Radio Resource Management Framework Research Articles

Intelligent Traffic Steering in Beyond 5G Open RAN Based on LSTM Traffic Prediction

Open radio access network (ORAN) Alliance offers a disaggregated RAN functionality built using open interface specifications between blocks. To efficiently support various competing services, <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">namely</i> enhanced mobile broadband (eMBB) and ultra-reliable and low-latency (uRLLC), the ORAN Alliance has introduced a standard approach toward more virtualized, open, and intelligent networks. To realize the benefits of ORAN in optimizing resource utilization, this paper studies an intelligent traffic steering (TS) scheme within the proposed disaggregated ORAN architecture. For this purpose, we propose a joint intelligent traffic prediction, flow-split distribution, dynamic user association, and radio resource management (JIFDR) framework in the presence of unknown dynamic traffic demands. To adapt to dynamic environments on different time scales, we decompose the formulated optimization problem into two long-term and short-term subproblems, where the optimality of the latter is strongly dependent on the optimal dynamic traffic demand. We then apply a long-short-term memory (LSTM) model to effectively solve the long-term subproblem, aiming to predict dynamic traffic demands, RAN slicing, and flow-split decisions. The resulting non-convex short-term subproblem is converted to a more computationally tractable form by exploiting successive convex approximations. Finally, simulation results are provided to demonstrate the effectiveness of the proposed algorithms compared to several well-known benchmark schemes.

A joint cluster-based RRM and Low-latency framework using the full-duplex mechanism for NR-V2X networks

The radio resource management (RRM) problem in new radio vehicle-to-everything (NR-V2X) communication systems is a combinatorial optimization problem, and it is hard to achieve an optimum result in polynomial time. To reduce the complexity, linear algorithms or meta-heuristics can solve the problem. In this study, we proposed a joint cluster-based resource management and low-latency framework using a full-duplex mechanism (JCRRM-FD) for NR-V2X networks, which is used for vehicle-to-vehicle (V2V)/ device-to-device (D2D) and vehicle-to-infrastructure (V2I) transmission in NR-V2X networks. The delay, resource management, and system throughput analysis of the full-duplex mechanism in this framework was performed to deal with resource utilization and latency constraints. Moreover, joint cluster-based radio resource management and ant colony optimization (ACO) algorithms were proposed to manage and optimize resources efficiently to achieve user separation. The swarm intelligence algorithm is a standard meta-heuristic algorithm employed to deal with the optimization concern by exploiting the V2X communication network’s unlimited speed (maximizing the data transfer capacity and overall network performance) while considering the quality-of-service (QoS) requirements. A comprehensive experiment analysis was enacted to evaluate the efficiency of the developed JCRRM-FD framework with baseline approaches. Based on the simulation results, the proposed JCRRM-FD framework enhances the fairness index, average delay, packet drop rate, best cost value, CDF, and throughput compared to the benchmark approaches.

Hybrid Radio Resource Management for 6G Subnetwork Crowds

In-X subnetworks are expected to be located at the very edge of the 6G 'network of networks' and provide localized wireless connectivity for in-vehicle, in-robot, in-body communication. By nature in-X subnetworks can lead to dense crowds, calling for efficient radio resource management techniques. In this article, we introduce a hybrid radio resource management framework where the decision capabilities can either happen at a global agent operating on an umbrella 6G network, or locally at each subnetwork, depending on the quality of the backhaul link, the crowd density and the local spectrum sensing capabilities. We present a possible application of the proposed framework for the problem of radio channel selection in a dense crowd. Possible research directions leveraging the proposed framework are also described.

RadiOrchestra: Proactive Management of Millimeter-Wave Self-Backhauled Small Cells via Joint Optimization of Beamforming, User Association, Rate Selection, and Admission Control

Millimeter-wave self-backhauled small cells are a key component of next-generation wireless networks. Their dense deployment will increase data rates, reduce latency, and enable efficient data transport between the access and backhaul networks, providing greater flexibility not previously possible with optical fiber. Despite their high potential, operating dense self-backhauled networks optimally is an open challenge, particularly for radio resource management (RRM). This paper presents, RadiOrchestra, a holistic RRM framework that models and optimizes beamforming, rate selection as well as user association and admission control for self-backhauled networks. The framework is designed to account for practical challenges such as hardware limitations of base stations (e.g., computational capacity, discrete rates), the need for adaptability of backhaul links, and the presence of interference. Our framework is formulated as a nonconvex mixed-integer nonlinear program, which is challenging to solve. To approach this problem, we propose three algorithms that provide a trade-off between complexity and optimality. Furthermore, we derive upper and lower bounds to characterize the performance limits of the system. We evaluate the developed strategies in various scenarios, showing the feasibility of deploying practical self-backhauling in future networks.

A QoS-aware and channel-aware Radio Resource Management framework for multi-numerology systems

To support the diversified 5G usage scenarios, the 5G New Radio (NR) physical layer is enriched with the flexible multi-numerology feature, where multiple frame structures with different sub-carrier spacings coexist in one frequency band. However, this flexibility brings with it a further challenge in Radio Resource Management (RRM), how to best allocate the available band spectrum among the different non-orthogonal numerologies. This work presents a novel Quality of Service (QoS)-aware and channel-aware RRM framework that manages diversified type of services, i.e., Best Effort (BE) services and Guaranteed Bit Rate (GBR) services with different priorities. The goal is to maximize the number of satisfied GBR services, according to the priority, and the throughput of the BE services. To provide a feasible solution, the proposed framework consists of two control levels. The first one appropriately splits the available bandwidth among the various numerologies, with the aim of achieving the above goal, trying to assign continuous portion of band to the same Numerology, in order to mitigate the Inter-Numerology Interference (INI) phenomenon. Moreover, it includes an advanced dropping strategy to support overload conditions. The second level distributes the Physical Resource Blocks (PRBs) to the User Equipments (UEs) belonging to the same numerology, by exploiting scheduling algorithms available in literature. Through an extensive simulation campaign, under different traffic load and channel conditions, we compare the proposed RRM framework with other reference schemes available in literature. The results show that the proposed framework outperforms them in any simulation setup.

Multicriterion Resource Management in Energy-Harvested Cooperative UAV-Enabled IoT Networks

Cooperative communication by employing unmanned aerial vehicle (UAV)-based relays with radio-frequency (RF) energy harvesting (EH) has been emerged as a prominent solution to provide extended coverage, connectivity, capacity, energy efficiency, and reliability in the future Internet-of-Things (IoT) systems. For successful integration of UAV relays in IoT networks, efficient radio resource management (RRM) is critical. We developed a multicriterion framework for energy-efficient RRM in a cooperative IoT network. We considered UAVs as relays, onboard EH facilities and deployed to relay the messages from a satellite terminal to the network IoT devices. We adopted a power splitting (PS)-based EH scheme, i.e., PS relaying protocol, for RF-EH at UAV relays. We formulate a joint optimization problem for IoT device selection, UAV relay assignment, source power allocation, and PS ratio selection. In our multicriterion framework, we consider three conflicting objectives by applying a weighted-sum method: 1) maximizing the network sum rate; 2) maximizing the number of IoT devices to be served; and 3) minimizing the carbon dioxide emissions. We propose an outer approximation algorithm (OAA) to solve the formulated problem, which is a mixed-integer nonlinear programming (MINLP) problem. Simulation results of the proposed algorithm are compared with two existing solutions, namely, the nonlinear optimization by mesh adaptive direct search (NOMAD) algorithm and an evolutionary algorithm (EA). The performance of the NOMAD algorithm is better in terms of computational complexity. However, the simulation results reveal the supremacy of the proposed OAA in terms of network sum rate, the number of selected IoT devices, and network utility.

Joint Radio Resource Management of Channel-Assignment and User-Association for Load Balancing in Dense WLAN Environment

IEEE 802.11-based wireless local area network (WLAN) is currently the most popular communication system and a lot of access points (APs) have been densely deployed over heavily populated areas. In such dense WLAN environment, the WLAN users can suffer from performance degradation due to high co-channel and adjacent-channel interference among APs. To tackling such user performance degradation problem, we propose a two-phase radio resource management (RRM) framework, of which the first phase is channel assignment (CA) and the second phase is user association for channel load balancing (UA-ChLB). In designing the RRM framework, we take account of typical WLAN environment where dual bands are supported and two types of APs coexist: controlled-APs, which are managed by a centralized controller, and stand-alone APs, which independently operate for themselves. The proposed CA method utilizes a channel bonding technique, which provides a high data rate by integrating multiple basic channels while reducing the interference among neighboring APs. The proposed hybrid UA-ChLB scheme, which is composed of distributed/centralized UA-ChLB, efficiently coordinates the channel load among neighboring APs and between two bands of 2.4 and 5 GHz, under consideration of the wireless channel quality, interference, and traffic conditions. We implement a prototype system in practice using the proposed RRM scheme. The experimental results show that the proposed RRM scheme greatly improves both throughput and fairness in dense WLAN environment, as compared with some existing schemes.

Radio resource management framework for energy‐efficient communications in the Internet of Things

AbstractThe Internet of Things (IoT) is the vision of a global network that connects various physical world objects to the IT infrastructure through a wireless medium. Despite the availability of a number of mature radio access technologies such as GSM, Long‐Term Evolution, Wi‐Fi, and due to the current progress made in developing 5G technology, more and more IoT operators are opting to use low‐power wide‐area (LPWA) technologies due to their low cost and easy deployment. However, recent studies show that the radio resource allocation used in these technologies is not scalable. This limitation often results in packet collisions, retransmission and unnecessary waste of scarce energy resources. In this paper, we propose a radio resource management framework, based on software‐defined network (SDN), to overcome the inefficient radio resource allocation of LPWA technologies. This is possible through the centralized nature of SDN, which allows collecting network monitoring information to analyze and calculate the optimal channel assignment configuration across the IoT network. We perform software‐defined radio‐based spectrum monitoring within the real IoT network platform in 868 MHz bands in which the latest IoT technologies, ie, LoRa and SigFox, operate. We demonstrate, through extensive simulations, that the proposed approach provides a better radio resource allocation for LPWA, reduces the number of packet collisions, and significantly improves the energy efficiency of the IoT communications.

Power cognition: Enabling intelligent energy harvesting and resource allocation for solar-powered UAVs

Solar-powered unmanned aerial vehicles (SUAVs) are a promising solution to increase the flight time of unmanned aerial vehicles (UAVs) in the sky, reducing human interventions for battery charging. Exploiting networked SUAVs for providing long-duration wireless communication cannot only improve the signal transmission reliability but realize energy autonomy. To reap these benefits, in this article, we propose an efficient energy and radio resource management framework based on intelligent power cognition at the SUAVs. Thereby, power-cognitive SUAVs can learn the environment including the spatial distributions of solar energy density, the channel state evolution, and the traffic patterns of wireless communication applications in adaption to the environment changes. These SUAVs intelligently adjust the energy harvesting, information transmission, and flight trajectory to improve the utilization of solar energy for two primary goals: staying aloft over a long time period and achieving high communication performance. We adopt reinforcement learning to compute the optimal decisions for maximization of the total system throughput within the lifetime of the SUAV. Simulation results show that the proposed power cognition scheme can simultaneously improve the communication throughput and the harvested energy for SUAVs.

A Biological Model for Resource Allocation and User Dynamics in Virtualized HetNet

Virtualization technology is considered an effective measure to enhance resource utilization and interference management via radio resource abstraction in heterogeneous networks (HetNet). The critical challenge in wireless virtualization is virtual resource allocation on which substantial works have been done. However, most existing researches on virtual resource allocation focus on improving total utility. Different from the existing works, we investigate the dynamic‐aware virtual radio resource allocation in virtualization based HetNet considering utility and fairness. A virtual radio resource management framework is proposed, where the radio resources of different physical networks are virtualized into a virtual resource pool and mobile virtual network operators (MVNOs) compete for virtual resources from the pool to provide service to users. A virtual radio resource allocation algorithm based on biological model is developed, considering system utility, fairness, and dynamics. Simulation results are provided to verify that the proposed virtual resource allocation algorithm not only converges within a few iterations, but also achieves a better trade‐off between total utility and fairness than existing algorithm. Besides, it can also be utilized to analyze the population dynamics of system.

An enhanced radio resource management based MIH policies in heterogeneous wireless networks

The require of omnipresent wireless access and high data rate services are expected to increase extensively in the near future. In this context, heterogeneous networks, which are a mixture of different wireless technologies (LTE-advanced, LTE-advanced Pro, C-IoT (Cellular Internet of Thing), 5G WiFi, etc) are invited to enable important capabilities, such as high data rates, low latencies and efficient resource utilization in order to provide dedicated capacity to offices, homes, and urban hotspots. Mixing these technologies in the same system, with their complementary characteristics, to afford a complete coverage to users can cause various challenges such as seamless handover, resource management and call admission control. This article proposes a general radio resource management framework which can be supported by future network architectures. A combined call admission control, resource reservation algorithm and bandwidth adaptation based IEEE 802.21 MIH standard approach for heterogeneous wireless network is detailed in this framework. Our aims are to guarantee quality of service (QoS) requirements of all accepted calls, reduce new call blocking probability and handover call dropping probability, and maintain efficient resource utilization. Performance analysis shows that our proposed approach best guarantees QoS requirements.

Resource Management in Non-Orthogonal Multiple Access Networks for 5G and Beyond

Non-orthogonal multiple access techniques have been proposed recently for 5G wireless systems and beyond to improve access efficiency by allowing many users to share the same spectrum. Due to the strong co-channel interference among mobile users introduced by NOMA, it poses significant challenges for system design and resource management. This article reviews resource management issues in NOMA systems. The main taxonomy of NOMA is presented by focusing on the two main categories of resource reuse: power-domain and code-domain NOMA. Then a novel radio resource management framework is proposed based on game-theoretic models for uplink and downlink transmissions. Finally, potential applications and open research directions in the area of resource management for NOMA are provided.

Green Networking in Cellular HetNets: A Unified Radio Resource Management Framework With Base Station ON/OFF Switching

In this paper, the problem of energy efficiency in cellular heterogeneous networks (HetNets) is investigated using radio resource and power management combined with the base station (BS) ON/OFF switching. The objective is to minimize the total power consumption of the network while satisfying the quality of service (QoS) requirements of each connected user. We consider the case of co-existing macrocell BS, small cell BSs, and private femtocell access points (FAPs). Three different network scenarios are investigated, depending on the status of the FAPs, i.e., HetNets without FAPs, HetNets with closed FAPs, and HetNets with semi-closed FAPs. A unified framework is proposed to simultaneously allocate spectrum resources to users in an energy efficient manner and switch off redundant small cell BSs. The high complexity dual decomposition technique is employed to achieve optimal solutions for the problem. A low complexity iterative algorithm is also proposed and its performances are compared to those of the optimal technique. The particularly interesting case of semi-closed FAPs, in which the FAPs accept to serve external users, achieves the highest energy efficiency due to increased degrees of freedom. In this paper, a cooperation scheme between FAPs and mobile operator is also investigated. The incentives for FAPs, e.g., renewable energy sharing and roaming prices, enabling cooperation are discussed to be considered as a useful guideline for inter-operator agreements.

A Unified Approach for Efficient Delivery of Unicast and Multicast Wireless Video Services

Recently, mobile multimedia services have represented an increasingly large source of revenue for the telecommunication industry. Subscribers are often interested in simultaneously receiving the same data flow and, hence, they fuel the growing demand for multicast multimedia services. Therefore, the design of efficient radio resource management strategies to jointly handle multicast and more traditional unicast traffic and to increase user satisfaction is of primary importance for the successful deployment of future mobile networks. This paper proposes an efficient radio resource management framework to handle unicast and multicast multi-layer video services. The proposed virtual unified group (VUG) approach makes use of a channel-aware subgrouping principle to provide fair throughput to both unicast and multicast users. The idea is to assign unicast subscribers to a virtual group, thus allowing them to compete for network resources on an equal footing with multicast users. Simulations highlight the benefits that VUG provides in a long term evolution network scenario, under different traffic load conditions, in terms of throughput and fairness in the distribution of resources to unicast and multicast users.

Joint TDD Backhaul and Access Optimization in Dense Small-Cell Networks

This paper addresses the problem of joint backhaul (BH) and access link optimization in dense small-cell networks with a special focus on time-division duplexing (TDD) mode of operation in BH and access link transmission. Here, we propose a framework for joint radio resource management, 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 with a single user. It is shown that the optimization can be decomposed into separate power and subchannel allocation in both BH and access links, where a set of rate-balancing parameters in conjunction with duration of transmission governs the coupling across both links. Moreover, a novel algorithm is proposed based on grouping the cells to achieve rate balancing in different small cells. Next, in the second stage, the problem is generalized for multiaccess small cells. Here, each small cell is associated with multiple users to provide the service. The optimization is similarly decomposed into separate subchannel and power allocation by employing auxiliary slicing variables. It is shown that similar algorithms, as in the previous stage, are applicable by a slight change with the aid of slicing variables. Additionally, for the special case of line-of-sight BH links, simplified expressions for subchannel and power allocation are presented. The developed concepts are evaluated by extensive simulations in different case studies from full orthogonalization to dynamic clustering and full reuse in the downlink, and it is shown that the proposed framework provides significant improvement over the benchmark cases.

Technology demonstrator of a novel software defined radio‐based aeronautical communications system

This study presents the architectural design, software implementation, the validation and flight trial results of an aeronautical communications system developed within the Seamless Aeronautical Networking through integration of Data links Radios and Antennas (SANDRA) project funded by the European 7th Framework Aeronautics and Transport Programme. Based on software defined radio (SDR) techniques, an integrated modular radio (IMR) platform was developed to accommodate several radio technologies. This can drastically reduce the size, weight and cost in avionics with respect to current radio systems implemented as standalone equipment. In addition, the modular approach ensures the possibility to dynamically reconfigure each radio element to operate on a specific type of radio link. A radio resource management (RRM) framework is developed in the IMR consisting of a communication manager for the resource allocation and management of the different radio links and a radio adaptation manager to ensure protocol convergence through IP. The IMR has been validated although flight trials held at Oberpfaffenhofen, Germany in June 2013. The results presented in the paper validate the flexibility and scalability of the IMR platform and demonstrate seamless service coverage across different airspace domains through interworking between the IMR and other components of the SANDRA network.

Efficient radio resource management algorithms in opportunistic cognitive radio networks

ABSTRACTCognitive radio (CR) bands following an interference‐free opportunistic manner. However, introduction of CR networks creates new challenges that are highly related to the fluctuation of TVWS, as they vary over time and location, as well as issues related to diverse Quality of Service requirements. In this context, this paper proposes two radio resource management (RRM) algorithms, enabling for the opportunistic exploitation of TVWS in a centralised CR networking architecture. Efficient administration of radio spectrum resources is achieved, by exploiting a novel RRM framework, adopted in a spectrum broker, which is in charge to effectively orchestrate the available wireless networking resources. Efficient RRM algorithms performance, as a matter of maximum‐possible spectrum broker benefit and radio spectrum utilisation, as well as minimum‐possible spectrum fragmentation is evaluated, by considering a fixed‐price and an auction‐based optimization approach. Experimental tests that were conducted under controlled simulation conditions, confirmed the validity of both RRM algorithms adopted in the proposed CR networking architecture, identifying fields for further research and experimentation. Copyright © 2013 John Wiley &amp; Sons, Ltd.

Cognitive radio resource management exploiting heterogeneous primary users and a radio environment map database

The efficient utilization of radio resources is a fundamental issue in cognitive radio (CR) networks. Thus, a novel cognitive radio resource management (RRM) is proposed to improve the spectrum utilization efficiency. An optimization framework for RRM is developed that makes the following contributions: (i) considering heterogeneous primary users (PUs) with multiple features stored in a radio environment map database, (ii) allowing variable CR demands, (iii) assuring interference protection towards PUs. After showing that the optimal solution is computationally infeasible, a suboptimal solution is consequently proposed. Performance evaluation is conducted in terms of total achieved data rate and satisfaction of CR requirements.

Radio Resource Management of Composite Wireless Networks: Predictive and Reactive Approaches

Recently, the IEEE 1900.4 standard specified a policy-based radio resource management (RRM) framework in which the decision making process is distributed between network-terminal entities. The standard facilitates the optimization of radio resource usage to improve the overall composite capacity and quality of service (QoS) of heterogeneous wireless access networks within a composite wireless network (CWN). Hence, the study of different RRM techniques to maintain either a load- or QoS-balanced system through dynamic load distribution across a CWN is pivotal. In this paper, we present and evaluate three primary RRM techniques from different aspects, spanning across predictive versus reactive to model-based versus measurement-based approaches. The first technique is a measurement-based predictive approach, known as predictive load balancing (PLB), commonly employed in the network-distributed RRM framework. The second technique is a model-based predictive approach, known as predictive QoS balancing (PQB), typically implemented in the network-centralized RRM framework. The third technique is a measurement-based reactive approach, known as reactive QoS balancing (RQB), anchored in the IEEE 1900.4 network-terminal distributed RRM framework. Comprehensive performance analysis between these three techniques shows that the IEEE 1900.4-based RQB algorithm yields the best improvement in QoS fairness and aggregate end-user throughput while preserving an attractive baseline QoS property.

A Radio Resource Management Framework for Multi-User Multi-Cell OFDMA Networks Based on Game Theory

This work proposes a radio resource management framework employing game theoretic concepts for orthogonal frequency division multiple access, the most prevalent multiple access technique for the next generation wireless networks. The subcarrier allocation problem is encountered as a combinatorial auction, where the base station auctions the subcarriers and the users bid for and buy bundles of subcarriers, aiming at minimising their required transmit power. Subsequently, each allocated subcarrier is loaded with a number of bits, decided by each user independently, and the power control process is set up as a non-cooperative game. Each user responds to the interference sensed in his environment and, through a best responses process, the game converges to the unique, Pareto optimal, Nash equilibrium. In order to guarantee convergence, a limit is imposed to the maximum modulation level for each subcarrier. Simulation results show that the auction algorithm follows closely the performance of the optimal algorithm, whereas it is of lower computational complexity and requires less feedback information. Similarly, the proposed distributed bit loading and power control scheme achieves lower transmit power per offered bit rate unit. However, the distributed nature of the algorithm results in lower total offered bit rate, because of the partial knowledge and exploitation of channel state information.