Multi-cell Downlink System Research Articles

Joint User Scheduling and Phase Shift Design for RIS Assisted Multi-Cell MISO Systems

This letter investigates the joint user scheduling and phase shift design for reconfigurable intelligent surface (RIS) assisted multi-cell downlink systems. A closed-form ergodic sum spectral efficiency (SE) approximation is utilized as the optimization metric. Based on this approximation, we schedule the users, whose cascaded channels are mostly correlated with each other’s, to maximize each user’s effective signal. Moreover, the RIS phase shift is designed to be the mean of the scheduled users’ cascaded channel phases. With the proposed transmission design, we find the optimal RIS deployment to achieve the highest maximum throughput which depends only on the relative locations of the BSs and RIS. In addition, we consider a more practical discrete RIS phase shift design based on a discrete Fourier transform (DFT) codebook. Simulation results show that the proposed low-complexity scheduling algorithm performs well.

5G Ultra-Reliable and Low Latency Communication Resource Scheduling for Power Business Quality Assurance

The resource scheduling method of 5G URLLC (Ultra-Reliable and Low Latency Communication) is studied in this paper to assure the Quality of Service (QoS) of various power business, which utilizes the limited spectrum and power in low-band cellular communication system to meet the requirement of power terminal about transmission rate, scheduling delay and fairness in an efficient manner. Firstly, based on the high reliability and low latency characteristics of URLLC, a multi-cell downlink system model is built. Then, a resource allocation problem model for maximizing downlink throughput is proposed and solved step by step. Power allocation algorithm bases on pricing mechanism and non-cooperative game and an Delay-based Proportional Fair algorithm (DPF）is designed to schedule channel resource dynamicly..Simulation results show that the proposed resource scheduling method can reduce the scheduling delay of power terminals under the constraints of transmission reliability and fairness while meeting the different QoS requirements. The proposed method outperforms some known algorithms.

SIR Coverage Analysis in Multi-Cell Downlink Systems With Spatially Correlated Queues

In this article, we analyze the signal-to-interference ratio (SIR) coverage probability of a multi-cell downlink system with random traffic arrivals. Based on a theoretical model, in which the base stations (BSs) and devices are deployed as independent Poisson point processes (PPPs), we first present the sufficient and necessary conditions of the e-stable region for the queueing system. Then, by taking into account the impact of spatially queueing interactions among BSs, we focus on the steady status of the queues and introduce a two-queue-length approximated model for the system. Specifically, in the studied new model, the BSs are separated into two sets in the steady state: the long-queue BS set and the short-queue BS set. The locations of the former are modeled by a Neyman-Scott process and those of the latter are modeled as a residual hole process. By applying the first-order statistic approximation, we further approximate the hole process by a homogeneous PPP with the same density. To model the deployment of the BSs precisely, the related parameters in the approximated model are fitted. Using tools from stochastic geometry and queueing theory, we derive the SIR coverage probability in the steady state. An iterative algorithm is proposed to calculate the active probability of the BSs. To reduce the computational complexity, Beta distribution is applied to approximate the probability density function of the service rate in each iteration of the algorithm. Finally, the effect of the fitting parameters and the accuracy of our analysis are presented via Monte Carlo simulations.

A low-complexity algorithm for the joint antenna selection and user scheduling in multi-cell multi-user downlink massive MIMO systems

The massive MIMO (multiple-input multiple-output) technology plays a key role in the next-generation (5G) wireless communication systems, which are equipped with a large number of antennas at the base station (BS) of a network to improve cell capacity for network communication systems. However, activating a large number of BS antennas needs a large number of radio-frequency (RF) chains that introduce the high cost of the hardware and high power consumption. Our objective is to achieve the optimal combination subset of BS antennas and users to approach the maximum cell capacity, simultaneously. However, the optimal solution to this problem can be achieved by using an exhaustive search (ES) algorithm by considering all possible combinations of BS antennas and users, which leads to the exponential growth of the combinatorial complexity with the increasing of the number of BS antennas and active users. Thus, the ES algorithm cannot be used in massive MIMO systems because of its high computational complexity. Hence, considering the trade-off between network performance and computational complexity, we proposed a low-complexity joint antenna selection and user scheduling (JASUS) method based on Adaptive Markov Chain Monte Carlo (AMCMC) algorithm for multi-cell multi-user massive MIMO downlink systems. AMCMC algorithm is helpful for selecting combination subset of antennas and users to approach the maximum cell capacity with consideration of the multi-cell interference. Performance analysis and simulation results show that AMCMC algorithm performs extremely closely to ES-based JASUS algorithm. Compared with other algorithms in our experiments, the higher cell capacity and near-optimal system performance can be obtained by using the AMCMC algorithm. At the same time, the computational complexity is reduced significantly by combining with AMCMC.

An Interference Cancellation Technique for Distributed MIMO Systems

This study proposes an uncooperative-multi-cell-multiple-input–multiple-output system to increase the throughput of an ill-conditioned wireless MIMO downlink system. The proposed algorithm suppresses the inter-cell interference at each mobile station using a set of adaptive receive beamformers, but handles the intra-cell interference at each base station using a Tomlinson Harashima precoder. Using the singular vectors of channel matrices, the receive beamformers can be designed based on a linear-constrained-minimum-variance criterion. On the other hand, to obtain high enough degree of freedoms for cancelling the intra-cell interference at each base station, the precoder is applied to the re-assembled downlink virtual channel formed by the simplified channel state information. Using the proposed interference suppression techniques, a multi-cell downlink system with multiple uncooperative base stations can be decomposed into a set of parallel subchannels. The resulting system has better performance in channel capacity than conventional MIMO systems. Computer simulations support the validity of the proposed approach.

다중 셀 MIMO 하향채널에서의 UCD를 이용한 블록 대각 분해

In this paper, we design non-linear transmitter and receiver using a uniform channel decomposition (UCD) to take account of inter-cell interference in multi-cell downlink systems. The designed UCD scheme brings forth the same SINR for all sub-channels in each cell. It provides great convenience to modulation/coding process and achieves the maximum diversity gain. The simulation results confirm that it exhibits a lower BER than the conventional method.

Energy efficient coordinated precoding design for a multicell system with RF energy harvesting

This paper investigates multicell multiuser downlink systems with simultaneous wireless information and power transfer. We study the energy efficient transmission design for maximizing the system energy efficiency of data transmission (bits/Joule delivered to the receivers) which is defined as the ratio of the weighted sum rate to the total power consumption. In particular, we focus on the energy efficient coordinated precoding for the downlink of multicell system using the power splitting technique where each receiver divides the received signal into two power streams for concurrent information decoding and energy harvesting. To solve the non-convex optimization problem under consideration, first, we transform the primal optimization problem in fractional form into a tractable parameterized subtractive form optimization problem. Then, the tractable problem is solved by applying the technique of the Lagrangian optimization technique and semidefinite relaxation. An efficient solution to the considered optimization problem is finally developed. Numerical results validate the effectiveness of the proposed algorithm and reveal that the proposed algorithm can achieve better energy efficiency performance than the traditional energy efficient transmission.

Low-Complexity User Scheduling with Switched Tilting for 3D Cellular Systems

We propose a low-complexity user scheduling scheme to enhance the sum rate performance for a multicell downlink system, in which the base station (BS) is equipped with a large-scale active antenna array. First, we divide each cell intoNregions according to the vertical beamwidth of the BS antennas. Next, candidate user equipment (UE) items are assigned to corresponding groups to their locations. Each scheduling slot is also divided intoNequal-time subslots. Then, at each subslot, we focus on one UE group, select the optimal number,K*, of UEs for simultaneous data transmission in the manner of round-robin scheduling, and adjust the BS antenna tilting to the optimal angleθtilt*. In particular,K*andθtilt*for each UE group are both obtained by means of large-system asymptotic analysis. Benefiting from the random matrix theory tools, the asymptotic analytical results are independent of instantaneous channel state information of UE, which make it possible to solveK*andθtilt*offline, therefore saving the online computational resources significantly. Numerical results verify that the proposed scheme achieves good sum rate performance with extremely low computational complexity.

Robust precoding for joint transmission in multicell multiuser downlink systems

This study considers the joint transmission precoding design for downlink multicell multiuser multiple-input single-output systems where imperfect channel variances are available at the base stations. The authors aim to tackle the robust signal-to-interference-plus-noise ratio (SINR) balancing problem to maximise the minimum worst-case user rate. To solve the non-convex problem, a duality relationship between the downlink max–min worst-case SINR optimisation problem and the virtual uplink min–max worst-case SINR optimisation problem is first revealed. Based on this, a new algorithm is developed to solve the virtual problem by using jointly the sub-gradient method and the geometric programming methods, whose achieved solution is finally converted to the downlink. Their analysis shows that the proposed algorithm is guaranteed to converge and has lower computational complexity than conventional approaches. Moreover, computer simulations validate the effectiveness of the proposed method and show that the proposed algorithm has fast convergence and achieves a performance close to that of the brute search method.

Robust Transceiver Design for Multicell MIMO Downlink Systems

The robust linear transceiver design for the more general model of multicell MIMO downlink system with imperfect channel state information is discussed in this paper. Our aim is to minimize the total power of the network while the quality of service (QoS) in terms of mean-square error (MSE) for every user should be strictly guaranteed for every channel realization in the uncertain region. Unfortunately, this problem may be infeasible due to the MSE constraints. Therefore, we provide a complete analysis of this problem by dividing the solutions into two phases. In phase I, a novel approach is devised to check the feasibility of this problem by considering one alternative problem which is always feasible. This alternative problem is troublesome due to infinite nonconvex MSE constraints. To handle this, we propose an iterative algorithm that performs optimization alternatively by switching between the precoders and decoders. The two subproblems in the algorithm can be recast as semidefinite programming problems which can be efficiently solved. In phase II, one novel iterative algorithm is proposed to solve the original robust problem. Finally, simulation results show that our proposed algorithms converge rapidly and can provide guaranteed QoS for all users. Moreover, we also show that, the more antennas at the users, the more power savings it can provide.

Coordinated beamforming for sum rate maximization in multi-cell downlink systems

In this paper, we propose novel approaches to maximize the sum rate in a multi-cell multi-input single-output (MISO) downlink system. First, considering a total power constraint, the uplink–downlink duality theory and the convex approximation approach are used to recast the original non-convex problem into an approximate problem of minimizing the sum of weighted inverse signal-to-interference-plus-noise-ratio (SINR). Then a lower complexity alternative optimization method with provable convergence is developed to solve this problem. Further, considering per-BS power constraints, the above algorithm is generalized and two novel lower complexity block coordinated beamforming methods are proposed to solve the sum rate maximization problem. Numerical results validate the effectiveness of the proposed algorithms and show that our algorithms converge to near-optimal performance within just a couple of iterations.

Distributed Precoding for Worst-Case MSE Optimization in Multi-Cell Downlink Systems

To mitigate the inter-cell interference in multi-cell downlink systems, robust precoder design is important for multi-cell cooperation as the channel state information (CSI) available at the base station is imperfect. In this letter, we investigate the worst-case performance optimization problem with bounded CSI error. According to the leakage-based schemes, the sum mean-square-error (MSE) minimization problem is reformulated as a series of parallel sub-problems that can be recast as a semidefinite programming (SDP) problem, and solved in a distributed manner. Moreover, a modified cost function is proposed to get a distributed solution for the Min-Max MSE optimization problem. In the modified MSE, inter-cell leakage is introduced to replace inter-cell interference in the traditional MSE. Simulation results indicate that the inter-cell leakage is comparable to the inter-cell interference for each user. Moreover, simulation results demonstrate the robustness of the distributed precoder designs through the bit error rate (BER) performance.

Large System Analysis of Cooperative Multi-Cell Downlink Transmission via Regularized Channel Inversion with Imperfect CSIT

In this paper, we analyze the ergodic sum-rate of a multi-cell downlink system with base station (BS) cooperation using regularized zero-forcing (RZF) precoding. Our model assumes that the channels between BSs and users have independent spatial correlations and imperfect channel state information at the transmitter (CSIT) is available. Our derivations are based on large dimensional random matrix theory (RMT) under the assumption that the numbers of antennas at the BS and users approach to infinity with some fixed ratios. In particular, a deterministic equivalent expression of the ergodic sum-rate is obtained and is instrumental in getting insight about the joint operations of BSs, which leads to an efficient method to find the asymptotic-optimal regularization parameter for the RZF. In another application, we use the deterministic channel rate to study the optimal feedback bit allocation among the BSs for maximizing the ergodic sum-rate, subject to a total number of feedback bits constraint. By inspecting the properties of the allocation, we further propose a scheme to greatly reduce the search space for optimization. Simulation results demonstrate that the ergodic sum-rates achievable by a subspace search provides comparable results to those by an exhaustive search under various typical settings.

Joint Beamforming and Power Control in Coordinated Multicell: Max-Min Duality, Effective Network and Large System Transition

This paper studies joint beamforming and power control in a coordinated multicell downlink system that serves multiple users per cell to maximize the minimum weighted signal-to-interference-plus-noise ratio. The optimal solution and distributed algorithm with geometrically fast convergence rate are derived by employing the nonlinear Perron-Frobenius theory and the multicell network duality. The iterative algorithm, though operating in a distributed manner, still requires instantaneous power update within the coordinated cluster through the backhaul. The backhaul information exchange and message passing may become prohibitive with increasing number of transmit antennas and increasing number of users. In order to derive asymptotically optimal solution, random matrix theory is leveraged to design a distributed algorithm that only requires statistical information. The advantage of our approach is that there is no instantaneous power update through backhaul. Moreover, by using nonlinear Perron-Frobenius theory and random matrix theory, an effective primal network and an effective dual network are proposed to characterize and interpret the asymptotic solution.

Multi-Cell Random Beamforming: Achievable Rate and Degrees of Freedom Region

Random beamforming (RBF) is a practically favourable transmission scheme for multiuser multi-antenna downlink systems since it requires only partial channel state information (CSI) at the transmitter. Under the conventional single-cell setup, RBF is known to achieve the optimal sum-capacity scaling law as the number of users goes to infinity, thanks to the multiuser diversity enabled transmission scheduling that virtually eliminates the intra-cell interference. In this paper, we extend the study of RBF to a more practical multi-cell downlink system with single-antenna receivers subject to the additional inter-cell interference (ICI). First, we consider the case of finite signal-to-noise ratio (SNR) at each receiver. We derive a closed-form expression of the achievable sum-rate with the multi-cell RBF, based upon which we show an asymptotic sum-rate scaling law as the number of users goes to infinity. Next, we consider the high-SNR regime and for tractable analysis assume that the number of users in each cell scales in a certain order with the per-cell SNR. Under this setup, we characterize the achievable degrees of freedom (DoF) for the single-cell case with RBF. Then we extend the analysis to the multi-cell RBF case by characterizing the DoF region. It is shown that the DoF region characterization provides useful guideline on how to design a cooperative multi-cell RBF system to achieve optimal throughput tradeoffs among different cells. Furthermore, our results reveal that the multi-cell RBF scheme achieves the "interference-free DoF" region upper bound for the multi-cell system, provided that the per-cell number of users has a sufficiently large scaling order with the SNR. Our result thus confirms the optimality of multi-cell RBF in this regime even without the complete CSI at the transmitter, as compared to other full-CSI requiring transmission schemes such as interference alignment.

SINR Balancing Techniques in Coordinated Multi-Cell Downlink Systems

In this paper, we study coordinated multi-cell downlink systems where multiple base stations jointly design a transmission strategy by sharing channel state information. Particularly, we tackle the signal-to-interference-plus-noise ratio (SINR) balancing problem to maximize the worst-user rate. We consider single-input single-output (SISO) interference channels (IFC) where all nodes are equipped with a single antenna, and there is one active user in each cell. First, achievable rate regions with symmetric complex (SC) and asymmetric complex (AC) signaling techniques are investigated. Then, we present the optimal and near-optimal SINR balancing algorithms with the SC signaling for two and three user SISO IFC. Due to residual interference, the worst-user rate of the SC signaling is saturated at high signal-to-noise-ratio region. To alleviate this problem, we also propose efficient balancing schemes based on the AC signaling for both two and three-user cases. Simulation results confirm effectiveness of the proposed SINR balancing algorithms and show that a substantial gain of the AC signaling is achieved over the SC signaling in terms of the maximum worst-user rate.

MSE-Based Robust Precoder Design in Multicell Downlink Systems

SUMMARY To mitigate the inter-cell interference in mul-ticell downlink systems, this letter consider the robust precoderdesign for multicell cooperation where the knowledge of chan-nel state available at the base station is imperfect. Assumingthat imperfect channel state information (CSI) can be exchangedamong cells but with no data sharing, we investigate the worst-case performance optimization problem with bounded CSI error.Our objective is to minimize the weighted sum mean-square-error(MSE) subject to per-base-station power constraints. A dis-tributed solution is obtained by reformulating the upper bound ofMSE and exploiting the Lagrangian method for the optimal prob-lem. Simulation results demonstrate that the proposed algorithmis robust to guarantee the worst-case sum rate performance andhas lower computational complexity than the SINR-based design. key words: Multi-cell cooperation, robust precoder, upper boundof MSE, weighted sum MSE minimization. 1. IntroductionMulticell cooperation communication between base sta-tions (BSs) has emerged as a promising approach toimprove the performance of systems and reduce the in-tercell interference with universal frequency reuse. De-pending on the BS coordinated level, it can be per-formed by two mode[1],[2]. In the first mode, the BSsshare both data and channel state information (CSI),where the system is termed as “network multiple inputmultiple output (MIMO)” or “joint processing (JP)”.In the second mode, the cooperative BSs exchange CSIonly, and the system is called “multicell system” or “co-ordinated beamforming (CBF)”. In this paper, we focuson the latter.One crucial processing for the cooperative multi-cell system is how to optimize the cooperation trans-mission between the cells. Without data sharing, [3]consider the sum-power minimization problem subjectto signal-to-interference plus noise ratio (SINR) con-straint. In [4], the weighted sum-rate maximizationproblem is solved through virtual SINR maximization,but the distributed solution is only suitable for highsignal-to-noise ratio (SNR) regime. To achieve a fair-ness basedrate optimality, [5] proposeda distributedal-gorithm to approach the performance of the centralized

Receiver Design for MIMO Relay Stations in Multi-Cell Downlink System

In multi-cell downlink system, quality of service (QoS) of multiuser MIMO is an important issue, in particular, for cell-edge users. For QoS of cell-edge users in the system, the utilization of multiple-input multiple-output (MIMO) relay stations (RSs) is a promising solution that improves the signal-to-interference plus noise ratio (SINR) from neighboring base stations (BSs) to each RS. Since the capacity between the BS and the RS relies heavily on the receive performance, it is necessary to reflect interference channels in the design of the receive weight vector. In this paper, we use a geometric approach to derive the effective channel gain of the RS according to the receive weight vector. The geometric relationship between the desired and interference channels is also described. Using the description, we propose a receiver, called the maximum lower bound of expected SINR receiver (MLESR). Through a performance analysis of the MLESR over the interference-limited regime, we derive closed terms for the performance bounds in terms of the number of RS antennas, the interference channel rank, and the BS transmit power according to the rate gap of the MLESR with respect to an ideal case, i.e., a no interference case.

Adaptive Cooperation Switching for Multicell Downlink Using Statistical CSI

This letter proposes an adaptive scheme that switches between cooperative and non-cooperative transmission for multicell downlink systems in Kronecker spatially correlated channels, which exploits statistical channel state information (CSI). Based on the received signal-to-noise ratios (SNRs) and a cooperation metric, we propose a simple base station (BS) association method and then derive low-SNR capacity approximations for both cooperative and non-cooperative systems. Using the results, we provide a low-complexity efficient cooperation switching method to enhance the system capacity. Results show that the proposed method is more efficient than the conventional method to search the switching point.

Robust Monotonic Optimization Framework for Multicell MISO Systems

The performance of multiuser systems is both difficult to measure fairly and to optimize. Most resource allocation problems are non-convex and NP-hard, even under simplifying assumptions such as perfect channel knowledge, homogeneous channel properties among users, and simple power constraints. We establish a general optimization framework that systematically solves these problems to global optimality. The proposed branch-reduce-and-bound (BRB) algorithm handles general multicell downlink systems with single-antenna users, multiantenna transmitters, arbitrary quadratic power constraints, and robustness to channel uncertainty. A robust fairness-profile optimization (RFO) problem is solved at each iteration, which is a quasi-convex problem and a novel generalization of max-min fairness. The BRB algorithm is computationally costly, but it shows better convergence than the previously proposed outer polyblock approximation algorithm. Our framework is suitable for computing benchmarks in general multicell systems with or without channel uncertainty. We illustrate this by deriving and evaluating a zero-forcing solution to the general problem.