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This paper studies the electronically steerable parasitic array radiator (ESPAR) antenna as a solution for beam pattern switching to implement blind interference alignment (BIA). The ESPAR antenna uses a single radio-frequency (RF) chain, and its beamforming is achieved by simply tuning the reactance loads of parasitic elements. Furthermore, ESPAR's beam pattern can be designed to enhance the received signal-to-noise ratio (SNR), thereby improving the performance of BIA. The focus of this work is the design of the ESPAR beam pattern to realize and improve BIA. In the first approach, each receiver ESPAR antenna dynamically selects the proper beam patterns from among the sector beam patterns, which are pre-designed to access different angular sectors with constant optimal antenna efficiency. We also propose a singular value decomposition (SVD) beamforming method, by means of the available channel state information at the receiver and a dynamic matching network for a high antenna efficiency. The simulation results illustrate the improvement of BIA with proposed methods, especially in the low and moderate SNR region, due to ESPAR's beamforming capability. [ABSTRACT FROM PUBLISHER]

As the number of antennas increases, the massive multiple-input multiple-output (MIMO) system can precisely point to user equipments (UEs) with narrow beams. Accurate and timely channel state information (CSI) feedback is crucial to keep UEs in service. Mobile UEs, however, may suffer from the narrow beam nature of the massive MIMO system since UEs can move out of the beam coverage. When intelligent reflecting surface (IRS) is applied to the massive MIMO system, the adjustment of the IRS cannot be frequent as the IRS is controlled remotely by the base station (BS). Limiting the number of CSI feedback and the number of both BS and IRS adjustments significantly reduces the overhead of transmission and computation to the system. In this paper, we consider the UEs’ mobility adaptation problem in an IRS assisted multiuser massive MIMO downlink system with infrequent CSI feedback. We propose a beam control method that adapts to UEs’ mobility. The problem is constructed as a sum rate problem where both the BS and IRS are taken into account to jointly optimize the beamforming matrices. Simulation results show that our proposed algorithm can converge quickly and provide satisfactory performance for mobile UEs. At the same time, our proposed algorithm reduces the frequency of updating the beamforming matrices effectively both at the BS and at the IRS. [ABSTRACT FROM AUTHOR]

This paper investigates a heterogeneous cellular network (HCN) topology, where multiple sub-6 GHz bands are available for access and device-to-device (D2D) links with the full-duplex (FD) mode, and an integrated millimeter-wave (mm-wave) band is added to the D2D links’ sub-channel resource options. D2D links share spectrum resources of cellular users or dedicated mm-wave sub-channel. Densely deployed small cells scenario provides an ideal platform to implement mm-wave, FD and D2D communications. With large bandwidths available, mm-wave has the potential to meet the expected extreme data rate demands. Leveraging highly directional beamforming, severe transmission loss and the blockage phenomenon of mm-wave can be alleviated. Theoretically, FD communications can double the spectral efficiency due to bi-directional communications with the same spectral and temporal resources. However, such benefit comes at the cost of residual self-interference (RSI). Multi-user interference (MUI) and RSI among network uplink, downlink and D2D links are the main challenges. In the paper, we formulate an optimization problem for FD access and D2D links sub-channel allocation underlaying HCNs combining sub-6 GHz and mm-wave bands to make the system transmission rate maximized. Then, a scheme based on the coalition formation game is proposed to deal with this challenging NP-complete optimization. Finally, performance evaluation investigates the suitable conditions for FD and HD operations. Under different network parameter settings, we quantify associated performance of proposed scheme. [ABSTRACT FROM AUTHOR]

This paper studies a cognitive radio network (CRN), in which a multiple-input single-output (MISO) secondary transmitter (SU-Tx) aims to send confidential messages to its receiver (SU-Rx) in the presence of unknown eavesdroppers, while having to control its generated interference to the primary users (PUs) under a given threshold. Because the eavesdroppers are unknown, this paper considers the artificial noise (AN) approach or masked beamforming to provide the information secrecy. The objective of this paper is to maximize the power of AN in order to degrade the eavesdroppers, subject to the signal-to-interference-plus-noise ratio (SINR) constraint at the SU-Rx, as well as the interference temperature limits (ITLs) for the PUs. In the case of perfect channel state information (CSI), we reveal that the optimal strategy for the information-bearing signal is beamforming. Imperfect CSI cases of bounded and stochastic uncertainties are investigated. In the case of ellipsoid-bounded errors, we derive the equivalent forms for the SINR and ITL constraints and then transform the optimization problem into a form of semidefinite programming (SDP). For the case of probabilistic CSI uncertainties, we propose an outage-constrained robust formulation where the CSI errors are Gaussian distributed. With the aid of two kinds of Bernstein-type inequalities, we reexpress the probabilistic constraints into deterministic forms, which results in a safe approximate solution. [ABSTRACT FROM PUBLISHER]

This paper investigates distributed beamforming networks composed of multiple groups of transmitters where each group consists of two transmitters. The goal is to jointly optimize the phase components at the transmitters and the orientation angles of the groups that maximize the radiated energy over an angular sector containing an adversary while placing a null towards the client in order not to obfuscate the client. Since the optimization problem is non-convex, it is intractable to obtain the optimal solution in closed-form. To this end, we propose a non-iterative closed-form solution by adopting a group-wise nullforming approach where each group independently creates a null to the client. The effectiveness of the proposed scheme is validated through numerical simulations. [ABSTRACT FROM AUTHOR]

In this paper, we investigate hybrid beamforming, user scheduling, and resource allocation optimization based on spectrum coexisting forward transmission in integrated terrestrial-satellite network (ITSN) with the purpose of improving system sum rate and energy efficiency. Considering the limitation of on-board beamforming, a hybrid analog-digital beamforming scheme is designed under the scenario of millimeter wave (mmWave) coexisting in the ITSN framework. Besides, in order to further mitigate intra-beam and inter-beam interference, we propose an adaptive user scheduling scheme, which first determines the cluster center based on adaptive threshold, and then selects users with less channel correlation into a scheduling cluster. Moreover, we model system sum rate maximization problem that incorporates maximum power constrains and minimum data rate requirements. Combined with the aforementioned hybrid beamforming and user scheduling strategy, we formulate the sum rate maximizing problem to a pure power allocation issue. In view of the non-convexity and high complexity, we propose a feasible optimization method based on the minimum mean square error (MMSE) criterion and logarithmic linearization to optimize the power allocation for each user terminal (UT). Simulation results show that our proposed joint beamforming and resource allocation optimization scheduling scheme can achieve an attractive gain in system sum rate and energy efficiency compared with conservative beamforming and allocations. [ABSTRACT FROM AUTHOR]

In this paper, we study the joint cooperative beamforming and user association scheme for multi-cell multi-user full-dimension massive multiple-input multiple-out (MIMO) networks, where each user equipment is dynamically associated with one of the base stations. We propose to maximize the network capacity by jointly optimizing the beamforming vectors and a binary vector that represents the user association decisions. The binary vectors are further transformed into binary beam association factors (BAFs) as indicators of both the beamforming and the user association decisions. A three-step Gaussian belief propagation (GaBP) based distributed solver is proposed to solve the non-convex NP-hard problem considered in this paper. By relaxing the binary BAFs, we first transform the optimization problem into a linear programming. Subsequently, GaBP is used to efficiently obtain a feasible solution on BAFs in parallel, and two mapping algorithms are proposed to achieve different performance-complexity tradeoffs. Simulation results show that the proposed cooperative beamforming methods significantly outperform the benchmarks in the literature in terms of the network capacity with a low computation complexity. [ABSTRACT FROM AUTHOR]

In this paper, we consider nonregenerative multipair two-way relaying systems, where a relay node supports $K$ pairs of two-way communications in the presence of imperfect channel state information (CSI). We employ a stochastic approach to model the channel uncertainties and study a robust beamforming design at the relay. It is shown that the proposed beamformer provides a reduction in the computational complexity, as well as good robustness against channel errors, compared with conventional designs. To reduce the estimation overhead at the users, we also suggest an efficient downlink training method to inform the users of the channel-dependent self-interference cancellation (SIC) parameters. With the training method, the additive noise may incur a malfunction of the SIC. To address this issue, we develop an intelligent selection criterion that decides whether the SIC should be adopted or not. Finally, from simulation results, we demonstrate the efficiency of our proposed schemes. [ABSTRACT FROM PUBLISHER]

We study multiuser detection for uplink transmissions using only analog beamformers in a millimeter-wave (mm-wave) communication system under a limited scattering environment in this paper. In particular, the successive interference cancelation (SIC)-based detection at a base station (BS) that is equipped with an antenna array is considered with the outputs of analog beamformers. We employ beam selection from a codebook of analog beams for low-complexity analog beamforming in the SIC-based detection and compressive sensing (CS)-based channel estimation that is suitable for the BS that only has analog beamformers. Through simulation results, we can see that the performance of the joint analog beam selection and SIC-based detection is comparable with the performance of the SIC-based detection (with ideal digital beamforming) at the cost of more antenna elements with lower complexity. [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 studies user-specific clustering for a downlink cloud radio access network (C-RAN), where a central unit (CU), connected to all base stations (BSs) via limited-capacity backhaul links, coordinates the BSs to form cooperative clusters for every user. By taking into account the training overhead for channel estimation in C-RAN, we design the clustering scheme aimed at maximizing the average net throughput of the network subject to the constraint on backhaul capacity, where a hybrid coordinated multipoint (CoMP) transmission mode is considered. The proposed clustering scheme can be operated in a semi-dynamic manner merely based on large-scale channel information, has low computational complexity, and performs close to the optimal scheme found by exhaustive searching. Under two special cases where the backhaul capacity is very stringent and unlimited, the proposed scheme is then tailored for pure coordinated beamforming (CB) mode and pure joint transmission (JT) mode to further reduce the clustering complexity. Simulation results show that the proposed semi-dynamic clustering schemes are superior to the dynamic clustering scheme due to the reduction of required training overhead. [ABSTRACT FROM AUTHOR]

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 article studies the transmit beamforming security in the dual-functional radar-communication (DFRC) system, where the radar beam also serves as the artificial jamming to suppress potential eavesdropper channels. Unlike previous researches, which assumed that the lone target might be a potential eavesdropper on the adversary side, this paper analyses a more generic scenario in which the eavesdroppers’ channel state information (CSI) is unknown. The eavesdropping rate should be limited to guarantee transmission confidentiality, which is intractable owing to the unknown CSI. To address this issue, we present the long-term eavesdropping signal-to-interference-plus-noise ratio (SINR) as a more tractable security metric. Based on the long-term eavesdropping SINR, the sum rate maximization (SRM) and jamming power maximization (JPM) transmission scheme are investigated. For the SRM scheme, we propose a zero-forcing (ZF) suboptimal algorithm to solve the non-convex fractional programming (FP) problem, and for the JPM scheme, the resultant problem is solved via the semidefinite relaxation (SDR) technique. Finally, numerical results validate the effectiveness of the proposed beamforming approaches. [ABSTRACT FROM AUTHOR]

Traditional beamforming (BF) methods can be used to suppress the Gaussian interferers in underwater acoustic communication systems. However, multiple unsteady sub-Gaussian interferers may exist that degrade the performance of BF. In this paper, a new beamformer is proposed for the suppression of multiple unsteady sub-Gaussian interferers (SMUSGI). The proposed BF combines the multiplier and the quasi-Newton methods to obtain the optimal weight vector. Simulation experiments are conducted to validate the performance of the proposed beamformer. In addition, the proposed beamformer is compared with the traditional methods including the MVDR, Subspace, Bartlett, MDDR, LCMD and LCMV beamformers. The obtained results demonstrate that the proposed beamformer can effectively suppress multiple unsteady sub-Gaussian interferers with a higher output signal-to-interference-plus-noise ratio (SINR), and faster convergence compared with the traditional methods. [ABSTRACT FROM AUTHOR]

In this paper, we propose a cost-efficient beamforming optimization algorithm for multi-user wireless communication systems associated with the intelligent reflecting surface (IRS). From the baseline successive refinement algorithm, which gives a sub-optimal solution for the power minimization problem under the signal-to-interference-plus-noise-ratio (SINR) constraint at each user, several optimization techniques are proposed to reduce the computation complexity while maintaining the algorithm-level performance. To reduce the number of required multiply-accumulate (MAC) operations, we first simplify the complicated matrix inversion by utilizing the channel hardening effect. We also present the two-phase refinement process for the group-level optimization of phase-shift elements, further relaxing the computation complexity as well as the processing latency. Applying the proposed optimization techniques, as a result, numerical results show that the fully-optimized algorithm can reduce the computational costs by up to 89.4% while showing less than 1 dB power loss, leading to the practical solution for the next-generation IRS-aided communication. [ABSTRACT FROM AUTHOR]

In this paper, we investigate the secure vehicle communications in cybertwin-enabled integrated satellite-terrestrial networks, where the digital twins (DTs) in the cybertwin space reflects the physical entities (i.e., satellite, terrestrial base station (BS), and vehicles). Particularly, considering the channel similarity between different satellite links versus the randomness difference in terrestrial links, it is challenging to reach the secure transmission in satellite and terrestrial links independently with limited resources. Considering the information exchange in the cybertwin space can support an information sharing between such physical entities, the secure transmission design by using the heterogeneous satellite-terrestrial resources can be conducted from a global perspective. With the channel feedback information of vehicles gathered at the cybertwin, the co-channel interference caused by the spectrum sharing is leveraged to assist the implementation of secure transmissions in the integrated satellite-terrestrial vehicle network. Specifically, the problems of maximizing the secrecy rate of satellite-to-vehicle link and the terrestrial BS-to-vehicle link are formulated, respectively. To solve such two problems, we propose two corresponding beamforming optimization approaches, where semi-definite relaxation (SDR) and semi-definite programming (SDP) are adopted due to the non-convexity. In addition, the tightness of SDR is proved and the complexity of proposed approaches is also analyzed. Finally, extensive numerical simulations are carried out and results show the effectiveness of our proposed approach. [ABSTRACT FROM AUTHOR]

In this paper, we consider suboptimal cooperative downlink beamforming strategies to maximize the user sum rate in a clustered multicell system with limited feedback. We decouple the problem into multiple independent subproblems through the uplink–downlink duality theorem. Semidefinite relaxation is applied to transform the original nonconvex problem into a convex semidefinite programming problem, which can be efficiently solved in polynomial time. We propose a novel limited feedback scheme based on a compressive sensing technique that can yield high-quality channel state information (CSI) feedback. We also consider a limited feedback scenario where base stations within one cluster have different estimates of the same CSI knowledge. We investigate the channel quantization criterion by choosing the codeword vector that has the minimum norm of the difference between the codeword and the normalized channel. Numerical results demonstrate the effectiveness of the proposed beamforming algorithm and limited feedback schemes. [ABSTRACT FROM PUBLISHER]

A full-duplex (FD) two-way relay channel (TWRC) with multiple antennas is considered. For this three-node network, the beamforming design needs to suppress self-interference (SI). While a traditional way is to apply zero forcing (ZF) for SI mitigation, it may harm the desired signals. In this paper, a design that reserves a fraction of SI is proposed by solving a quality-of-service (QoS) constrained beamforming design problem. Since the problem is challenging due to the loop SI, a convergence-guaranteed alternating optimization (AO) algorithm is proposed to jointly design the relay–user beamformers. Numerical results show that the proposed scheme outperforms ZF method and achieves a transmit power value close to the ideal case. [ABSTRACT FROM PUBLISHER]

To address the asynchronous interference issue in a generalized scenario with multiple primary and multiple secondary receivers, in this paper, we propose an innovative cooperative beamforming technique. The cooperative beamforming design is formulated as an optimization problem that maximizes the weighted sum transmission rate of secondary destinations while maintaining the asynchronous interference at the primary receivers below their target thresholds. In particular, we obtain the optimum beamforming directions and allocate power values among such beamforming directions. In light of the intractability of the power allocation problem, an approximation is used to convert the nonconvex and nonlinear optimization problem into a convex and linear one. Due to the multiple interference constraints corresponding to multiple primary receivers, the power allocation scheme is still complex. Therefore, we also propose a low-complexity power allocation algorithm. The proposed beamforming technique is extended for the case when cooperating CR nodes (CCRNs) have statistical channel knowledge of the primary receivers by developing an upper bound on the probability of introducing asynchronous interference at a given primary receiver beyond a given threshold value. We also propose two CCRN selection strategies for cooperative beamforming. [ABSTRACT FROM PUBLISHER]

Multiple-input–multiple-output (MIMO) full-duplex relaying (FDR) has been considered an efficient technique to provide coverage to users where their direct links from the base station (BS) are too weak for reliable signal reception. However, when multiple MIMO full-duplex relays are deployed in a network, the signal reception quality relies on the effective suppression of multiple types of interference. In this paper, the distributed beamforming is studied for the MIMO FDR network by formulating a power minimization problem with a nonstrict convex objective function (total transmit power of both the BS and all the relays in the network) under individual user rate constraints. We come up with two iterative distributed beamforming algorithms, Algorithm 1 for relays equipped with single receive antenna and Algorithm 2 with multiple receive antennas. The former can yield a global optimal solution of the power minimization problem, whereas the latter can yield only a local optimal solution due to conservative successive convex approximations (SCAs) performed at each iteration, and a rigorous analysis on the upper bounds of step sizes is proposed to guarantee their convergence. The proposed two algorithms only require local information exchange between relays and hence are scalable for different network sizes and topologies. An “early termination” strategy in the operation of the proposed two algorithms is also presented to acquire an acceptable transmit power solution with less computation time consumption and thus suitable for realistic applications. Finally, some simulation results are provided to demonstrate that the proposed two algorithms perform well and significantly better than the existing state-of-the-art scheme reported in the work of Lee and Shin. [ABSTRACT FROM PUBLISHER]

This paper applies combinatorial optimization techniques to improve the downlink transmission to multiple users in a network with N cells, where intercell interference is a performance-limiting factor. A fair distribution of the signal-to-interference-plus-noise ratio (SINR) is desirable. A well-known technique to get a fair (balanced) SINR is max–min unicast beamforming (MBF). However, in a multicell network, there are conditions where MBF can result in a low balanced SINR. This happens if, e.g., two users are geographically close together and served by two base stations (BSs) from, e.g., two different interfering sectors. Then, the mutual interference among the two links will be large, and the balanced SINR among all jointly optimized links decreases. Therefore, the users must be scheduled such that these situations are avoided. In this paper, smart selection of active beams to avoid intercell interference is called beam scheduling and leads to a combinatorial optimization problem. This paper proposes a graph-theory-based problem that is closely related to the beam scheduling problem. The proposed algorithms maximize the sum rate or the minimum SINR among all users and slots. For the two-cell case, an optimal algorithm exists. In the N > \2-cell case, the beam scheduling problem is proven to be NP-hard. Based on the close relation between the beam scheduling problem and the multidimensional assignment problem (MAP), this paper presents suboptimal algorithms for N > \2 to maximize either the sum rate or the minimum SINR among all users and slots. The performance gain in terms of the mean sum rate or the minimum SINR is significant compared with random scheduling. [ABSTRACT FROM PUBLISHER]

Employing dynamic time division duplexing can increase the system-wide spectral efficiency of applications with varying and unbalanced uplink and downlink data traffic requirements. However, in order to achieve this efficiency gain, it is necessary to manage the effects of cross-link interference, which are generated among cells transmitting in opposite link directions. This paper considers bidirectional sum-power minimization beamforming as a means to deal with this cross-link interference, by forcing a minimum signal-to-interference-plus-noise ratio constraint for both uplink and downlink. We propose two iterative approaches to solve this beamforming problem. The first approach assumes centralized processing and requires the availability of global channel state information. The second approach is performed in a decentralized manner, based on the alternating direction method of multipliers and requires only local channel state information and reduced signaling load. Both approaches are shown to converge to a minimum network power expenditure, whereas close-to-optimum performance can be obtained when limiting the number of iterations. [ABSTRACT FROM AUTHOR]

As a conventional metric, spectrum efficiency has been investigated widely in communication systems due to the limited bandwidth resources and increasing subscriber density. Beamforming technology is used in TD-LTE-A and 5G downlink to improve the spectrum efficiency and system capacity, and a proper resource allocation algorithm can also be used to improve the spectrum efficiency on the premise of satisfying user equipment (UE) requirements. In recent years, energy efficiency has attracted more and more attention as the power consumption on the communication systems increases rapidly, which lays a heavy burden on the environment. In this paper, we propose a joint resource allocation and power allocation algorithm in multi-user beamforming mode, which aims at maximizing the energy efficiency, and also takes UE requirements and spectrum efficiency into consideration. The proposed algorithm first calculates the user priorities and gives a UE grouping method to allocate resource blocks (RBs) to different UEs under the assumption that the power is equally allocated among UEs on the same RB. After that, on each RB, it calculates the power allocation among different UEs to realize the improvement of energy efficiency. In addition, the satisfaction of UE requirements is added in the algorithm as a constraint. Simulation results demonstrate that the proposed algorithm shows an excellent performance on system capacity, i.e., spectrum efficiency, and also provides higher energy efficiency than existing algorithms. Moreover, the UE requirements can be better satisfied and system fairness can also be improved. [ABSTRACT FROM AUTHOR]

In this correspondence paper, we analytically characterize the benefit of digital processing in uplink massive multiple-input multiple-output (MIMO) with a sub-connected hybrid architecture. By assuming that the number of radio frequency chains is equal to that of users, we characterize achievable rates of both pure analog detection and hybrid detection under the independent identically distributed Rayleigh fading channel model. From the derived expressions, we discover that the analog processing can outperform the hybrid processing using the maximal ratio combining or zero-forcing criterion in cases under some engineering assumptions. Performance comparison of the schemes are presented under tests with various number of users and number of antennas at the base station. [ABSTRACT FROM AUTHOR]

This paper investigates rate splitting multiple access (RSMA) for multigroup multicast beamforming in a cache-enabled cloud radio access network (C $^2$ -RAN). To maximize the minimum weighted rate among all users and ensure the base station (BS) transmit power and backhaul capacity constraints, we jointly optimize the dynamic BS clustering, message splitting, and beamforming vectors. More specifically, users requesting the same content are collected to a multicast group and served by a cluster of BSs. Each BS has limited storage space and connects to the central processor (CP) via a backhaul link. However, the resulting optimization problem is discrete and nonconvex and has a non-smooth objective function. To alleviate these problems, we construct two surrogates to approximate the objective function locally. These surrogates are strictly lower bounds and concave. Also, we build an upper-bound convex surrogate function to handle the backhaul cost constraint and derive a minorization-maximization (MM)-based iterative algorithm. Besides, we adopt a quadratic transform method to recast the achievable rate and binary BS selection problems to a more tractable form to reduce time cost. We transform the resulting problem into a two-tier alternating optimization problem; the outer-tier problem is handled with closed-form expressions while the inner-tier one is solved by a convex framework. We thus develop a quadratic transform-based alternating (QTA) algorithm. Extensive simulation results confirm the superiority of our proposed transmit strategy and algorithms. [ABSTRACT FROM AUTHOR]

In this paper, we consider joint beamforming, power, and channel allocation in a multiuser and multichannel underlay multiple-input–single-output (MISO) cognitive radio network (CRN). In this system, the primary users' spectrum can be reused by secondary-user transmitters (SU-TXs) to maximize spectrum utilization, whereas intrauser interference is minimized by implementing beamforming at each SU-TX. After formulating the joint optimization problem as a nonconvex mixed-integer nonlinear programming problem, we propose a solution that consists of two stages. In the first stage, a feasible solution for power allocation and beamforming vectors is derived under a given channel allocation by converting the original problem into a convex form with an introduced optimal auxiliary variable and a semidefinite relaxation approach. In the second stage, two explicit searching algorithms, i.e., genetic algorithm (GA) and simulated annealing (SA)-based algorithm, are proposed to determine suboptimal channel allocations. Simulation results show that the beamforming and power and channel allocation with SA algorithm can achieve a close-to-optimal sum rate while having lower computational complexity compared with the beamforming and power and channel allocation with the GA algorithm. Furthermore, our proposed allocation scheme has significant improvement in achievable sum rate compared with the existing zero-forcing beamforming. [ABSTRACT FROM AUTHOR]

This paper considers the problem of optimizing the downlink capacity in a heterogeneous home area network (HAN) with a beamforming technique at the smart meter for smart grid applications. We model the communication scenario between the smart meter and in-home appliances as a heterogeneous multiuser network. The optimal power-allocation algorithm is developed under the constraints that 1) each user satisfies individual signal-to-interference-plus-noise ratio (SINR) requirement for successful heterogeneous communication, 2) the sum of transmit power allocated to each user is equal to the permissible total transmit power at the smart meter, and 3) the allocated transmit power to each user is feasible. The optimization problem is mathematically shown to be convex, and the optimal power allocation is thus derived. By employing the theorem that the sum of independent exponentially distributed random variables follows Erlang distribution, the probability distribution function (pdf) of the smallest allocated transmit power is mathematically obtained from the properties of downlink indoor Saleh–Valenzuela (S–V) channels. It is analytically shown that the allocated transmit power has a lower limit, which is determined by the SINR threshold and the total number of active users in the HAN. Furthermore, numerical results verify the capacity performance improvements of the proposed optimal power-allocation scheme. It is also shown that beamforming technique contributes to the optimal power-allocation scheme. [ABSTRACT FROM PUBLISHER]

Hybrid antenna array consisting of analog subarrays provides a low-cost solution to achieving high beamforming gain in millimeter-wave/terahertz communication systems. For beam adjustment, beam scanning can be readily applied to find the line-of-sight (LoS) path. Conventionally, the beam scanning process is inefficient because the number of grids, together with the scanning period, has to be large to achieve high resolution. To improve the beam scanning efficiency, in this paper we propose an off-gird direction-of-arrival (DoA) estimation method utilizing unequal-sized subarrays. Specifically, we first provide a new solution to overcome the ambiguity problem, i.e., the hybrid antenna array may render the DoA estimate non-unique. That new solution is formed by designing the antenna numbers in the subarrays and can be easily combined with the beam scanning process. We then propose a three-stage beam scanning strategy. At the first stage, coarse scanning is applied to roughly steer the subarrays along the LoS path. At the second stage, a simple DoA estimator is employed to obtain the candidate DoAs. Finally, beam scanning is performed again on the candidate DoAs to obtain the final DoA estimate. Simulation results demonstrate that the proposed method can achieve higher DoA estimation accuracy in shorter scanning period than the conventional beam scanning method; and compared with existing methods developed for equal-sized subarrays, the proposed method is more robust against multi-path interference. [ABSTRACT FROM AUTHOR]

Coordinated beamforming has been optimized to maximize the sum rate under the transmit power constraints or to minimize the transmit power under the data rate constraints. In this paper, we design a precoder that maximizes the energy efficiency (EE) of multicell multiantenna downlink network with coordination and, in the meantime, ensures the minimal data rate requirement of each user. To find a solution of such a nonconvex optimization problem, we construct a convex subset of the original constraint set and a quasi-concave lower bound of the EE. Then, we propose an iterative algorithm to maximize the lower bound of the EE within the convex subset, which is shown to rapidly converge to a local optimum of the original problem. We evaluate the EE of the proposed algorithm through simulations under different user locations, data rate requirements, and numbers of antennas. The results demonstrate that the proposed precoder performs closely to an upper bound derived from interference-free assumption. When the circuit power consumption is dominant, the proposed precoder is much more energy efficient than the transmit power minimization precoder, as well as a precoder, including an interference alignment (IA) beamformer and the optimized transmit power maximizing the EE. [ABSTRACT FROM PUBLISHER]

To ensure link reliability and signal receiving quality, robust adaptive beamforming (RAB) is vital important in mobile communications. In this paper, we propose a new RAB algorithm based on interference-plus-noise covariance (INC) matrix reconstruction and steering vector (SV) estimation. In this method, the INC matrix is reconstructed by estimating all interferences SVs and powers, as well as the noise power. The interference SVs are estimated by using the Capon spatial spectrum together with robust Capon beamforming principle, subsequently the interference powers are estimated based on the orthogonality between different signal SVs. On the other hand, the desired signal SV is estimated via maximizing the beamformer output power by solving a quadratic convex optimization problem. The proposed algorithm only needs to know in advance the array geometry and angular sector, in which the desired signal lies. Simulation results indicate that the proposed algorithm outperforms the existing RAB techniques in terms of the overall performance in cases of various mismatches. [ABSTRACT FROM AUTHOR]

This paper studies the optimal joint beamforming and power control strategy for device-to-device (D2D) communication underlaying multiuser multiple-input multiple-output cellular networks. We consider multiple antennas at the base station (BS) and a single antenna at each cellular user (CU), D2D transmitter (DT), and D2D receiver (DR). We aim to minimize the total transmission power of the system by jointly designing the transmit beamforming at the BS and the transmit powers for both BS and DTs, while satisfying the signal-to-interference-plus-noise ratio based quality-of-service constraints for both CUs and DRs. Due to the nonconvex nature of the problem, we apply the semidefinite relaxation technique to find the optimal solution, which always satisfies the rank-one constraint. We also investigate three suboptimal fixed beamforming schemes: zero-forcing (ZF), regularized ZF, and hybrid maximum ratio transmission-ZF, where the focus is to minimize the total transmission power while reducing complexity. When perfect channel information is not available, we propose a robust transmit power minimization strategy with ZF beamforming that only requires limited feedback based channel direction information at the BS. Finally, computer simulation results are presented to demonstrate the effectiveness of the proposed schemes. [ABSTRACT FROM AUTHOR]

Physical layer security has drawn significant attention in recent years because of its advancement in preventing confidential information leakage to eavesdroppers. In this paper, we focus on secure beamformer designs in multiuser multi-input multi-output (MU-MIMO) systems with a multi-antenna eavesdropper attempting to overhear the confidential information of a particular user. Unlike the existing work, we aim to exploit the inherent multi-user interference to disrupt the reception of the eavesdropper and enhance the security of wireless communications. Particularly, utilizing practical signal-to-interference-noise metrics, the objective of the secure beamformer design is to minimize the eavesdropper's SINR, while providing legitimate users with required prespecified SINRs to guarantee the qualify of service. We propose secure beamformer designs for both downlink and uplink transmissions, under the conditions of known and unknown eavesdropper's channels, respectively. With the proposed secure beamformers, the multiuser signal behaves like the artificial noise or distributed friendly jamming, which can further degrade the reception of the eavesdropper. Extensive simulation studies confirm our analytical performance predictions and illustrate the advantages of proposed beamformer designs for secure MU-MIMO transmissions. [ABSTRACT FROM AUTHOR]

In this paper, we propose a non orthogonal multiple access (NOMA) system using coexisting narrow and wide beams to increase total capacity. In our proposed system, a base station considers the state of user mobility and supports users with either a narrow or a wide beam according to the mobility state. The proposed NOMA system uses the beam gain between a narrow and a wide beam, as well as the power gain from the pathloss difference in NOMA scheme. The system is thus less influenced by user mobility at the point of NOMA pairing. In the proposed system, we consider two power allocation schemes, namely the max and fair power allocation methods, and propose a pairing algorithm to maximize the capacity by minimizing the interference between narrow and wide beam user. The result verifies that the proposed system improves sum throughput, compared with the OMA and conventional NOMA system. [ABSTRACT FROM AUTHOR]

Achieving a good tradeoff between spectral efficiency (SE) and energy efficiency (EE) is important for the emerging wireless communication systems. Motivated by this, we study multiuser downlink beamforming in massive multiple-input multiple-output systems for maximizing a new metric of resource efficiency (RE), which is defined as a weighted combination of EE and SE. The new measure has more flexibility in striking the balance between SE and EE, but brings enormous difficulties in solving the resulting problem. To this end, we first investigate an uplink–downlink duality for the RE maximization. The conventional uplink–downlink duality only applies in the SE or the capacity region, and has yet not been explicitly established for the EE or the RE. In this paper, we prove with rigorous derivation that the duality has a more general form, which can be directly used to tackle the EE or RE beamforming optimization problem. Based on the duality, we then develop an optimization algorithm to realize spectral and energy efficient multiuser beamforming with either instantaneous or statistical channel state information (CSI). Numerical results finally verify that the design based on statistical CSI is able to asymptotically achieve the performance obtained with instantaneous CSI, but with much lower complexity. [ABSTRACT FROM AUTHOR]

Cognitive satellite–terrestrial networks (CSTNs) have been recognized as a promising network architecture for addressing spectrum scarcity problem in next-generation communication networks. In this paper, we investigate the secure transmission for CSTNs where the terrestrial base station serving as a green interference resource is introduced to enhance the security of the satellite link. Adopting a stochastic model for the channel state information uncertainty, we propose a secure and robust beamforming framework to minimize the transmit power, while satisfying a range of outage (probabilistic) constraints concerning the signal-to-interference-plus-noise ratio (SINR) recorded at the satellite user and the terrestrial user, the leakage-SINR recorded at the eavesdropper, as well as the interference power recorded at the satellite user. The resulting robust optimization problem is highly intractable and the key observation is that the highly intractable probability constraints can be equivalently reformulated as the deterministic versions with Gaussian statistics. In this regard, we develop two robust reformulation methods, namely \mathcalS-procedure and Bernstein-type inequality restriction techniques, to obtain a safe approximate solution. In the meantime, the computational complexities of the proposed schemes are analyzed. Finally, the effectiveness of the proposed schemes are demonstrated by numerical results with different system parameters. [ABSTRACT FROM PUBLISHER]

We investigate the performance of multiuser multiple-input-single-output (MU-MISO) downlink in the presence of the mutual coupling effect at the transmitter. Contrary to traditional approaches that aim at eliminating this effect, in this paper we propose a joint analog–digital (AD) beamforming scheme that exploits this effect to further improve the system performance. A jointly optimal AD beamformer is first obtained by iteratively maximizing the minimum received signal-to-interference-plus-noise ratio in the digital domain, followed by an optimization on the load impedance of each antenna element in the analog domain. We further introduce a decoupled low-complexity approach, with which existing closed-form beamforming schemes can also be efficiently applied. For the consideration of hardware imperfections in practice, we study the case where the analog load values are quantized, and propose a sequential search scheme based on greedy algorithm to efficiently obtain the desired quantized load values. Moreover, we also investigate the imperfect channel state information (CSI) scenarios, where we prove the optimality for closed-form beamformers, and further propose the robust schemes for two typical CSI error models. Simulation results show that with the proposed schemes the mutual coupling effect can be exploited to further improve the performance for both perfect CSI and imperfect CSI. [ABSTRACT FROM PUBLISHER]

Non-orthogonal multiple access (NOMA) is an enabling and key wireless technology for the next generation of mobile communications. Generally, previous works focus on the performance in NOMA networks, based on some assumptions. For instance, the number of users in a NOMA group, is two-category: 1) only two users; 2) any number of users without restriction. And just a certain factor of channel gain, e.g., the propagation path loss or the small scale fading, is subjectively considered to be dominant. Besides, there is lack of the closed-form expression for the probability density function (PDF) of ordered channel gain. In this paper, we investigate a downlink cellular NOMA network with the directional beamforming and the upper limit for user number (ULFUN) in a NOMA group. Meanwhile, the path loss and the Rayleigh fading are taken into account as a whole for ordering channel gains. Based on the derived closed-form PDF of ordered channel gain, we analyze the performance for an arbitrary user in the network and derive closed-form expressions for the outage probability, the approximate meta distribution of conditional success probability (CSP) and the ergodic data rate. The provided simulations demonstrate that the proposed analytical results are accurate and reasonable. Moreover, it illustrates that the existence of ULFUN can have a positive impact on the performance gain in low SINR environments, while in terms of the outage probability, ULFUN would become a non-negligible and negative factor in the improvement of performance for high SINR environments. [ABSTRACT FROM AUTHOR]

This paper considers an intelligent reflecting surface (IRS)-aided simultaneous wireless information and power transfer (SWIPT) network, where multiple users decode data and harvest energy from the transmitted signal of a transmitter. The proposed design framework exploits the cost-effective IRS to establish favorable communication environment to improve the fair energy efficient. In particular, we study the max-min energy efficiency (EE) of the system by jointly designing the transmit information and energy beamforming at the base station (BS), phase shifts at the IRS, as well as the power splitting (PS) ratio at all users subject to the minimum rate, minimum harvested energy, and transmit power constraints. The formulated problem is non-convex and thus challenging to be solved. We propose two algorithms namely penalty-based and inner approximation (IA)-based to handle the non-convexity of the optimization problem. As such, we divide the original problem into two sub-problems and apply the alternating optimization (AO) algorithm for both proposed algorithms to handle it iteratively. In particular, in the penalty-based algorithm for the first sub-problem, the semi-definite relaxation (SDR) technique, difference of convex functions (DC) programming, majorization-minimization (MM) approach, and fractional programming theory are exploited to transform the non-convex optimization problem into a convex form that can be addressed efficiently. For the second sub-problem, a penalty-based approach is proposed to handle the optimization on the phase shifts introduced by the IRS with the proposed algorithms. For the IA-based method, we jointly optimize beamforming vectors and phase shifts while the PS ratio is solved optimally in the first sub-problem. Simulation results verify the effectiveness of the IRS, which can significantly improve the system EE as compared to conventional benchmark schemes and also unveil a trade-off between convergence and performance gain for the two proposed algorithms. [ABSTRACT FROM AUTHOR]

In this paper, system design for a multiple-input–multiple-output (MIMO) relay broadcast channel with individual signal-to-interference-plus-noise ratio (SINR) constraints at mobile stations (MSs) is considered. By exploring the structure of downlink–uplink duality at either the base station (BS) or the relay station (RS), we propose two schemes of joint power allocation and beamforming design at the BS and the RS. The problem of the existence of feasible solutions under practical power constraints at the BS and the RS with given SINR targets is considered first. Then, the problem of sum power minimization is considered. Each design problem can be efficiently solved using optimal joint power allocation and beamforming under the framework of convex optimization. We also show that with subchannel pairing at the RS, the transmission power can be further reduced. Finally, a generalization to the multihop scenario is provided to further improve power efficiency. [ABSTRACT FROM PUBLISHER]

This paper studies robust interference mitigation against the imperfectness of channel knowledge involved in the interference link of a multiple-input–multiple-output (MIMO) cognitive radio (CR) system that coexists with a primary radio (PR) system via opportunistic spectrum-sharing (OSS) scheme. The robust design leads to a seemingly complicated matrix-valued problem. Nevertheless, we show that it can be equivalently converted into two convex semidefinite programs (SDPs). One of them is identified as a null-forcing approach with closed-form power allocation, and the other can be reduced to a convex scalar power allocation subproblem by properly relaxing the interference constraint. Numerical results demonstrate significant performance gain of the robust design over previous nonrobust strategies. [ABSTRACT FROM PUBLISHER]

Combining intelligent reflecting surface (IRS) and non-orthogonal multiple access (NOMA) is an effective solution to enhance communication coverage and energy efficiency. In this paper, we focus on an IRS-assisted NOMA network and propose an energy-efficient algorithm to yield a good tradeoff between the sum-rate maximization and total power consumption minimization. We aim to maximize the system energy efficiency by jointly optimizing the transmit beamforming at the BS and the reflecting beamforming at the IRS. Specifically, the transmit beamforming and the phases of the low-cost passive elements on the IRS are alternatively optimized until the convergence. Simulation results demonstrate that the proposed algorithm in IRS-NOMA can yield superior performance compared with the conventional OMA-IRS and NOMA with a random phase IRS. [ABSTRACT FROM AUTHOR]

This paper investigates the multicell coordinated beamforming (MCBF) design for high-speed railway communications, where a high-mobility train goes through a multicell multiuser system in urban areas. All passengers in the train are aggregated to be a high-mobility “big user.” In order to explore the downlink system performance limit, we formulate an optimization problem to maximize the information rate from the serving base station to the high-mobility train while guaranteeing the minimal required achievable information rates of all low-mobility users outside the train by optimizing MCBF vectors. The interchannel interference (caused by Doppler effect to the train), the intercell interference and the interuser interference are jointly considered. Since the problem is nonconvex and cannot be solved directly, we first introduce an auxiliary variable (AV), and for a given AV, we find the optimal MCBF vectors via solving a second-order cone programming. Then, by updating the AV with the bisection method, the corresponding global optimal MCBF vectors are numerically obtained. Simulation results show that our proposed MCBF design greatly enhances the information throughput of the high-mobility train without degrading the required data rates of low-mobility users. [ABSTRACT FROM PUBLISHER]

In this paper, a novel cognitive cooperation model is investigated, in which one multiantenna secondary user (SU) cooperates with two primary users (PUs), reaching a three-party agreement on how to jointly share the authorized spectrum bands, and meanwhile, energy harvesting (EH) and spatial multiplexing (SM) are introduced to make full use of the specious spectrum and energy resources. We call it the EH-SM-enabled two-plus-one cognitive cooperation and focus on the design of the optimal forwarding vector for PUs, optimal beamforming vector for the SU, and optimal power splitting factor for EH, with the objective to maximize SU's achievable transmission rate subject to both minimum transmission rate requirements for two PUs and maximum power consumption constraint for the SU. The formulated problem is nonconvex and difficult to solve directly. For an effective solution, the original rate maximization problem is transformed into the contrapositive power minimization problem, and a low-complexity optimal algorithm is proposed by jointly employing the bisection and golden section methods. Simulation results show that compared with the existing one-plus-one scheme and non-EH-enabled scheme, the proposed scheme greatly improves the SU's achievable transmission rate and, meanwhile, provides more reliable data transmission for PUs. [ABSTRACT FROM AUTHOR]

This paper considers a downlink unicast transmission in a multicell network with multiple users. In a network with frequency reuse factor of one, intercell interference is a limiting factor. The max–min beamforming technique enables a balancing of the signal-to-interference-plus-noise ratio (SINR) among all users in a network under a power budget. Thus, a fair distribution of the achievable rate can be achieved. The max–min beamforming problem (MBP) is nonconvex in general. However, if instantaneous channel-state information (CSI) is available, the MBP has an equivalent quasiconvex form and can optimally be solved with an efficient algorithm based on a convex solver. In addition to this convex-solver-based solution, this paper considers the so-called surrogate dual problem of the MBP with per-antenna and per-antenna-array power constraints. The surrogate dual problem combines multiple power constraints to a single power constraint. Furthermore, the surrogate dual problem can efficiently be solved for long-term CSI in the form of spatial correlation matrices. Strong duality is proved for instantaneous and long-term CSI in the form of higher rank spatial correlation matrices. With the surrogate dual problem, a fast algorithm for the MBP is presented. The convergence issue is discussed. Numerical results verify the convergence and the performance of the proposed algorithm. [ABSTRACT FROM PUBLISHER]

In this paper, the problem of mode switching in multiple-input–multiple-output (MIMO) broadcast channels utilizing the Per-User Unitary Rate Control (PU2RC) transmission scheme is considered. Since there is no mathematical expression for the achievable rate of the PU2RC systems, it is proposed to approximate them using proper bounds. It is shown that certain simple feedback distributions will lead to tight bounds on the achievable rates of the PU2RC systems. The proposed bounds are used for the calculation of mode-switching points and maximization of the system rate under different multiplexing order conditions. The asymptotic behavior of the preferred modes is also studied under high-signal-to-noise-ratio (SNR) and low-SNR conditions, and it is shown that the single-user transmission mode is the preferred mode for both regimes. [ABSTRACT FROM PUBLISHER]

In this paper, we investigate a novel relaying technique employing scheduling, precoding, beamforming, and power allocation for a multiuser multiple-input–multiple-output (MU-MIMO) relay network. In the proposed scheme, the interference is canceled by using a combination of transmit–receive weights and Tomlinson–Harashima precoding (THP). To achieve data rate fairness among different users and further improve the performance of MU-MIMO relay networks, we propose a low-complexity power allocation (LC-PA) to allocate power to each user. In addition, an algorithm to select a subset of users that has maximum sum rate performance is proposed. The performance of the proposed scheme is investigated by examining the sum rate of the system for different user selection algorithms and different power allocation schemes. [ABSTRACT FROM PUBLISHER]

Millimeter wave (mmWave) has been widely considered as a promising technology in the next generation wireless communication systems. Various beam-based directional transmission techniques have been proposed to compensate high pathloss caused by mmWave properties. However, mobility effect of users potentially causes severe inter-beam interferences, which has not been fully-investigated in existing studies. In this paper, we have analyzed different types of mobility-aware beam interference models. Additionally, the theoretical SINR is derived based on channel statistics and the trajectories of users. We propose a mobility-aware subband and beam resource allocation (MSBA) scheme for the mmWave subband-beam massive multi-input multi-output (SB-MMIMO) systems. The proposed MSBA benefits from reduced computation complexity for maximizing system throughput constrained by quality-of-service (QoS) demands of users. The first phase of MSBA scheme is responsible for resource block assignment; while the second phase allocates beamwidth and beam directions according to the analytical derivations. Numerical results show that the proposed MSBA scheme can effectively achieve the highest system throughput and the lowest complexity compared to existing schemes in open literatures. [ABSTRACT FROM AUTHOR]

Non-orthogonal multiple access (NOMA) has been deemed a promising technology to achieve high spectral efficiency. This paper investigates transmit power optimization for a multi-user multiple-input single-output (MISO) NOMA downlink system endowed with energy harvesting and physical layer security. More specifically, the system is designed to satisfy the data-link security requirements of its legitimate users while charging energy harvesting devices. To achieve that goal, we investigate a joint design on beamforming vectors and artificial noise (AN) covariance matrix. Then, an iterative difference-of-two-convex-function (D.C.) algorithm is proposed by applying the successive convex approximation (SCA) approach. To further reduce the computational complexity, a sub-optimal alternating D.C. (ADC) algorithm is devised by decoupling beamforming and mapping the AN to the null space of legitimate users. In the ADC algorithm, optimal null space and beamforming vectors are alternately solved by convex programming. Furthermore, the convergence of the proposed D.C. and ADC algorithms is derived. Extensive simulation results show that the two proposed AN-aided algorithms significantly reduce the transmit power cost over OMA and NOMA scheme without AN, especially when there are more eavesdroppers. [ABSTRACT FROM AUTHOR]

Massive multiple-input multiple-output (MIMO) is a key technology in fifth-generation (5G) networks. By using a large number of antenna elements placed in a 2-D array, an evolved Node B (eNB) can form multiple vertical beams in the elevation domain and adaptively track moving vehicles. Thus, the throughput and spatial multiplexing efficiency in vehicle-to-infrastructure (V2I) access can be significantly increased. However, multipath spatial propagation in wireless channels results in the angular spread of the power arrival profile, which causes intersector interference in massive-MIMO beamforming and becomes a limiting factor for the system capacity. In this paper, we investigate the channel propagation characteristics and the intervehicle interference in the elevation-domain spatial multiplexing for V2I mobile access and develop a packet-level channel model that can be used for system design and network protocol simulations. First, a measurement campaign for the urban macrocell street uplink channels was performed using a 3-D MIMO channel sounder, and the elevation power spectra (EPSs) on the eNB side were measured. Then, the intersector interference in the vertical beamforming is evaluated based on the antenna array pattern and channel EPS. Finally, a packet-level channel model in the form of a finite-state Markov chain (FSMC) is proposed for the V2I elevation multiplexing uplinks. The model describes the received signal-to-interference ratio (SIR) variation when a vehicle is moving toward an eNB. The steady-state distribution of the FSMC is analytically derived and verified by simulations. Based on the stochastic process of the SIR, the concepts of “large/small-scale multiplexing fading” are proposed. The field measurement results in this work have revealed the spatial power arrival profiles of the V2I street channels that are critical for the MIMO system design, and the proposed packet-level channel model provides an enabling tool for the analysis and simulation of the V2I elevation multiplexing links in the 5G networks. [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]