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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 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]

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 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]

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]

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]

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]

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]

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]

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 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]

The cellular communications scenario involving the coexistence of the one-hop direct transmission and the two-hop relaying is studied in this paper. In contrast to conventional cellular systems featuring orthogonal information flows served by decoupled channel resources via, e.g., time division, we propose a novel protocol in which two information flows are served simultaneously via the shared channel resource, thus constituting a spectral-efficient solution for cellular communications. On the other hand, however, an inevitable issue associated with the proposed protocol is the interflow interference, which may lead to serious deterioration on both information flows. To tackle this issue, we develop an overhearing-based protocol that utilizes the overheard interference as useful side information in the receiver design. Specifically, depending on the interference levels, an adaptive linear minimum mean squared error (MMSE) and nonlinear MMSE successive interference cancellation (SIC) receiver exploiting the overheard interference at the direct mobile terminal is developed. To balance between the two information flows, we develop the asymptotically optimum superposition coding at the base station (BS) in the high-power regime. Furthermore, the optimum relay beamforming matrix maximizing the bottleneck of the achievable rates of the two information flows is developed subject to a finite power constraint. Finally, simulations demonstrate a remarkable throughput gain over the conventional cellular systems. [ABSTRACT FROM AUTHOR]

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

In this paper, we consider unicast/multicast multicell cooperative transmission with interference among users/user groups. In this context, we derive the optimal minimum variance distortionless response (MVDR)-based successive minimum variance beamforming (SMVB) scheme that achieves a superior sum-rate performance compared with the block diagonalization scheme. Subsequently, we derive an optimal joint power allocation and broadcast beamforming scheme for multicell cooperative broadcast transmission for a pooled power constraint at the base stations. This scheme is extended to include per base station power constraints by formulating it as a relaxed semi-definite program (SDP). We also consider the multicell cooperative uplink scenario and derive a successive multiuser (MU) uplink beamforming (SMUB) scheme that maximizes the signal-to-interference-plus-noise ratio (SINR) of each user. Next, we consider imperfect channel state information (CSI) and derive a robust beamforming scheme for the downlink, based on minimizing the worst-case interference by formulating it as a second-order cone program. It is demonstrated that the corresponding robust beamformer for the uplink transmission can be derived by employing the multidimensional covariance fitting approach. Simulation results are presented to demonstrate the superior sum-rate performance of the proposed multicell transmission schemes. The performance is also compared with several bounds for cooperative multicell scenarios. [ABSTRACT FROM AUTHOR]

The network densification brings favored channel gain. However, its performance is limited by the increased interference and feedback overhead. In this paper, we focus on the channel state information (CSI) measure error induced by feedback delay and study the effect of the uplink feedback on the downlink transmission performance in ultra-dense networks (UDNs). Using a mobility-aware sparse feedback scheme (MSFS), we analyze the feedback transmission delay and feedback waiting delay separately, considering the impact of CSI feedback probability and measurement range. To capture the effect of proposed MSFS, the coverage probability for a typical mobile user is derived, where the imperfect-CSI-based zero-forcing precoding is employed to mitigate the severe inter-cell interference in UDNs. Numerical results reveal that MSFS succeeds in providing the effective CSI for downlink precoding while keeping a lower feedback load, and the optimal feedback probability and measurement range are provided for diverse mobility cases and different network densities. [ABSTRACT FROM AUTHOR]

This paper addresses the problem of coordinated beamforming across a group of base stations (BSs) and frequency slots in the downlink of a multiple-input multiple-output (MIMO) orthogonal frequency-division multiple-access (OFDMA) cellular network. Three figures of merit are considered for system design under a per-BS power constraint: 1) the weighted sum of the rates (WSR) on the frequency slots of the coordinated BSs; 2) the global energy efficiency (GEE), defined as the ratio between the network sum rate and the corresponding consumed power; and 3) the weighted sum of the energy efficiencies (WSEE) on the frequency slots of the coordinated BSs. The Karush–Kuhn–Tucker (KKT) conditions of the considered optimization problems are first derived to gain insight into the structure of the optimal beamformers. Then, we propose a suboptimal design method that can be applied to all considered figures of merit. Numerical results are provided to assess the performance of the proposed beamforming strategies. [ABSTRACT FROM PUBLISHER]

Coordinated beamforming (CBF) provides improved sum rate with less overhead and computational complexity, and its theoretical performance analysis is mostly finished in a spatially uncorrelated environment. In this paper, we investigate the effect of spatial correlation on the sum rate of a two-cell multiuser downlink channel. We analyze the scaling laws of the sum rate with a large number of users. To improve the performance, different roles are assigned to the two cells: One is a master cell, and the other is a slave cell. The base station (BS) of the master cell requests the interference cancelation to the slave cell, and the BS of the slave cell performs precoding to cancel the interference influencing over the master cell, although the achievable rate of the slave cell is decreased. The decision of the master and slave cells can be affected by the number of users and the degree of spatial correlations. From computer-based simulations, we show that our proposed algorithm can achieve a higher sum rate than other previous approaches with lower overhead. [ABSTRACT FROM PUBLISHER]

In this paper, we exploit the concept of array pattern synthesis to design cluster beamformers with partial channel state information for non-orthogonal multiple access downlink multiuser transmission in millimeter wave channels. Using the information on angle-of-departure region for each user, we formulate an array pattern nulling problem to design each cluster beamformer for minimizing inter-cluster interferences, which is solved by using semidefinite programing. Further, we exploit linear programing to solve a power allocation problem for each user in the cluster to achieve a target signal-to-interference-and-noise ratio. Numerical results are presented to verify the validity of the proposed schemes. [ABSTRACT FROM AUTHOR]

Two-stage beamforming is a transmit strategy that uses two types of beamformers to reduce the feedback overhead of frequency-division-duplexing massive multiple-input multiple-output systems that are spatially correlated. In this paper, we present large system analysis of two-stage beamforming when a transmitter has limited channel information from feedback and when regularized-zero-forcing (RZF) is used as a second-stage beamformer. We consider two random-vector-quantization-based feedback schemes that can be respectively applied when users have perfect channel information or perfect effective channel information. For each feedback method, we analyze both expected signal-to-interference-plus-noise ratio (SINR) and expected rate loss and then characterize their bounds as a function of the number of feedback bits. From the characterization, we reveal that the sum rate of two-stage beamforming is very sensitive to the regularization parameter of RZF, especially when the number of feedback bits is limited. Motivated by this, we derive the optimal regularization parameter that maximizes the SINR of two-stage beamforming with limited feedback. Using simulations, we verify the tightness of the characterized bounds and quantify how the use of the optimal regularization parameter improves the sum rate of two-stage beamforming. [ABSTRACT FROM AUTHOR]

Recently, an adaptive intercell interference (ICI) mitigation technique has been proposed for downlink cellular systems as a means to increase throughput with low system complexity. However, in the prior work, the issue of intercell user scheduling has not been considered. In this paper, we study multiple-input–single-output (MISO) downlink cellular systems that jointly design adaptive ICI cancelation (ICIC) and intercell user scheduling assuming that partial channel state information (CSI) is shared among base stations (BSs). Since the optimal solution would require high complexity, we investigate a new low-complexity algorithm that selects the best users and their beamforming strategies in terms of maximizing the weighted sum rate (WSR). To this end, we first develop a simple threshold criterion for each user to decide the preferred beamforming strategy based on the derivation of the expected signal-to-interference-plus-noise ratio (SINR). Then, according to users' feedback about their decisions, a successive user and beamforming selection algorithm is performed at the BSs. From simulation results, we show that, combined with proportional fair scheduling, the proposed scheme provides excellent throughput performance with very low computational complexity and the amount of inter-BS CSI exchange. Furthermore, we discuss an extension of the proposed scheme to limited feedback systems and observe that our algorithm also provides similar advantages over conventional schemes with quantized feedback. [ABSTRACT FROM PUBLISHER]

Coordinated beamforming (CBF) or coordinated scheduling is regarded as an effective means of mitigating the intercell interference (ICI) in orthogonal frequency-division multiplexing (OFDM) systems. In this paper, a new CBF scheme is presented based on leakage-controlled minimum-mean-square-error (LC-MMSE) precoding. We use a regularized factor to maximize the signal-to-interference-plus-noise-ratio (SINR) of LC-MMSE and analyze its achievable throughput loss compared with the conventional minimum-mean-square-error (MMSE) scheme. The results show that the upper bound of the achievable throughput loss increases as the number of serving users grows. A joint frequency and spatial coordination scheme is proposed as an effort to simplify the design of transmit beamforming weights by separating the objective functions that maximize the expected signal strength and minimize the interference leakage in different resource groups. System-level simulation results demonstrate that the proposed scheme can significantly reduce the ICI, and it performs well even with imperfect channel-state information (CSI) at the transmitter. [ABSTRACT FROM PUBLISHER]

This paper is concerned with the performance analysis of the maximum eigenmode beamforming (MEB) scheme with equal power allocation (EPA) over practically coded multiple-input multiple-output (MIMO) multiple-access channels (MACs). Both the scenarios of perfect and imperfect channel state information (CSI) at the transmitters (CSIT) are considered. Analytical and numerical results show that MEB with EPA can achieve significant power savings and is less sensitive to CSIT error than other alternatives. [ABSTRACT FROM PUBLISHER]

Opportunistic beamforming is a promising technique for downlink channels in cellular systems. When opportunistic beamforming is to support multiple users at a time, it is important to form multiple beams with as little interference as possible. In the conventional approach with limited feedback of channel state information from users, the best beamforming matrix can be selected from a set of multiple beamforming matrices. Thus, at each time when a different set of users are selected by a scheduler, a new beamforming matrix has to be found, which may require excessive complexity. To avoid this problem, in this paper, we propose an opportunistic beamforming approach with a single fixed beamforming matrix for stationary users. Since the beams are fixed, we only need to perform user selection (and power allocation if necessary). It is also shown that the proposed approach is suited for a distributed virtual antenna array built by multiple base station or access point units, since the intercorrelation between the users' channel vectors in this case allows effective opportunistic beamforming based on user classification in conjunction with a single beamforming matrix. [ABSTRACT FROM PUBLISHER]

In this paper, we propose a practical downlink multiuser multiple-input–multiple-output (MU-MIMO) system. The proposed MU-MIMO system focuses on improving two limiting factors for practical implementations of MU-MIMO: 1) feedback rate and 2) computational complexity. First, users efficiently feed their channel-state information back with low feedback rate based on the proposed channel-quantization method. Second, the beamforming matrix at the base station is easily computed using singular value decomposition (SVD). Numerical results show that the proposed MU-MIMO system achieves a higher sum rate than conventional MU-MIMO systems, particularly at low feedback rate, while the computational complexity is kept reasonable. [ABSTRACT FROM PUBLISHER]

In this paper, we consider a new cognitive radio network (CRN), where cooperative beamforming is designed to enhance the physical-layer security for both primary users (PUs) and secondary users (SUs). In particular, the proposed design aims to minimize the transmit power of a secondary transmitter (ST) while providing an eavesdropper (Eve), a primary receiver (PR), and multiple secondary receivers (SRs) with different signal-to-interference-plus-noise ratio (SINR). The semi-definite relaxation (SDR) technique is utilized to convert the initial nonconvex problem into a convex semi-definite programming (SDP), which can be efficiently solved. Then, the optimality of SDR is strictly proved by showing that optimal transmit covariances must be rank one. Numerical examples are provided to corroborate the proposed studies. [ABSTRACT FROM AUTHOR]

We consider a multiuser two-way amplify-and-forward relay channel with finite feedforward (FF) of the beamforming weights. The relay decides the beamforming weights of the users based on the full channel knowledge and feeds the beamforming information to the users through the band-limited FF channels. Assuming random vector quantization (RVQ), the performance analysis of the finite-FF system is presented. Similar to the multiuser broadcast channel, the average sum rate saturates at a high SNR with a fixed-size codebook. The scaling rate of the codebook size necessary to overcome the saturation and to follow up the multiplexing gain is presented. Supporting simulation results are presented. [ABSTRACT FROM PUBLISHER]

A new transmission strategy that consists of a spatial scheduling algorithm and two precoding algorithms is developed for multicarrier multiuser (MU) multiple-input–multiple-output (MIMO) systems. The scheduling algorithm, which is called efficient multicarrier ProSched (EMC-ProSched), adopts a novel and effective scheduling metric for each user and can efficiently search for a suitable group of users to be served at the same time on the same frequency. Two precoding techniques are then designed to handle different antenna configurations. For the case where the number of transmit antennas at the base station (BS) is not smaller than the total number of receive antennas at the user terminals (UTs), the linear-precoding-based geometric mean decomposition (LP-GMD) algorithm is proposed. It suppresses the MU interference (MUI) and enables an effective implementation of the same modulation and coding scheme (MCS) on all spatial streams of each user. Consequently, smaller signaling overhead is required compared with the case where a different MCS is applied on each spatial stream. When the total number of receive antennas at the UTs exceeds the number of transmit antennas at the BS, we propose the low-complexity coordinated beamforming (LoCCoBF) algorithm to accomplish the goal of the MUI mitigation and to achieve a high capacity. A system-level simulator with a link-to-system interface is further developed under the framework of the IEEE 802.11ac standard to evaluate the performance of the proposed transmission strategy. The simulation results indicate that a promising performance can be achieved by employing the proposed transmission strategy. [ABSTRACT FROM PUBLISHER]

In this paper, we propose a distributed beamforming scheme to realize a full-duplex relay (FDR) in multiuser multiple-input–multiple-output (MIMO) relaying systems. In the proposed scheme, each mobile and relay station performs distributed transmit beamforming and receive combining, whereas the base station performs ordinary multiuser MIMO transmission. Using the channel reciprocity, the proposed FDR scheme can be realized in an iterative way with no cooperation among nodes. Numerical results show that the proposed scheme provides substantial performance improvement compared with the half-duplex relay. [ABSTRACT FROM PUBLISHER]

A precoding scheme is proposed for the downlink of multibeam satellite communication in the face of phase perturbations. In order to alleviate the signal-to-interference and noise ratio degradation induced by phase uncertainty and to minimize the transmission power consumption, we formulated the beamforming problem as a chance-constrained optimization. Moreover, we provided a large deviation inequality aided conservative approximation for the chance-constraints, followed by conceiving a tractable solution. Our simulation results showed the accuracy and the efficiency of our proposed approximation algorithm compared to the benchmarks. [ABSTRACT FROM AUTHOR]

This paper examines a novel multichannel multi-antenna-based cross-tier interference coordination strategy for two-tier femtocell networks. To reduce the cross-tier interference and improve the aggregate throughput of two-tier femtocell networks without exchanging feedback information by backhauling, we propose a beamforming codebook restriction strategy. The beamforming codebook restriction strategy enables the femtocell users to select the best channel that is robust to cross-tier interference before the transmission by reducing the size of the macrocell beamforming codebook. Although restricting the beamforming codebook increases the quantization error for macrocell users, the opportunistic channel selection strategy and the proportional fair scheduler compensate for the increased quantization error by exploiting the channel selection diversity gain and the multiuser diversity gain. Both analytical and numerical results demonstrate that the proposed strategies collaboratively improve the aggregate throughput of two-tier femtocell networks. [ABSTRACT FROM PUBLISHER]

In the fifth-generation (5G) system, millimeter-wave (mmWave) communication has been recognized as a candidate technique. To overcome the high transmission loss of mmWave, distributed antenna systems that consist of a base station (BS) connecting with multiple remote radio units (RRUs) were studied to improve the energy efficiency. In this letter, by utilizing the propagation characteristics of mmWave, we propose a cooperative multiuser beamforming method for energy-efficient transmissions in mmWave distributed antenna systems. The RRUs first decide the local serving group separately and the BS makes an optimization globally with limited data exchange. From simulation results, compared to the state-of-the-art method, it is shown that higher energy efficiency can be realized by the proposed method for the multiuser mmWave distributed system. [ABSTRACT FROM AUTHOR]

Dual-functional radar-communication (DFRC) has been viewed as a promising component in future networks. The considered two-cell interfering DFRC network involves two multi-antenna base stations (BSs), and each BS serves multiple single-antenna users and receives echo signals to detect the target. An optimization framework for the joint transmit beamforming and receive filters design of a coordinated two-cell network is formulated. Specifically, we minimize the transmit power at two BSs subject to the signal-to-interference-and-noise ratio constraints. To solve the formulated non-convex optimization problem, an alternating optimization-based method is invoked. For the transmit beamforming design, a locally optimal successive inner convex approximation (SICA)-based method with fast convergence is firstly proposed. To further reduce the computational complexity, we also provide three zero-forcing (ZF)-based sub-optimal methods for practical consideration. For the receive filters design, a power iterations-based method is proposed. Simulation results validate the effectiveness of our proposed SICA-based locally optimal and ZF-based sub-optimal schemes. [ABSTRACT FROM AUTHOR]

Consider a two-hop relay network that consists of multiple pairs of single-antenna users and multiple multiantenna relays. We assume that the relays employ a linear decode-and-forward (DF) strategy to cooperatively forward information from sources to their destinations. To provide fairness among users and alleviate the network backhaul load, we consider a signal-to-interference-plus-noise-ratio (SINR)-based max–min fairness (MMF) problem by jointly optimizing the power allocation at sources, the receive and transmit beamformers at relays, and the size of the cooperating relays. This problem is a nonlinear mixed-integer program, which is challenging to solve. As a compromise, we seek some efficient approximate solutions to it. Specifically, we first approximate the hard backhaul constraint by a group sparse constraint and then employ the uplink–downlink duality transformation, advocated by Luo et al. [IEEE Transactions on Wireless Communications 2015], to eliminate trivial nonsparse solutions. From there, a custom-made distributed algorithm is developed for the approximated problem by fitting it into the alternating direction method of multipliers (ADMM) framework. Each iteration of ADMM can be computed in closed form, thus giving it very low complexity. The effectiveness of the proposed algorithm was validated by extensive numerical simulations. [ABSTRACT FROM PUBLISHER]

To achieve the full multiplexing gain of a $K$-multiuser multiple-input–multiple-output (MIMO) overlay cognitive radio (CR) network, a distributed opportunistic interference alignment (DOIA) technique using a new beamforming algorithm, namely, threshold-based beamforming (TBF), is proposed. The MIMO overlay CR system consists of one primary user (PU) and $K$ secondary users (SUs) where the local channel state information is available at both the transmitters and receivers of SUs. Assuming that the receiver and the transmitter of the PU have perfect knowledge of their own channel matrix, the PU uses a maximum eigenmode beamforming (MEB) scheme for its data transmission to release some of its eigenmodes for the SUs. For this virtual cooperation, the SUs align their transmitted signals to the spatial directions (SDs) associated with the unused PU's eigenmodes to ensure orthogonality between the links of the PU and the SUs. In addition, the MEB scheme overcomes the limitation of the conventional water-filling power allocation (WPA) in releasing the PU's eigenmodes for the SUs at high signal-to-noise ratios (SNRs). A noniterative and distributed power-allocation strategy, namely, TBF, is proposed, in which the SUs' links with a maximum eigenvalue above a certain threshold transmit data at full power, and the rest remain silent. This protocol, along with the MEB scheme, enables the MIMO overlay CR system to enhance the throughput of the network described as the average sum rate of both the PU and SUs. The proposed DOIA-TBF protocol allows the opportunistic SUs to use the same frequency band of a preexisting PU and guarantees that no interference is imposed on the PU's performance for such a MIMO overlay CR system. [ABSTRACT FROM PUBLISHER]

We consider regenerative multi-group multi-way (MGMW) relaying. A half-duplex regenerative multi-antenna relay station (RS) assists multiple communication groups. In each group, multiple half-duplex nodes communicate with each other. The number of communication phases is defined by the maximum number of nodes among all groups. In the first phase, i.e., the multiple-access (MAC) phase, all nodes simultaneously transmit to the RS, and the RS decodes the data streams of all nodes. In the following broadcast (BC) phases, the RS transmits to the nodes. We propose three BC strategies, i.e., unicasting, multicasting, and hybrid uni/multicasting. For the multicasting strategy, either superposition or exclusive or (xor ) network coding is applied. On the one hand, we propose a transmit beamforming algorithm minimizing the RS's transmit power while guaranteeing that each node receives with a rate equal to the rate received at the RS for each particular data stream. The transmit beamforming algorithm is designed by coupling the MAC and BC phases. On the other hand, for the case where the RS's transmit power is fixed, we design low-complexity transmit beamforming algorithms: matched filter, zero forcing, minimization of mean square error, and MGMW-aware transmit beamforming. It is shown that the multicasting strategy with xor network coding improves the achievable sum rate and requires the least transmit power compared with other strategies. [ABSTRACT FROM PUBLISHER]

Although there have been extensive studies on transmit beamforming in multi-input single-output (MISO) multicell networks, achieving optimal sum-rate with limited channel state information (CSI) is still a challenge even with a single user per cell. A novel cooperative downlink multicell MISO beamforming scheme is proposed with highly limited information exchange among the base stations (BSs) to maximize the sum-rate. In the proposed scheme, each BS can design its beamforming vector with only local CSI based on limited information exchange on CSI. Unlike previous studies, the proposed beamforming design is non-iterative and does not require any vector or matrix feedback but requires only quantized scalar information. The proposed scheme closely achieves the optimal sum-rate bound in almost all signal-to-noise ratio regime based on non-iterative optimization with lower amount of information exchange than existing schemes, which is justified by numerical simulations. [ABSTRACT FROM AUTHOR]

This study proposes a multiobjective beamforming power control scheme to achieve the robust quality of service (QoS), the optimal signal-to-interference-plus-noise ratio (SINR) target tracking and the minimum average power consumption simultaneously in multicell multiuser MIMO (multicell MU-MIMO) wireless system with imperfect channel. The advantages of the proposed multiobjective beamforming power control optimization method are threefold. First, the proposed power control scheme can simultaneously achieve three objectives as follows: 1) the optimal $H_{\infty }$ robust SINR target tracking performance, 2) the optimal $H_{2}$ SINR target tracking performance, and 3) the minimum average power consumption in the multicell MU-MIMO wireless system. The proposed multiobjective optimization problem (MOP) is equivalently derived as a MOP by minimizing their corresponding upper bounds under the constraint of a set of linear matrix inequalities (LMIs). Second, without the requirement of statistical information and the norm-bounded condition of the channel uncertainty, the effect of inter-cell interference, intra-cell interference, channel uncertainty, round trip delay, and noise can be efficiently attenuated by the proposed robust SINR target tracking method. Third, through the proposed LMIs-constrained multiobjective evolution algorithm (MOEA), the designer can obtain Pareto optimal solutions efficiently with a good distribution along the Pareto front to achieve the proposed multiobjective power control design. Several simulation examples with the proposed multiobjective control scheme are produced with favourable results under the guarantee of the theoretical analysis. [ABSTRACT FROM AUTHOR]

A multi-user multiple-input single-output simultaneous wireless information and power transfer full-duplex system with imperfect channel state information is considered. Under the assumption that the unknown channel estimation error is within a certain range, the self-interference cannot be suppressed completely at the base station and at the multi-user nodes. The proposed design problem is formulated as maximizing the average harvested energy of multi-user nodes and maintaining the signal-to-interference-plus-noise ratio requirement, which is proved to be non-convex. Utilizing semidefinite relaxation and S-procedure methods, a convex optimization problem of optimal beamforming vector for the multi-antenna transmitter is obtained. Moreover, semidefinite relaxation is proved to be tight by showing the existence of rank-one optimal solutions. Simulation results are presented to verify the robustness and effectiveness of the proposed algorithm compared with the non-robust design. [ABSTRACT FROM AUTHOR]