Search

In this paper, we propose a new transmission scheme, called integer forcing-and-forward (IFF), for communications among multipair multiple-antenna users in which each pair exchanges their messages with the help of a single multiple-antenna relay in the multiple-access and broadcast phases. The proposed scheme utilizes integer forcing linear receiver (IFLR) at the relay, which uses equations, i.e., linear integer combinations of messages, to harness intrapair interference. Accordingly, we propose the design of a mean square error (MSE)-based transceiver, including precoder and projection matrices for the relay and users, assuming that perfect channel state information (CSI) is available. In this regard, in the multiple-access phase, we introduce two new MSE criteria for the related precoding and filter designs, i.e., the sum of the equations MSE (Sum-Equation MSE) and the maximum of the equations MSE (Max-Equation MSE), to exploit the equations in the relay. In addition, the convergence of the proposed criteria is proven as well. Moreover, in the broadcast phase, we use the two traditional MSE criteria, i.e., the sum of the users% mean square errors (Sum MSE) and the maximum of the users% mean square errors (Max MSE), to design the related precoding and filters for recovering the relay%s equations by the users. Then, we consider a more practical scenario with imperfect CSI. For this case, the IFLR receiver is modified, and another transceiver design is proposed, which takes into account the effect of channel estimation error. We evaluate the performance of our proposed strategy and compare the results with the conventional amplify-and-forward (AF) and denoise-and-forward (DF) strategies for the same scenario. The results indicate the substantial superiority of the proposed strategy in terms of outage probability and sum rate. [ABSTRACT FROM PUBLISHER]

In this paper, we study the impact of carrier frequency offset (CFO) estimation/compensation on the information rate performance of the zero-forcing (ZF) receiver in the uplink of a multiuser (MU) massive multiple-input–multiple-output (MIMO) system. Analysis of the derived closed-form expression of the per-user information rate reveals that, with the increasing number of BS antennas $M$, an \mathcal{O}(\sqrt{M}) array gain is achievable, which is the same as that achieved in the ideal zero CFO scenario. Moreover, it is observed that, compared with the ideal zero CFO case, the performance degradation in the presence of residual CFO (after CFO compensation) is the same for both ZF and MRC. [ABSTRACT FROM PUBLISHER]

In this paper, we study a multiple-antenna two-way relaying (TWR) cognitive radio (CR) system. A space alignment (SA) technique is adopted by the secondary users (SUs) to avoid interference with the primary users (PUs). We derive the optimal power allocation that maximizes the TWR achievable SU sum-rate while respecting the total power budget and the relay power constraints. We also analyze the case in which the relay is able to optimize its gain matrix structure to enhance the SU sum-rate. In the numerical results, we quantify the sum-rate gain of using the SA in the TWR CR and we show that the SU sum-rate is very limited when the relay power is low or the PU power and its resulting interference are high. In addition, we optimize the relay gain using an iterative algorithm and compare between different relay matrix structures. [ABSTRACT FROM PUBLISHER]

Due to the high flight speed of the aircraft and long range to access the ground station (GS), the channel state information (CSI) is not accurately obtained by direct feedback from the receiver to the transmitter. This makes it difficult to achieve stable transmission when the feedback codebook selection scheme is directly employed in the multiple-input multiple-output (MIMO) for the air-to-ground (A/G) communications. Therefore, in this paper, a stable transmission scheme for the A/G MIMO transmission is proposed to handle these issues. Two contributions are proposed: 1) an open-loop calibrated channel estimation scheme based on navigation filtering to offer a more accurate CSI estimation; and 2) an adaptive precoding scheme based on the correlation coefficient for stable transmission. The simulations confirm the validity and efficiency of the proposed schemes. For the 4 $\times$ 4 MIMO transmission, compared with the traditional scheme of a single antenna, the spectrum efficiency can be improved by 2 $\sim$ 4 times with a stable transmission. [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 a massive multiple-input–multiple-output (MIMO) uplink system, the pilot sequence reuse in neighboring cells causes pilot contamination, causing the decoding performance to degrade significantly. In this paper, a blind decoding method based on independent component analysis (ICA) is proposed without using pilot sequences. The proposed blind decoding method uses ICA to separate the received signals (from in-cell and neighboring cells) and estimate channels. The energy levels of the estimated channels are used to differentiate an in-cell signal from neighboring cell signals. The analytical performance results of the blind decoding method are derived. Numerical results show that the proposed blind decoding scheme outperforms minimum-mean-square-error (MMSE) decoding and zero-forcing (ZF) decoding with imperfect channel state information (CSI). The proposed scheme has only a negligible performance loss compared with MMSE decoding and ZF decoding with perfect CSI. [ABSTRACT FROM PUBLISHER]

Multiple-input–multiple-output orthogonal frequency-division multiplexing (MIMO-OFDM) receivers with an electronically steerable passive array radiator (ESPAR) antenna can improve bit error rate (BER) performance and obtain additional diversity gain without increasing the number of radio-frequency (RF) front-end circuits. In this scheme, a minimum-mean-square-error (MMSE) channel estimator is employed for obtaining the channel state information. However, its drawbacks include insufficient estimation accuracy and high computational complexity due to the inverse matrix calculation. In this paper, we propose using a low-complexity compressed-sensing-based channel estimation for the MIMO-OFDM receiver with an ESPAR antenna. We also propose a low-complexity detection scheme to further reduce the computational cost required for the detection. The simulation results show that with using the proposed channel estimation method, the BER performance is improved, while its complexity is reduced. The simulations also show that the proposed detection scheme successfully reduces the required computational cost while minimizing the degradation in the BER. [ABSTRACT FROM PUBLISHER]

In this paper, we analyze the spectral efficiency of multicell massive multiple-input–multiple-output (MIMO) systems with downlink training and a new pilot contamination precoding (PCP) scheme. First, we analyze the spectral efficiency of the beamforming training (BT) scheme with maximum-ratio transmission (MRT) precoding. Then, we derive an approximate closed-form expression of the spectral efficiency to find the optimal lengths of uplink and downlink pilots. Simulation results show that the achieved spectral efficiency can be improved due to channel estimation at the user side, but in comparison with a single-cell scenario, the spectral efficiency per cell in multicell scenario degrades because of pilot contamination. We focus on the practical case where the number of base station (BS) antennas is large but still finite and propose the BT and PCP (BT-PCP) transmission scheme to mitigate the pilot contamination with limited cooperation between BSs. We confirm the effectiveness of the proposed BT-PCP scheme with simulation, and we show that the proposed BT-PCP scheme achieves higher spectral efficiency than the conventional PCP method and that the performance gap from the perfect channel state information (CSI) scenario without pilot contamination is small. [ABSTRACT FROM AUTHOR]

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]

A companding-based transformation method is introduced to quasi-maximum-likelihood (ML) detectors, such as the QR-decomposition-based M-algorithm (QRD-M) and list sphere decoding, for coded multiple-input–multiple-output (MIMO) systems in this paper. The key idea of the proposed companding technique is to compress (i.e., down-weight) the dubious observation of the accumulated branch metric by taking into account its statistical characteristics so that, after companding, the estimation error of the unreliable detected information bits due to insufficient candidate size and/or channel estimation error is significantly mitigated without disproportionate compromise of the reliable information bits. By employing the proposed companding method, the original leptokurtically distributed log-likelihood ratio of the detected information bits becomes more Gaussian distributed. As an illustrative example, the QRD-M detector is employed in this paper. Numerical results show that the QRD-M detector based on the proposed companding paradigm achieves significant performance gain over the conventional method and approaches the performance of the ML detector for a 16-ary quadrature-amplitude-modulated (16-QAM) \4 \times \4 MIMO multiplexing system with lower-than-linear-detector computational complexity. [ABSTRACT FROM PUBLISHER]

In this paper, the problem of estimating multiple-input multiple-output (MIMO) channels in a realistic environment involving correlated channel fading and multiuser interference is considered. Four estimation schemes are studied, including the linear minimum mean squared error (LMMSE), least squares (LS),and Gauss-Markov (GM) estimators, as well as a novel scheme which is derived here as an alternative to LMMSE estimation. The MSE-optimal training sequences for each of them are provided and their requirements for side information feedback are assessed. The new scheme is shown to exhibit a performance comparable to or even better than LMMSE, at a significantly lower feedback and computational cost. The analytical comparison of the estimation schemes is supported by numerous simulation results that cover a wide range of antenna configurations, relative interference power, and channel correlation strengths. The results of this paper provide a complete picture for a palette of estimation schemes, with their relative performance and costs of training. [ABSTRACT FROM AUTHOR]

This paper employs a high resolution quantization framework to study the effects of finite-rate feedback of the channel state information (CSI) on the performance of multiple-input-multiple-output (MIMO) systems over independently and identically distributed (i.i.d.) Rayleigh flat fading channels. The contributions of this paper are twofold. First, we extend the general distortion analysis of vector quantizers to deal with complex source variables. Necessary and sufficient conditions that guarantee a concise high-resolution distortion analysis in the complex domain is presented. Second, as an application of the proposed complex distortion analysis, tight lower bounds on the capacity loss due to the finite-rate channel quantization are provided for MIMO systems employing a fixed number of equal power spatial beams. Based on the obtained closed-form analytical results, it is shown that the system capacity loss decreases exponentially as the ratio of the quantization rate to the total degrees of freedom of the channel state information to be quantized. Moreover, MIMO CSI-quantizers using mismatched codebooks that are only optimized for high-signal-to-noise ratio (SNR) and low-SNR regimes are also investigated to quantify the penalties incurred by the use of mismatched codebooks. In addition, the analysis is extended to deal with MIMO systems using multi-mode spatial multiplexing transmission schemes with finite-rate CSI feedback. Finally, numerical and simulation results are presented which confirm the tightness of the derived theoretical distortion bounds. [ABSTRACT FROM PUBLISHER]

In this paper, we consider the uplink of a single-cell multi-user single-input multiple-output (MU-SIMO) system with in-phase and quadrature-phase imbalance (IQI). In particular, we investigate the effect of receive (RX) IQI on the performance of MU-SIMO systems with large antenna arrays employing maximum-ratio combining receivers. In order to study how IQI affects channel estimation, we derive a new channel estimator for the IQI-impaired model and show that the higher the value of signal-to-noise ratio, the higher the impact of IQI on the spectral efficiency (SE). Moreover, a novel pilot-based joint estimator of the augmented multiple-input multiple-output (MIMO) channel matrix and IQI coefficients is described, and then, a low-complexity IQI compensation scheme is proposed, which is based on the IQI coefficients’ estimation and it is independent of the channel gain. The performance of the proposed compensation scheme is analytically evaluated by deriving a tractable approximation of the ergodic SE assuming transmission over Rayleigh fading channels with large-scale fading. Furthermore, we investigate how many mobile stations should be scheduled in massive MIMO systems with IQI and show that the highest SE loss occurs at the optimal operating point. Finally, by deriving asymptotic power scaling laws and proving that the SE loss due to IQI is asymptotically independent of the number of BS antennas, we show that massive MIMO is resilient to the effect of RX IQI. [ABSTRACT FROM AUTHOR]

The small wavelength at millimeter wave (mmWave) allows to pack antenna arrays in a very small area enabling practical large-scale MIMO. However, wideband and high-precision analog-to-digital converters (ADCs) are very expensive and power-hungry. In this paper, we propose a mmWave MIMO communication system that employs simple one-bit ADCs at the receiver and MIMO precoding at the transmitter. One significant challenge associated with this system is efficient channel estimation based on one-bit quantized channel output. We propose a novel continuous-time channel estimator based on level-triggered sampling, a nonuniform sampling scheme that offers estimation performance similar to that of the estimator based on full-precision channel output. We also develop the transmitter precoding scheme that reduces the transmit peak-to-average power ratio (PAPR) and, at the same time, equalizes the antenna cross-talks so that receiver demodulation can be implemented simply by symbol-rate slicing. Extensive simulation results are provided to demonstrate the effectiveness of the proposed channel estimation and precoding techniques for mmWave systems. [ABSTRACT FROM AUTHOR]

This paper proposes an amplify-and-forward-based massive multiple-input–multiple-output full-duplex relaying (AF-MM-FDR) protocol, where multiple source–destination pairs communicate simultaneously with a common full-duplex relay. A zero-forcing (ZF)-based relay transceiver design with semiblind gain control is presented, considering the problem of relay oscillation. The asymptotic expression of end-to-end signal-to-interference-plus-noise power (SINR) is derived under the general power scaling scheme. The scaling behaviors of echo interference (EI) and effective channel estimation error are determined. Scaling results show that the deployment of very large antenna arrays at the relay has the potential to eliminate the effect of EI due to the full-duplex operation, if the power scaling scheme is selected properly. Moreover, based on the asymptotic analysis, a low-complexity power control scheme is proposed to optimize the spectral efficiency of AF-MM-FDR. Simulation results show that the proposed power control scheme improves the spectral–energy efficiency tradeoff of AF-MM-FDR significantly. [ABSTRACT FROM AUTHOR]

In this paper, the bit error rate (BER) performance of spatial modulation (SM) systems under a novel 3-D vehicle-to-vehicle (V2V) multiple-input multiple-output (MIMO) channel model is investigated both theoretically and by simulations. The impact of vehicle traffic density, Doppler effect, and 3-D and 2-D V2V MIMO channel models on the BER performance are thoroughly investigated. Simulation results show that the performance of SM is mainly affected by the spatial correlation of the underlying channel model. Compared with other MIMO technologies, the SM system can offer a better tradeoff between spectral efficiency and system complexity. [ABSTRACT FROM AUTHOR]

Operation of full-duplex systems requires efficient mitigation of the self-interference signal caused by the simultaneous transmission/reception. In this paper, we propose a maximum-likelihood (ML) approach to jointly estimate the self-interference and intended channels by exploiting its own known transmitted symbols and both the known pilot and unknown data symbols from the other intended transceiver. The ML solution is obtained by maximizing the ML function under the assumption of Gaussian received symbols. A closed-form solution is first derived, and subsequently, an iterative procedure is developed to further improve the estimation performance at moderate-to-high signal-to-noise ratios (SNRs). We establish the initial condition to guarantee the convergence of the iterative algorithm to the ML solution. In the presence of considerable phase noise from the oscillators, a phase noise estimation method is proposed and combined with the ML channel estimator to mitigate the effects of the phase noise. Illustrative results show that the proposed methods offer good cancelation performance close to the Cramer–Rao bound (CRB). [ABSTRACT FROM AUTHOR]

We address the problem of noise and interference corrupted channel estimation in massive MIMO systems. Interference, which originates from pilot reuse (or contamination), can in principle be discriminated on the basis of the distributions of path angles and amplitudes. In this paper, we propose novel robust channel estimation algorithms exploiting path diversity in both angle and power domains, relying on a suitable combination of the spatial filtering and amplitude based projection. The proposed approaches are able to cope with a wide range of system and topology scenarios, including those where, unlike in previous works, interference channel may overlap with desired channels in terms of multipath angles of arrival or exceed them in terms of received power. In particular, we establish analytically the conditions under which the proposed channel estimator is fully decontaminated. Simulation results confirm the overall system gains when using the new methods. [ABSTRACT FROM AUTHOR]

Coarse quantization at the base station (BS) of a massive multi-user (MU) multiple-input multiple-output (MIMO) wireless system promises significant power and cost savings. Coarse quantization also enables significant reductions of the raw analog-to-digital converter data that must be transferred from a spatially separated antenna array to the baseband processing unit. The theoretical limits as well as practical transceiver algorithms for such quantized MU-MIMO systems operating over frequency-flat, narrowband channels have been studied extensively. However, the practically relevant scenario where such communication systems operate over frequency-selective, wideband channels is less well understood. This paper investigates the uplink performance of a quantized massive MU-MIMO system that deploys orthogonal frequency-division multiplexing (OFDM) for wideband communication. We propose new algorithms for quantized maximum a posteriori channel estimation and data detection, and we study the associated performance/quantization tradeoffs. Our results demonstrate that coarse quantization (e.g., four to six bits, depending on the ratio between the number of BS antennas and the number of users) in massive MU-MIMO-OFDM systems entails virtually no performance loss compared with the infinite-precision case at no additional cost in terms of baseband processing complexity. [ABSTRACT FROM AUTHOR]

In systems employing pilot-symbol aided channel estimation, the pilot-to-data power ratio is known to have a large impact on performance. Therefore, previous works proposed methods setting the pilot power such that either the weighted sum of the mean squared error (MSE) of the estimated data symbols is minimized or the overall spectral efficiency (SE) is maximized. However, previous works did not take into account the impact of correlated antennas and channel state information (CSI) errors on the optimal pilot power setting. In this paper, we consider the uplink of a multiuser multiple-input multiple-output (MU MIMO) system employing a receiver that minimizes the MSE of the received data symbols in the presence of CSI errors and derive closed-form expressions for the MSE and the achievable SE. These expressions take into account the impact of antenna correlation and CSI errors, and are a function of pilot power and the number of receive antennas. The analytical and numerical results can help set the pilot power, minimizing the MSE in multiple antenna systems. [ABSTRACT FROM AUTHOR]

This paper considers a multiple-input multiple-output (MIMO) receiver with very low-precision analog-to-digital convertors (ADCs) with the goal of developing massive MIMO antenna systems that require minimal cost and power. Previous studies demonstrated that the training duration should be relatively long to obtain acceptable channel state information. To address this requirement, we adopt a joint channel-and-data (JCD) estimation method based on Bayes-optimal inference. This method yields minimal mean square errors with respect to the channels and payload data. We develop a Bayes-optimal JCD estimator using a recent technique based on approximate message passing. We then present an analytical framework to study the theoretical performance of the estimator in the large-system limit. Simulation results confirm our analytical results, which allow the efficient evaluation of the performance of quantized massive MIMO systems and provide insights into effective system design. [ABSTRACT FROM AUTHOR]

In massive multiple-input multiple-output (MIMO) systems, it may not be power efficient to have a pair of high-resolution analog-to-digital converters (ADCs) for each antenna element. In this paper, a near maximum likelihood (nML) detector for uplink multiuser massive MIMO systems is proposed where each antenna is connected to a pair of one-bit ADCs, i.e., one for each real and imaginary component of the baseband signal. The exhaustive search over all the possible transmitted vectors required in the original maximum likelihood (ML) detection problem is relaxed to formulate an ML estimation problem. Then, the ML estimation problem is converted into a convex optimization problem which can be efficiently solved. Using the solution, the base station can perform simple symbol-by-symbol detection for the transmitted signals from multiple users. To further improve detection performance, we also develop a two-stage nML detector that exploits the structures of both the original ML and the proposed (one-stage) nML detectors. Numerical results show that the proposed nML detectors are efficient enough to simultaneously support multiple uplink users adopting higher-order constellations, e.g., 16 quadrature amplitude modulation. Since our detectors exploit the channel state information as part of the detection, an ML channel estimation technique with one-bit ADCs that shares the same structure with our proposed nML detector is also developed. The proposed detectors and channel estimator provide a complete low power solution for the uplink of a massive MIMO system. [ABSTRACT FROM PUBLISHER]

Several distributed coordinated precoding methods exist in the downlink multicell multiple-input–multiple-output (MIMO) literature, many of which assume perfect knowledge of received signal covariance and local effective channels. In this paper, we let the notion of channel state information (CSI) encompass this knowledge of covariances and effective channels. We analyze what local CSI is required in the weighted minimization of the mean square error (WMMSE) algorithm for distributed coordinated precoding, and we study how this required CSI can be obtained in a distributed fashion. Based on pilot-assisted channel estimation, we propose three CSI acquisition methods with different tradeoffs between feedback and signaling, backhaul use, and computational complexity. One of the proposed methods is fully distributed, meaning that it only depends on over-the-air signaling but requires no backhaul, and it results in a fully distributed joint system when coupled with the WMMSE algorithm. Naïvely applying the WMMSE algorithm together with the fully distributed CSI acquisition results in catastrophic performance however; therefore, we propose a robustified WMMSE algorithm (RB-WMMSE) based on the well-known diagonal loading framework. By enforcing properties of the WMMSE solutions with perfect CSI onto the problem with imperfect CSI, the resulting diagonally loaded spatial filters are shown to perform significantly better than the naïve filters. The proposed robust and distributed system is evaluated using numerical simulations and is shown to perform well compared with benchmarks. Under centralized CSI acquisition, the proposed algorithm performs on par with other existing centralized robust WMMSE algorithms. When evaluated in a large-scale fading environment, the performance of the proposed system is promising. [ABSTRACT FROM AUTHOR]

In this paper, we consider time-domain vector perturbation (TDVP) in a large-system limit when channel state information (CSI) is imperfect due to pilot contamination, and we derive the system's achievable sum rate per cell. We use random matrix methods to avoid time-consuming Monte Carlo simulations. Numerical results show that, in general, linear precoding ensures a higher sum rate than TDVP in the massive multiple-input–multiple-output (MIMO) regime. [ABSTRACT FROM AUTHOR]

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 MIMO system where the base station (BS) acquires quantized phase-only (PO) measurements of the complex baseband signal by our introduced stage-wised phase quantizer. PO-MIMO requires only one-bit ADCs for data sampling, so it successfully overcomes the ADC bottleneck that appears when the signal bandwidth is extremely wide. We construct a PO generalized approximate message passing (POG-AMP) algorithm for solving the linear mixing problem with quantized or unquantized phase measurements. POG-AMP has low computational complexity, exploits the signal prior statistical distribution, and handles the nonlinear distortions exerted on the measurements (e.g., losing magnitude and quantization). Then, POG-AMP is successfully applied to construct practical channel estimator and multiuser detector for PO-MIMO. Numerical results show that the POG-AMP estimator (POG-AMPE) and POG-AMP detector (POG-AMPD) are robust to the phase-quantization loss. POG-AMPE acquires high-quality channel side information at the receiver (CSIR), and POG-AMPD is robust to the CSIR errors when the BS antennas are massive enough. By introducing moderately more BS antennas, PO-MIMO with phase measurements even performs similarly to MIMO with full measurements containing both magnitude and phase. In order to maximize the transmit energy-efficiency, the lengths of the channel training sequences should be gradually increased with the increase of the channel coherence time. Antenna correlations at the BS degrade the convergence and bit-error rate performances of POG-AMPD, but can be handled by the analog spatial filtering technique. [ABSTRACT FROM AUTHOR]

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]

Previous correlation-based wideband multiple-input–multiple-output (MIMO) channel models have been limited to non-line-of-sight (NLoS) scenarios where simple antenna correlation measurements are sufficient to describe the correlated Gaussian process representing the fading statistics. However, in the presence of line-of-sight (LoS) or specular reflections, simple antenna correlation measurements unfortunately do not lead to accurate fading characterizations. In this paper, we propose a trend-stationary correlation-based analytical model for wideband LoS and NLoS MIMO channels and design and validate a wideband multitone MIMO channel sounder and estimator to, respectively, measure channel impulse responses (CIRs) and estimate channel parameters for this model. Joint maximum-likelihood (ML) estimation of the LoS component and the NLoS complex correlation matrix is presented. In addition to enabling the estimation of the path correlation matrix of a wireless channel in the presence of LoS components, the modeling approach also provides tools to evaluate the validity of the assumption of uncorrelated scattering (US), even in LoS environments, which is commonly used in channel modeling. A more general model that does not assume US is also presented, which can likewise be estimated using the proposed estimator. To validate the channel sounding/model estimator techniques, we employ channel sounding waveforms and collect snapshots from the outputs of a state-of-the-art hardware MIMO channel emulator capable of emulating multipath channels under the US assumption. Receiver signal processing and channel parameter estimation are performed on the collected data, and the results are compared with the emulated channel parameters. The work is unique in that it provides a correlation-based multipath MIMO channel modeling approach that addresses LoS environments, it describes corresponding estimation techniques to extract the full complex correlation matrix, and the model estimators are backed using channel sounding with emulation-based validation. [ABSTRACT FROM PUBLISHER]

In this paper, we investigate the performance of a multiple-input–multiple-output (MIMO) maximal ratio combining (MRC) system with feedback delay and channel estimation error. By taking these practical imperfect factors into account, we first formulate the system model and derive the moment-generating function of the output signal-to-noise ratio (SNR), which serves as the basis for further system performance analysis. Then, we compute the probability density function (pdf) and the cumulative distribution function (cdf) of the output SNR. Furthermore, we derive the analytical expressions of the exact and approximate average symbol error rates (SERs) of the MIMO MRC system, which are used to investigate the performance loss in terms of the array gain and diversity order. Finally, computer simulations are conducted to show the efficacy of the analytical results and the effect of feedback delay and channel estimation error on the system performance. [ABSTRACT FROM PUBLISHER]

Multiple-input—multiple-output (MIMO) orthogonal frequency-division multiple access (OFDMA) has been selected as the core physical-layer access scheme for the downlink of state-of-the-art and next-generation wireless communications standards. In these systems, scheduling and resource allocation (SRA) algorithms, jointly assigning transmission data rates, bandwidth, and power, become crucial in optimizing resource utilization while providing support to multimedia applications with heterogeneous quality-of-service (QoS) requirements. To this end, the transmitter is assumed to have channel state information at the transmitter (CSIT) that will typically be imperfect. This paper introduces a unified analytical framework for robust channel- and queue-aware QoS-guaranteed cross-layer SRA algorithms for the downlink of MIMO-OFDMA networks with imperfect CSIT. The framework is based on the statistical characterization of the signal-to-noise ratio (SNR) under imperfect CSIT and is general enough to encompass spatial correlation effects in the Tx and Rx antenna arrays, different types of traffic, uniform and continuous power allocation, discrete and continuous rate allocation, and protocols with different amounts of channel and queue awareness. Simulation results using parameters drawn from the Third-Generation Partnership Project Long-Term Evolution (3GPP-LTE) standard demonstrate the validity and advantages of the proposed robust cross-layer unified approach. [ABSTRACT FROM PUBLISHER]

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]

Common signals in public channels of cellular systems should be transmitted omnidirectionally from the base station (BS) to ensure cell-wide coverage. In this paper, we propose an omnidirectional precoding (OP) based transmission for public channels in massive MIMO systems, which leads to a significant reduction in the downlink pilot overhead while providing omnidirectional signaling. For this OP based transmission, we present three necessary conditions that the OP matrix should satisfy, to meet the requirements for omnidirectional transmission for reliable cell-wide coverage, equal average power on each antenna to sufficiently utilize all the available power amplifier capacities of BS antennas, and achievable ergodic rate maximization for the i.i.d. channel, respectively. Then, several examples of the OP matrix satisfying the three necessary conditions simultaneously are designed by utilizing the Zadoff-Chu sequence and its properties. We also analyze the system performance in terms of achievable ergodic rate, outage probability, and peak-to-average power ratio (PAPR) for these designs. It is shown that a dedicated design of the OP matrix has the following advantages: 1) it asymptotically maximizes the achievable ergodic rate, maximizes the achievable diversity order, and minimizes the outage probability in the large-scale array regime, not only for the i.i.d. channel, but also for spatially correlated channels; 2) it preserves the PAPR of the transmitted signal after precoding. [ABSTRACT FROM AUTHOR]

Massive multiple-input–multiple-output (MIMO) antenna system implies the use of a large number of base station (BS) antennas to serve a relatively small number of user terminals (UTs) for extraordinary spectral efficiency. However, its performance is limited by pilot contamination due to unavoidable reuse of pilot sequences from UTs in different cells. In this paper, we analyze the performance of massive MIMO zero-forcing (ZF) systems using the time-shifted pilot scheme, which was known to combat pilot contamination effectively using conjugate beamforming if there is a very large number of antennas. We derive expressions for achievable sum rates and the signal-to-interference-plus-noise ratios (SINRs) of forward and reverse links if the number of BS antennas is finite. Then, the impact of system parameters, such as cell radius, transmit power, group number (a parameter related to the time-shifted pilot scheme), BS antenna number, etc., on the system performance are revealed. Our model embraces a series of previous works as special cases. Moreover, we compare the performance of conjugate and ZF precoders, from which a simple but effective large-scale fading-based UT scheduling scheme is proposed to enhance the system throughput. [ABSTRACT FROM PUBLISHER]

This paper proposes a spatially common sparsity based adaptive channel estimation and feedback scheme for frequency division duplex based massive multi-input multi-output (MIMO) systems, which adapts training overhead and pilot design to reliably estimate and feed back the downlink channel state information (CSI) with significantly reduced overhead. Specifically, a nonorthogonal downlink pilot design is first proposed, which is very different from standard orthogonal pilots. By exploiting the spatially common sparsity of massive MIMO channels, a compressive sensing (CS) based adaptive CSI acquisition scheme is proposed, where the consumed time slot overhead only adaptively depends on the sparsity level of the channels. In addition, a distributed sparsity adaptive matching pursuit algorithm is proposed to jointly estimate the channels of multiple subcarriers. Furthermore, by exploiting the temporal channel correlation, a closed-loop channel tracking scheme is provided, which adaptively designs the nonorthogonal pilot according to the previous channel estimation to achieve an enhanced CSI acquisition. Finally, we generalize the results of the multiple-measurement-vectors case in CS and derive the Cramér–Rao lower bound of the proposed scheme, which enlightens us to design the nonorthogonal pilot signals for the improved performance. Simulation results demonstrate that the proposed scheme outperforms its counterparts, and it is capable of approaching the performance bound. [ABSTRACT FROM AUTHOR]

This paper develops hybrid analog-digital channel estimation and beamforming techniques for multiuser massive multiple-input multiple-output (MIMO) systems with limited number of radio frequency (RF) chains. For these systems, first, we design novel minimum-mean-squared error (MMSE) hybrid analog-digital channel estimator by considering both cases with perfect and imperfect channel covariance matrix knowledge. Then, we utilize the estimated channels to enable beamforming for data transmission. When the channel covariance matrices of all user equipments (UEs) are known perfectly, we show that there is a tradeoff between the training duration and throughput. Specifically, we exploit the fact that the optimal training duration that maximizes the throughput depends on the covariance matrices of all UEs, number of RF chains, and channel coherence time ($T_c$). We also show that the training time optimization problem can be formulated as a concave maximization problem where its global optimal solution can be obtained efficiently using existing tools. The analytical expressions are validated by performing extensive Monte Carlo simulations. [ABSTRACT FROM AUTHOR]

This paper investigates the achievable sum-rate of massive multiple-input multiple-output (MIMO) systems in the presence of channel aging. For the uplink, by assuming that the base station (BS) deploys maximum ratio combining (MRC) or zero-forcing (ZF) receivers, we present tight closed-form lower bounds on the achievable sum-rate for both receivers with aged channel state information (CSI). In addition, the benefit of implementing channel prediction methods on the sum-rate is examined, and closed-form sum-rate lower bounds are derived. Moreover, the impact of channel aging and channel prediction on the power scaling law is characterized. Extension to the downlink scenario and multicell scenario is also considered. It is found that, for a system with/without channel prediction, the transmit power of each user can be scaled down at most by 1/\sqrtM (where $M$ is the number of BS antennas), which indicates that aged CSI does not degrade the power scaling law, and channel prediction does not enhance the power scaling law; instead, these phenomena affect the achievable sum-rate by degrading or enhancing the effective signal to interference and noise ratio, respectively. [ABSTRACT FROM AUTHOR]

Massive multi-input multi-output (MIMO) systems require channel state information (CSI) at base stations. In multicell environments, adjacent cells using the same spectrum cause a pilot contamination problem and degrade CSI quality. Under such practical environments, it is important to assess achievable rate of the system for a proper system design. There exist achievable rate bounds of uplink (UL) systems in the literature, but general closed-form rate bounds for downlink (DL) systems are not available yet. This paper studies a practical massive MIMO DL system with or without pilot-aided coherent detection under the scenario of pilot contamination, and derives closed-form approximate achievable DL rate expressions for both maximum ratio combining (MRC) and zero forcing (ZF) precoders. For the case with DL pilot, a corresponding DL effective channel estimator is also developed. Numerical simulation results corroborate accuracy of the closed-form rate expressions and they also show performance characteristics of pilot-contaminated massive MIMO DL systems with MRC and ZF precoders. [ABSTRACT FROM AUTHOR]

In this paper, we consider minimum-mean-square error (MMSE) training-based channel estimation for two-hop multiple-input multiple-output (MIMO) orthogonal frequency division multiplexing (OFDM) relaying systems. The channel estimation process is divided into two main phases. The relay-destination channel is estimated in the first phase and can be obtained using well-known point-to-point MIMO OFDM estimation methods. In the second phase, the source-relay channel is estimated at the destination with the use of a known training sequence that is transmitted from the source and forwarded to the destination by a nonregenerative relay. To obtain an estimate of the source-relay channel, the source training sequence, relay precoder, and destination processor, require to be optimized. To solve this problem, we first derive an iterative algorithm that involves sequentially solving a number of convex optimization problems to update the source, relay, and destination design variables. Since the iterative algorithm may be too computationally expensive for practical implementation, we then derive simplified solutions that have reduced computational complexity. Simulation results demonstrate the effectiveness of the proposed algorithms. [ABSTRACT FROM AUTHOR]

This paper proposes a novel precoding scheme for multiuser multiple-input–multiple-output (MIMO) relay systems in the presence of imperfect channel state information (CSI). The base station (BS) and the MIMO relay station (RS) are both equipped with the same codebook of unitary matrices. According to each element of the codebook, we create a latent precoding matrix pair, namely, a BS precoding matrix and an RS precoding matrix. The RS precoding matrix is formed by multiplying the appropriate unitary matrix from the codebook by a power scaling factor. Based on the given CSI and a block of transmit symbols, the optimum precoding matrix pair, within the class of all possible latent precoding matrix pairs derived from the various unitary matrices, is selected by a suitable selection mechanism for transmission, which is designed to minimize the squared Euclidean distance between the pre-estimated received vector and the true transmit symbol vector. We develop a minimum mean-square-error (MMSE) design algorithm to construct the latent precoding matrix pairs. In the proposed scheme, rather than sending the complete processing matrix, only the index of the unitary matrix and its power scaling factor are sent by the BS to the RS. This significantly reduces the overhead. Simulation results show that, compared with other recently reported precoding algorithms, the proposed precoding scheme is capable of providing improved robustness against the effects of CSI estimation errors and multiuser interference. [ABSTRACT FROM PUBLISHER]

This paper focuses on precoding design for sum-rate maximization while considering the effects of residual self-interference for multiuser multiple-input–multiple-output (MU-MIMO) full-duplex (FD) systems. The problem formulation leads to a nonconvex matrix-variable optimization problem, where we develop two efficient sum-rate maximization algorithms using sequential convex programming (SCP), namely, the difference of convex functions (DC)-based and the sequential convex approximations for matrix-variable programming (SCAMP) algorithms. In addition, we derive the achievable sum rate under the effect of residual self-interference. Simulation results show that, even in cases of high self-interference and high estimation error, the SCAMP algorithm provides approximately 20%–30% sum-rate improvements over both conventional optimized half-duplex (HD) transmission and the existing state-of-the-art FD algorithm in a wide range of scenarios. Finally, the convergence results indicate that the DC-based algorithm tends to initially give the best performance; however, at convergence, the SCAMP algorithm tends to significantly outperform the other algorithms. [ABSTRACT FROM AUTHOR]

While the impact of antenna mutual coupling on multiple-input multiple-output (MIMO) communication performance has been well documented, little work has appeared demonstrating how antenna coupling impacts the performance of multiantenna radios that use reciprocal electromagnetic propagation to establish secret encryption keys. This paper uses network theory to analyze the performance of such systems. The presentation includes an eavesdropper whose antennas may be very near and therefore coupled to the antennas of a legitimate node and further demonstrates the signal and noise model when the systems must remove the impact of nonreciprocal circuit elements through calibration. Representative results obtained with the model quantify the impact of coupling and the type of impedance matching used to connect the antennas to the radio circuitry on the system key establishment performance. [ABSTRACT FROM PUBLISHER]

The impulse response of wireless channels between the N_t transmit and N_r receive antennas of a MIMO-OFDM system are group approximately sparse (ga-sparse), i.e., the N_tN_r channels have a small number of significant paths relative to the channel delay spread and the time-lags of the significant paths between transmit and receive antenna pairs coincide. Often, wireless channels are also group approximately cluster-sparse (gac-sparse), i.e., every ga-sparse channel consists of clusters, where a few clusters have all strong components while most clusters have all weak components. In this paper, we cast the problem of estimating the ga-sparse and gac-sparse block-fading and time-varying channels in the sparse Bayesian learning (SBL) framework and propose a bouquet of novel algorithms for pilot-based channel estimation, and joint channel estimation and data detection, in MIMO-OFDM systems. The proposed algorithms are capable of estimating the sparse wireless channels even when the measurement matrix is only partially known. Further, we employ a first-order autoregressive modeling of the temporal variation of the ga-sparse and gac-sparse channels and propose a recursive Kalman filtering and smoothing (KFS) technique for joint channel estimation, tracking, and data detection. We also propose novel, parallel-implementation based, low-complexity techniques for estimating gac-sparse channels. Monte Carlo simulations illustrate the benefit of exploiting the gac-sparse structure in the wireless channel in terms of the mean square error (MSE) and coded bit error rate (BER) performance. [ABSTRACT FROM PUBLISHER]

This paper addresses energy-efficient power control for time-division-duplexing (TDD) multiple-input–single-output (MISO) systems. Assuming ideal channel reciprocity, the beamforming at the transmitter (TX) is based on the estimated channel state information (CSI) obtained from the pilot signal of the receiver (RX). We define the energy efficiencies of the TX and the RX separately and study their energy efficiency region. In addition, we seek a power control scheme that can achieve all the points on the Pareto boundary of the energy efficiency region. We propose to maximize the weighted system energy efficiency, which is the data rate divided by the weighted sum of the power consumption at the TX and the RX. A method based on the quasi-concavity of the weighted system energy efficiency is developed to solve the problem. Simulation results show that the proposed power control can achieve all the points on the Pareto boundary of the energy-efficiency region by varying the weight. [ABSTRACT FROM PUBLISHER]

This paper investigates the prediction of multiple-input–multiple-output (MIMO) narrow-band multipath fading channels for mobile-to-mobile (M-to-M) wireless communication systems. Using a statistical model for M-to-M communication in urban and suburban environments, we derive a parameterized double directional model and utilize a multidimensional extension of the ESPRIT algorithm to jointly estimate the angles of departure (AoD), angles of arrival (AoA), and effective Doppler frequencies. A simple method is also proposed for mobile velocity estimation. The parameter estimates are then used to forecast the M-to-M channel. The bound on the prediction error is derived using a vector formulation of the Cramer–Rao lower bound (CRLB). Simulations are used to evaluate the performance of the prediction scheme and to compare it to the derived error bound. [ABSTRACT FROM PUBLISHER]

Recently, a structure of an optimal linear precoder for multi cell downlink systems has been described, and many other references have used simplified versions of this precoder to obtain promising performance gains. These gains have been hypothesized to stem from the additional degrees of freedom that allow for interference mitigation through interference relegation to orthogonal subspaces. However, no conclusive or rigorous understanding has yet been developed. In this paper, we build on an intuitive interference induction trade-off and the aforementioned preceding structure to propose an interference aware RZF (iaRZF) preceding scheme for multi cell downlink systems, and we analyze its rate performance. Special emphasis is placed on the induced interference mitigation mechanism of iaRZF. For example, we will verify the intuitive expectation that the precoder structure can either completely remove induced inter-cell or intra-cell interference. We state new results from large-scale random matrix theory that make it possible to give more intuitive and insightful explanations of the precoder behavior, also for cases involving imperfect channel state information (CSI). We remark especially that the interference-aware precoder makes use of all available information about interfering channels to improve performance. Even very poor CSI allows for significant sum-rate gains. Our obtained insights are then used to propose heuristic precoder parameters for arbitrary systems, whose effectiveness are shown in more involved system scenarios. Furthermore, calculation and implementation of these parameters does not require explicit inter base station cooperation. [ABSTRACT FROM PUBLISHER]

In this paper, we study beam selection for millimeter-wave (mm-wave) multiuser multiple input multiple output (MIMO) systems where a base station (BS) and users are equipped with antenna arrays. Exploiting a certain sparsity of mm-wave channels, a low-complexity beam selection method for beamforming by low-cost analog beamformers is derived. It is shown that beam selection can be carried out without explicit channel estimation using the notion of compressive sensing (CS). Due to various reasons (e.g., the background noise and interference), some users may choose the same BS beam, which results in high inter-user interference. To overcome this problem, we further consider BS beam selection by users. Through simulations, we show that the performance gap between the proposed approach and the optimal beamforming approach, which requires full channel state information (CSI), becomes narrower for a larger number of users at a moderate/low signal-to-noise ratio (SNR). Since the optimal beamforming approach is difficult to be used due to prohibitively high computational complexity for large antenna arrays with a large number of users, the proposed approach becomes attractive for BSs and users in mm-wave systems where large antenna arrays can be employed. [ABSTRACT FROM PUBLISHER]

The full-duplex (FD) single-user multiple-input–multiple-output (SU-MIMO) sum-rate maximization problem leads to a nonconvex optimization problem. In this paper, we introduce a maximum self-interference threshold (SIT) constraint to the sum-rate maximization problem to transform the problem into a convex optimization problem and develop the SIT-based FD precoding (FDP-SIT) algorithm. In particular, the FDP-SIT algorithm consists of an inner loop that assumes fixed threshold values and an outer loop that optimizes each node's threshold values. Illustrative examples show how the proposed FDP-SIT algorithm can provide a tradeoff between the separate and joint FDP algorithms. As well, the measured data-based results suggest that the FDP-SIT algorithm can offer increased sum rates when low cancelation power is used. [ABSTRACT FROM PUBLISHER]

In this paper, we investigate the impact of channel estimation error on symbol error rate (SER) for joint diversity (i.e., combining space–time block coding and user scheduling) in multiple-input–multiple-output systems with a channel estimation error. Under a Gaussian distributed channel estimation error, the exact expressions of the joint diversity for SER with both M-ary quadrature amplitude modulation (QAM) and phase-shift keying (PSK) modulation are derived. Using probability density function approximation, we quantify the diversity order and signal-to-noise ratio (SNR) gain of SER for joint diversity in the presence of a channel estimation error. From the analysis results, we show that the diversity order is enhanced as the number of user terminals increases, regardless of the modulation type. This is because user scheduling provides multiuser diversity. Moreover, the SNR gain decreases as the number of user terminals increases. This is because the peak effective SNR in joint diversity decreases as the number of user terminals increases. [ABSTRACT FROM PUBLISHER]

In this paper, we derive the maximum a posteriori (MAP) symbol detector for a multiple-input multiple-output system in the presence of Wiener phase noise due to noisy local oscillators, and quasi-static fading channels. As in single-antenna systems, the computation of the optimum receiver is an analytically intractable problem and is unimplementable in practice. In this purview, we propose three suboptimal, low-complexity algorithms for approximately implementing the MAP symbol detector, which involve joint phase noise estimation and data detection. Our first algorithm is obtained by means of the sum-product algorithm, where we use the multivariate Tikhonov canonical distribution approach. In our next algorithm, we derive an approximate MAP symbol detector based on the smoother-detector framework, wherein the detector is properly designed by incorporating the phase noise statistics from the smoother. The third algorithm is derived based on the variational Bayesian framework. By simulations, we evaluate the performance of the proposed algorithms for both uncoded and coded data transmissions and observe that the proposed techniques significantly outperform the other important algorithms from prior works, which are considered herein. [ABSTRACT FROM PUBLISHER]