Multi-cell Multiple-input Single-output Research Articles

Joint Beamforming for IRS-Aided Multi-Cell MISO System: Sum Rate Maximization and SINR Balancing

This paper studies joint beamforming problems for an intelligent reflecting surface (IRS)-aided multi-cell multiple-input single-output (MISO) system, and the goal is to maximize the sum rate by jointly optimizing the transmit beamforming vectors at BSs and the reflective beamforming vector at the IRS, subject to the individual maximum transmit power constraints at BSs, and the reflection constraints at the IRS. Due to the formulated optimization problem is highly non-convex, we propose an alternating optimization (AO) algorithm based on successive convex approximation (SCA) such that the transmit and reflective beamforming vectors can be optimized alternately. We further consider the SINR balancing beamforming design scheme by maximizing the minimum SINR among all users to enhance the fairness among users, in which the transmit and reflective beamforming vectors are optimized in an alternating manner. The transmit beamforming vectors are optimized by the second-order-cone programming (SOCP) based on bisection method and the reflective beamforming vector is updated based on the technique of semidefinite relaxation (SDR). Simulation results show that the two proposed algorithms considerably outperform the benchmark zero-forcing (ZF) scheme. Moreover, the AO algorithm based on SCA has good communication performance than the other two schemes. And the AO algorithm based on bisection search guarantees the fairness for all users.

Weighted Sum Rate Maximization in IRS-BackCom Enabled Downlink Multi-Cell MISO Network

This letter investigates intelligent reflecting surface backscatter communication (IRS-BackCom) assisted downlink multi-cell multiple-input single-output network for the first time. In such multi-cell network, each IRS instead of active transmit antennas serves as a cell base station (BS) to communicate with its single-antenna users through modulation and backscatter of ambient wireless signal from a power beacon (PB). To maximize weighted sum rate (WSR) of network subject to total power budget, coordinated multipoint transmission (CoMP) is implemented at the IRSs. By alternately optimizing the beamforming vectors at the PB and all the IRSs, the sub-optimal WSR is obtained. Simulations show the achievable WSR and verify the feasibility of the proposed IRS-BackCom enabled CoMP scheme.

User Scheduling Based on Multi-Agent Deep Q-Learning for Robust Beamforming in Multicell MISO Systems

Maximizing the rate in multiple input single output (MISO) systems using distributed algorithms is an important task that typically incurs high computational cost. In this work, we propose two deep Q-learning-based user scheduling schemes to solve the beamforming problem of sum-rate maximization with per base station power constraints in multicell MISO scenarios. The two key features of the proposed algorithms are that they are executed in a distributed fashion and are robust with respect to channel state information (CSI) errors. Simulation results show that in the presence of CSI errors the proposed schemes outperform state-of-the-art algorithms both in terms of average spectral efficiency and execution time.

Max-Min Fairness in IRS-Aided Multi-Cell MISO Systems With Joint Transmit and Reflective Beamforming

This paper investigates an intelligent reflecting surface (IRS)-aided multi-cell multiple-input single-output (MISO) network with a set of multi-antenna base stations (BSs) each communicating with multiple single-antenna users, in which an IRS is dedicatedly deployed for assisting the wireless transmission and suppressing the inter-cell interference. Under this setup, we jointly optimize the coordinated transmit beamforming vectors at the BSs and the reflective beamforming vector (with both reflecting phases and amplitudes) at the IRS, for the purpose of maximizing the minimum weighted signal-to-interference-plus-noise ratio (SINR) at the users, subject to the individual maximum transmit power constraints at the BSs and the reflection constraints at the IRS. To solve the non-convex min-weighted-SINR maximization problem, we first present an exact-alternating-optimization approach to optimize the transmit and reflective beamforming vectors in an alternating manner, in which the transmit and reflective beamforming optimization subproblems are solved exactly in each iteration by using the techniques of second-order-cone program (SOCP) and semi-definite relaxation (SDR), respectively. However, the exact-alternating-optimization approach has high computational complexity, and may lead to compromised performance due to the uncertainty of randomization in SDR. To avoid these drawbacks, we further propose an inexact-alternating-optimization approach, in which the transmit and reflective beamforming optimization subproblems are solved inexactly in each iteration based on the principle of successive convex approximation (SCA). In addition, to further reduce the computational complexity, we propose a low-complexity inexact-alternating-optimization design, in which the reflective beamforming optimization subproblem is solved more inexactly. Via numerical results, it is shown that the proposed three designs achieve significantly increased min-weighted-SINR values, as compared with benchmark schemes without the IRS or with random reflective beamforming. It is also shown that the inexact-alternating-optimization design outperforms the exact-alternating-optimization one in terms of both the achieved min-weighted-SINR value and the computational complexity, while the low-complexity inexact-alternating-optimization design has much lower computational complexity with slightly compromised performance. Furthermore, we show that our proposed design can be applied to the scenario with unit-amplitude reflection constraints, with a negligible performance loss.

Energy and Spectral Efficiency Tradeoff via Rate Splitting and Common Beamforming Coordination in Multicell Networks

Rate splitting (RS) has the potential to significantly enhance both energy efficiency (EE) and spectral efficiency (SE) of wireless networks. In this paper, we propose joint design of the beamforming and rate allocation to maximize both EE and SE of a downlink multicell multiple-input single-output (MISO) system with rate splitting and common beamforming coordination (RS-CBC). This design problem is formulated as a non-convex quadratically-constrained multi-objective optimization problem (MOOP). By investigating the quasi-concavity relationship between EE and SE, the formulated MOOP is transformed into a single-objective optimization problem (SOOP) to offer a tradeoff between EE and SE by maximizing EE in any achievable SE region. A series of transformations are then applied to make the SOOP tractable, after which an efficient iterative algorithm based on successive convex approximation (SCA) is proposed to solve the problem. Simulation results demonstrate the effectiveness of the proposed algorithm and unveil interesting tradeoffs between EE and SE under different parameter settings.

Energy Efficient Coordinated Beamforming for Multicell System: Duality-Based Algorithm Design and Massive MIMO Transition

In this paper, we investigate joint beamforming and power allocation in multicell multiple-input single-output (MISO) downlink networks. Our goal is to maximize the utility function defined as the ratio between the system weighted sum rate and the total power consumption subject to the users’ quality of service requirements and per-base-station (BS) power constraints. The considered problem is nonconvex and its objective is in a fractional form. To circumvent this problem, we first resort to an virtual uplink formulations of the the primal problem by introducing an auxiliary variable and applying the uplink-downlink duality theory. By exploiting the analytic structure of the optimal beamformers in the dual uplink problem, an efficient algorithm is then developed to solve the considered problem. Furthermore, to reduce further the exchange overhead between coordinated BSs in a large-scale antenna system, an effective coordinated power allocation solution only based on statistical channel state information is reached by deriving the asymptotic optimization problem, which is used to obtain the power allocation in a long-term timescale. Numerical results validate the effectiveness of our proposed schemes and show that both the spectral efficiency and the energy efficiency can be simultaneously improved over traditional downlink coordinated schemes, especially in the middle-high transmit power region.

대용량 데이터 전송을 위한 다중 셀 MISO 하향 능동 안테나 시스템에서 3D 빔포밍 기법

본 논문은 다중 셀 다중입력 단일 출력 (MISO : multiple-input single-output) 하향링크 능동 안테나 시스템 (AAS : active antenna systems)에서 기지국의 수직 각도를 최적화하는 새로운 기법을 제시한다. 본 연구에서는 기존의 전역 탐색 방식 대신 간단하면서 최적의 값에 근사한 알고리듬들을 제안한다. 먼저, random matrix theory의 특성을 반영하여 평균 전송용량에 large system approximation이 적용된 수직적 빔포밍 알고리듬을 소개한다. 다음으로, signal-to-leakage-and-noise ratio (SLNR)을 기반으로 최적화 문제를 간단하게 만드는 수직적 빔포밍 알고리듬을 제안한다. 실험 결과를 통해 제안된 알고리듬들의 성능이 기존의 전역 탐색 알고리듬에 비해 복잡도가 매우 감소됨에도 불구하고 거의 비슷한 성능을 보임을 증명하였다. In this paper, we provide a new approach which optimizes the vertical tilting angle of the base station for multi-cell multiple-input single-output (MISO) downlink active antenna systems (AAS). Instead of the conventional optimal algorithm which requires an exhaustive search, we propose simple and near optimal algorithms. First, we represent a large system approximation based vertical beamforming algorithm which is applied to the average sum rate by using the random matrix theory. Next, we suggest a signal-to-leakage-and-noise ratio (SLNR) based vertical beamforming algorithm which simplifies the optimization problem considerably. In the simulation results, we demonstrate that the performance of the proposed algorithms is near close to the exhaustive search algorithm with substantially reduced complexity.

Decentralized Energy-Efficient Coordinated Beamforming for Multicell Systems

Energy-efficient transmission is crucial for future wireless communication systems and has attracted much attention. In this paper, we study coordinated beamforming optimization for multicell multiple-input-single-output (MISO) downlink systems using energy efficiency as a criterion, which is still an open problem to the best of our knowledge. The optimization problem of interest is nonconvex and in a fractional form. To solve it, we first reveal that finding the solution of the problem is equivalent to searching for a particular point on the Pareto boundary of the newly defined energy-efficiency rate tuples. Then, we propose to use a set of interference temperature (IT)-constrained beamforming optimization problems to characterize the energy-efficiency rate Pareto boundary. Based on that, two efficient iterative algorithms are developed to reach the Pareto optimality and thus to solve the primary problem. We show that the proposed algorithms can be carried out in a decentralized manner and are guaranteed to converge. Then, these algorithms are extended to consider imperfect channel state information (CSI) using the worst-case design. Numerical results are finally provided to verify the effectiveness of the proposed schemes and they exhibit the great potential of the coordinated beamforming optimization in improving the energy efficiency of the cellular network. In particular, the results also illustrate that our proposed algorithms achieve most of the achievable performance gain with a small number of iterations and thereby with limited backhaul overhead in a time-division duplexing (TDD) system.

A Multi-Cell Beamforming Design by Uplink-Downlink Max-Min SINR Duality

In this paper, we address the problem of the coordinated beamforming design for multi-cell multiple input single output (MISO) downlink system subject to per-BS power constraints. The objective is taken as the maximization of the minimum signal-to-interference plus noise ratio (SINR), while a complete analysis of the duality between the multi-cell downlink and the virtual uplink optimization problems is provided. A hierarchical iterative scheme is proposed to solve the virtual uplink optimization problem, whose solution is then converted to derive the one of the multi-cell downlink beamforming problem. The proposed algorithm is proved to converge to a stable point. Additional, the complexity of the proposed algorithm is analyzed. Simulation results show that, in contrast to existing multi-cell beamforming schemes, the proposed algorithm achieves better performance in terms of both rate per energy (RPE) and the worst-user rate.

Capacity Analysis of a Multi-Cell Multi-Antenna Cooperative Cellular Network with Co-Channel Interference

Characterization and modeling of co-channel interference is critical for the design and performance evaluation of realistic multi-cell cellular networks. In this paper, based on alpha stable processes, an analytical co-channel interference model is proposed for multi-cell multiple-input multi-output (MIMO) cellular networks. The impact of different channel parameters on the new interference model is analyzed numerically. Furthermore, the exact normalized downlink average capacity is derived for a multi-cell MIMO cellular network with co-channel interference. Moreover, the closed-form normalized downlink average capacity is derived for cell-edge users in the multi-cell multiple-input single-output (MISO) cooperative cellular network with co-channel interference. From the new co-channel interference model and capacity, the impact of cooperative antennas and base stations on cell-edge user performance in the multi-cell multi-antenna cellular network is investigated by numerical methods. Numerical results show that cooperative transmission can improve the capacity performance of multi-cell multi-antenna cooperative cellular networks, especially in a scenario with a high density of interfering base stations. The capacity performance gain is degraded with the increased number of cooperative antennas or base stations.