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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]

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]

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

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

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

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]

Large-scale antennas systems have been attracted significant attention to provide considerable array gains for millimeter wave (mmWave) communications systems. However, huge hardware complexity and power consumptions make the conventional precoding architectures impractical to realize. Recently, hybrid precoding structures for mmWave communications systems are widely investigated to save the cost and achieve satisfactory performance. Unfortunately, most existing precoder designs lie in the assumption of infinite or high-resolution phase shifters (PSs), which are also difficult to realize in real-world. Motivated by this fact, a low complexity hybrid precoder and combiner design for multi-user systems with two-bit resolution PSs is proposed in this paper, which provides an appropriate trade-off between energy efficiency and system sum-rate. Specifically, the candidate sets for analog beamforming are constructed by introducing the auxiliary variables, in which the number of elements is the same as that of antennas. Accordingly, the computational complexity of searching optimal beamformer is significantly reduced over proposed codebooks. Then, to avoid high complexity singular value decomposition (SVD) of channel matrix, we propose a rank-1 approximate decomposition algorithm based-on principal component analysis technique. At last, the user scheduling algorithm is presented to further improve the performance in multiuser-scenario. Simulation results verified the effectiveness of the proposed algorithms, the performance of which is close to the optimal unquantized precoder. [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]

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]

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]

In this paper, we propose a novel low-complexity beamforming algorithm for multiuser successive maximum ratio transmission (MS-MRT) toward space-division multiplexing to achieve video quality maximization in the downlink of a multiuser (MU) multiple-input–multiple-output (MIMO) orthogonal frequency-division multiple-access (OFDMA)-based fourth-generation (4G) wireless network. We compare the performance of this algorithm in terms of system throughput and video quality with the two popular precoding techniques, namely, block diagonalization (BD) and successive optimization (SO). We also compare its performance with the generalized versions of these two algorithms termed coordinated transmit–receive processing (CTR)-BD and CTR-SO algorithms, respectively. Further, we propose an extension of the MS-MRT scheme to broadcast scenarios, termed broadcast successive maximum ratio transmission (BS-MRT), which computes the optimal broadcast beamforming vector to maximize the video quality at each of the broadcast group members while maintaining orthogonality to the previously scheduled user groups. We also demonstrate that the proposed MS-MRT and BS-MRT schemes can be naturally adapted to the context of various MU OFDMA scheduling algorithms such as proportional fairness (PF) and round robin (RR), to name a few, to maximize video quality through optimal scheduling in the presence of a large number of users. We discuss implementations of the proposed MS-MRT scheme in association with these scheduling algorithms for MU unicast and broadcast video transmission scenarios. The simulation results presented in this work rely on the rate models derived using the Joint Scalable Video Model (JSVM) software developed by the Joint Video Team (JVT) and are, thus, readily applicable in practice. The presented results clearly demonstrate the ability of the proposed algorithm to maximize the video quality in comparison with the other competing MU MIMO precoding techniques. Further, employing the proportional fair scheduling algorithm in conjunction with the MS-MRT results in an overall enhancement in the received video quality compared with the RR and max-rate algorithms. [ABSTRACT FROM PUBLISHER]

In multi-user downlink small cell networks, cooperative resource allocation (RA) within a small cell cluster is a key technique to enhance network capacity. However, capacity-maximizing RA in frequency-selective fading channels requires global channel state information (CSI) of users within a small cell cluster, which makes it infeasible in practical networks with limited direct link capacity. To circumvent this global CSI assumption, most of the existing studies on RA have been based on several CSI assumptions such as local CSI and local CSI at the transmitters (CSIT). Nevertheless, cost functions with local CSI or local CSIT in the literature rely on heuristic formulations, because the sum-rate cannot be computed if without global CSI. In this paper, we propose a deep reinforcement learning-based RA algorithm to maximize the sum-rate for any given limited information on instantaneous CSI or sum-rate at the previous period. The proposed scheme is not restricted to certain CSI assumptions, but attempts to find the best RA for any given information such as quantized local CSI and quantized local CSIT; thus, it is applicable to any given direct link capacity. The proposed algorithm is self-adaptive in time-varying channels, since it is not divided into training and test phases. We modify the target neural network (TNN) scheme to enhance the sum-rate and the convergence speed. Numerical simulations confirm that: i) the proposed algorithm outperforms the conventional algorithms even under the same CSI assumption such as local CSI and local CSIT; ii) a flexible trade-off between the amount of CSI and the sum-rate is realizable in practical systems. [ABSTRACT FROM AUTHOR]

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]

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]

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]

This paper investigates the precoding design for multicast transmission in multicell massive multiple-input multiple-output (MIMO) systems with finite-alphabet inputs. The users within each cell are interested in common information, and different cells provide distinct information. Focusing on the weighted max–min fairness (MMF) problem with only statistical channel state information at the base station, we provide the necessary conditions of the optimal precoding vectors to maximize the minimum weighted achievable ergodic rate, and an iterative algorithm is proposed to optimize the precoding vectors. To achieve lower computational complexity, we then derive a lower bound on the achievable ergodic rate for finite-alphabet inputs. Considering the problem of the minimum weighted rate lower bound maximization, we utilize the concave–convex procedure (CCCP) to develop a CCCP-based algorithm, which is proven to converge to a local optimum. Furthermore, exploiting the channel characteristic in massive MIMO systems, we prove that the optimal precoding vectors, maximizing the minimum weighted rate lower bound, are linear combinations of eigenvectors of transmit correlation matrices, and the original problem can be shifted into a lower dimensional space. Motivated by this insight, a relation-based algorithm is devised to obtain the optimal solution of the weighted MMF problem by using the duality between the MMF problem and the quality of service problem. Numerical results illustrate the tightness of the achievable ergodic rate lower bound and the significant performance of the devised algorithms. [ABSTRACT FROM AUTHOR]

The challenges of millimeter-wave (mmWave) radio propagation in dense crowded environments require dynamic re-associations between the available access points (APs) to reduce the chances of losing the line-of-sight path. However, the antenna beamsearching functionality in the mmWave systems may introduce significant delays in the course of AP re-association. In this paper, we analyze user performance in dense urban mmWave deployments that are susceptible to blockage by the dynamically moving crowd. Our approach relies on the ergodic capacity as the key parameter of interest. We conduct a detailed evaluation with respect to the impact of various system parameters on the ergodic capacity, such as AP density and height, blocker density and speed, number of antenna array elements, array switching time, degree of multiconnectivity, and employed connectivity strategies. Particularly, we demonstrate that dual connectivity delivers the desired performance out of all possible degrees of multiconnectivity, and there is an optimal density of mmWave APs that maximizes the capacity of cell-edge users. We also show that the use of low complexity “reactive” multiconnectivity design, where the beamtracking is only performed when the currently active connection is lost, together with the utilization of iterative beamsearching algorithms, does not significantly deteriorate the ergodic capacity. [ABSTRACT FROM AUTHOR]

In this paper, we develop beamforming (BF) schemes to minimize the total transmitted power of both uplink (UL) and downlink (DL) in two-tier network with in-band wireless backhaul (WB) while the target rates of user equipment (UE) are satisfied under the backhaul constraints. We propose a centralized BF scheme with an alternate update algorithm optimizing the BF matrices and power allocation vectors. Then the UL-DL feasible target rate region of the proposed scheme is analyzed, which can be enlarged in both UL and DL by increasing the number of antennas of small cell base stations (SBSs). Simulation results show that the proposed in-band WB system for two-tier network can save the power consumption by 33.5% and 82.1% compared with the out-of-band WB system and conventional one-tier network, respectively, at the UE target rates of 80 Mb/s in DL and 40 Mb/s in UL. In addition, we propose a distributed BF scheme to locally optimize the variables at each SBS, which can achieve above 90% of the feasible target rate region of the centralized scheme without the instantaneous information exchange. [ABSTRACT FROM AUTHOR]

This paper considers the $K$-user multiple-input–single-output (MISO) broadcast channel with delayed channel state information at the transmitter (CSIT) where the base station has only two antennas. Based on the MAT

Named after M. A. Maddah-Ali and D. Tse [1]. [ABSTRACT FROM PUBLISHER]

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]

In this paper, we propose a new three-user network information flow model, referred to as the triangular multiple-input–multiple-output (MIMO) relay (TMR) channel, which consists of three users and three relays equipped with nU and nR antennas, respectively. Each user sends two independent messages to the other two users via the adjacent relays in two time slots, which are referred to as the multiple-access and broadcast stages. We derive a novel simultaneous signal and interference alignment for the proposed TMR channel in a scenario where there are fewer antennas at each relay than at each user (nR < nU ) . An optimized pseudo-inverse scheme based on an efficient gradient projection algorithm is proposed to solve the simultaneous alignment problem. By deriving a gradient over weighted sum-rate maximization and applying a gradient descent method, the optimal beamforming vectors are obtained to maximize the effective signal-to-noise ratios. Furthermore, to obtain rapid convergence speed and reduce computational complexity, we introduce a quasi-Newton method, which is referred to as the Broyden–Fletcher–Goldfarb–Shanno algorithm, by approximating the Hessian matrix of a pure Newton method. The convergence of the proposed gradient algorithm is guaranteed by proposing a line search algorithm. Finally, a performance evaluation shows that the proposed scheme offers a higher sum rate, produces a better outage probability, and achieves a higher multiplexing gain than the existing schemes. [ABSTRACT FROM PUBLISHER]

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]

Multicell dynamic time division duplexing (TDD) systems make it possible to adapt the number of uplink and downlink time slots in each cell to the prevailing cell-wide traffic demand. Although dynamic TDD systems can be advantageously deployed in scenarios in which the uplink and downlink traffic demands are asymmetric and time varying, dynamic TDD systems give rise to base station to base station (BS-to-BS) interference and user equipment to user equipment (UE-to-UE) interference that negatively impact the system performance. In this paper, we propose employing a distributed beamforming scheme to mitigate the BS-to-BS interference and thereby to improve the uplink performance. Specifically, the proposed scheme uses interference pricing to find the appropriate precoder vectors at the BSs, which also improves the signal-to-interference-plus-noise ratio (SINR) performance in the downlink. We compare the performance of the pricing-based (PB) beamforming scheme with that of zero-forcing beamforming in an outdoor picocell environment specified by the 3rd Generation Partnership Project using a realistic system simulator. We find that the proposed PB scheme boosts the SINR in the uplink at the expense of a small degradation of the downlink SINR compared with the zero-forcing scheme. On the other hand, the PB beamforming approach can significantly reduce the downlink transmit power levels and thereby improve the overall energy efficiency of the system. [ABSTRACT FROM PUBLISHER]

This paper presents optimal sum-rate precoding and beamforming strategies, to enable serving multiple and single data streams per user, respectively, for multi-user MIMO-OFDM based interweave cognitive radio (CR) networks. The proposed schemes maximize the rate of the CR users subject to spatial interference constraints. Initially, a beamforming scheme is derived for a multiuser CR scenario. A framework is also presented for regularized secondary user (SU) beamforming followed by an iterative procedure to compute the optimal value of the regularization parameter based on decomposition into convex subproblems. For a multiuser CR scenario with multiple receive antennas at each user, a scheme is proposed for the downlink interference limited block diagonalization and power allocation considering both the absence and presence of channel state information (CSI) of the primary user. SU rate maximization for MIMO-OFDM scenarios is also considered with CSI uncertainty, for both stochastic and worst case CSI uncertainty models. Finally, in order to multiplex several CR users with a limited number of base-station antennas, a paradigm is also introduced for coordinated secondary user beamforming. Simulation results demonstrate the improved performance of the various proposed multiuser MIMO-OFDM CR transmission schemes. [ABSTRACT FROM AUTHOR]

The nested array, implemented by nonuniform antenna placement, is an effective approach to achieve $O(N^2)$ degrees of freedom (DOF) with an antenna array of $N$ antennas. Such DOF refers to the number of directions of incoming signals that can be resolved. With the increased number of DOF, an important application of nested array is to nullify interference signals from multiple directions. In this paper, we apply nested array in a massive multiple input multiple output (MIMO) heterogeneous network (HetNet) for interference management. With a nested array, a base station (BS) can nullify a certain number of interference signals based on their directions. Then, a key design issue is how to select the set of interference sources to be nullified at each BS. As the DOF of each BS is used to resolve both desired signals and interference, the number of interference signals that can be nullified depends on the number of users served by the BS. Thus, user association is another factor that impacts the system performance and should be jointly considered with interference nulling. We formulate the joint interference nulling scheduling and user association problem as an integer programming problem, aiming to maximize the sum rate of all users subject to BS DOF constraints. We first investigate the case of interference nulling with a given user association, and propose a scheme to solve a relaxed problem as well as derive a performance upper bound. Then, we propose a distributed joint interference nulling and user association scheme based on a poly matching between users and BSs. Simulation results show that the proposed schemes effectively improve the sum rate and achieves a near optimal performance. [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 investigate the interference alignment (IA) for the multiple-input–multiple-output (MIMO) multicell downlink networks in the context of a square channel matrix. We propose a novel joint user grouping and base station (BS) association algorithm. Furthermore, we design the transmit and receive beamforming matrices in closed forms by using a generalized eigenvalue decomposition (GEVD)-based IA scheme, which can exploit the special structure of a square channel matrix against the existing algorithm. Moreover, we provide three theorems to characterize the maximum achievable degrees of freedom (DoF) of the proposed GEVD-based IA scheme. Compared with the existing IA scheme, the proposed GEVD-based IA scheme can reduce the dimension of subspace occupied by the intercell interference (ICI) more efficiently, hence providing achievable DoF higher than or equal to that of the existing IA scheme. [ABSTRACT FROM AUTHOR]

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]

For next-generation network topologies, the distributed wireless ad-hoc network is one of the most challenging issues because of the inherent node intelligence decision. Nevertheless, due to the randomness of node distribution, it is difficult to achieve high efficiency without signaling to neighboring nodes to coordinate random interference among the nodes, which, in turn, leads to high computational overhead in general. In this paper, we propose a framework of distributed multiple-input–multiple-output (MIMO) ad-hoc networks to improve link capacity while achieving computational simplicity without interworking with other links in a fully distributed manner. To achieve this goal, we align three main resource management algorithms, i.e., the distributed link-scheduling algorithm (DLSA), the distributed power allocation algorithm (DPAA), and the distributed cooperative beamforming algorithm (DCBA). In the simulation results, performance has proven to be promising in terms of the average sum rate, compared with that of other conventional schemes. [ABSTRACT FROM PUBLISHER]

This paper studies the rate maximization problem and designs the beamforming strategy for relay-aided multiuser multiantenna cognitive radio (CR) network that coexists with a primary radio (PR) system via opportunistic spectrum sharing (OSS). The beamforming is characterized by two layers: 1) Cognitive beamforming (CB) is employed by CR base station (BS), CR relay station (RS), and secondary users (SUs) to mitigate the interference to and from the PR. 2) Joint CR-BS and CR-RS beamforming is used to maximize the weighted sum rate of the SUs. Since the optimization turns out to be nonconvex, we apply the classic Blahut-Arimoto algorithm and solve the original problem through an iterative approach. Interestingly, each iterative step involves only convex optimizations whose closed forms can be found. Moreover, we derive an upper bound for the sum rate to assist system analysis. Numerical results show that the proposed approach achieves higher performance, compared with the widely studied diagonally structured beamformer design. [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]

The multiuser beamforming (MUBF) scheme has attracted tremendous attention as a suboptimal linear precoding technique to realize the benefits of system capacity in multiple-input–multiple-output (MIMO) systems. Due to mutual interference between cochannel users, the objective of maximizing the system capacity and fair service provisioning to different users should be well balanced in the design of the MUBF scheme. To this end, we propose a framework for the joint multiuser beamforming and partitioning (MUBFP) design problem, which attempts to partition users into a set of disjoint groups, where each group of users is served using the MUBF scheme. By jointly optimizing the grouping number, the user partitioning, and the beamformer design, the average sum capacity can be maximized without sacrificing user fairness. A decomposition approach is presented to decouple this problem into the user partitioning and beamformer design subproblems with the given number of groups, which can be solved using the agglomerative hierarchical clustering (AHC) algorithm and sequential parametric convex approximation (SPCA) approach, respectively. An iterative algorithm is proposed to search the optimal grouping number for the decoupled MUBFP problem by incorporating the algorithms of these two subproblems, and a suboptimal algorithm is developed to reduce the searching times with good approximation performance. Simulation results are provided to show the effectiveness of the proposed schemes in terms of convergence, average sum capacity, and complexity. [ABSTRACT FROM PUBLISHER]

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]

This work investigates a robust energy-efficient solution for multiple-input–multiple-output (MIMO) transmissions in cognitive vehicular networks. Our goal is to design an optimal MIMO beamforming for secondary users (SUs), considering imperfect interference channel-state information (CSI). Specifically, we optimize the energy efficiency (EE) of SUs, given that the transmission power constraint, the robust interference power constraint, and the minimum transmission rate are satisfied. To solve the optimization problem, we first characterize the uncertainty of CSI by bounding it in a Frobenius-norm-based region and then equivalently convert the robust interference constraint to a linear matrix inequality (LMI). Furthermore, a feasible ascent direction approach is proposed to reduce the optimization problem into a sequential linearly constrained semidefinite program, which leads to a distributed iterative optimization algorithm for deriving the robust and optimal beamforming. The feasibility and convergence of the proposed algorithm is theoretically validated, and the final experimental results are also supplemented to show the strength of the proposed algorithm over some conventional schemes in terms of the achieved EE performance and robustness. [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 a multicast scenario that involves an ad hoc network of cochannel multiple-input–multiple-output (MIMO) nodes in which a source node attempts to share a streaming message with all nodes in the network through some predefined multihop routing tree. The message is assumed to be broken down into packets, and the transmission is conducted over multiple frames. Each frame is divided into time slots, and each link in the routing tree is assigned one time slot in which to transmit its current packet. We present an algorithm for determining the number of time slots and the scheduling of the links in these time slots to optimize the connectivity of the network, which we define to be the probability that all links can achieve the required throughput. In addition to time multiplexing, the MIMO nodes also employ beamforming to manage interference when links are simultaneously active, and the beamformers are designed with the maximum connectivity metric in mind. The effects of outdated channel-state information are taken into account in both the scheduling and the beamforming designs. We also derive bounds on the network connectivity and sum transmit power to illustrate the impact of interference on network performance. Our simulation results demonstrate that the choice of the number of time slots is critical in optimizing network performance and illustrates the significant advantage provided by multiple antennas in improving network connectivity. [ABSTRACT FROM PUBLISHER]

This correspondence proposes a novel multiuser computational offloading scheme for a fog-based scenario. Specifically, a controlling user (CU) distributes its computational tasks to multiple trusted helping users (HUs) by exploiting both nonorthogonal multiple access (NOMA) and beamforming. First, social relationships between the CU and HUs are exploited for the selection of trusted HUs. Then, the HUs having sufficient computing resources and high social trust are selected and grouped into multiple pairs. For the sake of improving the spectral efficiency, NOMA is invoked for simultaneously supporting several HUs. Meanwhile, a process of zero-forcing beamforming is used by the CU to completely eliminate the interpair interference, hence further enhancing the sum rate. By analyzing the achievable rate and energy consumption of the uplink and downlink transmission, an energy consumption minimization problem is formulated subject to the constraints of signal-to-interference plus noise ratio and transmission latency. We design a user-pairing and resource allocation algorithm for successively optimizing the power allocation and computational task distribution, while satisfying the classic Karush–Kuhn–Tucker conditions. Our numerical results demonstrate that by combining the social relationships, the computational capacities, and the channel conditions, the proposed multiuser computational offloading scheme relying on social trust, NOMA, and beamforming improves the energy consumption, sum rate, and transmission latency. [ABSTRACT FROM AUTHOR]

In this correspondence, we propose an efficient hybrid beamforming algorithm for multipair two-way relaying systems with a large number of antenna elements. We first investigate an asymptotic behavior of the fully digital (FD) case where the number of radio frequency (RF) chains equals that of antenna elements. Our analysis reveals that the optimal RF filters can be determined by the array response vectors in the asymptotic region without consideration of the baseband (BB) filters. From this observation, we provide a new RF filter design based on the Gram–Schmidt process. Also, in order to manage interpair interference, we design BB filters based on the weighted sum minimum mean square error for given RF filters. Our simulation results demonstrate that as the number of antenna elements grows, our method achieves the optimal FD performance with much reduced computational complexity. [ABSTRACT FROM AUTHOR]

We tackle the problem of minimizing the maximum expected delivery time of all transmission pairs in 60 GHz mmWave networks. The network is dual-hop with multiple transmitter-receiver pairs, relays, and a centralized controller (termed PicoNet coordinator). We jointly optimize relay and link selection for the transmitter–receiver pairs to minimize delivery time, while the reflected non-line-of-sight transmission links are exploited to get around obstacles. To reduce computational complexity, we develop a decomposition principle to transform the joint-optimization problem into a link selection subproblem and a relay assignment subproblem when there are a sufficient amount of relays. A tight performance bound for the proposed algorithm is proved. We also develop a heuristic scheme to handle the case when there are no enough relays. The superior performance of our proposed algorithms is validated with extensive simulations and comparisons with benchmark schemes. [ABSTRACT FROM AUTHOR]

We consider a multiple-input–multiple-output (MIMO) relaying broadcast channel in downlink cellular networks, where the base station and the relay stations are both equipped with multiple antennas, and each user terminal has only a single antenna. In practical scenarios, channel estimation is imperfect at the receivers. Aiming to maximize the signal-to-interference noise ratio (SINR), at each user, we develop two robust linear beamforming schemes for the single-relay case and the multirelay case, respectively. The two proposed schemes are based on singular value decomposition (SVD), minimum mean square error (MMSE), and regularized zero forcing (RZF). Simulation results show that the proposed scheme outperforms the conventional schemes with imperfect channel estimation. [ABSTRACT FROM PUBLISHER]