Conventional Beamforming Schemes Research Articles

Joint beamforming and power splitting design for MISO downlink communication with SWIPT: a comparison between cell-free massive MIMO and small-cell deployments

Simultaneous wireless information and power transfer (SWIPT) has been advocated as a highly promising technology for enhancing the capabilities of 5G and 6G devices. However, the challenge of dealing with large propagation path loss poses a significant hurdle. To address this issue, massive multiple-input multiple-output (MIMO) is employed to enhance the efficiency of SWIPT in cellular-based networks with multiple small cells, and especially increase the energy for cell-edge users. In addition, by leveraging a large set of spatially distributed base stations to collaboratively serve SWIPT-enabled user equipment, the cell-free massive MIMO has the potential to provide even better performance than the conventional small-cell systems. In this work, we extend the investigation to include the application of SWIPT technology with alternating current (AC) logic in the cell-free networks and the small-cell networks and propose joint beamforming and power splitting optimization frameworks to maximize the system sum-rate, subject to the constraints on harvested energy, AC logic energy supply, and total transmit power. The optimization problem is shown to be non-convex, posing a significant challenge. To address this challenge, we resort to a two-stage decomposition approach. Specifically, we first introduce quadratic transform-based fractional programming (FP) algorithms to iteratively solve the non-convex optimization problems in the first stage, achieving near-optimal solutions with low time complexities. To further reduce the complexities, we also incorporate conventional schemes such as zero forcing, maximum ratio transmission, and signal-to-leakage-and-noise ratio for the design of beamforming vectors. Second, to determine the optimal power splitting ratio within the framework, we develop a one-dimensional (1-D) search algorithm to tackle the single variable optimization problem reduced in the second stage. These algorithms are then evaluated in the context of cell-free MIMO and small-cell networks with numerical experiments. The results show that the FP-based algorithms can consistently outperform those utilizing the conventional beamforming schemes, and the solutions of this work can achieve up to fivefold improvement in the system sum-rate than the small-cell counterpart while providing different but comparable performance trends in energy harvesting (EH).

On the Application of Quasi-Degradation to Network NOMA in Downlink CoMP Systems

The application of network non-orthogonal multiple access (N-NOMA) technique to coordinated multi-point (CoMP) systems has attracted significant attention due to its superior capability to improve connectivity and maintain reliable transmission for CoMP users simultaneously. Based on the concept of quasi-degraded channel for N-NOMA, this paper studies the precoding design for downlink N-NOMA scenarios with two base stations (BSs) equipped with multiple antennas. In specific, under quasi-degraded channels, simple linear precoding based N-NOMA can achieve the same minimal total transmission power as theoretically optimal but complicated dirty paper coding (DPC) scheme, when the users’ target rates and minimal transmission power of each BS are given. In this paper, the channel quasi-degradation (QD) condition is first rigorously derived for the scenario with single CoMP user and two NOMA users. The closed-form optimal precoders for N-NOMA under quasi-degraded channels are also provided. Then, based on QD condition, a novel hybrid N-NOMA (H-N-NOMA) scheme is proposed, which is a mixture of N-NOMA and conventional zero-forcing beamforming (ZFBF) scheme. Further, for the scenarios with more users, a low-complexity QD based user pairing (QDUP) algorithm is proposed, which is then combined with H-N-NOMA to make a novel scheme termed H-N-NOMA/QDUP into being. Numerical results are presented to reveal the impact factors on QD channels, and also demonstrate the superior performance of the proposed H-N-NOMA/QDUP scheme. It is shown that the proposed H-N-NOMA/QDUP scheme can effectively exploit the benefit of multi user diversity.

Reinforcement Learning-Based Joint Beamwidth and Beam Alignment Interval Optimization in V2I Communications

The directional antenna combined with beamforming is one of the attractive solutions to accommodate high data rate applications in 5G vehicle communications. However, the directional nature of beamforming requires beam alignment between the transmitter and the receiver, which incurs significant signaling overhead. Hence, we need to find the optimal parameters for directional beamforming, i.e., the antenna beamwidth and beam alignment interval, that maximize the throughput, taking the beam alignment overhead into consideration. In this paper, we propose a reinforcement learning (RL)-based beamforming scheme in a vehicle-to-infrastructure system, where we jointly determine the antenna beamwidth and the beam alignment interval, taking into account the past and future rewards. The simulation results show that the proposed RL-based joint beamforming scheme outperforms conventional beamforming schemes in terms of the average throughput and the average link stability ratio.

Feedback-Based Beam Training for Intelligent Reflecting Surface Aided mmWave Integrated Sensing and Communication

The intelligent reflecting surface (IRS) aided integrated sensing and communication (ISAC) is a promising technique in mmWave systems. However, most of the existing works on IRS-aided ISAC assume the availability of channel state information (CSI) along with high-overhead channel estimation. Besides, none has taken the impact of the echo interference from the communication user on the practical sensing performance into account. In this paper, we focus on the beamforming design in the presence of the destructive interference echo signal from the communication user and accordingly formulate a beam training problem for enhancing both sensing and communication performances. Considering that no CSI is available at the base station (BS), we propose a feedback-based joint active and passive beam training scheme by invoking a modified particle swarm optimization (PSO) method without requiring high-overhead cascaded channel estimation. Specifically, the BS updates the active and passive beams based on its received echo signal and user feedback information in the beam training period and then assigns them for more accurate sensing and more efficient communication in the ISAC period. Simulation results demonstrate the advantages of the proposed feedback-based beam training scheme over the conventional beamforming schemes without considering the interference echo signal, in terms of both sensing and communication performances. Moreover, it is shown that the destructive echo interference from the communication user can be effectively mitigated by limiting the communication signal-to-noise ratio, thereby achieving better sensing performance.

Robust adaptive Bayesian beamforming against stationary and nonstationary interferences

In this work, we aim to enhance the robustness of conventional adaptive beamforming schemes against both signal steering vector uncertainties and interference nonstationarity here. For this purpose, a random model with Watson prior distribution is applied to the steering vector of interest, and cluster structured prior belief about the interferer motion is incorporated into the statistical analysis. However, the difficulty in handling the normalization factor of the Watson distribution makes the Bayesian inference of this probabilistic model prohibitive. Towards this end, an efficient algorithm using variational Bayesian expectation-maximization is proposed, by iteratively majorizing a lower bound on the variational free energy to refine the latent parameters and circumvent intractable moment computation. A specific beamformer encompasses a trade-off among accuracy, speed and ease of implementation is thus obtained. Further, an extension of the algorithm to the case of moving interferences is presented by assigning a Dirichlet process prior to the hidden parametric space. By doing so, the underlying time-aware clusters embedded in the interference-plus-noise vectors can be automatically revealed. Both simulated and experimental results validate the superiority of the algorithms over the existing algorithms.

Holographic Radar: Target Detection Enabled by Reconfigurable Holographic Surfaces

Radars are a widely used technology for target detection, commonly enabled by phased arrays. However, complex hardware circuits such as phase shifters in phased arrays lead to relatively high power and cost. To tackle this issue, the reconfigurable holographic surface (RHS), a novel metamaterial antenna with simple hardware and compact element spacing, has been proposed. In this letter, we propose the holographic radar enabled by RHSs for target detection to achieve high accuracy with low power and cost. Due to the amplitude-controlled structure of the RHS which generates beams by tuning radiation amplitudes of its elements, conventional phase-controlled beamforming schemes for phased array radars do not work anymore. Therefore, we design an RHS amplitude-controlled beamforming scheme to optimize the detection accuracy. Theoretical analysis shows the holographic radar consumes less power than phased array radars with the same detection accuracy and cost, which is also verified by simulation results.

Physical Beam Sharing Multiuser Systems: Beamforming Designs and OTA Testings

This paper proposes a novel physical beam sharing system to use few radio-frequency (RF) chains to support more users. The concept of beam sharing is to design a beam pattern to simultaneously benefit all users. In this proposed system, every user can receive a number of data streams equal to the number of RF chains at the base station (BS) and achieve full multiplexing gain. A special design example is that the BS can support multiple users simultaneously even with only one RF chain. As a result, the computational and hardware costs of the BS can be significantly reduced due to the decreased RF chains in deploying a dense communications network. We first derive beamforming designs for this proposed system in a MISO channel with one data stream. Then, the results are further extended to MIMO channels with multiple data streams. Simulation results show that the proposed schemes with the derived solutions outperform conventional systems in several interested parameter settings, and have advantages in terms of fairness and complexity compared with NOMA (non-orthogonal multiple access) systems. Moreover, we also implemented 28 GHz hardware platforms with an <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"> <tex-math notation="LaTeX">$8 \times 8$ </tex-math></inline-formula> antenna array to verify the feasibility and advantages of the proposed schemes as well as measure the resulting beam patterns. The over-the-air (OTA) testings validate the concept of the proposed physical beam sharing and show that it outperforms conventional physical beamforming schemes.

Interference Exploitation for Ambient Backscatter Communication Networks via Symbol Level Precoding

Although ambient backscatter communications (AmBCs) are capable of achieving high spectrum- and energy-efficiency in numerous cutting-edge Internet-of-Things applications, the legacy user devices (UDs) will suffer from severe interference caused by the backscatter device (BD). In this letter, we leverage the symbol level precoding (SLP) technique to convert the harmful interference perceived at the UD into a useful signal, thereby improving the communication quality of the legacy users. Specifically, we consider an AmBC system where one access point (AP) simultaneously transmits information to all UDs and receives the BD&#x2019;s reflected signal. To reduce the power consumption of the AP, we formulate a joint transmit SLP and receive beamforming optimization problem while guaranteeing the quality of service of all UDs and the BD. An efficient algorithm is proposed to solve the formulated non-convex problem. Simulation results show that the proposed SLP scheme can achieve superior performance over conventional beamforming schemes in the sense of saving power consumption.

DoA Prediction Based Beamforming with Low Training Overhead for Highly-Mobile UAV Communication with Cellular Networks

In supporting communications with unmanned aerial vehicles (UAVs) as aerial user equipments (aUEs) in cellular systems, the current beamforming schemes based on channel state estimation are facing severe challenges from the pilot contamination effect, especially in 5G and future networks where the cell size becomes small and the user density is high. Beamforming schemes based on signal direction of arrival (DoA) are regarded as a highly promising alternative to solve this problem. However, to achieve optimal performance for DoA-based beamforming, the error to DoA estimation during pilot signal intervals, caused by the high mobility of UAVs, must be addressed. In the meantime, the training overheads of traditional DoA estimation algorithms must be reduced to save the bandwidth for data communication. This paper investigates uplink beamforming performance enhancement based on signal DoA estimation to support UAV-cellular network communication. We propose a novel DoA estimation algorithm to predict angle variations during the intervals, which achieves high precision even when UAVs are at high mobility. The prediction process requires no pilot signals and enables timely adjustment of the steering vector when calculating the beamforming weight vector. The proposed algorithm contributes to the realisation of a beamforming scheme with real-time steering vector updates, which simultaneously maintains high beamforming gains and low training overheads. Simulation results show that, compared with the conventional DoA-based beamforming scheme, the proposed method yields more accurate DoA estimation output and higher gains. Furthermore, simulation experiments also suggests that applying the proposed scheme can reduce up to 100 pilot signal transmissions per second.

Adaptive Perturbation-Aided Opportunistic Hybrid Beamforming for mmWave Systems

In this paper, we propose an adaptive perturbation-aided opportunistic hybrid beamforming (AP-OHBF) scheme for temporally correlated millimeter wave (mmWave) channels. The basic idea is that the proposed scheme perturbs the channel parameters to track the channels of the scheduled mobile stations (MSs), whose channels are in favorable conditions, based on only the signal-to-interference-plus-noise ratio (SINR) information of MSs. Specifically, in each time slot, the proposed scheme makes random perturbations to the “best” known channel parameters in the memory, which corresponds to the maximum sum scheduling metric. Then the base station (BS) constructs the channel estimates from these parameters, which are subsequently used to generate the precoders. For data transmission, the BS adopts the successful perturbation, which achieves performance improvements. By contrast, if a perturbation results in performance degradation, it is discarded, whereas the precoders corresponding to the best-known parameters in the memory are instead used for transmission. Simulation results show that in mmWave channels, the proposed AP-OHBF scheme significantly improves the system throughput with respect to the conventional opportunistic beamforming schemes for the same amount of feedback information.

Decentralized transmit beamforming scheme with zero forcing and user selection for multicell system dynamical systems

Conventional distributed beamforming schemes in multicell system often ignore two practical conditions, (1) the coexistence of intra-cell interference (InCI) and inter-cell interference (ICI), (2) the number of users is larger than the number of base station (BS) antennas. In this paper, we consider these two conditions and analyze the decentralized beamforming of the multicell system from the viewpoint of zero forcing (ZF). A novel user selection method based on ZF algorithm is proposed to divide the users into two categories, i.e. one of the categories is eligible to participate in ZF algorithm and the other is not. The proposed criterion of partitioning users is based on the degree of parallelity between local channels. In addition, each BS utilizes only the local channel state informations (CSIs) to derive its beamforming vector. The simulation results demonstrate that our proposed algorithm can achieve higher rates and are more robust against InCI and ICI than the traditional ones under the different number of transmitting antennas and users.

Turbo-Like Beamforming Based on Tabu Search Algorithm for Millimeter-Wave Massive MIMO Systems

For millimeter-wave (mmWave) massive multiple-input-multiple-output (MIMO) systems, codebook-based analog beamforming (including transmit precoding and receive combining) is usually used to compensate the severe attenuation of mmWave signals. However, conventional beamforming schemes involve complicated search among predefined codebooks to find out the optimal pair of analog precoder and analog combiner. To solve this problem, by exploring the idea of turbo equalizer together with the tabu search (TS) algorithm, we propose a Turbo-like beamforming scheme based on TS, which is called Turbo-TS beamforming in this paper, to achieve near-optimal performance with low complexity. Specifically, the proposed Turbo-TS beamforming scheme is composed of the following two key components: 1) Based on the iterative information exchange between the base station (BS) and the user, we design a Turbo-like joint search scheme to find out the near-optimal pair of analog precoder and analog combiner; and 2) inspired by the idea of the TS algorithm developed in artificial intelligence, we propose a TS-based precoding/combining scheme to intelligently search the best precoder/combiner in each iteration of Turbo-like joint search with low complexity. Analysis shows that the proposed Turbo-TS beamforming can considerably reduce the searching complexity, and simulation results verify that it can achieve near-optimal performance.

Exploiting Known Interference as Green Signal Power for Downlink Beamforming Optimization

We propose a data-aided transmit beamforming scheme for the multi-user multiple-input-single-output (MISO) downlink channel. While conventional beamforming schemes aim at the minimization of the transmit power subject to suppressing interference to guarantee quality of service (QoS) constraints, here we use the knowledge of both data and channel state information (CSI) at the transmitter to exploit, rather than suppress, constructive interference. More specifically, we design a new precoding scheme for the MISO downlink that minimizes the transmit power for generic phase shift keying (PSK) modulated signals. The proposed precoder reduces the transmit power compared to conventional schemes, by adapting the QoS constraints to accommodate constructive interference as a source of useful signal power. By exploiting the power of constructively interfering symbols, the proposed scheme achieves the required QoS at lower transmit power. We extend this concept to the signal to interference plus noise ratio (SINR) balancing problem, where higher SINR values compared to the conventional SINR balancing optimization are achieved for given transmit power budgets. In addition, we derive equivalent virtual multicast formulations for both optimizations, both of which provide insights of the optimal solution and facilitate the design of a more efficient solver. Finally, we propose a robust beamforming technique to deal with imperfect CSI, that also reduces the transmit power over conventional techniques, while guaranteeing the required QoS. Our simulation and analysis show significant power savings for small scale MISO downlink channels with the proposed data-aided optimization compared to conventional beamforming optimization.

Beamforming in Non-Regenerative Two-Way Multi-Antenna Relay Networks for Simultaneous Wireless Information and Power Transfer

Simultaneous wireless information and power transfer (SWIPT) is able to prolong the lifetime of energy constrained wireless networks. In this paper, we consider the relay beamforming design problem for SWIPT scheme in a non-regenerative two-way multi-antenna relay network. Our objective is to maximize the sum rate of two-way relay network under the transmit power constraint at relay and the energy harvesting (EH) constraint at EH receiver. For the non-convex EH-constrained relay beamforming optimization problem, we propose an iterative algorithm to find the global optimal solution based on semidefinite programming and rank-one decomposition theorem. To reduce computational complexity of global optimal solution, we transform the EH-constrained optimization problem to a difference of convex programming and propose a constrained concave convex procedure based iterative algorithm to find a local optimum. To further reduce the complexity, we propose a suboptimal solution based on the generalized eigenvectors method. When the case of multi-antenna sources is considered, we propose the alternating optimization based iterative algorithms. It is shown from simulations that considering the EH constraint, our proposed schemes outperform conventional relay beamforming schemes in the literature.

Downlink Vertical Beamforming Designs for Active Antenna Systems

In this paper, we study a vertical beamforming technique for multiple-input multiple-output downlink multi-user systems. In general, the transmit antenna gain is controlled by adjusting the boresight of antennas in directional antennas, and thus the cell average rate varies according to the angle of the boresight. First, we compute the tilting angles for directional antenna systems which maximize the cell average rate. To this end, the probability density function of a three-dimensional user distribution is derived. Based on the result, we analyze the average rate gain of active antenna systems over passive antenna systems for a single user case. Furthermore, for a multi-user active antenna system, beamforming designs to maximize the weighted sum rate are proposed by optimizing the transmit antenna gain and power allocation. Since finding joint optimal parameters requires prohibitively high computational complexity, we separate the optimization problem into two sub-problems of the vertical beamforming and the power allocation. Then a simple vertical beamforming algorithm based on a high signal-to-noise ratio assumption is presented. Also, for a multi-user passive antenna system, we provide a beamforming scheme based on a multi-sector concept. Simulation results show that the proposed beamforming schemes outperform the conventional beamforming schemes.

SER Performance of Large Scale OFDM-SDMA Based Cognitive Radio Networks

This paper proposes an adaptive large scale multiple input multiple output-beamforming scheme (LSMIMO-BF) for uplink (UL) access in broadband wireless cognitive networks with multiple primary users (PUs) and secondary users (SUs) sharing the same spectrum and employing orthogonal frequency-division multiplexing (OFDM). The proposed algorithm seeks the optimal transmit/receive weight vectors that maximize the UL MIMO channel capacity for each cognitive user while controlling the interference levels to PUs. Under the assumption of very large number of antennas at the base station, a closed-form expression for the symbol error rate (SER) performance of the cognitive LSMIMO-OFDM system is derived and compared with the one based on conventional beamforming schemes such as MIMO-maximum ratio combining (MIMO-MRC). The analysis and simulation show that when SUs are transmitting with the proposed constrained capacity-aware (CCA) scheme, the total interference level at the primary base station is reduced and the SER of PUs is improved compared to the case when the secondary network is using conventional MIMO-MRC. It was also shown that, as the number of base station antennas becomes larger, the constraints imposed by the primary network could be relaxed and the symbol error rate (SER) of SUs is improved without harming the PUs performance.

Relay Beamforming Design with SIC Detection for MIMO Multirelay Networks with Imperfect CSI

In this paper, we consider a dual-hop multiple-input-multiple-output (MIMO) wireless multirelay network, in which a source-destination pair, both equipped with multiple antennas, communicates through multiple half-duplex amplify-and-forward (AF) relay terminals, which are also with multiple antennas. Since perfect channel-state information (CSI) is difficult to obtain in a practical multirelay network, we consider imperfect CSI for all channels. We focus on maximizing the signal-to-interference-plus-noise ratio (SINR) at the destination. We propose a novel robust linear beamforming at the relays, based on the QR decomposition filter at the destination node, which performs successive interference cancellation (SIC). Using the law of large numbers, we obtain the asymptotic rate in the presence of imperfect CSI, upon which the proposed relay beamforming is optimized. Simulation results show that the asymptotic rate matches with the ergodic rate well. Analysis and simulation results demonstrate that the proposed beamforming outperforms the conventional beamforming schemes for any power of CSI errors and SNR regions.

Successive orthogonal beamforming for cooperative multi‐point downlinks

This study considers successive orthogonal beamforming (SOBF) for multicell downlink transmissions. A group of base stations (BSs) are in cooperation for effective downlink transmissions to cell-edge users, which is usually referred to as cooperative multi-point. As the number of cell-edge users is small and varying, conventional beamforming schemes, for example, zero-forcing beamforming (ZFBF), are not suitable for the multicell scenario. In the SOBF scheme considered here, the beamforming vectors are found in a successive manner when a new user comes in, so that the beamforming vectors of the existing users remain unchanged. With limited channel state information at the group of BSs, a new successive beam allocation (SBA) scheme is proposed for SOBF. In SBA, the candidate beams are broadcasted to all the users through downlink before the index of the best beam is sent back to the BS by each user. The sum rate performance of a system with two users is studied analytically. The numerical results are presented to show that the proposed SOBF with SBA outperforms ZFBF with conventional feedback strategy.

MIMO Broadcast Channels Based on SINR Feedback using a Non-Orthogonal Beamforming Matrix

The signal to interference plus noise ratio (SINR) feedback has been utilized in random beamforming (RBF) to select users for multiple-input multiple-output (MIMO) systems where a large number of users are required to obtain a multi-user diversity gain. However, if the number of users is not large enough, it may be difficult to obtain the performance gain and be easy to have a performance degradation conversely. To resolve this problem, it is necessary to find orthogonal random beams close to the channel matrix of selected users. In this paper, we present RBF using a non-orthogonal beamforming matrix (RBF NOBM), demonstrating an efficient search of random beams correlated with the channel matrix of selected users over a small number of users. For single user RBF using NOBM (SU-RBF NOBM) with two transmit antennas, the beamforming matrix for user data service is obtained through closed form expressions, and SU-RBF NOBM is then expanded to more than two transmit antennas. We also propose user selection algorithms for SU-RBF NOBM and multi-user RBF using NOBM (MU-RBF NOBM). Via simulation results, we demonstrate that the performances of RBF NOBM represent significant improvements compared to conventional beamforming schemes.

Capacity-aware linear MMSE detector for OFDM-SDMA systems

This paper presents a capacity-aware linear minimum mean square error (MMSE) detector for orthogonal frequency-division multiplexing/space-division multiple access (OFDM-SDMA) applications. We derive closed-form expressions for the signal-to-interference plus noise ratio (SINR) performance of OFDM-SDMA systems with linear MMSE detection method, where capacity enhancements have been introduced in the transmit/receive weight computations. Using the derived expressions, we compare the performance of the proposed scheme with the regular multiple-input multiple-output (MIMO)-MMSE system in OFDM-SDMA applications. It is shown that in enhancing the system capacity, the capacity-aware MIMO-MMSE scheme also enhances the SINR, resulting in lower symbol error rate (SER) compared to the conventional MIMO-MMSE beamforming schemes.