband signal processing are reduced, hence simplifying the overall system operation. At the same time, this impacts the quality of the channel estimation produced after the CSI acquisition process. To characterize this tradeoff, we explore the impact that the number of antennas with instantaneous CSI has on the performance, signal processing complexity, and energy efficiency of time-division duplex (TDD) systems. The analytical and simulation results presented in this paper show that the application of the proposed strategy in size-constrained antenna arrays is able to significantly enhance the energy efficiency against systems with full CSI availability, while approximately preserving their average performance. [ABSTRACT FROM AUTHOR]

Beamforming with antenna arrays has been considered as an enabling technology in future wireless communication systems. To conduct beamforming, one has to know the angle-of-departure (AoD) or angle-of-arrival (AoA). For data detection, the receiver also has to know channel response. In this paper, we propose a new joint AoD, AoA, and channel estimation scheme for pilot-assisted multiple-input-multiple-output–orthogonal-frequency-division-multiplexing (MIMO-OFDM) systems. First, a compressive-sensing technique is employed to estimate the channel impulse response, exploiting the sparsity property of wireless channels. Then, AoA and AoD are jointly estimated for each detected path by the maximum likelihood method. The Cramér–Rao lower bound (CRLB) is also derived and a transmit beamforming scheme is proposed accordingly. In the scenario of available prior information, a maximum a posteriori estimation is proposed. The Bayesian CRLB (BCRLB) for the problem is also derived and a transmit beamforming scheme is further proposed. It turns out that only two training OFDM symbols are required for the estimation. Simulation results show that the proposed methods can approach the CRLB/BCRLB in both scenarios and achieve the same spectral efficiency as that obtained with the ideal channel in millimeter-wave communications. [ABSTRACT FROM AUTHOR]

Pilot contamination attack is an important activity of active eavesdropping conducted by a malicious user during channel training phase. This attack is potentially harmful to the physical layer security. In this paper, motivated by the fact that frequency asynchronism could introduce divergence of the transmitted pilot signals between intended user and attacker, we propose a new uncoordinated frequency shift (UFS) scheme for detecting pilot contamination attack in multiple antenna system. During the reverse training phase of the UFS scheme, the legitimate user Bob deliberately introduces multiple random frequency shifts in the publicly known pilot sequence. Since eavesdropper Eve has no knowledge of these random frequency shifts, it is almost impossible for her to pretend exactly like Bob. This provides the opportunity to detect the presence of Eve. An attack detection algorithm is then developed based on source enumeration method. Both the asymptotic performance analysis and numerical results are provided to verify the proposed detection scheme. The proposed scheme is also enhanced based on noise power estimation to cope with attacks from a multi-antenna Eve. Furthermore, the proposed UFS scheme is extended by introducing general parameterized phase shifts. It is demonstrated that the proposed UFS scheme can achieve comparable detection performance as the existing superimposed random sequence based scheme, without sacrifice of legitimate channel estimation performance. [ABSTRACT FROM AUTHOR]

In this paper, we study the effect of downlink (DL) training on the achievable sum rate of multiuser massive MIMO channels with spatial correlations, and derive sufficient conditions of the DL channel estimation error covariance matrices that maintain the full multiplexing gain at high data signal-to-noise-ratios (SNRs). Given the derived conditions, a simple asymptotic upper bound on the average sum rate loss due to channel estimation is obtained. We derive, in closed-form, training sequences of limited duration that satisfy these conditions. The training duration is variable and increases with the data SNR, while the sequences lie in a subspace spanned by a variable number of user spatial covariance matrices’ eigenvectors. We additionally study the problem of sequence codebook design and find solutions to this problem for uniform linear and rectangular arrays using asymptotic results. For the aforementioned training structure, the designed codebooks are observed numerically to be near-optimal for a moderate number of base station antennas. Due to their ability to identify a sufficient limited number of channel directions to train, the proposed solutions can substantially reduce DL training overheads while providing achievable rates that are comparable with the rates achieved with perfect channel state information. [ABSTRACT FROM AUTHOR]

In this paper, we study the performance of the training based two-step channel estimation in two-way multiple-input multiple-output relay systems over Rayleigh channels. We first estimate the forward channel during the first phase. During the second phase for the composite channel estimation, singular value decomposition based maximum likelihood method is applied in two way relay systems, which is extended from one way relay systems. Hence, the backward channel is estimated with the given forward channel and composite channel. In general, the proposed estimation scheme can also be extended from the system with a single relay antenna to multiple relay antennas. Moreover, we derive the proposed training sequences and relay gain that aim to minimizes the trace of the approximate Bayesian Cramér–Rao lower bound with respect to the individual channels. The numerical results are shown that although the training sequence is designed to improve individual channel mean square error performance, it can also improve composite channel estimator performance. [ABSTRACT FROM AUTHOR]

In wireless communications, massive multiple-input multiple-output (MIMO) uplink systems in which the base station is equipped with a large number of antennas can provide significant spectral and energy efficiency by means of simple signal processing. Therefore, massive MIMO uplink systems are an attractive technology for next-generation wireless systems and for green communications. However, phase noise (PN) introduced by the impairment of oscillators can cause a severe performance loss in wireless communication systems. Solving the PN problem for massive MIMO uplink systems is challenging as it is a multivariate joint PN estimation and data detection problem. The optimal solution is difficult to derive and may lead to high complexity. In this paper, we propose a low-complexity PN suppression scheme based on the zero-forcing detector to solve this problem. By using the ideal PN free output signal-to-noise ratio (SNR) as a performance upper bound, simulation results show that when the required SNR is satisfied, the output SNR of the proposed scheme can approach the upper bound, except for very large PN variances. Moreover, the complexity of the proposed scheme is low because the proposed scheme can update the current PN estimation and data detection by the previous ones via a simple iterative process. [ABSTRACT FROM AUTHOR]

Channel estimation in multicarrier systems employing filter banks with offset quadrature amplitude modulation (FBMC/OQAM) is far less easy compared to orthogonal frequency division multiplexing (OFDM). Challenges come from the interference effect, inherent in this type of FBMC systems, and the (generally) nonnegligible frequency selectivity of their subchannels, due to the nature of this modulation and the cyclic prefix (CP)-free transmission. Most of the FBMC/OQAM channel estimation methods rely on the assumption of a low frequency selective channel (thus implying (almost) flat subchannels) in order to more easily (i.e., in a manner analogous to CP-OFDM) tackle this problem. However, they perform quite poorly when applied in more demanding propagation conditions. Others, more recent ones, relax the assumption of low frequency selectivity but they either depend on a parametric model for the channel or they concentrate on persubchannel estimation and often require long training sequences. This paper revisits this problem through a formulation that does not rely on any simplifying assumption about the channel. This gives rise to a new estimation method, applicable to channels of any frequency selectivity, which relies on a very short preamble for training. The latter is optimized with respect to the channel estimation mean squared error and is shown to result in a very significant simplification of the estimation procedure. Both full (block-type) and sparse (comb-type) preambles are considered. Applying these results in low-dispersion channels yields, as special cases, existing and new preamble-based estimation schemes for such scenarios. The superiority of the proposed method over classical ones is demonstrated via simulation results for both mildly and highly frequency selective channels. [ABSTRACT FROM PUBLISHER]

Massive multiple-input multiple-output (MIMO) is widely recognized as a promising technology for future 5G wireless communication systems. To achieve the theoretical performance gains in massive MIMO systems, accurate channel-state information at the transmitter (CSIT) is crucial. Due to the overwhelming pilot signaling and channel feedback overhead, however, conventional downlink channel estimation and uplink channel feedback schemes might not be suitable for frequency-division duplexing (FDD) massive MIMO systems. In addition, these two topics are usually separately considered in the literature. In this paper, we propose a joint channel training and feedback scheme for FDD massive MIMO systems. Specifically, we first exploit the temporal correlation of time-varying channels to propose a differential channel training and feedback scheme, which simultaneously reduces the overhead for downlink training and uplink feedback. We next propose a structured compressive sampling matching pursuit (S-CoSaMP) algorithm to acquire a reliable CSIT by exploiting the structured sparsity of wireless MIMO channels. Simulation results demonstrate that the proposed scheme can achieve substantial reduction in the training and feedback overhead. [ABSTRACT FROM PUBLISHER]

In this paper, a novel concept called a uniquely factorable constellation (UFC) is proposed for the systematic design of noncoherent full-diversity constellations for a wireless communication system that is equipped with a single transmitter antenna and multiple receiver antennas [single input–multiple output (SIMO)], where neither the transmitter nor the receiver knows channel-state information. It is proved that such a UFC design guarantees the unique blind identification of channel coefficients and transmitted signals in a noise-free case by processing only two received signals, as well as full diversity for the noncoherent maximum-likelihood (ML) receiver in a noise case. By using the Lagrange four-square theorem, an algorithm is developed to efficiently and effectively design various sizes of energy-efficient unitary UFCs to optimize the coding gain. In addition, a closed-form optimal energy scale is found to maximize the coding gain for the unitary training scheme based on the commonly used quadratic-amplitude modulation (QAM) constellations. Comprehensive computer simulations show that, using the same generalized likelihood ratio test (GLRT) receiver, the error performance of the unitary UFC designed in this paper outperforms the differential scheme, the optimal unitary training scheme presented in this paper, and the signal-to-noise ratio (SNR) efficient training scheme using the QAM constellation, which, so far, yields the best error performance in the current literature. However, with the same training ML receiver, the error performance of the UFCs is worse than the training scheme based on the QAM constellations. [ABSTRACT FROM PUBLISHER]

In this paper, adaptive training beam sequence design for efficient channel estimation in large millimeter-wave (mmWave) multiple-input multiple-output (MIMO) channels is considered. By exploiting the sparsity in large mmWave MIMO channels and imposing a Markovian random walk assumption on the movement of the receiver and reflection clusters, the adaptive training beam sequence design and channel estimation problem is formulated as a partially observable Markov decision process (POMDP) problem that finds non-zero bins in a two-dimensional grid. Under the proposed POMDP framework, optimal and suboptimal adaptive training beam sequence design policies are derived. Furthermore, a very fast suboptimal greedy algorithm is developed based on a newly proposed reduced sufficient statistic to make the computational complexity of the proposed algorithm low to a level for practical implementation. Numerical results are provided to evaluate the performance of the proposed training beam design method. Numerical results show that the proposed training beam sequence design algorithms yield good performance. [ABSTRACT FROM PUBLISHER]

This paper proposes a new compressive sensing-based downlink channel state information (CSI) estimation scheme for frequency division duplexing (FDD) massive multiple-input multiple-output (MIMO) systems. The proposed scheme, which involves two-stage weighted block \ell _1-minimization, exploits the block sparse nature of the angular domain representation of the MIMO channel matrices and the existence of common scattering paths in the realistic propagation environment. In the first stage of the implemented scheme, a conventional block \ell _1-minimization program is solved to extract the information about the common/individual supports of the multiuser channel matrices. In the second stage, a weighted block \ell _1-minimization algorithm, the weighting coefficients of which are suitably chosen to exploit the acquired support information, is used to estimate the channel matrices. The analytic performance guarantees of the proposed scheme are specified based on the block restricted isometry property of the sensing matrix. Specifically, the upper bounds of the \ell _2-norm reconstruction error are derived using various assumptions regarding the weighting. The obtained analytical results enable a discussion of the selection of weighting coefficients to enhance the CSI estimation performance, and the determination of a sufficient condition under which the proposed scheme outperforms the unweighted naive solution. Computer simulations show that the proposed method achieves higher estimation accuracy as compared to an existing greedy-based algorithm. [ABSTRACT FROM AUTHOR]

Massive multiple-input multiple-output (MIMO) communication is an emerging technology that uses an excess of transmit antennas to realize high spectral efficiency. Achieving potential gains with large-scale antenna arrays hinges on sufficient channel estimation accuracy. Much prior work focuses on time-division duplex (TDD)-based networks, relying on reciprocity between the uplink and downlink channels. However, most currently deployed commercial wireless systems are frequency-division duplex (FDD)-based, making it difficult to exploit channel reciprocity. In massive MIMO FDD systems, the problem of channel estimation becomes even more challenging due to the substantial training resources and feedback requirements which scale with the number of antennas. In this paper, we consider the problem of training sequence design and the mapping of training signals to training periods. We focus on reduced-dimension training sequence designs, along with transmit precoder designs, aimed at reducing both hardware complexity and power consumption. The resulting designs are extended to hybrid analog-digital beamforming systems, which employ a limited number of active RF chains for transmit precoding, by applying the Toeplitz distribution theorem to large-scale linear antenna systems. A practical guideline for training sequence parameter selection is presented along with performance analysis. [ABSTRACT FROM AUTHOR]

Radio-frequency (RF) enabled wireless energy transfer (WET), as a promising solution to provide cost-effective and reliable power supplies for energy-constrained wireless networks, has drawn growing interests recently. To overcome the significant propagation loss over distance, employing multi-antennas at the energy transmitter (ET) to more efficiently direct wireless energy to desired energy receivers (ERs), termed energy beamforming, is an essential technique for enabling WET. However, the achievable gain of energy beamforming crucially depends on the available channel state information (CSI) at the ET, which needs to be acquired practically. In this paper, we study the design of an efficient channel acquisition method for a point-to-point multiple-input multiple-output (MIMO) WET system by exploiting the channel reciprocity, i.e., the ET estimates the CSI via dedicated reverse-link training from the ER. Considering the limited energy availability at the ER, the training strategy should be carefully designed so that the channel can be estimated with sufficient accuracy, and yet without consuming excessive energy at the ER. To this end, we propose to maximize the net harvested energy at the ER, which is the average harvested energy offset by that used for channel training. An optimization problem is formulated for the training design over MIMO Rician fading channels, including the subset of ER antennas to be trained, as well as the training time and power allocated. Closed-form solutions are obtained for some special scenarios, based on which useful insights are drawn on when training should be employed to improve the net transferred energy in MIMO WET systems. [ABSTRACT FROM PUBLISHER]

In this paper, we study feedback optimization problems that maximize the users' signal to interference plus noise ratio (SINR) in a two-cell multiple-input multiple-output broadcast channel. Assuming the users learn their direct and interfering channels perfectly, they can feed back this information to the base stations (BSs) over the uplink channels. The BSs then use the channel information to design their transmission scheme. Two types of feedback are considered: 1) analog and 2) digital. In the analog feedback case, the users send their unquantized and uncoded channel state information (CSI) over the uplink channels. In this context, given a user's fixed transmit power, we investigate how he/she should optimally allocate it to feed back the direct and interfering (or cross) CSI for two types of BS cooperation schemes, namely, multicell processing (MCP) and coordinated beamforming. In the digital feedback case, the direct and cross link channel vectors of each user are quantized separately, each using the random vector quantization scheme, with different size codebooks. The users then send the index of the quantization vector in the corresponding codebook to the BSs. Similar to the feedback optimization problem for the analog feedback, we investigate the optimal bit partitioning for the direct and interfering link for both types of cooperation. We focus on regularized channel inversion precoding structures and perform our analysis in the large system limit in which the number of users per cell $(K)$ and the number of antennas per BS (N)$ tend to infinity with their ratio \beta=({K}/{N}) held fixed. We show that for both types of cooperation, for some values of interfering channel gain, usually at low values, no cooperation between the BSs is preferred. This is because, for these values of cross channel gain, the channel estimates for the cross link are not accurate enough for their knowledge to contribute to improving the SINR and there is no benefit in doing BS cooperation under that condition. We also show that for the MCP scheme, unlike in the perfect CSI case, the SINR improves only when the interfering channel gain is above a certain threshold. [ABSTRACT FROM PUBLISHER]

In this paper, we analyze a multiple-input multiple-output (MIMO) interference channel where nodes are randomly distributed on a plane as a spatial Poisson cluster point process. A Poisson cluster point process consists of clusters with fixed number of points randomly distributed as with the cluster centers distributed randomly on the plane. The nodes in each cluster use interference alignment (IA) to suppress intra-cluster interference but unlike most work on IA, we do not neglect inter-cluster interference. We also connect the accuracy of channel state information to the distance between the nodes, i.e., the quality of CSI degrades with increasing distance. Accounting for the training and feedback overhead, we derive the transmission capacity of this MIMO IA ad hoc network and then compare it to open-loop (interference-blind) spatial multiplexing. Finally, we present exemplary system setups where spatial multiplexing outperforms IA due to the imperfect channel state information or the non-aligned inter-cluster interference. [ABSTRACT FROM PUBLISHER]

This work examines the use of two-way training to efficiently discriminate the channel estimation performances at a legitimate receiver (LR) and an unauthorized receiver (UR) in a multiple-input multiple-output (MIMO) wireless system. This work improves upon the original discriminatory channel estimation (DCE) scheme proposed by Chang where multiple stages of feedback and retraining were used. While most studies on physical layer secrecy are under the information-theoretic framework and focus directly on the data transmission phase, studies on DCE focus on the training phase and aim to provide a practical signal processing technique to discriminate between the channel estimation performances (and, thus, the effective received signal qualities) at LR and UR. A key feature of DCE designs is the insertion of artificial noise (AN) in the training signal to degrade the channel estimation performance at UR. To do so, AN must be placed in a carefully chosen subspace, based on the transmitter's knowledge of LR's channel, in order to minimize its effect on LR. In this paper, we adopt the idea of two-way training that allows both the transmitter and LR to send training signals to facilitate channel estimation at both ends. Both reciprocal and nonreciprocal channels are considered and a two-way DCE scheme is proposed for each scenario. For mathematical tractability, we assume that all terminals employ the linear minimum mean square error criterion for channel estimation. Based on the mean square error (MSE) of the channel estimates at all terminals, we formulate and solve an optimization problem where the optimal power allocation between the training signal and AN is found by minimizing the MSE of LR's channel estimate subject to a constraint on the MSE achievable at UR. Numerical results show that the proposed DCE schemes can effectively discriminate between the channel estimation and, hence, the data detection performances at LR and UR. [ABSTRACT FROM PUBLISHER]

The communication between a multiple-antenna transmitter and multiple receivers (users) with either a single or multiple-antenna each can be significantly enhanced by providing the channel state information at the transmitter (CSIT) of the users, as this allows for scheduling, beamforming and multiuser multiplexing gains. The traditional view on how to enable CSIT has been as follows: In time-division duplexed (TDD) systems, uplink (UL) and downlink (DL) channel reciprocity allows the use of a training sequence in the UL direction, which is exploited to obtain an UL channel estimate. This estimate is in turn recycled in the next downlink transmission. In frequency-division duplexed (FDD) systems, which lack the UL and DL reciprocity, the CSIT is provided via the use of a dedicated feedback link of limited capacity between the receivers and the transmitter. In this paper, we focus on TDD systems and put their classical approach in question. We show that the traditional TDD setup above fails to fully exploit the channel reciprocity in its true sense. In fact, we show that the system can benefit from a combined CSIT acquisition strategy mixing the use of limited feedback and that of a training sequence. This combining gives rise to a very interesting joint estimation and detection problem for which we propose two iterative algorithms. An outage rate based framework is also developed which gives the resource split between training and feedback. We demonstrate the potential of this hybrid combining in terms of the improved CSIT quality under a global training and feedback resource constraint. [ABSTRACT FROM PUBLISHER]

In this paper, we design the training symbol placement for channel prediction in time-division-duplex multiple-antenna systems, where training symbols in a finite-length uplink frame are used for predicting downlink channels at the base station (BS). Aimed at minimizing the normalized sum mean square error of the Wiener predictor, we first prove that, for the first-order Gauss–Markov fading channel model, the optimal positions of training symbols lie at the end of the uplink frame, which is commonly recognized by intuition. For general channel models, we show that the ending placement is no longer optimal, and we propose a low-complexity method for designing training symbol placement based on alternating searching. In practice, the BS finds the optimal positions based on each user's temporal correlation, spatial correlation, and uplink signal-to-noise ratio (SNR) and then transmits the positions to the user, which needs low signaling overhead. Numerical results show significant prediction performance gain of the proposed training symbol placement over typical training placements under various SNR and Doppler frequency. [ABSTRACT FROM PUBLISHER]