Multi-cell Wireless Networks Research Articles

Interference Management for Over-the-Air Federated Learning in Multi-Cell Wireless Networks

Federated learning (FL) over resource-constrained wireless networks has recently attracted much attention. However, most existing studies consider one FL task in single-cell wireless networks and ignore the impact of downlink/uplink inter-cell interference on the learning performance. In this paper, we investigate FL over a multi-cell wireless network, where each cell performs a different FL task and over-the-air computation (AirComp) is adopted to enable fast uplink gradient aggregation. We conduct convergence analysis of AirComp-assisted FL systems, taking into account the inter-cell interference in both the downlink and uplink model/gradient transmissions, which reveals that the distorted model/gradient exchanges induce a gap to hinder the convergence of FL. We characterize the Pareto boundary of the error-induced gap region to quantify the learning performance trade-off among different FL tasks, based on which we formulate an optimization problem to minimize the sum of error-induced gaps in all cells. To tackle the coupling between the downlink and uplink transmissions as well as the coupling among multiple cells, we propose a cooperative multi-cell FL optimization framework to achieve efficient interference management for downlink and uplink transmission design. Results demonstrate that our proposed algorithm achieves much better average learning performance over multiple cells than non-cooperative baseline schemes.

Interference-aware path planning optimization for multiple UAVs in beyond 5G networks

Integrating unmanned aerial vehicles (UAVs) as flying base stations (FBSs) is expected to be an important architectural element in the beyond 5G/6G mobile wireless networks. A key operational aspect in UAV-aided 5G/6G networks is the UAV path optimization (or trajectory planning as it is also commonly referred to). In this paper, we address this problem using a mixed integer linear programming (MILP) formulation for FBS path optimization in terms of traveling time considering co-channel interference and time windows in serving ground users (GUs) at cluster points (CPs) for constructing the multiple UAV paths in both a single-cell and a multi-cell wireless network. In the proposed technique, we assume that all FBSs depart and return to a single depot, which is considered to be a terrestrial 5G/6G base station (BS). The novelty of the proposed solution compared to previous techniques is that we take explicitly into account the interference under the unit disk graph (UDG) model to create interference-aware UAV's trajectory. Numerical investigations reveal that the proposed interference-aware path optimization approaches improve the overall performance of the network up to approximately 21% in terms of throughput for the FBSs. Notably, these gains come with an unnoticeable increase in the total completion time, which is comparable to well-known previously proposed solutions. Additionally, the proposed heuristic algorithms provide competitive decision making with low underlying computational complexity rendering them amenable for real-time implementation.

On Meeting a Maximum Delay Constraint Using Reinforcement Learning

Several emerging applications in wireless communications are required to achieve low latency, but also high traffic rates and reliabilities. From a latency point of view, most of the state-of-the-art techniques consider the average latency which may not directly apply to scenarios with stringent latency constraints. In this paper, we consider scheduling under a max-delay constraint; this is an NP-hard problem. We propose a novel approach to tackle the scheduling problem by directly addressing the constraint. We consider the downlink of a multi-cell wireless communication network with nodes communicating with users each facing their own delay constraint on randomly arrived packets. Packets must be scheduled to meet the users’ delay constraints. Our main contributions are first, proposing a new search approach, Super State Monte-Carlo Tree Search (SS-MCTS), as a version of regular MCTS modified for large-scale probabilistic environments; second, developing trained value and policy networks to reduce computational complexity, and finally, addressing the scheduling problem through a reinforcement learning framework. Our numerical results demonstrate that the proposed approach significantly improves the packet delivery rate over a baseline approach while meeting the max-delay constraint, and addressing the scalability as the main issues in large action-state spaces.

RIS Configuration, Beamformer Design, and Power Control in Single-Cell and Multi-Cell Wireless Networks

Reconfigurable Intelligent Surfaces (RISs) are recently attracting a wide interest due to their capability of tuning wireless propagation environments in order to increase the system performance of wireless networks. In this paper, a multiuser wireless network assisted by a RIS is studied and resource allocation algorithms are presented for several scenarios. First of all, the problem of channel estimation is considered, and an algorithm that permits separate estimation of the mobile user-to-RIS and RIS-to-base stations components is proposed. Then, for the special case of a single-user system, three possible approaches are shown in order to optimize the Signal-to-Noise Ratio with respect to the beamformer used at the base station and of the RIS phase shifts. Then, for a multiuser system with two cells, assuming channel-matched beamforming, the geometric mean of the downlink Signal-to-Interference plus Noise Ratios across users is maximized with respect to the base stations transmit powers and RIS phase shifts configurations. In this scenario, the RIS is placed at the cell-edge and some users are jointly served by two base stations to increase the system performance. Numerical results show that the proposed procedures are effective and that the RIS brings substantial performance improvements to wireless system.

Intelligent Reflecting Surface-Aided Joint Processing Coordinated Multipoint Transmission

This article investigates intelligent reflecting surface (IRS)-aided multicell wireless networks, where an IRS is deployed to assist the joint processing coordinated multipoint (JP-CoMP) transmission from multiple base stations (BSs) to multiple cell-edge users. By taking into account the fairness among cell-edge users, we aim at maximizing the minimum achievable rate of cell-edge users by jointly optimizing the transmit beamforming at the BSs and the phase shifts at the IRS. As a compromise approach, we transform the non-convex max-min problem into an equivalent form based on the mean-square error method, which facilities the design of an efficient suboptimal iterative algorithm. In addition, we investigate two scenarios, namely the single-user system and the multiuser system. For the former scenario, the optimal transmit beamforming is obtained based on the dual subgradient method, while the phase shift matrix is optimized based on the Majorization-Minimization method. For the latter scenario, the transmit beamforming matrix and phase shift matrix are obtained by the second-order cone programming and semidefinite relaxation techniques, respectively. Numerical results demonstrate the significant performance improvement achieved by deploying an IRS. Furthermore, the proposed JP-CoMP design significantly outperforms the conventional coordinated scheduling/coordinated beamforming coordinated multipoint (CS/CB-CoMP) design in terms of max-min rate.

High system capacity pre-scheduler for multi-cell wireless networks

With the constant increase of throughput demands, maximizing the wireless network capacity has always been a crucial issue. Network densification becomes one of the adopted strategies to guarantee a high Quality of Experience (QoE). These new access points are included in the traditional 5G coverage scheme and offload the network in dense areas where the demand is high. A mobile may be located in the coverage area of several base stations which implies the need to extend traditional scheduling to a multicell context. This leads to the emergence of new issues concerning resource allocation strategies. When one user is suitable to receive resources from different cells, a well designed users' distribution is essential to limit unbalanced and overloaded cells. The main contribution of this paper is to propose a pre-scheduler called Multi-Cell Pre-Scheduler (MCPS) that adequately allocates user traffic between different access points, intervening before the scheduling application. Compatible with any type of simple cell existing schedulers, MCPS adapts schedulers to multi-cell context and improves their performance by delaying the system congestion and making the system more robust to unexpected traffic peaks. Performance evaluations show that the use of MCPS solution allows to increase the throughput capacity and energy efficiency while improving QoE fairness between users.

Diverse Cell Association Schemes for Fifth-Generation Wireless Networks

One of the underlying challenges in infrastructure-based multi-cell wireless networks is ensuring the proper association of a particular mobile User Equipment (UE) to the appropriate base stations (BSs). In the network terminology, this is usually referred to as cell association, user association, cell selection, or BS assignment. For now, the term cell association will be used for further discussion within this paper. In a wireless network with dense deployment of the base stations (BSs), the number of potential BSs with which a UE can be associated increases significantly with the increasing network complexity. The network densification demands the need for designing optimal or distributed cell association schemes. This is because, in case of improper association of UEs with BSs, it may result in increased interference, reduced throughput, inefficient energy consumption, load imbalance and higher latency in both uplink and/or downlink. 5G cellular networks aim to improve the connection speed and provide ultra-low latency through heterogeneous environments. While maintaining these parameters, the Quality of Service (QoS) needs to be maintained along with a sustained data rate. In such a case, the design of efficient cell association scheme for smooth flow of the traffic in the network is a real challenge which needs to be tackled. The aim of this paper is to study cell association techniques for 5G and beyond wireless networks, analyze the benefits of the methods and propose evolved and better solutions for cell association schemes.

Multi-User Adaptive Video Delivery Over Wireless Networks: A Physical Layer Resource-Aware Deep Reinforcement Learning Approach

In this paper, we investigate the adaptive video delivery for multiple users over time-varying and mutually interfering multi-cell wireless networks. The key research challenge is to jointly design the physical-layer resource allocation scheme and application-layer rate adaptation logic, such that the users' long-term fair quality of experience (QoE) can be maximized. Due to the timescale mismatch between these two layers and the asynchrony of user requests, however, it is difficult to directly model the cross-layer stochastic control problem by using a reinforcement learning framework. To address this difficulty, we propose a novel two-level decision framework where an optimization-based beamforming scheme (performed at the base stations) and a deep reinforcement learning (DRL)-based rate adaptation scheme (performed at the user terminals) are, respectively, developed, such that a highly complex long-term multi-user QoE fairness problem is decomposed into some relatively simple problems and solved effectively. Our strategy represents a significant departure from the existing schemes with consideration of either a short-term multi-user QoE maximization or a long-term single-user point-to-point QoE maximization. Extensive simulations demonstrate that the proposed cross-layer design is effective and promising.

Joint Task Offloading and Resource Allocation for Multi-Server Mobile-Edge Computing Networks

Mobile-edge computing (MEC) is an emerging paradigm that provides a capillary distribution of cloud computing capabilities to the edge of the wireless access network, enabling rich services and applications in close proximity to the end users. In this paper, an MEC enabled multi-cell wireless network is considered where each base station (BS) is equipped with a MEC server that assists mobile users in executing computation-intensive tasks via task offloading. The problem of joint task offloading and resource allocation is studied in order to maximize the users’ task offloading gains, which is measured by a weighted sum of reductions in task completion time and energy consumption. The considered problem is formulated as a mixed integer nonlinear program (MINLP) that involves jointly optimizing the task offloading decision, uplink transmission power of mobile users, and computing resource allocation at the MEC servers. Due to the combinatorial nature of this problem, solving for optimal solution is difficult and impractical for a large-scale network. To overcome this drawback, we propose to decompose the original problem into a resource allocation (RA) problem with fixed task offloading decision and a task offloading (TO) problem that optimizes the optimal-value function corresponding to the RA problem. We address the RA problem using convex and quasi-convex optimization techniques, and propose a novel heuristic algorithm to the TO problem that achieves a suboptimal solution in polynomial time. Simulation results show that our algorithm performs closely to the optimal solution and that it significantly improves the users’ offloading utility over traditional approaches.

Dynamic User-Centric Clustering for Uplink Cooperation in Multi-Cell Wireless Networks

In this paper, dynamic user-centric cell clustering, which is capable of exploiting dynamics in the channel states, is investigated for joint intra-cluster interference cancellation in a multi-cell environment. A resource efficient cluster size minimization problem is formulated to dynamically group the cells into clusters based on user channel states such that quality-of-service provisioning for cell-edge users can be improved. The integer programming problem depends largely on the intra-cluster interference weight. A subgradient algorithm is employed to solve the relaxed problem when no constrain on cooperation cost is present. To reduce extra burden on backhaul due to base station (BS) cooperation, constraints on the number of per-BS cooperative links and the maximum user-centric cluster size are introduced to the optimization problem, which is solved efficiently by a greedy algorithm. Numerical results show that the proposed dynamic user-centric clustering algorithms achieve significant improvements over existing static and fixed-size dynamic clustering schemes in terms of cell-edge performance and backhaul efficiency. The proposed greedy algorithm, in particular, can effectively alleviate the overall and per-BS cooperation cost while guaranteeing the cooperative gain. With similar resource consumption and outage performance, the proposed scheme achieves 12.6% higher rate gain compared with existing fixed size dynamic clustering strategy.

Fundamental Analysis of Full-Duplex Gains in Wireless Networks

Full-duplex radio technology is becoming mature and holds potential to boost the spectrum efficiency of a point-to-point wireless link. However, a fundamental understanding is still lacking, with respect to its advantages over half-duplex in multi-cell wireless networks with contending links. In this paper, we establish a spatial stochastic framework to analyze the mean network throughput gain from full duplex, and pinpoint the key factors that determine the gain. Our framework extends classical stochastic geometry analysis with a new tool set, which allows us to model a tradeoff between the benefit from concurrent full-duplex transmissions and the loss of spatial reuse, particularly for CSMA-based transmitters with random backoff. We analytically derive closed-form expressions for the full-duplex gain as a function of link distance, interference range, network density, and carrier sensing schemes. It can be easily applied to guide the deployment choices in the early stage of network planning.

A performance comparison of in-band full duplex and dynamic TDD for 5G indoor wireless networks

In-band full duplex has emerged as a solution for high data rate and low access delay for 5G wireless networks after its feasibility has been demonstrated. However, the impact of the in-band full duplex on the system-level performance of multi-cell wireless networks has not been investigated thoroughly. In this paper, we conduct an extensive simulation study to investigate the performance of in-band full duplex for indoor 5G small cell wireless networks. Particularly, we compare the in-band full duplex with static and dynamic time division duplexing schemes which require much less hardware complexity. We examine the effects of beamforming and interference cancellation under various traffic demands and asymmetry situations in the performance comparison. Our objective is to identify under which condition and with which technology support the in-band full duplex becomes advantageous over the simpler duplexing schemes. Numerical results indicate that for highly utilized wireless networks, in-band full duplex should be combined with interference cancellation and beamforming in order to achieve a performance gain over traditional duplexing schemes. Only then in-band full duplex is considered to be advantageous at any number of active mobile stations in the network and any downlink to uplink traffic proportion. Our results also suggest that in order to achieve a performance gain with the in-band full duplex in both links, the transmit power of the access points and the mobile stations should be comparable.

Energy-Efficient Joint Scheduling and Power Control in Multi-Cell Wireless Networks

Traditional design of wireless networks mainly focuses on system capacity and spectral efficiency. As green networking is an inevitable trend, energy-efficient design for future wireless networks becomes paramount. In this paper, we address energy-efficient resource management in downlink orthogonal frequency division multiple access networks. The focus is targeted toward multi-cell networks, which are composed of multiple base stations (BSs) sharing the available radio resources. Consequently, greater emphasis is given to techniques that take inter-cell interference into account. Resource management in our context refers to the task of allocating the radio resources in order to maximize energy efficiency. We devise resource management techniques that jointly tackle the problems of scheduling and power control. Accordingly, we adopt two different approaches: a centralized approach , where BSs coordinate in order to reach a globally optimal energy-efficient solution, and a distributed approach , where BSs selfishly strive to maximize their own energy efficiency. We portray the centralized approach as a convex optimization problem, whereas we have recourse to non-cooperative game theory to model the distributed approach. In particular, we show that the non-cooperative game converges to a unique Nash equilibrium in low- and high-interference scenarios. We perform thorough numerical simulations to quantify the discrepancy between the centralized and distributed approaches, and identify the conditions where they have precedence over the state of the art. Moreover, the simulation results highlight the fast convergence of our algorithms, which is a precious asset for realistic deployments.

WiFlix: Adaptive Video Streaming in Massive MU-MIMO Wireless Networks

We consider the problem of simultaneous on-demand streaming of stored video to multiple users in a multicell wireless network where multiple unicast streaming sessions are run in parallel and share the same frequency band. Each streaming session is formed by the sequential transmission of video “chunks,” such that each chunk arrives into the corresponding user playback buffer within its playback deadline. We formulate the problem as a network utility maximization (NUM) where the objective is to fairly maximize users’ video streaming quality of experience (QoE) and then derive an iterative control policy using Lyapunov optimization, which solves the NUM problem up to any level of accuracy and yields an online protocol with control actions at every iteration decomposing into two layers interconnected by the users’ request queues : 1) a video streaming adaptation layer reminiscent of dynamic adaptive streaming over HTTP (DASH), implemented at each user node; and 2) a transmission scheduling layer where a max-weight scheduler is implemented at each base station. The proposed chunk request scheme is a pull strategy where every user opportunistically requests video chunks from the neighboring base stations and dynamically adapts the quality of its requests based on the current size of the request queue. For the transmission scheduling component, we first describe the general max-weight scheduler and then particularize it to a wireless network where the base stations have multiuser multiple-input multiple-output (MU-MIMO) beamforming capabilities. We exploit the channel hardening effect of large-dimensional MIMO channels (massive MIMO) and devise a low complexity user selection scheme to solve the underlying combinatorial problem of selecting user subsets for downlink beamforming, which can be easily implemented and run independently at each base station. Furthermore, through simulations, we show that deploying MU-MIMO significantly improves video streaming performance and also that the proposed cross-layer approach is able to serve users more fairly than a baseline scheme representative of current systems running independently designed protocol layers.

Hybrid Energy-Efficient Transmission Protocol for Heterogeneous Wireless Sensor Networks

Heterogeneous Wireless Sensor Networks (WSNs) have different sensing ranges and computing power in the midst of various resource constraints such as limited battery power, reduced transmit power and network potential. This paper proposes new hybrid energy efficient transmission protocol called Hybrid Transmission Protocol (HTP). The proposed algorithm focuses on the issues of throughput dreadful conditions and limited power on mobile nodes due to conflicts in multi-cell wireless networks. The design principle of the proposed routing algorithm is to introduce a new border cluster between the sink nodes and the cluster-heads in order to lengthen the lifetime of the network nodes with minimized energy consumption to attain energy efficiency. The creation of clusters is done by making use of the sensor nodes formation that has a Cluster-Head (CH) used for performing the data aggregation in the sensor nodes of the cluster. Later the data that is aggregated is transmitted with multiple hops to the base station and in turn, this process leads to the reduction in the bandwidth through the elimination of the redundant data present in a cluster. The performance results indicate that the HTP proposed gained greater network lifetime, better performance and higher throughput compared to the other available algorithms.

Distributed Scheduling and Discrete Power Control for Energy Efficiency in Multi-Cell Networks

Distributed resource allocation for energy efficiency is an important issue in wireless cellular networks. While most of the existing works in literature consider continuous transmit power, in practical systems such as 3GPP LTE networks, the downlink transmit power is quantized into discrete levels. Moreover, how to apply those power control schemes into such systems is not specified, so one can consider a simple rounding scheme, however, the optimality, convergence, and uniqueness may not be preserved. Therefore, in this letter, we propose a distributed resource allocation for energy efficiency in multi-cell wireless networks, considering discrete power control. We formulate the problem as a fractional discrete optimization problem, show its NP-hardness, and propose a suboptimal polynomial time solution by exploiting the convenience of non-linear fractional programming. The algorithm is executed before each transmission interval, and in each iteration, each base station first assigns the user for the radio resource and then updates the transmit power using a randomized procedure. Simulations are provided to evaluate the convergence and advantages of the proposed algorithm.

Iterative beamforming algorithm for improved throughput in multi‐cell multi‐antenna wireless systems

Mitigating the effects of inter-cell interference in a multi-cell wireless network is essential to improve the achieved performance. In this study, the authors develop an iterative distributed beamforming scheme that addresses the effect of inter-cell interference problem on the downlink of a multi-cell multi-antenna wireless system with linear beamformers. The proposed scheme attempts at improving the overall system performance by exploiting network duality principle to jointly compute the optimal transmit and receive beamforming weights such that the desired received power is maximised while minimising the caused interference to other users. Network duality allows the serving base stations and the intended users to jointly compute the beamforming weights of the transmit/receive antenna elements, which improve network performance. The proposed scheme considers variable levels of channel information feedback and different transmit–receive antenna configurations. Simulation results show that a significant performance improvement is achieved through the joint optimisation of the transmit/receive beamforming vectors. Results also indicate that compared with reference schemes, their scheme improves the achieved sum-rate by up to 50%.

High-performance SINR assignment in multi-cell wireless networks

The problem of finding an optimal power vector for maximizing the number of supported links (i.e., links who may obtain their desired target-SINRs) in wireless networks is generally NP-hard. Recently, distributed power-control algorithm with temporary removal and feasibility check (DFC) has been proposed in which if the system is infeasible (i.e., the target-SINRs of all links may not be simultaneously reachable), some of the unsupported links are hardly removed (by stopping power transmission) aiming at the maximum number of supported links. DFC has been designed based on the single-cell CDMA network model and therefore, not taking the interference variations from other cells into account, its performance can be shown to degrade for multi-cell or ad-hoc networks. In this paper, we propose high-performance power control algorithm with feasibility check (HFC) for wireless networks in which, aiming at the minimal number of removed links, each non-supported link is removed from the set of active links in favour of others. While finding a Pareto and energy efficient power/SINR vector for a general model of a wireless network has been an open problem so far, we specially show that the fixed-points of HFC are Pareto-efficient, meaning that no link is unnecessarily removed.

A Two-Level Temporal Fair Scheduler for Multi-Cell Wireless Networks

We propose a two-level scheduler for a frequency reuse-1 multi-cell wireless network satisfying inter-and intra-cell weighted temporal fairness constraints. As opposed to hard partitioning of the entire frequency band to different cell patterns in frequency reuse-M systems (M > 1), we propose sharing this band opportunistically in time by these patterns. Through numerical examples, we show notable gains in overall network throughput due to improved multi-user diversity in comparison with a conventional frequency reuse-3 system.

Adaptive Soft Frequency Reuse Scheme for Wireless Cellular Networks

Spectrum scarcity and intercell interference (ICI) are two fundamental limiting factors in wireless cellular systems. Soft frequency reuse (SFR) has been proposed as an effective way to manage the spectrum and reduce ICI in cellular systems. In a static SFR scheme, the allocations of transmit power or subcarriers in each cell are fixed prior to system deployment. This limits the potential performance of the SFR scheme. In this paper, we propose an intercell resource-allocation algorithm, which is referred to as the adaptive SFR algorithm (ASFR), that dynamically optimizes subcarrier and power allocations for multicell wireless networks to improve system capacity. The ASFR first finds the subcarrier and power allocations in each cell by using exhaustive search and greedy descend methods and then iteratively repeats among cells until a predefined convergence criterion is satisfied. The theoretical analysis proves the convergence of the ASFR algorithm. Simulation results show that the ASFR achieves a higher system throughput and better cell edge user performance than existing frequency reuse schemes.