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This paper investigates the secrecy outage performance of transmit antenna selection (TAS)/maximal ratio combining (MRC) in multiple-input–multiple-output (MIMO) cognitive radio networks (CRNs) over Rayleigh fading channels. In the considered system, a secondary user (SU-TX) equipped with NA (NA\geq \text{1}) antennas uses TAS to transmit confidential messages to another secondary user (SU-RX), which is equipped with NB (NB\geq \text{1}) antennas and adopts the MRC scheme to process multiple received signals. Meanwhile, an eavesdropper equipped with NE (NE\geq \text{1}) antennas also adopts the MRC scheme to overhear the transmitted information between SU-TX and SU-RX. SU-TX adopts the underlay strategy to guarantee the quality of service (QoS) of the primary user (PU) without spectrum sensing. In this paper, we derive the exact and asymptotic closed-form expressions for the secrecy outage probability (SOP). Simulations are conducted to validate the accuracy of the analysis. [ABSTRACT FROM PUBLISHER]

This paper investigates the issues on optimal antenna deployment at a base station to maximize downlink ergodic sum-rate capacity. We analyze the ergodic sum-rate capacity of a multiuser multiple-input–multiple-output (MIMO) system using random matrix theory, and the analysis reveals that such a capacity depends largely on the deployed antenna structure. Moreover, we have found an optimal antenna configuration based on the proposed particle swarm optimization (PSO) algorithm to maximize the ergodic sum-rate capacity. The theoretical results on downlink ergodic sum-rate capacity match simulation results very well, validating the accuracy of the theoretical analysis. The simulation results show that the proposed optimal antenna configuration offers the largest downlink sum-rate capacity with the best bit error rate (BER) performance compared with uniform linear arrays (ULAs) and uniform circular arrays (UCAs). It is concluded that the optimal antenna coordinates can be predetermined before deploying the antennas in a given base station. [ABSTRACT FROM PUBLISHER]

In this paper, we address the problem of securing information transmission in very adverse environments, i.e., no eavesdropper channel state information (CSI) and imperfect legitimate channel CSI, by introducing a massive multiple-input–multiple-output (M-MIMO) relay. The relay is powered through wireless power transfer from the source, such that it can flexibly adjust its position according to the secrecy requirement. The benefits of a M-MIMO relay are fully exploited to enhance both power transfer efficiency and information transmission security. In particular, the challenging issues incurred by short interception distance and long transfer distance are well addressed simultaneously. The focus of this paper is on investigating the secrecy performance of two classical relaying protocols, namely amplify-and-forward (AF) and decode-and-forward (DF), and obtaining explicit expressions of secrecy outage capacity in terms of transmit power, channel condition, and antenna number. Furthermore, we propose two effective performance optimization schemes, i.e., power control at the source, and relaying protocol switch at the relay. Finally, our theoretical claims are validated by simulation results. [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]

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 study the distributed precoding problem for simultaneous wireless information and power transfer (SWIPT) in decode-and-forward (DF)-based multiple-input–multiple-output (MIMO) relay networks. The system model considered here consists of a source, a relay, an information decoding (ID) receiver, and an energy harvesting (EH) receiver, all mounted with multiple antennas. Since full channel state information (CSI) is usually unavailable, a practical scenario, where only local CSI is required, is considered in this paper. Such a practical scenario naturally constitutes a noncooperative game, where the source and the relay can be regarded as two rational game players. With properly designed utilities, it can be further shown that the existence and uniqueness of the pure-strategy Nash equilibrium (NE) of the proposed game can both be guaranteed under some mild conditions. Therefore, a distributed iterative precoding algorithm can be developed based on the best-response dynamic to obtain the unique NE solution for the proposed game. Moreover, a proximal-point-based regularization approach is also pursued to ensure the convergence of the proposed algorithm without requiring special restrictions on the channel ranks. Numerical simulations are also provided to demonstrate the proposed algorithm. Results show that our algorithm can converge quickly to a satisfactory solution with guaranteed convergence. [ABSTRACT FROM PUBLISHER]

Current and future mobile network access technologies increasingly rely on multiantenna transmission techniques and on cell miniaturization to achieve orders-of-magnitude gains in capacity. In this paper, we propose and evaluate the idea of multiuser multiple-input–multiple-input-based (MU-MIMO) cell reassignments. Dense small-cell deployments make it possible to pair user equipment units (UEs), which were originally assigned to different cells, enabling multiuser MIMO (MU-MIMO) and resulting in performance improvements to both UEs. We discuss how such pairings can be accomplished, and we assess the potential gains in the resulting spectral efficiency and MU-MIMO usage. These are assessed through both discussion of performance bounds and detailed system-level simulation in a realistic outdoor small-cell scenario. [ABSTRACT FROM PUBLISHER]

In this paper, we address the ordering of transmit antennas according to reliability for unequal error protection (UEP) in spatially multiplexed (SM) multiple-input multiple-output (MIMO) systems with joint detection at the receiver. When zero-forcing (ZF) detection is adopted, the reliabilities of transmit antennas are explicitly expressed as postequalization signal-to-noise ratios (SNRs). Thus, multiantenna UEP can be implemented by assigning data of high priority to transmit antennas with a high postequalization SNR. Unfortunately, the overall error performance is generally unsatisfactory. Various joint signal detection techniques that achieve maximum likelihood (ML) or near-ML performance have been developed as alternatives to ZF, but it is not obvious how to discriminate the reliabilities of the transmit antennas or the jointly detected symbols. Recently, an ordering technique for antenna reliabilities was proposed that exploits the structure of the previous near-optimal QR-decomposition-based least-reliable-layer joint detection. In this paper, we divide the log-likelihood ratio of each symbol into collaborative and individual components, assuming joint ML detection. We derive a statistical connection between the magnitude order of each component of the multiple symbols (or the corresponding transmit antennas) and the post-ZF-equalization SNRs. Based on the statistical connections, we propose the use of post-ZF-equalization SNR as a criterion for antenna reliability ordering of SM MIMO with various near-optimal, as well as optimal, joint detections. The differentiated error performance of transmit antennas is also shown to be mainly attributed to the difference of individual components, and the tendency becomes stronger as the constellation size increases. Simulations demonstrate that the proposed ordering technique better meets the UEP requirement with lower computational complexity than the conventional ordering. Assuming N_t \times N_t MIMO systems, the previous ordering and the proposed ordering require \mathcal{O}(N_t^3)+\sum_{N=2}^{N_t-1}\mathcal{O}(NN_t^2+N^3) and \mathcal{O}(N_t^3)$, respectively. [ABSTRACT FROM PUBLISHER]

In this paper, we propose a novel receiver scheme for multiple-input–multiple-output (MIMO) single-carrier frequency-division multiple access (SC-FDMA) systems. Various optimal and near-optimalMIMO detection techniques developed for flat-fading channels are not readily applied to MIMO SC-FDMA systems due to the large dimensions of the effective channels. Recently, the system equation of single-input–single-output (SISO) SC-FDMA systems was divided into a number of disjoint subsystems with moderate dimensions, and a previous MIMO detection technique was applied to the subsystems. In this paper, instead of naively extending the previous scheme for SISO SC-FDMA to MIMO SC-FDMA systems, we first express the large system equation of MIMO SC-FDMA, such that the effective channels are block circulant for an arbitrary number of transmit and receive antennas. The block circulant channel structure is then exploited to lower the computational complexity of projections to construct nonoverlapping subsystems. The proposed circulant projection also takes advantage of the sparse channel structure, offering higher postprojection signal-to-noise ratios (SNRs) of the resulting subsystems than those of a naive projection that does not exploit the channel structure. The simulations confirm the desirable performance of the proposed scheme when a relatively small number of subcarriers are used. The proposed technique is also compared with the previous iterative block decision feedback equalization (IB-DFE) and minimum-mean-square-error–prewhitened-maximum-likelihood (MMSE-prewhitened-ML) detection techniques. [ABSTRACT FROM PUBLISHER]

Hybrid precoding is used in millimeter wave (mmWave) massive multiple-input multiple-output (MIMO) systems instead of full digital precoding to reduce the cost and power consumption. Conventional hybrid precoding however requires complex channel estimation before beamforming. In this paper, we propose a low complexity low-cost channel estimation-free deep direct beamforming method composed of two basic steps-beam impulse and beamforming for time division duplex (TDD). This method is built upon randomized singular value decomposition (RSVD) and can directly complete the process of beamforming. Furthermore, a realistic dataset of mmWave channel-DeepMIMO is adopted to evaluate and determine the practicability of this method. The simulation results demonstrate that the proposed deep direct beamforming achieves high performance while its complexity and power consumption are very low. Compared with other methods without perfect channel status information (CSI), the proposed method costs less and requires lower computational resources while achieves higher spectral efficiency. Furthermore, the RSVD can be easily combined with conventional beamforming methods hence the proposed method is easily extendable to other applications. [ABSTRACT FROM AUTHOR]

In this paper, we study the effects of joint transmit and receive antennas’ selection on full-duplex (FD) multiple-input-multiple-output (MIMO) networks’ performance. The antennas’ selection problem is, in general, a combinatorial problem whose complexity grows exponentially with the number of antennas. To fully understand the effects of antennas’ selection, we study the sum rate maximization problem in a single-cell network with an FD-MIMO base-station (BS). First, we consider a system with a normal-scale MIMO full-duplex BS, i.e., a normal-scale FD MIMO system. The sum rate maximization problem is studied for two different scenarios; in the first scenario, we consider jointly optimizing the transmit and receive antennas’ selection with the precoder and the receiver weights. A Generalized Bender's Decomposition based algorithm is proposed to solve the mixed-integer nonlinear sum rate maximization problem. In the second scenario, we consider self-interference cancellation via zero-forcing (ZF) transmission. A heuristic algorithm is proposed to solve the sum rate maximization problem by optimizing the selection of the transmit antennas, receive antennas, and the receive antennas at which self-interference is nulled. Second, in a very-large scale, i.e., massive MIMO system, we derive lower bounds for the uplink and downlink rates with ZF receiver and precoder, respectively. The sum rate is maximized by jointly optimizing the transmit to receive antennas ratio and the ratio of the receive antennas at which self-interference is nulled. Finally, via numerical analysis, we evaluate the performance of the formulated sum rate maximization problems. [ABSTRACT FROM AUTHOR]

This paper proposes a novel cooperative amplify and forward (CAF) architecture for MIMO WLANs to improve their coverage and transmission rates, without requiring any modifications in the IEEE 802.11 standards at the clients. System models are used for analyzing the physical layer of CAF networks, and compared with the conventional amplify-and-forward (AF) and decode-and-forward (DF) networks. Numerical results demonstrate that the CAF network clearly outperforms conventional 802.11 networks, and AF and DF networks in terms of coverage and throughput. [ABSTRACT FROM AUTHOR]

In this paper, we propose a compressive random access (CRA) scheme using multiple resource blocks (RBs) to support massive connections for machine type communications. The proposed CRA scheme is scalable. As a result, if the number of devices increases, more RBs can be added to support them. Thanks to multiple RBs, we can employ fast retrial between RBs for re-transmissions of collided packets, which can result in short access delay. For stable CRA with fast retrial, we derive conditions (with a rate control scheme), and analyze the steady-state performance to find the throughput and delay. Through analysis and simulation results, we can see that the proposed scheme can perform better than conventional multichannel ALOHA and enjoy a tradeoff between the performance and complexity in terms of the number of RBs. [ABSTRACT FROM AUTHOR]

In this paper, a low complexity channel estimation method is proposed for two-dimensional (2-D) massive MIMO systems with uniform rectangular arrays of antennas. Instead of assuming independent fading between different transmit–receive antenna pairs, an array-structure-based physical channel model that characterizes the realistic scattering environments via the angles and gains associated with different propagation paths is studied. A 2-D Fourier-transform (FT) based on elevation and azimuth steering factors is applied to derive the spatial spectrum of received signals in angular domain at base station. Based on the spectrum analysis, the spatial spectrum of the received data is used to estimate the channel parameters, including direction of arrival (incident azimuth and elevation angles) and the path amplitude of each resolvable path. Since the channel coefficients are estimated in spatial domain and the proposed 2-D FT can be realized by fast-Fourier-transform, the computational complexity is reduced significantly. Simulation results are provided to verify the accuracy and the complexity of the proposed scheme. [ABSTRACT FROM AUTHOR]

Tire force distribution with the consideration of practical constraints is a key part of automatic driving. This paper is intended to improve the operational capability of the vehicle under low friction coefficient. The speed and front wheel angle are restricted within a specific range caused by the limitation of executable drive space, which is defined as an input constrained problem of the nonlinear system. The hierarchical design concept is adopted in this paper, including algorithms of both control and allocation to determine the driving performance cooperatively. The methodology of multi-input and multi-output sliding mode control (MIMO SMC) is applied in the high-level controller to guarantee the vehicle system stable. Confronted with circumstances that the vehicle may be seriously unstable due to longitudinal forces and lateral forces beyond the feasible region in the friction circle, the scheme of dynamic adjustment of control target is provided owing to there is no reasonable solution for tire force distribution in the low-level controller. The coefficients of reconstruction and attenuation are introduced to reconstruct the optimized controller and weaken inputs of the closed loop system, respectively. The simulation results verify that the proposed design can redistribute generalized forces and moments between the wheels especially for the poor road condition. [ABSTRACT FROM AUTHOR]

In this paper, we study the downlink resource assignment problem of maximizing the total data rate subject to minimum satisfaction guarantees in multiservice scenarios assuming the use of multiuser (MU) multiple-input–multiple-output (MIMO) techniques. With the use of MU MIMO techniques, the same frequency resource can be shared by different terminals at the same time by assigning different spatial channels to each terminal. Different from single-user (SU) access per frequency resource where the resource assignment problem consists of searching for the best association between frequency resources and terminals, with MU access per resource, we aim at finding which space-division multiple-access (SDMA) group should be associated with each frequency resource. The first contribution of this paper is the formal presentation of this generalized problem as an optimization problem. Then, after some mathematical manipulations, we managed to convert this problem from a nonlinear integer to an integer linear problem (ILP) that can be optimally solved by standard techniques instead of resorting to brute-force methods. Motivated by the high computational complexity of the optimal solution, we propose a low-complexity algorithm. By analyzing outage rate and total data rate performance metrics, we show that the proposed solution presents a good performance–complexity tradeoff compared with the method for obtaining the optimal solution. [ABSTRACT FROM PUBLISHER]

New emerging services, such as real-time video streaming or video on demand, are causing rapid growth in packet transmission over wireless networks. Unlike voice calls, for which the duration is usually not very long, video streaming applications require continuous transmission for a long time. Therefore, video streaming applications in mobile networks consume more energy compared with voice calls. Thus, the task of optimizing data transmission algorithms has become more important during the last few years. Apparently, the majority of multimedia traffic is video transmission. These applications consume much more power, compared with audio or general data transmission, because of higher throughput requirements. This paper addresses the problem of decreasing power consumption due to video transmission applications in Long-Term Evolution (LTE) networks. There are existing solutions for managing power consumption during video transmission. In particular, Third-Generation Partnership Project LTE Advanced (LTE-A) has defined the discontinuous reception/transmission (DRX/DRT) mechanism to allow devices to turn off their radio interfaces and go to sleep in various patterns. Some other similar solutions suggest DRX/DRT optimization to maximize the sleep periods of devices while guaranteeing quality of service in multimedia applications. However, existing solutions for packet transmission optimization are not very effective without physical-layer optimization. However, existing solutions for packet transmission optimization are not very effective without physical-layer optimization. We suggest a new method of modulation for improving energy efficiency of wireless video transmission. Four different schemes of quasi-quadrature modulation using multiple-input–multiple-output (MIMO) techniques with different quality of service performances are proposed in this paper. We simulate H.264/AVC video transmission. Results confirm the theoretical analysis. The proposed approach is able to improve energy efficiency while providing the same packet loss probability. [ABSTRACT FROM PUBLISHER]

Virtual multiple-input–multiple-output (MIMO) systems using multiple antennas at the transmitter and a single antenna at each of the receivers have recently emerged as an alternative to point-to-point MIMO systems. This paper investigates the relationship between energy efficiency (EE) and spectral efficiency (SE) for a virtual-MIMO system that has one destination and one relay using compress-and-forward (CF) cooperation. To capture the cost of cooperation, the power allocation (between the transmitter and the relay) and the bandwidth allocation (between the data and cooperation channels) are studied. This paper derives a tight upper bound for the overall system EE as a function of SE, which exhibits good accuracy for a wide range of SE values. The EE upper bound is used to formulate an EE optimization problem. Given a target SE, the optimal power and bandwidth allocation can be derived such that the overall EE is maximized. Results indicate that the EE performance of virtual-MIMO is sensitive to many factors, including resource-allocation schemes and channel characteristics. When an out-of-band cooperation channel is considered, the performance of virtual-MIMO is close to that of the MIMO case in terms of EE. Considering a shared-band cooperation channel, virtual-MIMO with optimal power and bandwidth allocation is more energy efficient than the noncooperation case under most SE values. [ABSTRACT FROM PUBLISHER]

Recently, ultra-reliable and low-latency communications (URLLC) using short-packets has been proposed to fulfill the stringent requirements regarding reliability and latency of emerging applications in 5G and beyond networks. In addition, multiple-input multiple-output non-orthogonal multiple access (MIMO NOMA) is a potential candidate to improve the spectral efficiency, reliability, latency, and connectivity of wireless systems. In this paper, we investigate short-packet communications (SPC) in a multiuser downlink MIMO NOMA system over Nakagami-m fading, and propose two antenna-user selection methods considering two clusters of users having different priority levels. In contrast to the widely-used long data-packet assumption, the SPC analysis requires the redesign of the communication protocols and novel performance metrics. Given this context, we analyze the SPC performance of MIMO NOMA systems using the average block error rate (BLER) and minimum blocklength, instead of the conventional metrics such as ergodic capacity and outage capacity. More specifically, to characterize the system performance regarding SPC, asymptotic (in the high signal-to-noise ratio regime) and approximate closed-form expressions of the average BLER at the users are derived. Based on the asymptotic behavior of the average BLER, an analysis of the diversity order, minimum blocklength, and optimal power allocation is carried out. The achieved results show that MIMO NOMA can serve multiple users simultaneously using a smaller blocklength compared with MIMO OMA, thus demonstrating the benefits of MIMO NOMA for SPC in minimizing the transmission latency. Furthermore, our results indicate that the proposed methods not only improve the BLER performance, but also guarantee full diversity gains for the respective users. [ABSTRACT FROM AUTHOR]

Intelligent reflecting surfaces (IRSs) can improve the wireless link between the transmitter and receiver by configuring the propagation environments of the system. In order to achieve intelligence of the configuration, the channel state information is required. In this paper, we study the downlink channel estimation problem in an IRS-aided frequency division duplexing (FDD) multiple-input multiple-output (MIMO) system. We propose a two-phase training scheme such that the direct channel (non-IRS channel) and the cascaded channel (IRS-aided channel) can be tracked by two Kalman filters separately. The pilots transmitted by the base station (BS) and the reflective coefficients at the IRS are then jointly optimized to improve the channel estimation accuracy. Since the formulated problem is non-convex, the optimal solution is not straightforward. A lower bound of the objective function is derived to serve as the baseline for evaluating the performance of a certain pilot and reflective coefficients design. In two special cases, namely the direct channel dominant scenario and the cascaded channel dominant scenario, we find that the optimal pilots and the optimal reflective coefficient matrix can be approximated by some columns of the discrete Fourier transform (DFT) matrices as the number of antennas at the BS and the number of reflective elements at the IRS get larger. We further propose a codebook-based low-complexity design for a general scenario which can significantly reduce the control overhead of the IRS and cut the computational burden. Numerical results are provided to verify the efficiency of the proposed channel estimation method. [ABSTRACT FROM AUTHOR]

In large scale multiple-input multiple-output (MIMO) systems (or massive MIMO systems), hybrid beamforming is a promising technique due to its versatile tradeoff between implementation cost (including hardware cost and power consumption) and system performance. In this paper, we investigate the downlink millimeter wave (mmWave) multi-user massive MIMO system and propose an interference cancellation (IC) framework on hybrid beamforming design. Based on the proposed framework, three successive interference cancellation (SIC) aided hybrid beamforming algorithms are proposed to deal with inter-user and intra-user interference. Specifically, for the first proposed algorithm, we use zero-forcing (ZF) to cancel inter-user interference and use SIC to cancel intra-user interference. For the second one, SIC is used to cancel inter-user interference and ZF is used to cancel intra-user interference. Both inter-user interference and intra-user interference are suppressed by SIC in the third algorithm. Furthermore, the optimal detection order of data streams is derived according to the post-detection signal-to-interference-plus-noise ratio (SINR). Numerical results show that the proposed SIC-aided hybrid beamforming algorithms outperforms the existing approaches in terms of spectral efficiency (SE) at the cost of computational complexity for the SIC procedure. Moreover, the results indicate that the proposed algorithms can achieve good SE performance with 2-bit finite resolution phase shifters and channel estimation error. [ABSTRACT FROM AUTHOR]

Massive MIMO is a promising air interface technology for 5G wireless communications. Such antenna systems offer capabilities to utilize channel correlations to create beams suitable for user specific transmissions. Most of the prior research is devoted to user-specific transmission in such systems, where a narrow beam is formed towards a user to improve its reception. Meanwhile, public-channel transmission to multiple users at once by means of broad beams has been understudied. In this paper, we introduce the concept of array-size invariant (ASI) beamforming, enabling generation of broad beams from very large antenna arrays where all elements are transmitting at maximum power. The ASI technique offers the possibility to achieve a perfectly flat array factor by exploiting the additional degree of freedom coming from the dual-polarization beamforming. The proposed technique is applicable to both uniform linear and rectangular arrays. The benefits of the method are shown by means of several numerical examples. In particular, the practical usefulness of the ASI beamforming, in terms of coverage and energy efficiency, is illustrated on an example of designing a cell-specific broad beam for public-channel transmission in a realistic network deployment. [ABSTRACT FROM AUTHOR]

Mobile edge computing (MEC) and millimeter wave (mmWave) communications are capable of significantly reducing the network's delay and enhancing its capacity. In this paper we investigate a mmWave and device-to-device (D2D) assisted MEC system, in which user A carries out some computational tasks and shares the results with user B with the aid of a base station (BS). We assume partial offloading model and the task can be partitioned into two portions: the first part is computed locally at user A, while the second part is transmitted to the BS and computed by the MEC server. The computational results are then sent to user B through a D2D link and via the link from the BS to user B, respectively. To support computation offloading, both the users and the BS are equipped with multiple antennas and employ analog and digital (A/D) hybrid beamforming. Moreover, we propose a novel two-timescale joint hybrid beamforming and task allocation algorithm to reduce the system latency whilst cut down the required signaling overhead. Specifically, the high-dimensional analog beamforming matrices are updated in a frame-based manner based on the channel state information (CSI) samples, where each frame consists of a number of time slots, while the low-dimensional digital beamforming matrices and the offloading ratio are optimized more frequently relied on the low-dimensional effective channel matrices in each time slot. A stochastic successive convex approximation (SSCA) based algorithm is developed to design the long-term analog beamforming matrices. As for the short-term variables, the digital beamforming matrices are optimized relying on the innovative penalty-concave convex procedure (penalty-CCCP) for handling the mmWave non-linear transmit power constraint, and the offloading ratio can be obtained via the derived closed-form solution. Simulation results verify the effectiveness of the proposed algorithm by comparing the benchmarks. [ABSTRACT FROM AUTHOR]

Multi-numerology transmission is a promising multiple access scheme for flexible communication systems, which can simultaneously support users with different service requirements. To this end, both inter- and intra-numerology interference (inter-NI and intra-NI) should be suppressed, which is not straightforward. This paper proposes a novel zero-forcing (ZF) precoding design, which perfectly eliminates all the interference, for massive multiple-input multiple-output (MIMO) systems with multi-numerology. First of all, we express the signal-to-interference-plus-noise ratio (SINR) of the multi-numerology system with spectrum sharing. From the SINR representation, we identify the channels that precoders must nullify to suppress inter-NI. After that, we propose a ZF precoder suppressing both inter-NI and intra-NI perfectly. The lower bound of ergodic achievable rate with perfect channel state information at the transmitter (CSIT) is analyzed. Based on the lower bound of ergodic achievable rate, we present the feasible rate region. Also, we propose two power allocation schemes: user fairness and the ergodic sum rate maximization. The simulation results demonstrate the advantage of the proposed precoding design and verify our analysis results. [ABSTRACT FROM AUTHOR]

Reconfigurable intelligent surface (RIS) is a passive antenna array composed of a large number of reflecting elements. In a RIS-assisted communication system, it is a challenging task to acquire accurate channel state information (CSI). In this paper, we propose a semi-blind channel estimation method for a RIS-assisted massive multiple-input single-output (MISO) system. Assuming a Gaussian priori on the data symbols, an iterative expectation maximization (EM)-based algorithm is developed to obtain the maximum likelihood (ML) estimate of the cascaded RIS channel. Different from existing pilot-based methods, the proposed semi-blind channel estimation method exploits the data symbols for channel estimation enhancement and only a fraction of full-pilot signaling overhead is required. Simulation results verify that the proposed method achieves significant improvement in the accuracy of the channel estimation while it noticeably reduces the training pilot overhead. [ABSTRACT FROM AUTHOR]

This paper proposes a selection relaying (SR) protocol for a cooperative multiple-input–multiple-output (MIMO) relay system that consists of a direct link between a source and a destination. The system has only receive-side channel state information (CSI) and spatially correlated MIMO channels, and the receiver nodes observe spatially correlated noise. The transmit nodes employ orthogonal space–time block codes (OSTBCs), whereas the receiver nodes employ optimum minimum mean square error (MMSE) detection. The SR protocol, which transmits via the relay only when the direct link between the source and destination is in outage, is compared with the fixed relaying (FR) protocol, which always uses the relay. By deriving novel asymptotic expressions of the outage probabilities, it is analytically shown that both protocols provide the same diversity gain. However, the coding gain (CG) of the SR protocol can be much better than that of the FR protocol. In particular, when all MIMO links have the same effective rank, irrespective of its value, the SR protocol provides better CG than the FR scheme if the target information rate is greater than \ln_2(\mbox3) bits per channel use. Simulation results support theoretical analysis and show that the SR scheme can significantly outperform the FR method, which may justify the increased complexity due to the one-bit feedback requirement in the SR protocol. [ABSTRACT FROM AUTHOR]

In this paper, the downlink of a clustered coordinated multicell multiuser multiple-input multiple-output (MIMO) system is considered, and various low-complexity user scheduling algorithms to maximize the sum rate are proposed. The users' data signals are transmitted cooperatively via the base stations (BSs) of a cluster within the sectorized multicell heterogeneous cellular network. Exhaustive search as the optimal scheduling method is extremely complex, particularly for large numbers of users requesting service. We therefore propose suboptimal scheduling algorithms based on the lower complexity metaheuristic stochastic optimization techniques of simulated annealing (SA) and particle swarm (PS). Moreover, a hybrid algorithm combining the traits of PS and a greedy scheduler is also proposed. The performance and complexity of these algorithms are evaluated for a heterogeneous system model employing different precoding methods. Assessing the algorithms' performance via computer simulations, the effectiveness of the proposed schemes in terms of sum rate is demonstrated, yielding performance very close to that of the exhaustive search at much lower complexity. For each precoding method used in the employed system model, the best corresponding approach to scheduling from our proposed algorithms is determined. [ABSTRACT FROM AUTHOR]

This paper investigates the joint design of fronthaul compression and precoding for the downlink of cloud radio access networks (C-RANs). In a C-RAN, a central unit (CU) controls a cluster of radio units (RUs) through low-latency fronthaul links. Most previous works on the design of fronthaul compression and precoding assume constant channels and instantaneous channel state information (CSI) at the CU. This paper, in contrast, concentrates on a more practical scenario with block-ergodic channels and considers either instantaneous or stochastic CSI at the CU. Moreover, the analysis encompasses two types of CU–RU functional splits at the physical layer, which we refer to as compression-after-precoding (CAP) and compression-before-precoding (CBP). With the CAP approach, which is the standard C-RAN solution, all baseband processing is done at the CU. With the CBP scheme, channel encoding and precoding are instead performed at the RUs: The CU does not perform precoding but rather forwards separately the information messages of a subset of mobile stations (MSs) along with the compressed precoding matrices to each RU. Optimization algorithms over fronthaul compression and precoding for both CAP and CBP are proposed, which are based on a stochastic successive upper bound minimization (SSUM) approach. Numerical results yield insights into the optimal RU–CU functional split for C-RANs. As a general conclusion, the relative advantages of the two functional splits depend on the interplay between the enhanced interference management abilities of CAP, particularly for dense networks, and the lower fronthaul requirements of CBP in terms of precoding information overhead, particularly for large coherence periods and with stochastic, rather than instantaneous, CSI. [ABSTRACT FROM AUTHOR]

In this paper, a spectrally efficient nonorthogonal time–frequency training sequence (TS)-based channel state information (CSI) acquisition approach is proposed for multiple-input–multiple-output orthogonal frequency-division multiplexing (MIMO-OFDM) systems under the framework of structured compressed sensing (SCS). The scheme first relies on a time-domain TS that is identical for all transmit antennas to acquire the partial channel common support by utilizing the spatial correlation property of the MIMO channels and then uses the frequency-domain TS for accurate CSI recovery based on the proposed adaptive spatially–temporally joint simultaneous orthogonal matching pursuit algorithm. Here, the obtained partial channel common support can be utilized to reduce the complexity of the classical SCS algorithm and improve the signal recovery probability. Simulation results show that the proposed scheme could significantly reduce the TS overhead and demonstrate better performance than the existing MIMO-OFDM systems, which might be suitable for massive MIMO systems in future fifth-generation communications. [ABSTRACT FROM AUTHOR]

This paper presents the detailed design and the key system performance results of a comprehensive laboratory demonstrator (testbed) for a hybrid satellite/terrestrial S-band mobile digital broadcasting system. The physical layer is based on an enhanced version of the digital video broadcasting—satellite to handheld (DVB-SH) standard, exploiting dual-polarization multiple-input–multiple-output (MIMO) technology. This complete digital MIMO demonstrator, which is the first of its kind, allows for an in-depth verification and optimization of the MIMO techniques applied to satellite broadcasting networks. Moreover, this demonstrator allows for complementing and confirming the theoretical or simulation-based findings published thus far. It is shown that dual-polarization MIMO diversity is able to provide remarkable gains in terms of satellite/terrestrial transmit power reduction and/or capacity increase compared with more conventional non-MIMO solutions. It is also demonstrated that the adoption of a relatively simple spatial multiplexing MIMO technique represents the best way to grasp these gains. This paper provides an extensive set of laboratory measurement results for existing stochastic satellite and hybrid MIMO channels, as well as results based on an S-band satellite-measured dual-polarization time series recently collected during a campaign sponsored by the European Space Agency. Results obtained using MIMO techniques are also compared with a dual- and single-polarization single-input–single-output (SISO) DVB-SH benchmark system and with computer simulation results. [ABSTRACT FROM AUTHOR]

In practical applications, an imperfect channel estimate over a noisy channel is inevitable. This paper investigates an interference-aware receiver in which colored-estimation-noise-based maximum-likelihood (CEN-ML) detection is employed to mitigate the impact of imperfect channel estimates. It is shown that the CEN-ML detection, which treats the channel estimation errors as colored noise, has superior performance and is robust to imperfect channel estimation. However, a CEN-ML interference-aware detector usually requires computational efforts that are too complex for multiple-input–multiple-output (MIMO) interference-limited systems. To reduce the complexity, this paper proposes new methods for both the preprocessing and tree-search stages of an interference-aware sphere detector (SD). First, a channel extended matrix is constructed using the information from channel estimation errors in a preprocessing stage. The channel extension model can reduce the complexity without performance loss. Second, two tree pruning (TP) criteria are developed in a tree-search stage to make the implementation of the CEN-ML SD feasible. The first criterion is based on a lower bound of the CEN-ML metric and can achieve the same result as the optimal brute-force method. The second criterion is derived from an approximated metric and can further reduce the complexity with small performance loss. Simulation results for typical Third-Generation Partnership Project Long-Term Evolution (LTE)/LTE-Advanced scenarios show that the new methods provide the ability to deal with the channel estimation errors and to achieve a better tradeoff between complexity and performance (e.g., in terms of throughput or error rate) in interference-limited scenarios. The proposed low-complexity CEN-ML SD can have a gain of more than 10 dB than the other investigated detection for 16-ary quadrature amplitude modulation (QAM) or 64-QAM at a target coded block error rate (BLER) of \mbox10^-2. [ABSTRACT FROM AUTHOR]

Multicell coordinated beamforming (CB) can mitigate intercell interference (ICI). However, existing work on CB focuses on systems that maximize overall throughput. This paper considers CB for systems with multiple receive antennas and further extends the CB to multiuser and multistream transmission. We consider fairness and pay close attention to cell-edge users. CB that maximizes the harmonic sum of signal-to-interference-plus-noise ratio (SINR) can be derived into a low-complexity algorithm that favors cell-edge users. Two iterative algorithms are developed. From the simulation results, the proposed algorithms can provide a better fifth-percentile user throughput compared with the CB algorithms minimizing mean square error, maximizing uplink SINR, and maximizing weighted sum rate. Furthermore, the computational complexity of the proposed algorithms is significantly lower than that maximizing the minimum SINR of users. [ABSTRACT FROM AUTHOR]

This paper discusses over-the-air (OTA) testing for multiple-input–multiple-output (MIMO) capable terminals with emphasis on wideband MIMO channel capacity analysis in a multiprobe anechoic chamber setup. In the literature, the spatial correlation simulation accuracy at the receiver (Rx) side has been used to determine the test area size for a limited number of probes. However, it is desirable that the test area size is defined in terms of data rate deviation of the simulated channel in the laboratory from that of the target channel model. This paper reports MIMO capacity analysis results for wideband spatiotemporal channel models, with emphasis on the impact of the spatial correlation at the transmit (Tx) side, the channel model, and the spatial correlation at the Rx side on the capacity simulation accuracy. Simulation results show that the number of probes is irrelevant to capacity simulation accuracy when the spatial correlation at the Tx side is in the high region (e.g., \rho > \mbox0.7). Furthermore, when correlation at the Tx side is low, the spatial correlation accuracy is less critical with small correlation at the Rx side. The simulation results are further supported by measurements in a practical multiprobe anechoic chamber setup. The capacity simulation accuracy is shown to be a valid measure to determine the test area size. [ABSTRACT FROM AUTHOR]

In this paper, we study user selection (US) strategies for a multiple-input–multiple-output (MIMO) cognitive radio (CR) downlink network, where the r-antenna underlay CR secondary users (SUs) coexist with a primary user (PU), and all terminals are equipped with multiple antennas. Two main scenarios are considered: 1) The t-antenna cognitive base station (CBS) has perfect or partial channel state information at the transmitter (CSIT) from the CBS to the PU receiver (RX), and 2) the CBS has absolutely no PU CSIT. For these scenarios, we propose and evaluate multiple SU selection schemes that are applicable to both best-effort PU interference mitigation and hard interference temperature (IT) constraints. The computational complexity of the proposed schemes can be significantly smaller than that of an exhaustive search with negligible performance degradation. For the selection of C SUs out of K, whereas an exhaustive search is on the order of \mathcal{O}(\binom{K}{C}C^4r^3). When t$ and r$ are of the same order, the computational complexity of the proposed scheme can be \binom{K}{C}C^4/K times smaller. Mathematical complexity analysis and numerical simulations are provided to show the advantage of our schemes. [ABSTRACT FROM PUBLISHER]

In this paper, we analyze the ergodic achievable rate of a large uplink multiuser multiple-input–multiple-output (MU-MIMO) system over generalized- $K$ fading channels. In the considered scenario, multiple users transmit their information to a base station equipped with a very large number of antennas. Since the effect of fast fading asymptotically disappears in massive MIMO systems, large-scale fading becomes the most dominant factor for the ergodic achievable rate of massive MIMO systems. Regarding this fact, in our work, we concentrate our attention on the effects of large-scale fading for massive MIMO systems. Specifically, some interesting and novel asymptotic expressions of ergodic achievable rate have been derived with both perfect channel state information (CSI) and imperfect CSI. Simulation results assess the accuracy of these analytical expressions. [ABSTRACT FROM PUBLISHER]

This paper presents a coherent scheme to optimize an in-band full-duplex multiple-input–multiple-output (MIMO) relay via beamforming and transmit power allocation in a two-hop single-input–single-output (SISO) link under full channel knowledge and perfect hardware assumptions. First, we derive in closed form the optimal pair of transmit power and receive filter for a fixed transmit filter by unifying the minimum-mean-square-error (MMSE) filtering with the SISO-equivalent power allocation, as an iterative approach is not guaranteed to converge to global optimum. Second, we propose a heuristic algorithm to approximate the optimal transmit filter for a fixed receive filter. Furthermore, we study the well-known null-space projection constraint and derive a singular value decomposition (SVD)-based solution for the arbitrary-rank self-interference channel by generalizing the optimal solution under the assumption of rank-1 self-interference channel. Finally, we combine these solutions into a partially iterative algorithm in order to address the global optimization as our observations justify that some of the aforementioned schemes converge to the optimal solution under certain criteria. The numerical analysis of the proposed iterative algorithm demonstrates close-to-optimal performance relative to the theoretical upper bound of the end-to-end link in terms of maximum achievable throughput. [ABSTRACT FROM PUBLISHER]

This paper considers limited feedback for a two-cell interfering multiple-input–multiple-output multiple-access channel (MIMO-MAC). We propose a QR decomposition-based interference alignment (QRD-IA) scheme for efficient limited feedback and investigate the conditions for the antenna configuration. We characterize the interference leakage caused by quantization error as a function of the number of feedback bits. To minimize the interference leakage, we further propose a quantized-precoder-based IA (QP-IA) scheme by modifying the proposed QRD-IA scheme. By characterizing the interference leakage of the QP-IA scheme, we investigate the number of feedback bits to maintain the degrees of freedom (DOFs) in the two-cell interfering MIMO-MAC and a feedback bit allocation algorithm to improve the sum rate. From simulation results, we demonstrate that our proposed schemes can provide better sum-rate performance than the existing scheme using the same number of feedback bits, and our feedback bit allocation algorithm provides a higher sum rate compared with equal feedback bit allocation. [ABSTRACT FROM PUBLISHER]

Multiuser multiple-input–multiple-output (MUMIMO) techniques, such as scheduling and precoding, have shown to improve the spectral efficiency of wireless communication systems. However, these techniques require an accurate knowledge of the channel of the different users at the transmitter. In frequency-division duplex (FDD) systems, this information has to be provided by the different users, motivating the research of efficient limited feedback schemes. This paper presents a novel statistical characterization of the spatial multiple-input–single-output (MISO) channel. In this characterization, one antenna is selected as the reference, and the channel fading experienced from this antenna is also considered to be the reference. The conditional probability density functions (CPDFs) of the envelope and phase of the channel fading coefficients from the rest of the antennas (denoted as nonreference channel fading and nonreference antennas) are obtained given the reference one. Based on this statistical characterization, this paper proposes a channel quantization scheme that individually quantizes the channel fading coefficient of each transmit antenna that is seen by each user. The envelope and phase of the reference channel fading are quantized considering a Rayleigh distribution and a uniform distribution, respectively. The nonreference channel fading coefficients are quantized according to their respective CPDFs, which in turn depend on the spatial correlation between each channel fading and the reference channel fading. Numerical simulations have been carried out to compare the performance of the proposed conditional quantization (CQ) scheme with a polar quantization (PQ) and with a quantization based on the Karhunen–Loève (KL) transform. PQ does not consider spatial correlation, CQ needs one spatial correlation coefficient per nonreference antenna, and the KL scheme makes use of the full spatial correlation matrix. The results show that CQ achieves a lower quantization mean square error (MSE) than the other two schemes in highly and moderately correlated environments. When the spatial channel model (SCM) is considered, the proposed scheme allows the spatial correlation to be successfully exploited in arrays with N=\4 and N=\8 transmit antennas for antenna separations that are lower than d=\1.3\lambda and d=\0.75\lambda, respectively. [ABSTRACT FROM PUBLISHER]

Channel estimation is crucial to beamforming techniques in directional millimetre wave (mmWave) communications, which is generally designed based on channel state information static. However, due to the Doppler effect caused by the mobility of users, such as unmanned aerial vehicles, high-speed trains and autonomous vehicles, the mmWave channel is changing rapidly. Spatial channel covariance, defined by long-term statistic information of channels, is a promising solution to reduce channel estimation frequency and can be used to design hybrid precoders. In this paper, we first proposed a highly mobile hybrid mmWave multi-user (MU) multiple input multiple output (MIMO) system based on transition probabilities which can represent moving action of the MU. Secondly, we investigate compressive sensing based spatial channel covariance estimation based on the proposed dynamic system. We then propose a dynamic covariance forward-backward pursuit (DCFBP) algorithm which introduces forward and backward mechanisms to reconstruct the Hermitian sparse covariance matrix. We further explore the constructed MU sensing matrix quality for conventional sparse Bayesian learning (SBL) framework. The updated sparse Bayesian learning (Updated-SBL) algorithm is developed to reduce the total squared coherence of a constructed sensing matrix with updated receive precoder. Numerical analysis demonstrates the proposed DCFBP method outperforms the benchmark methods. The total squared coherence of the proposed Updated-SBL algorithm is dramatically reduced. and the superiority of this algorithm is validated compared with other benchmark methods with comparable computation complexity. [ABSTRACT FROM AUTHOR]

With the inherent benefits, such as, better cell coverage and higher area throughput, extra-large scale massive multiple-input multiple-output (MIMO) has great potential to be one of the key technologies for the next generation wireless communication systems. However, in practice, when the antenna dimensions grow large, spatial non-stationarities occur and users will only see a portion of the base station antenna array, which we call visibility region (VR). To assess the impact of spatial non-stationarities, in this paper, we investigate the uplink transmission of extra-large scale massive MIMO systems by considering VRs. In particular, we first propose a subarray-based system architecture for extra-large scale massive MIMO systems. Then, tight closed-form uplink spectral efficiency (SE) approximations with linear receivers are derived. With the objective of maximizing the achievable SE, we also propose schemes for the subarray phase coefficient design. In addition, based on the obtained ergodic achievable SE approximations, two statistical channel state information (CSI)-based greedy user scheduling algorithms are developed. Our results indicate that the statistical CSI-based greedy joint user and subarray scheduling algorithm collaborating with the on-off switch-based subarray architecture is a promising practical solution for extra-large scale massive MIMO systems. [ABSTRACT FROM AUTHOR]

As an emerging technology in 5G, ultra-dense unmanned aerial vehicles (UAVs) network can significantly improve the system capacity and networks coverage. However, it is still a challenge to reduce interference and improve energy efficiency (EE) of UAVs. In this paper, we investigate a downlink power control problem to maximize the EE in an ultra-dense UAV network. Firstly, the power control problem is formulated as a discrete mean field game (MFG) to imitate the interactions among a large number of UAVs, and then the MFG framework is transformed into a Markov decision process (MDP) to obtain the equilibrium solution of the MFG due to the dense deployment of UAVs. Specifically, a deep reinforcement learning-based MFG (DRL-MFG) algorithm is proposed to suppress the interference and maximize the EE by using deep neural networks (DNN) to explore the optimal power strategy for UAVs. The numerical results show that the UAVs can effectively interact with the environment to obtain the optimal power control strategy. Compared with the benchmarks algorithms, the DRL-MFG algorithm converges faster to the solution of MFG and improves the EE of UAVs. Moreover, the impact of the transmit power on EE under the different heights of the UAVs is also analyzed. [ABSTRACT FROM AUTHOR]

In this paper, we show how the absorption and re-radiation energy from molecules in the air can influence the Multiple Input Multiple Output (MIMO) performance in high-frequency bands, e.g., millimeter wave (mmWave) and terahertz. In more detail, some common atmosphere molecules, such as oxygen and water, can absorb and re-radiate energy in their natural resonance frequencies, such as 60 GHz, 180 GHz and 320 GHz. Hence, when hit by electromagnetic waves, molecules will get excited and absorb energy, which leads to an extra path loss and is known as molecular attenuation. Meanwhile, the absorbed energy will be re-radiated towards a random direction with a random phase. These re-radiated waves also interfere with the signal transmission. Although, the molecular re-radiation was mostly considered as noise in literature, recent works show that it is correlated to the main signal and can be viewed as a composition of multiple delayed or scattered signals. Such a phenomenon can provide non-line-of-sight (NLoS) paths in an environment that lacks scatterers, which increases spatial multiplexing and thus greatly enhances the performance of MIMO systems. Therefore in this paper, we explore the scattering model and noise models of molecular re-radiation to characterize the channel transfer function of the NLoS channels created by atmosphere molecules. Our simulation results show that the re-radiation can increase MIMO capacity up to 3 folds in mmWave and 6 folds in terahertz for a set of realistic transmit power, distance, and antenna numbers. We also show that in the high SNR, the re-radiation makes the open-loop precoding viable, which is an alternative to beamforming to avoid beam alignment sensitivity in high mobility applications. [ABSTRACT FROM AUTHOR]

This paper considers short-term power constrained cell-free massive multiple-input multiple-output (MIMO) scenarios where a large set of multi-antenna access points (APs) provide service to a group of single-antenna mobile stations (MSs) on a spatially correlated multipath environment. Based on a probabilistic approach, the spatially correlated propagation links are modeled using either Ricean or Rayleigh fading channel models that combine a deterministic line-of-sight (LOS) propagation path with a small-scale fading caused by non-line-of-sight (NLOS) multipath propagation. Assuming the use of minimum mean square error (MMSE) channel estimates, closed-form expressions for the downlink (DL) achievable spectral efficiency of a cellfree massive MIMO network with short-term power constraints (i.e., a vector normalized conjugate beamformer (NCB)) are derived and benchmarked against that provided by the conventional cell-free massive MIMO network with long-term power constraints (i.e., the conventional conjugate beamforming (CB)). These expressions, encompassing the effects of spatial antenna correlation, Ricean/Rayleigh fading and pilot contamination, are then used to derive both pragmatic and optimal max-min peruser power allocation strategies and to gain theoretical insight on the performance advantage provided by the use of short-term power constraints instead of the conventional long-term power constrained approach. [ABSTRACT FROM AUTHOR]

The performance of the hybrid beamforming system based on the full-connected structure is close to that of the full-digital one in millimeter-wave (mmWave) massive multiple-input multiple-output (MIMO) systems. However, due to the high complexity of circuit wiring in practical applications, it has been greatly restricted. This paper investigates the hybrid (analog/digital) beamforming design of a multiuser mmWave massive MIMO system with sub-connected structure. For this system, we focus on a sum-rate maximization probelm. Considering the joint design of the receiver, and transmitter, a two-stage design method is adopted. In the analog stage, the analog beamformer, and combiner are designed by the proposed piecewise dual joint iterative approximation (PDJIA) method, which not only can obtain closed-form solutions but also has linear property. Based on the criterion of avoiding the loss of information, the problem of digital beamforming is solved by exploiting the baseband piecewise successive approximation (BPSA) method, which can effectively increase the number of users served. Simulation results show that the proposed scheme outperforms the state-of-the art MIMO hybrid beamforming design schemes, and more accurately approximates to that of the full-digital system. [ABSTRACT FROM AUTHOR]

In the era of the fifth generation (5G) communication networks, massive multiple-input multiple-output (MIMO) systems demand even lower computation complexity and power consumption while catching up with good detection performance. In this paper, a low-complexity nonlinear detection algorithm is proposed for massive MIMO systems, which is based on partial tree search and successive interference cancellation (SIC). The proposed scheme allows us to expedite the detection process by coping with the transmit symbols group by group. As compared to vertical-Bell laboratories layered space time (V-BLAST), the major breakthrough of computation reduction lies in the fact that the partial tree search can assist the detection process to avoid the inversion of the detection matrix required in each recursion of the SIC process. Both computational complexity analysis and simulation results show that our proposed algorithm not only significantly reduces computational complexity, but also has better bit error rate (BER) performance. [ABSTRACT FROM AUTHOR]

To be differentiated from the literature, in this paper, we aim to alleviate the impact of pilot contamination (PC) on the massive MIMO systems by cooperatively scheduling users (rather than just assigning) among neighboring cells to share the limited orthogonal pilots. To this end, we develop the multi-cell cooperative scheduling (MCCS) algorithm together with the cooperative scheduling indexes (COSIs) for maximizing the data rate, maximizing the Jain's fairness index and reaching a better tradeoff in-between. For convenience, they are denoted by the COSIs for the CMDR, CMMF and CPF schedulers, respectively. However, its high computational complexity may somehow resists its from practical applications. Thus, a low-complexity cooperative proportional fairness (LC-CPF) algorithm is designed to well approach the MCCS algorithm using the CPF COSI; and at the meantime, the order of computational complexity can be significantly reduced. Moreover, its ability to reach a better tradeoff can still be maintained under the impact of high spectrum-sharing interference. In addition, it is interesting to find that using the proposed cooperative scheduling methods, the open-loop power control mechanism is no longer required for compensating the differences of received signal quality between users. [ABSTRACT FROM AUTHOR]

In recent years, intelligent transportation systems are a vital part of the development of smart cities. In intelligent transportation systems, it is necessary for each vehicle to know the states of all other running vehicles within a range, depends on the road speed limit. The states include location, travelling direction, speed, and possibly other useful information. Knowing the states of surrounding vehicles is critical in high-risk locations, such as traffic intersections and roundabouts. The development of 5G and its technologies, such as massive multiple-input multiple-output antenna, enables a new communication model, termed vehicle-to-infrastructure edge network (V2IEN), to achieve the above communication goal. In this paper, we study the optimization of the data throughput of the proposed V2IENs in 5G with the Time Division Duplex scheme. We show that due to the nature of information flow in the proposed V2IENs, the uplink and downlink traffic loads are asymmetric. This asymmetric characteristic allows maximizing the degrees of freedom of the V2IENs by optimizing the time-slot resources for uplink and downlink transmission. In this work, we present the sum-DoF capacity of the V2IENs with proof. We demonstrate that a significant DoF gain is achieved for the V2IENs by carefully allocate the system transmission time resources. We further propose an iterative algorithm for network sum-rate maximization via the optimization of the vehicle and base station precoders. Numerical results demonstrate that a careful design of the precoders at the base station and at the vehicles can considerably improve the V2IENs performance. [ABSTRACT FROM AUTHOR]

In order to improve on existing radio communications for railways, cognitive radio (CR) is a promising concept, allowing the combination of artificial intelligence and software-defined radios. To ensure the best environmental awareness, as required by CR receivers, automatic modulation identification is a key feature for promoting efficient and secure communications in the CR context. This paper deals with blind modulation identification in the railway transmission environment and specifically considers its two major constraints: the high-speed and the impulsive nature of the noise. To achieve this goal, we propose a feature-based process of blind identification, which is made up of three subsystems: impulsive noise mitigation (which is the main contribution of this paper), feature extraction, and classification. For the purpose of mitigating the impulsive noise, we introduce a blind filtering myriad-based approach, which, in turn, requires the estimation of the noise parameters. Simulation results prove that the developed filtering approach provides a good filtering performance, and consequently, high identification performances of the overall identification system. [ABSTRACT FROM AUTHOR]

Nonorthogonal multiple access (NOMA) has been considered as a promising technology for fifth-generation wireless communication systems. In this paper, we consider a two-cell multiple-input-multiple-output NOMA downlink interference channel with one multiantenna base station (BS) and multiple multiantenna users in each cell. Each user treats interferences from adjacent cell as additional noises and does not attempt to decode them. This paper aims to design interference channel beamformers to maximize weighted sum capacity of the strongest users in two cells under quality of service requirements at other users and transmit power budget constraint at each BS. Such problem is complicated and nonconvex due to the expression of a logarithm of a determinant $(\log \det)$ minus $\log \det$ in the objective function and constraints. For the nonconvex problem, we put forward a successive convex approximation (SCA) based iterative algorithm. Since the computational complexity of SCA-based iterative algorithm is high, we also put forward an efficient majorization-minimization based iterative algorithm. Based on simulation results, we show that our developed NOMA schemes are superior to the zero-forcing-based NOMA, orthogonal-NOMA, and orthogonal multiple access schemes. [ABSTRACT FROM AUTHOR]

A new approach of channel estimation for multiantenna systems is put forward in this paper, which can be adopted in high-mobility situations such as high speed trains. The channel impulse response is abstracted as three domains to improve the modeling accuracy. Both the time-domain preamble and the frequency-domain pilot are adopted in the orthogonal frequency division multiplexing (OFDM) frame. First, the training in time domain is exploited to obtain the partial common support of the channel. Then, the pilot location optimized by the genetic algorithm is employed to build the framework of structured compressive sensing and recover the channel. A novel compressive recovery algorithm called adaptive support-aware block orthogonal matching pursuit for multiple input multiple output-OFDM systems is proposed to solve the problem. It is manifested in the simulation that the scheme in this paper outperforms the traditional ones in both recovery probability, mean square error, and bit error rate over the doubly selective channel with low computational complexity. [ABSTRACT FROM AUTHOR]