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In this paper, we design receivers for filter bank multicarrier-based (FBMC-based) massive MIMO considering practical aspects such as channel estimation and equalization. In particular, we propose a spectrally efficient pilot structure and a channel estimation technique in the uplink to jointly estimate all the users’ channel impulse responses. We mathematically analyze our proposed channel estimator and find the statistics of the channel estimation errors. These statistics are incorporated into our proposed equalizers to deal with the imperfect channel state information (CSI) effect. We revisit the channel equalization problem for FBMC-based massive MIMO, address the shortcomings of the existing equalizers in the literature, and make them more applicable to practical scenarios. The proposed receiver in this paper consists of two stages. In the first stage, a linear combining of the received signals at the base station (BS) antennas provides a coarse channel equalization and removes any multiuser interference. In the second stage, a per subcarrier fractionally spaced equalizer (FSE) takes care of any residual distortion of the channel for the user of interest. We propose an FSE design based on the equivalent channel at the linear combiner output. This enables the applicability of our proposed technique to small and/or distributed antenna setups such as cell-free massive MIMO. Finally, the efficacy of the proposed techniques is corroborated through numerical analysis. [ABSTRACT FROM AUTHOR]

In this treatise, we conceive a hardware impairment aware receiver design of a large multiple-input multiple-output (MIMO) orthogonal frequency division multiplexing (OFDM) system, taking into account the error statistics of parameter estimations. The hardware impairment includes the quantization noise, phase noise etc. There are two key contributions in this work. Firstly, consideration of error statistics of residual parameters in the receiver design improves receiver performance. In this work, we propose a linear equalizer based data detector at the receiver for a large MIMO-OFDM system and the parameters of interest are the channel impulse response (CIR) and carrier frequency offset (CFO) along with the hardware impairments awareness both at transmitter and receiver. The design criterion chosen for designing the detector equalizer is based on the linear minimum mean square error (LMMSE) one and it will consider the statistics of estimation error and hardware impairments. However, in any practical communication system, the availability of statistics for these parameter errors is inconceivable. Hence, we propose further to consider the Bayesian Crammer Rao lower bound (BCRLB) as the variance of the error with the key assumption of zero mean. Numerical simulation demonstrates that, consideration of such statistics attains an SNR gain of $2$ dB with respect to a fixed symbol error ratio (SER). It also shows that the SER performance with BCRLB matches very closely with the case, when exact error variance is used. For the second contribution, an extensive theoretical analysis has been performed to evaluate the MSE improvement of data detection with the LMMSE criterion with parameter estimation error statistics consideration in the large MIMO-OFDM system. This theoretical analysis of improved MSE has been conceived with the usage of large dimension random matrix theory and a closed-form expression has been obtained. Numerical simulation demonstrates that the theoretical analysis for MSE improvement matches closely with the simulated one. [ABSTRACT FROM AUTHOR]

This correspondence paper aims at addressing the estimation of carrier frequency offset (CFO) for the uplink orthogonal frequency-division multiple access systems. Since the CFOs of the signals from different active users are sparsely distributed in the frequency domain, a sparse Bayesian learning (SBL) is tailored to determine the CFO in this paper, ending up with the SBL assisted CFO (SBL-CFO) estimator. In particular, the CFO estimation problem is first formulated as a sparse nonnegative least squares (S-NNLS) problem. Meanwhile, background noise and sampling errors are mitigated utilizing a selection matrix and a whitening filter, respectively. This enables us to exploit the SBL with nonnegative Laplace prior (SBL-NLP) to solve the S-NNLS problem. Furthermore, in order to make the convergence of the SBL-NLP algorithm faster and its estimation more accurate, the hyperprior inherent in the SBL-NLP algorithm is initialized by the traditional SBL with nonnegative Gaussian prior. Simulation results show that our proposed SBL-CFO estimator significantly outperforms the state-of-the-art estimators in terms of estimation accuracy, especially when the CFOs and the number of active users are large. [ABSTRACT FROM AUTHOR]

This correspondence paper studies the impact of nonidentically distributed channel estimation error on performance of a scheduled space-time block coding with mean-value power allocation in cognitive-radio multiple-input multiple-output system. Using the derived probability density function, the exact closed-form expressions of the proposed system for outage probability, ergodic capacity, and symbol-error rate (SER) with both $\rm M$ -ary quadrature amplitude modulation and phase shift keying modulation are derived. Using asymptotic analysis, this paper quantifies the diversity order for both outage probability and SER in the presence of nonidentically distributed error. From the analysis results, this paper shows that the diversity order is enhanced by the multiuser diversity as the number of user terminals increases regardless of outage probability and SER, while the spatial diversity is eliminated. [ABSTRACT FROM AUTHOR]

In this paper, we study the impacts of cochannel interference (CCI) and channel estimation error (CEE) on the performance of a cellular multiuser two-way relay network. The network comprises of one Na-antenna base station (BS), $M$ single-antenna users, and one L$-antenna relay with N_{r} number of interferers. In particular, by employing antenna selection at the relay, we propose beamforming and antenna selection transmission schemes at the BS with user scheduling in the presence of CEE and CCI. Then, we derive the tight closed-form lower bound expressions for the overall outage probability under Nakagami-m fading. We also deduce the asymptotic outage expressions in the high signal-to-noise ratio regime, from which the impacts of CEE and CCI on the achievable diversity order are highlighted. Moreover, using the asymptotic expressions, three optimization problems to minimize the outage probability are formulated and solved: first, optimal power allocation (OPA) with fixed relay location. Specifically, we develop two OPA approaches, namely, OPA I and OPA II. OPA I allocates the total power appropriately at each terminal, whereas OPA II allocates the fixed half of the total power at the relay and remaining power to base station and users; second, optimal relay location with fixed power allocation; and third, jointly optimal power allocation and relay location. Our results reveal that the OPA I outperforms OPA II, irrespective of different channel/system parameters, interferers, and relay locations. In addition, we demonstrate that the effect of interferers at the relay can be ignored if we allocate sufficient power to the base station and users. It is also shown that the CEE and CCI can severely degrade the outage performance, and even reduce the diversity order to zero. Further, it is highlighted that the impact of CCI can be alleviated by increasing the number of relay antennas. The numerical and simulation results validate our analytical findings, and also demonstrate that the antenna selection scheme offers comparable outage performance as beamforming scheme. [ABSTRACT FROM PUBLISHER]

This paper proposes a novel blind carrier frequency offset (CFO) estimator, namely the sparse recovery assisted CFO (SR-CFO) estimator, for the uplink orthogonal frequency-division multiple access (OFDMA) systems. By exploiting the sparsity embedded in the OFDMA data, the CFO estimation is formulated as an optimization problem of sparse recovery with high-resolution. Meanwhile, in order to enhance the estimation accuracy of CFOs, background noise and sampling errors are mitigated by exploiting the structure of the noise covariances matrix in the transformed observation data, and the asymptotic distribution of the sampling errors. Furthermore, we propose an approach for deriving the regularization parameter used by the SR-CFO estimator, so as to control the tradeoff between the data fitting error and the sparsity of solution. The performance of the proposed SR-CFO estimator along with other four existing estimators is investigated and compared. Numerical results show that the proposed SR-CFO estimator is superior to the state-of-the-art estimators in terms of the estimation reliability. [ABSTRACT FROM PUBLISHER]

In this paper, we study the problem of velocity estimation in vehicular networks by exploiting the sparsity of vehicle velocity trajectory. First, we exploit the sparsity of vehicular velocity trajectories to reduce the beaconing load on the channel. To this end, we propose a sublayer that reduces the number of transmitted packets through a superframe. At each superframe, each vehicle transmits a few samples of its velocity as well as an encoded measurement of its past velocities. Sparse recovery of each velocity vector is performed at the receiver. We propose the use of repetitions at the medium access control (MAC) layer. Moreover, we extend our scheme into a streaming estimation system without the superframe concept. The streaming system uses a sliding window concept. We thoroughly study the estimation error of the streaming system, and we propose an algorithm to find the best solution over a streaming sliding window. The proposed superframe and streaming schemes are tested with real velocity traces collected in the city of Toronto to capture the performance in the city and highway conditions. Experiment results show that the proposed schemes significantly reduce the number of exchanged packets while preserving the velocity information with an excellent accuracy at the receiver. We also demonstrate how past velocity can be used to enhance vehicle localization accuracy. [ABSTRACT FROM PUBLISHER]

In this paper, we study the secure multiantenna transmission with artificial noise (AN) under imperfect channel state information (CSI) in the presence of spatially randomly distributed eavesdroppers. We derive the optimal solutions of the power allocation between the information signal and the AN for minimizing the secrecy outage probability (SOP) under a target secrecy rate and for maximizing the secrecy rate under an SOP constraint, respectively. Moreover, we provide an interesting insight that channel estimation error affects the optimal power allocation strategy in opposite ways for the two given objectives. When the estimation error increases, more power should be allocated to the information signal if we aim to decrease the rate-constrained SOP, whereas more power should be allocated to the AN if we aim to increase the SOP-constrained secrecy rate. [ABSTRACT FROM PUBLISHER]

This paper concerns the uplink of multicell multiuser multiple-input multiple-output (MIMO) systems. To tackle the effect of pilot contamination that is generally viewed as a bottleneck in previous work, this paper presents a superimposed pilot (SP)-aided uplink channel estimation scheme and mathematically characterizes the impact that an SP has on the performance of such a very large MIMO system. It is shown that there are two types of interference components that do not vanish, even when the number of antennas $M$ grows to infinity. The first type, which is referred to as cross-contamination, is due to the correlation between the SP and data among different cells. The second type, which is referred to as self-contamination, is due to the dependence between channel estimation and estimation error. Cross-contamination is, in principle, similar to pilot contamination in a conventional pilot-based multicell MIMO system, whereas self-contamination is unique for the SP-aided scheme. Both theoretical and simulation results demonstrate that the SP-aided scheme can effectively reduce the estimation contamination by increasing the data frame size and, in turn, achieve a significant improvement over the spectral efficiency, in comparison with conventional pilot-based methods. [ABSTRACT FROM AUTHOR]

Contactless smart card systems have gained universal prevalence in modern metros. In addition to its original goal of ticketing, the large amount of transaction data collected by the smart card system can be utilized for many operational and management purposes. This paper investigates an important problem: how to extract spatiotemporal segmentation information of trips inside a metro system. More specifically, for a given trip, we want to answer several key questions: How long does it take for a passenger to walk from the station gantry to the station platform? How much time does he/she wait for the next train? How long does he/she spend on the train? How long does it take to transfer from one line to another? This segmentation information is important for many application scenarios such as travel time prediction, travel planning, and transportation scheduling. However, in reality, we only assume that only each trip's tap-in and tap-out time can be directly obtained; all other temporal endpoints of segments are unknown. This makes the research very challenging. To the best of our knowledge, we are the first to give a practical solution to this important problem. By analyzing the tap-in/tap-out event pattern, our intuition is to pinpoint some special passengers whose transaction data can be very helpful for segmentation. A novel methodology is proposed to extract spatiotemporal segmentation information: first, for nontransfer trips, by deriving the boarding time between the gantry and the platform, and then, for with-transfer trips, by deriving the transfer time. Evaluation studies are based on large-scale real-system data of the Shenzhen metro system, which is one of the largest metro systems in China and serves millions of passengers daily. Onsite investigations validate that our algorithm is accurate and that the average estimation error is only around 15%. [ABSTRACT FROM AUTHOR]

The longitudinal motion control of automotive vehicles is heavily reliant on information about the time-varying tire road friction coefficient. In the presence of varying road roughness profiles, the effective vertical load on each wheel varies dynamically, influencing the tire friction. In this paper, we integrated the vertical and longitudinal dynamics of a quarter wheel to form an integrated nonlinear model. In the modeled dynamics, the time-varying random road profile and the tire friction are treated as unknown inputs. To estimate these unknown inputs and states simultaneously, a combination of nonlinear Lipschitz observer and modified super-twisting algorithm (STA) observer is developed. Under Lipschitz conditions for the nonlinear functions, the convergence of the estimation error is established. Simulation results performed with the high-fidelity vehicle simulation software CarSim demonstrate the effectiveness of the proposed scheme in the estimation of states and unknown inputs. [ABSTRACT FROM PUBLISHER]