Learning Radio Resource Management in RANs: Framework, Opportunities, and Challenges

We consider resource management problems in multi-user wireless networks, which can be cast as optimizing a network-wide utility function, subject to constraints on the long-term average performance of users across the network. We propose a state-augmented algorithm for solving the aforementioned radio resource management (RRM) problems, where, alongside the instantaneous network state, the RRM policy takes as input the set of dual variables corresponding to the constraints, which evolve depending on how much the constraints are violated during execution. We theoretically show that the proposed state-augmented algorithm leads to feasible and near-optimal RRM decisions. Moreover, focusing on the problem of wireless power control using graph neural network (GNN) parameterizations, we demonstrate the superiority of the proposed RRM algorithm over baseline methods across a suite of numerical experiments.

In this paper, we develop a generic Markovian model to study the dynamics of data service in a multi-cell context, and apply it to cells served by HSDPA and WiMAX access networks. We first present analytical models for interference and throughputs in WiMAX and HSDPA. Then, we consider a cooperative WiMAX/HSDPA network considering both inter-cell and vertical handovers and show how to calculate the steady-state probabilities and the performance indicators. Our calculations take into account mobility within the cell and between different cells and make us able to calculate intra and inter-cell dropping. We compare the performances of three joint Radio Resource Management (RRM) policies: HSDPA/WiMAX traffic distribution policy, HSDPA filling policy and WiMAX filling policy. Our numerical results show that the policy favoring WiMAX access and considering HSDPA as a rescue system (WiMAX filling policy) offers the best performances.

Featuring edge caching and computing, fog radio access networks have been seen as promising architectures. However, the joint optimization of cache and radio resource can put heavy burden on the resource manager in the cloud. To overcome this issue, hierarchical radio and cache resource management is studied in this paper. The core idea is to fully utilize the resource management capabilities of fog access points (FAPs) to divide time-domain resource on a small timescale by participating a coalitional game while make the resource manager allocate cache resource on a large timescale to maximize a long-term utility. Based on defined FAP preference, a low-complexity and distributed coalition formation algorithm is first developed under per-FAP fronthaul capacity constraints. Then, facing the challenges incurred by no closed form, discrete variables and the curse of dimensionality, multi-agent reinforcement learning (MARL) based caching is proposed for the resource manager. In the proposal, multiple agents are created, one for each content-FAP pair, who jointly learn a caching strategy via the interaction with a history network environment. By simulation, the effectiveness of the MARL based caching is demonstrated.

As heterogeneous networks (HetNets) emerge as one of the most promising developments toward realizing the target specifications of Long Term Evolution (LTE) and LTE-Advanced (LTE-A) networks, radio resource management (RRM) research for such networks has, in recent times, been intensively pursued. Clearly, recent research mainly concentrates on the aspect of interference mitigation. Other RRM aspects, such as radio resource utilization, fairness, complexity, and QoS, have not been given much attention. In this paper, we aim to provide an overview of the key challenges arising from HetNets and highlight their importance. Subsequently, we present a comprehensive survey of the RRM schemes that have been studied in recent years for LTE/LTE-A HetNets, with a particular focus on those for femtocells and relay nodes. Furthermore, we classify these RRM schemes according to their underlying approaches. In addition, these RRM schemes are qualitatively analyzed and compared to each other. We also identify a number of potential research directions for future RRM development. Finally, we discuss the lack of current RRM research and the importance of multi-objective RRM studies.

This article provides an overview of radio resource management (RRM) in the context of network slicing considering a range of different deployment options and practicalities. Initially, it elaborates the 3GPP radio access network (RAN) sharing in terms of business and architecture. Slice‐tailored RRM solutions are then considered for various base station deployments focusing on RRM principles and customization, which result in different paradigms of resource sharing including full isolation, sharing, and hybrid approaches. The concept of slice broker is also elaborated, which aims to allocate radio resources related to slice requests considering the desired service performance, highlighting the need of traffic and mobility forecasting. The notion of resource sharing is further analyzed, considering various RRM and scheduling schemes including multi‐objective and hierarchical RRM, slice‐aware flexible function split, TDD service‐oriented solutions, and joint radio and backhaul optimization and licensed assisted configurations.

Wireless Sensor Networks (WSNs) use the unlicensed Industrial, Scientific, and Medical (ISM) band for transmissions. However, with the increasing usage of these networks for diverse applications, the currently available ISM band does not suffice for their transmissions and a new challenge appears before the WSNs' research community. One of the candidate approaches to tackle this spectrum insufficiency problem is to incorporate the opportunistic spectrum access capability of Cognitive Radio (CR) into the existing WSN, thus giving birth to Cognitive Radio Sensor Network (CRSN). Efficient spectrum utilization is another approach to overcome this challenging problem. Another challenge associated to WSN operation is the dependability of sensor nodes on battery supplied power where the batteries in general are not replaceable. Therefore, advanced and intelligent radio resource management schemes are very essential to perform dynamic and efficient spectrum allocation among multiple sensor nodes and to optimize the power consumption of each individual node in the network. Radio resource management enables the sensor nodes to efficiently utilize the available spectrum and power, which in turn ensures QoS transmissions, maximizes the network lifetime, and reduces the inter-node and inter-network interferences. In this chapter, the authors present a comprehensive overview of the recent advances in radio resource management for CRSN. Radio resource management in CRSN has been reviewed in various scenarios (i.e., centralized, cluster-based, and distributed). The related issues and challenges are discussed, and future research directions are highlighted.

Nowadays, The Long-term Evolution-advanced (LTEA) system is widely used to provide the fifth generation (5G) communication network due to its improved network capacity and less delay during the communication process. The main issues in 5G network are insufficient resources of users and Burst errors, because it creates losses in the data transmission, in order to overcome this effective radio resource management to be developed in the 5G network. In this paper the binary crow search algorithm is proposed to develop the radio resource management in the 5G network. The proposed radio resource management is used for assigning adequate power and bandwidth to the desired user equipments of the network. In radio resource management a request queue is used for avoiding the unwanted resource allocation in the network. Moreover, the losses during the Transmission are minimized by using frequency Interleaving and Guard level Insertion. The performance of the proposed system is analyzed in terms of bit rate and sum rate.KeywordsQuality-of-Service (QoS)Long-term evolution (LTE)Data rateRadio resource organization approach (RROA)Long-term evolution-advanced (LTE-A)

The ever increasing user QoS demands and emergence of new user applications make job of network operators and manufacturers more challenging for efficiently optimisation and managing radio resources in radio the radio resources pools of different wireless networks. A group of strategies or mechanisms which are collectively responsible for efficient utilisation of radio resources available within the Radio Access Technologies (RAT) are termed as Radio Resource Management (RRM). The traditional RRM strategies are implemented independently in each RAT, as each RRM strategy considers attributes of a particular access technology. Therefore traditional RRM strategies are not suitable for heterogeneous wireless networks. Common Radio Resource Management (CRRM) or joint radio resource management (JRRM) strategies are proposed for coordinating radio resource management between multiple RATs in an improved manner. In this paper a fuzzy algorithm based CRRM strategy is presented to efficiently utilise the available radio resources in heterogeneous wireless networks. The proposed CRRM strategy balances the load in heterogeneous wireless networks and avoids the unwanted congestion situation. The results such as load distribution, packet drop rate and average throughput at mobile nodes are used to demonstrate the benefits of load balancing in heterogeneous wireless networks using proposed strategy.

One of the challenges of the mobile industry is to cope with the growth of mobile traffic demand expected for the next years, primarily driven by the increasing usage of mobile video services. Indeed, the existence of increasingly powerful terminals is encouraging the consumption of high-quality video content. Usually, video services are identified with linear Television (TV) and scheduled broadcast (point-to-multipoint (p-t-m)) distribution. However, the consumption of video content over mobile networks is different from traditional fixed TV because contents are mainly consumed on-demand with unicast point-to-point (p-t-p) connections. Then, the convergence of linear TV and on-demand content delivery represents a challenge that requires a combined broadcast/unicast transmission model. This dissertation addresses the use of broadcasting technologies for the provision of mobile multimedia services in Fourth Generation (4G) mobile broadband networks and beyond. Specifically, the dissertation focuses on the broadcast technology included in 4G Long Term Evolution (LTE) and LTE Advanced (LTE-A) networks, known as Enhanced Multicast Broadcast Multimedia Services (eMBMS). It analyses the benefits of the eMBMS physical layer aspects regarding Multimedia Broadcast Multicast Services over a Single Frequency Network (MBSFN) deployments and identifies the current limitations of eMBMS at physical layer by comparing with the broadcast technology of the other 4G mobile system, the Institute of Electrical and Electronics Engineers (IEEE) 802.16m standard. Those limitations are the use of a dedicated carrier and Multiple-Input Multiple-Output (MIMO) techniques for broadcast transmissions. Our investigations employ a complete simulation platform including link-level and system-level simulations to evaluate the performance of broadcast transmissions in these real technologies. The research on eMBMS services is aimed at finding the optimum delivery of streaming and file download services focusing on the Radio Resource Management (RRM) problem and trade-off between Physical layer ? Forward Error Correction (PHY-FEC) and Application Layer - Forward Error Correction (AL-FEC). Concerning streaming services, results show that the use of AL-FEC increases the coverage level and, then, the maximum service data rate. The gain due to AL-FEC is greater in scenarios with high mobility users, although, this gain is limited if low zapping times are desired. Regarding file delivery services, this dissertation analyses the duration of the transmission required to guarantee the correct file reception and the reduction in the mean throughput of unicast users with different delivery modes. They are the unicast delivery, the eMBMS delivery and a hybrid approach, which combines a first eMBMS delivery with a post-delivery error repair phase with unicast transmissions. Our results show that the hybrid delivery is the most efficient configuration in terms of file download time, although it further reduces unicast performance. On the other hand, as an exemplary use case, this dissertation also investigates the use of LTE networks for the provision of vehicular safety services comparing both unicast and eMBMS delivery modes. Results highlight the significant benefits in terms of resource usage, end-to-end latency and cost delivery saving that can be achieved by using eMBMS for the delivery of road safety applications. In addition, research also addresses the problem associated with the support of road safety applications over the current eMBMS architecture, the configuration of the Intelligent Transportation Systems (ITS) server in charge of distributing safety messages as well as on its interaction with the mobile network operator. Finally, this dissertation analyzes feasible options of convergence between mobile and broadcast industry to ensure the success of the mobile broadcasting deployments in the future. A separated evolution of the broadcast technologies of both industries would lead to a scenario with two complete different industries, with different network infrastructures and business models competing for market and spectrum. This dissertation proposes an approach by which the definition of the future Fifth Generation (5G) mobile broadband communication system could bring together the cellular and broadcast industries to form a single fixed and mobile converged network and offer a full alternative to terrestrial TV broadcasting as a universal service

With the proliferation of IP-based bandwidth-intensive video services and smartphones, there has been an unprecedented exponential increase in mobile broadband data. This has resulted in increasing demand for additional wireless capacity. In order to increase the wireless capacity multifold, the next-generation radio access networks (RAN) boast of a number of sophisticated technologies, such as Carrier Aggregation (CA), Evolved-Multicast/Broadcast Multimedia Services (eMBMS) using Single-Frequency Networks (SFN), enhanced Inter-Cell Interference Coordination (eICIC) in self-organized Heterogeneous Networks (HetNets), Coordinated Multi-Point (CoMP) transmission in Multiple-Input–Multiple-Output (MIMO) systems using 2D/3D Beamforming, and full-duplex communication. Some of the above technologies are standardized in 3GPP Release 10+ systems like LTE-Advanced and are seen as a roadmap to 5G RANs. This chapter provides a comprehensive overview of each of these technologies and surveys the key open issues concerning them in terms of radio resource management (RRM) to facilitate maximum wireless capacity and provide Quality-of-Service (QoS) to the users. It also explores the synergies between these technologies towards developing holistic optimization techniques for the design of 4G+ and 5G systems.

The interface management and resource allocation in backhaul (BH)/access networks for the cloud/centralized radio access networks (C-RAN) are two of the largest challenges to enable the C-RAN architecture deployment successfully in cellular wireless systems. From the PHY perspectives, one major advantage of C-RAN is the ease of implementation of multicell coordination mechanisms to manage the interference and improve the system spectral efficiency (SE). Theoretically, a large number of cooperative cells leads to a higher SE; however, it may also cause significant delay due to extra channel state information feedback and joint processing computational needs at the cloud data center, which is likely to result in performance degradation. In order to investigate the delay impact on the throughput gains, we divide the network into multiple clusters of cooperative small cells and formulate a throughput optimization problem. We model various delay factors and the sum-rate of the network as a function of cluster size, treating it as the main optimization variable. For our analysis, we consider both base stations' as well as users' geometric locations as random variables for both linear and planar network deployments. The output signal-to-interference-plus-noise ratio and ergodic sum-rate are derived based on the homogenous Poisson-point-processing model. The sum-rate optimization problem in terms of the cluster size is formulated and solved. From the radio resource management (RRM) perspective, we consider the problem of joint BH and access links optimization in dense small-cell networks with special focus on time division duplexing mode of operation in BH and access links transmission. Here, we propose a framework for joint RRM where we systematically decompose the problem in BH and access links. To simplify the analysis, the procedure is tackled in two stages. At the first stage, the joint optimization problem is formulated for a point-to-point scenario where each small cell is simply associated to a single user. In the second stage, the problem is generalized for multiaccess small cells. In addition, the chapter addressed thejoint routing and BH scheduling in a dense small-cell networks using 60 GHz multihop BH, coordinated by a local C-RAN central unit. The problem is formulated as a generalized vehicle routing problem and decoupled into two subproblems, channel-aware path selection and queue-aware link scheduling.

Densification of the radio access network and massive connectivity are envisaged to significantly increase the capacity of future radio access networks, well beyond 4G capabilities. This comes at the price of a harsher interference environment and an increased complexity of radio resource management (RRM), to the extent that today's RRM protocols may no longer be efficient. In this article we propose an inter-cell interference coordination mechanism that departs from the conventional concept of trading cell-centre spectral efficiency for cell-edge spectral efficiency, but rather aims at improving spectral efficiency in the entire service area. This is achieved by coordinating and optimizing access point transmission blanking on a fast time-scale while scheduling mobile stations in the direction of least interference. Simulation results indicate that the spectral efficiency can effectively be improved over the entire service area, with up to 200% increase of cell-edge users capacity and an increased average network capacity up to 30% compared to traditional reuse-1 schemes.

Cognitive Radio (CR) is considered as a promising solution to improve wireless spectrum utilization. Moreover, through optimization frameworks, Radio Resource Management (RRM) procedures are designed to enhance the efficient utilization of resources in CR networks. To face this issue in a holistic perspective, the RRM solutions should take into account not only the optimization task but also their impact on the design task. Thus, in this paper, a system architecture of a novel RRM is proposed for CR networks. In particular, it is fundamental to construct flexible systems to work across heterogeneous systems and help realize their full potential. Therefore, in the proposed system, heterogeneous Primary Users (PUs) with multiple features and variable CR demands are considered. A Radio Environment Map (REM) is used to obtain the required PU features, which are exploited to improve the adaptability in CR networks and, thus, to design an efficient Cognitive RRM. The functionalities of the network elements are described in detail, along with the message exchange to carry out the resource management.

IEEE 802.16/WiMAX-based broadband and mobile wireless access is expected to be a significant component in the next generation (e.g., beyond 3G, 4G) wireless systems. IEEE 802.16/WiMAX standard, which incorporates several advanced radio transmission technologies such as orthogonal-frequency division multiplexing (OFDM), adaptive modulation and coding, and adaptive forward error correction (FEC), is designed to provide broadband wireless capability using a well-defined quality-of-service (QoS) framework. Therefore, this is a promising technology for wireless services requiring high- rate transmission (in the range of tens of Mbps) and strict QoS requirements in both indoor and outdoor environments. This technology is currently of big interest to researchers and practitioners involved in the design, analysis, installation, and management of mobile broadband wireless access systems and networks. The objective of this tutorial is to provide a complete overview on the IEEE 802.16/WiMAX standard and the current state-of-the-art research on the design, analysis, and optimization of IEEE802.16/WiMAX-based networks. The key technical challenges related to protocol engineering in these networks are described. The aim is to provide a survey on the emerging IEEE 802.16/WiMAX technology rather than an in-depth study on a specific protocol engineering problem. An overview of the different evolutions of the IEEE 802.16/WiMAX standard broadband wireless access (BWA) is presented and the open research issues in protocol engineering (e.g., for radio resource management, mobility support, security) are identified. A comprehensive survey on radio resource management, traffic scheduling, admission control, mobility management, and security mechanisms for IEEE 802.16/WiMAX are provided. A comparative performance study among the third-generation (3G) and the IEEE 802.16-based systems is provided. Several applications (e.g., wireless telemedicine and e-health services), services and deployment scenarios are described and the necessary protocol design and engineering issues are outlined.

This paper presents a new technique for jointly optimizing energy consumption and quality of service (QoS) in heterogeneous cellular networks employing fractional frequency reuse (FFR) in the macrocell tier. The proposed technique combines radio resource management (RRM) and network routing principles to produce an effective way of utilizing the spectrum. Based on a novel method of interference mapping, the RRM is invoked, in distributed or centralized manner, to allocate the resource. In the case of centralized RRM, network routing is applied to maximize spectrum utilization. It will be shown through matching mathematical analysis and computer simulations that, relative to existing well known techniques, the proposed technique offers significant QoS improvements, in terms of guaranteed data rate per user, as well as, enhancements in the overall energy efficiency of the network.