Strong Inter-cell Interference Research Articles

Joint Resource Management for MC-NOMA: A Deep Reinforcement Learning Approach

This paper presents a novel and effective deep reinforcement learning (DRL)-based approach to addressing joint resource management (JRM) in a practical multi-carrier non-orthogonal multiple access (MC-NOMA) system, where hardware sensitivity and imperfect successive interference cancellation (SIC) are considered. We first formulate the JRM problem to maximize the weighted-sum system throughput. Then, the JRM problem is decoupled into two iterative subtasks: subcarrier assignment (SA, including user grouping) and power allocation (PA). Each subtask is a sequential decision process. Invoking a deep deterministic policy gradient algorithm, our proposed DRL-based JRM (DRL-JRM) approach jointly performs the two subtasks, where the optimization objective and constraints of the subtasks are addressed by a new joint reward and internal reward mechanism. A multi-agent structure and a convolutional neural network are adopted to reduce the complexity of the PA subtask. We also tailor the neural network structure for the stability and convergence of DRL-JRM. Corroborated by extensive experiments, the proposed DRL-JRM scheme is superior to existing alternatives in terms of system throughput and resistance to interference, especially in the presence of many users and strong inter-cell interference. DRL-JRM can flexibly meet individual service requirements of users.

Graph Theory Based Beam Scheduling for Inter-Cell Interference Avoidance in MmWave Cellular Networks

The application of high directional beam in millimeter wave leads to a new downlink inter-cell interference (ICI) characteristic that the ICI is high only if the beam of a neighboring cell points towards a user in local cell. This characteristic indicates that the ICI can be avoided if the beams in the network are scheduled coordinately. In this paper, we investigate the time-domain beam scheduling problem for downlink ICI avoidance by modeling the entire network as a graph, where the number of time slots occupied by each beam is considered as a constraint to guarantee service quality. The beams in each cell are classified according to the neighboring cells they may interfere with. If two adjacent cells simultaneously use the beam which may interfere with each other, beam collision occurs, leading to strong ICI. Based on graph theory, we propose a least beam collision (LBC) algorithm to minimize the number of beam collisions, and we prove that this LBC algorithm is capable of acquiring the global minimum beam collision solution. Our simulation results verify that the strong ICI between two neighboring cells can be efficiently eliminated, which benefits the transmission reliability and the network's sum rate.

Interference Coordination and Resource Allocation Planning With Predicted Average Channel Gains for HetNets

Future average channel gains are recently reported predictable within a minute-level horizon for a mobile user. Predictive resource allocation for non-realtime service with future average channel gains in a time window of one or more minutes long has been demonstrated effective in improving user experience and network throughput as well as reducing energy consumption. Nonetheless, existing studies for predictive resource allocation never consider inter-cell interference (ICI), which severely limits the user experience and network performance in densely-deployed cellular networks. This paper investigates predictive resource allocation in heterogeneous networks, where some base stations generate strong ICIs to some mobile users requesting non-realtime service. Optimizing the resource allocation in a predictive manner for interference networks is challenging, since how to allocate future resources depends on the future signal to interference-plus-noise ratio, which in turn relies on the assigned resources. To deal with this difficulty, we introduce a predictive interference coordination scheme to divide all BSs and users into groups, where the BS-user pairs in each group can communicate simultaneously in a second-level frame. Then, we optimize a common resource allocation plan for all users in each group. The plan essentially determines which BSs in the group should be muted and the users should be associated with which active BS in each frame of the prediction window. By resorting to graph theory, we obtain the optimal solution and derive a low-complexity algorithm. Simulation results show that the proposed scheme outperforms existing relevant methods in terms of user satisfactory rate in heterogeneous networks with heavy traffic load.

Adaptive Centralized Clustering Framework for Software-Defined Ultra-Dense Wireless Networks

This paper develops a new centralized clustering framework to mitigate strong intercell interference in a software-defined ultra-dense network, where the number of clusters can be adapting to network conditions. A directed interference graph is designed to capture the dominant interference resulting from user mobility. An asymptotically optimal Max- K -Cut method is proposed to partition the graph, achieving a ( $1-1/K$ ) approximation of the optimum in a polynomial time-complexity, where $K$ is the number of clusters. As a result, $K$ can be adaptively adjusted to leverage among the optimality loss, throughput, and complexity. Numerical results show that our adaptive centralized framework performs significantly better than other centralized or semidistributed clustering schemes in terms of throughput.

User Association and Resource Allocation Optimization in LTE Cellular Networks

As the demand for higher data rates is growing exponentially, homogeneous cellular networks have been facing limitations when handling data traffic. These limitations are related to the available spectrum and the capacity of the network. Heterogeneous networks (HetNets), composed of macro cells (MCs) and small cells (SCs), are seen as the key solution to improve spectral efficiency per unit area and to eliminate coverage holes. Due to the large imbalance in transmit power between MCs and SCs in HetNets, intelligent user association (UA) is required to perform load balancing and to favor some SCs attraction against MCs. As long term evolution (LTE) cellular networks use the same frequency sub-bands, user equipment may experience strong intercell interference (ICI), especially at cell edge. Therefore, there is a need to coordinate the resource allocation (RA) among the cells and to minimize the ICI. In this paper, we propose a generic algorithm to optimize UA and RA in LTE networks. Our solution, based on game theory, permits to compute cell individual offset and a pattern of power transmission over frequency and time domain for each cell. Simulation results show significant benefits in the average throughput and also cell edge user throughput of 40% and 55% gains respectively. Furthermore, we also obtain a meaningful improvement in energy efficiency.

Joint Resource Optimization for Multicell Networks With Wireless Energy Harvesting Relays

This paper first considers a multicell network deployment where the base station (BS) of each cell communicates with its cell-edge user with the assistance of an amplify-and-forward (AF) relay node. Equipped with a power splitter and a wireless energy harvester, the self-sustaining relay scavenges radio frequency (RF) energy from the received signals to process and forward the information. Our aim is to develop a resource allocation scheme that jointly optimizes (i) BS transmit powers, (ii) received power splitting factors for energy harvesting and information processing at the relays, and (iii) relay transmit powers. In the face of strong intercell interference and limited radio resources, we formulate three highly-nonconvex problems with the objectives of sum-rate maximization, max-min throughput fairness and sum-power minimization. To solve such challenging problems, we propose to apply the successive convex approximation (SCA) approach and devise iterative algorithms based on geometric programming and difference-of-convex-functions programming. The proposed algorithms transform the nonconvex problems into a sequence of convex problems, each of which is solved very efficiently by the interior-point method. We prove that our algorithms converge to the locally optimal solutions that satisfy the Karush-Kuhn-Tucker conditions of the original nonconvex problems. We then extend our results to the case of decode-and-forward (DF) relaying with variable timeslot durations. We show that our resource allocation solutions in this case offer better throughput than that of the AF counterpart with equal timeslot durations, albeit at a higher computational complexity. Numerical results confirm that the proposed joint optimization solutions substantially improve the network performance, compared with cases where the radio resource parameters are individually optimized.

Channel Estimation and Data Detection for Multicell Massive MIMO Systems in Correlated Channels

In this study, multicell multiuser multiple-input multiple-output systems are considered with very large number of antennas at base station. We consider a physical channel model comprised finite number of angle of arrivals, and propose a channel estimation scheme based on the eigenvalue decomposition of the covariance matrix of the received signal vector. To improve the channel estimation performance under strong intercell interference, a joint channel estimation and optimal multiuser detection scheme is proposed. Furthermore, an optimal linear receiver is developed for the uplink by taking the channel estimation error into account, and derive a closed-form expression for the sum achievable rate of the optimal receiver. Moreover, using the channel reciprocity of a time-division duplex system, we present how to form an optimal precoding vector for the downlink, and how to compute the sum achievable rate in the downlink. Numerical results are provided to verify the analysis and evaluate the performance of the channel estimation and data detection.

Combinatorial Orthogonal Beamforming for Joint Processing and Transmission

Coordinated multiple point transmission (CoMP) technologies have recently been proposed to improve the performance of cell-edge users who suffer strong inter-cell interference. Nevertheless, as more transmitters get involved in cooperation, the complexity associated with the selection of multi-dimensional parameters increases exponentially. In this work, we investigate efficient multi-cell cooperation based on CoMP-joint processing and transmission (CoMP-JPT) with orthogonal beamforming using limited feedback. Through the utilization of combinatorial optimization, optimal user scheduling for joint transmission via multiple transmitters is accomplished, while the computational complexity is significantly reduced. In particular, a generalized beam assignment problem (GBAP) is formulated and solved using a combinatorial algorithm that is generalized in terms of the number of transmitters o\mathcal{B}o. The performance of the combinatorial orthogonal beamforming (COBF) scheme is mathematically analyzed so as to demonstrate its superiority and capability to maintain fairness among users in a multi-cell environment. In the simulation results, a performance gain of more than 50% for cell-edge users is obtained without a loss in the average throughput for the total number of users. In addition, the COBF method can reduce the complexity by more than 60% when compared to the conventional exhaustive search technique.

Heterogenous QoS‐guaranteed load balancing in 3GPP LTE multicell fractional frequency reuse network

ABSTRACTThe performance of 3GPP long term evolution (LTE) network can be significantly influenced by unbalanced user distribution. In heavily loaded cells, users cannot obtain satisfied services because of shortage of resources, whereas in lightly loaded cells, there are plenty of unoccupied resources. Load balancing (LB) is proposed to deal with this problem, through which the load distribution among nearby cells can be greatly balanced. However, because of the usage of full frequency reuse in LTE, the cell‐edge throughput is severely deteriorated by strong intercell interference. Meanwhile, LTE network usually serves users with heterogenous quality‐of‐service (QoS) requirements, which requires LB to be aware of it. Furthermore, call admission control (CAC) is indispensable in the process of LB, and it depends heavily on the long‐term average performance of the scheduling scheme. Thus, design of an efficient LB algorithm with the consideration of intercell interference, QoS, CAC and scheduling together becomes an important issue. To deal with the previous problem, a multi‐objective optimisation problem is formulated in this paper. Its complexity is analysed, and a practical algorithm, which includes hybrid scheduling, QoS‐guaranteed CAC, QoS‐aware intracell and intercell LB, is proposed. Extensive simulations are conducted. Results show that the proposed algorithm can approximate the optimal solution efficiently, thus leading to significantly better performance, that is, a lower new call blocking rate for users with QoS requirement, a larger total utility and a higher cell‐edge throughput for users without QoS requirement, although the cost is a bit degradation of the total throughput. Copyright © 2013 John Wiley & Sons, Ltd.

Power-efficient downlink transmission in multicell networks with limited wireless backhaul

This article shows that division of a cell into tiers of smaller cells reduces power consumption. However, using the same frequency-time resources within multiple divided cells causes strong intercell interference. Given this circumstance, three beamforming techniques for multicell networks are presented to tackle the resultant challenging intercell interference environment. The schemes minimize the total transmit power across the coordinating base stations while simultaneously considering the quality of service of each user so that the latter is not unduly affected. Since the beamforming approaches require the circulation of information, an energy-efficient backhaul protocol is demonstrated.

A Joint Power and Subchannel Allocation Scheme Maximizing System Capacity in Indoor Dense Mobile Communication Systems

Indoor mobile communication systems (or simply indoor systems) are expected to yield significant improvement of capacity and coverage at low cost on mobile communication systems. However, the indoor system will likely be in dense environments, in which many cells exist in a small region, and a great portion of the coverage of a cell is overlapped by that of other cells. In dense environments, the systems are exposed to strong intercell interference, which is critical to system performance and should be mitigated by a novel radio resource management (RRM) scheme. In this paper, we first derive the characteristics of an optimal allocation of power and subchannel in dense environments. We prove that a special form of power allocation, which is called binary power allocation, in which only one transmitter transmits a signal for each subchannel, performs better than power allocation by conventional schemes. Then, we propose an efficient scheme for jointly allocating power and a subchannel based on the binary power allocation. Simulation and numerical results show that the proposed scheme can achieve larger system capacity than conventional schemes.

Limited Feedback Multiuser MIMO Techniques for Time-Correlated Channels

This work presents limited feedback schemes for closed-loop multiple-input multiple-output systems using frequency division duplex. The proposed methods employ compact feedback messages in order to (a) feed back and track a complete frequency-flat channel matrix, to be used as input to multiuser multiplexing methods designed for full channel side information (CSI) at the transmitter, and (b) enable the receiver to command the transmit weight adaptation, in order to maximize the link reliability under strong intercell interference. Simulations show that the channel feedback accuracy provided by the proposed algorithms produces a negligible bit error probability (BEP) performance loss in low mobility scenarios compared to the full CSI performance, and that the proposed interference rejection techniques can effectively exploit an estimate of the interference statistics in order to enable multiple-stream communications under the permanent presence of intercell interference signals.