Radio Resource Management Techniques Research Articles

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.

A novel method for spectrum sensing in cognitive radio networks using fractional GWO[sbnd]CS optimization

The co-channel interference and radio resources in a wireless communication system are often systematically managed by employing Radio Resource Management (RRM) strategies. The parameters of radio resources such as transmit power, data rate, error coding, and user allocation can be controlled via the RRM techniques whose main aim is to better exploit the limited radio frequency spectrum resources. The unallocated spectrum resources become spectrum holes and the effective utilization of the spectrum holes is made possible through cognitive radio by improving the network throughput. To enhance the effective energy utilization capacity of the spectrum holes by considering different sensing states, this paper integrates the grey wolf optimization (GWO) with the Cuckoo search (CS) algorithm to form the Fractional GWOCS Optimization model. The Fractional Optimization Model (FOM) allows the Secondary User (SU) for performing periodic sensing and data transfer. The main aim of the Fractional GWOCS Optimization model is to optimize different parameters like power spectral density (PSD), transmission power, and sensing bandwidth (SB). When computing energy efficiency concerning detection probability, SB, PSD, mutual optimization of PSD and SB, and convergence, the proposed methodology offers improved outcomes when compared to the existing techniques.

Performance Evaluation of Call Admission Control Schemes in 3GPP Long Term Evolution Networks

Radio resource management (RRM) techniques are employed for resource utilization in wireless broadband networks. One of the major RRM techniques that are aimed at controlling the admission and rejection of new or handoff calls/connections in a network is known as Call Admission Control (CAC). CAC accepts or rejects a connection request based on a certain pre-determined criterion(s). Performance Evaluation of some existing CAC schemes was presented in this paper. The schemes evaluated are; Adaptive Call Admission Control Scheme with Bandwidth reservation termed as (ACAC), QoS-Aware Call Admission Control Scheme termed as (QA-CAC) and Enhanced Adaptive Call Admission Control Scheme with bandwidth reservation termed (EA-CAC). Vienna LTE system level simulator was used for the simulation experiments conducted. After several experiments, the results show that the QA-CAC and EA-CAC schemes perform better in terms of increasing the throughput of real-time (RT) traffic while all the three schemes almost have the same performance in terms of admitting the non-real-time (NRT) traffic.

Advanced NOMA-Based RRM Schemes for Broadcasting in 5G mmWave Frequency Bands

A relevant solution for the high demand for new multimedia applications and services is millimeter wave (mmWave) frequency band in 5G. However, in order to face the technological challenges of the present and those that will appear in the short-term future, it is necessary to improve the spectral efficiency of 5G systems. In particular, the Radio Resource Management (RRM) module is considered an essential component. Nevertheless, resource allocation techniques that combine orthogonal multiplexing (OMA) schemes, such as Time Division Multiple Access (TDMA), with Non-Orthogonal Multiple Access (NOMA) techniques have not been studied in-depth. Therefore, this article designs and evaluates different RRM models that combine TDMA with NOMA for innovative applications in the 5G mmWave frequency bands. To this end, the advantages and challenges associated with mmWave frequency bands and potential future applications are introduced. An innovative use case has been designed to test the RRM models. The use case is based on the on-demand distribution of multimedia content in high-density environments, such as museum halls. The on-demand content aims at offering Augmented Reality (AR) services to unicast users in order to provide personalized services. The models have been evaluated in terms of throughput, capacity, and availability. According to the results, the combined RRM techniques offer up to 50% more capacity than the single multiplexing technology models and can provide video-on-demand service to practically the entire cell.

Device-to-Device Relaying: Optimization, Performance Perspectives, and Open Challenges Towards 6G Networks

A relaying can significantly improve performance of contemporary mobile networks in terms of capacity and/or energy consumption. Nevertheless, an incorporation of conventional relay stations into the mobile networks is usually expensive in terms of both capital and operational expenditures for the mobile operators. With an evolution of Device-to-device (D2D) communication, the relaying can be also facilitated via a user equipment (UE) by means of D2D relaying. In the D2D relaying, the users relay data for others, thus significantly cutting the mobile network operators’ expenditures. The D2D relaying also offers a plenty of opportunities for the relaying as, in theory, each UE can play a role of the relay. To fully unlock all benefits of the D2D relaying, however, there are still many challenges to be addressed, such as an efficient radio resource and interference management, mobility management, security and trust issues, or finding proper incentives to motivate users to help others. In this survey, we introduce a detailed taxonomy of the D2D relaying concept encompassing an overview of various relaying scenarios, types of relays, and radio resource management techniques to be optimized. Then, we provide a comprehensive overview of research works targeting the D2D relaying addressing the above-mentioned challenges, compare these works from various perspectives, identify their potential drawbacks and limitations, and also draw some interesting lessons for the readers. Last, based on the gaps in the current literature, we identify key open research challenges deserving further attention of the researches to make the D2D relaying feasible and attractive option for mobile network operators as well as end-users in emerging 6G networks.

Energy-Efficient QoE-Driven Radio Resource Management Method for 5G and Beyond Networks

Energy-efficient Radio Resource Management (RRM) for 5G and beyond networks has become a key research challenge due to increasing Small Cells (SCs) densities and the high Quality of Experience (QoE) requirements of business users. Ensuring QoE and energy efficiency is essential in mobile networks, but these goals are often opposing and rarely addressed simultaneously in existing solutions. In this paper, we propose to include the QoE criterion in the RRM technique for 5G and beyond multi-layer networks, which will allow ordering individual QoE for business users. We developed a new radio resource allocation and optimization method to address changing user QoE requirements and reduce energy consumption in multi-layer 5G networks. The proposed method differs from the known ones in that it considers the QoE requirements of business users and load localization to optimally distribute the service process between Macro Cells (MCs) and SCs. This method uses a Voronoi diagram to energy-efficiently design the 5G Radio Access Network (RAN) by switching SCs to sleep mode when they are not serving active users. As a result, a balance is struck between user QoE requirements and network energy efficiency. Based on simulations, it is proved that the proposed method allowed more efficient use of accessible radio resources by 25% and reduced the energy consumption of the 5G RAN by 8.7% to provide the ordered QoE for users compared to traditional RRM methods.

An Improved Proportional Fair Scheduling Algorithm for Downlink LTE Cellular Network

Abstract: Network providers of LTE networks can achieve maximum gain and Quality of Service (QoS) requirement of their users by employing a radio resource management technique that has the ability to allocate resource blocks to users in a fair manner without compromising the capacity of the network. This implies that for a better performing LTE network, a fair scheduling and balanced QoS delivery for various forms of traffic are needed. In this paper an improved proportional fair scheduling algorithm for downlink LTE cellular network has been developed. This algorithm was implemented using a MATLAB-based System Level simulator by Vienna University. The developed algorithm was compared to other scheduling algorithms such as the Proportional Fair (PF) algorithm, Best Channel Quality Indicator (CQI), and Round Robin (RR) scheduling methods. The system performance was also analyzed under different scenarios using different performance metrics. The achieved results showed that the developed algorithm had a better throughput performance than the Round Robin and Proportional fair scheduling. The developed algorithm shows improved cell edge throughputs of about 19.2% (as at 20 users) and 9.1% higher for cell edge users without and with mobility impact respectively. The Best CQI algorithm had higher peak throughput values but the fairness was highly compromised. The developed algorithm outperforms the Best CQI by 136.6% without the impact of mobility. Finally, in dense conditions, the developed algorithm still outperforms the other algorithms with a QoS metric of 4.6% increment when compared to the PF algorithm which was the closest competitor. Keywords: UE, eNodeB, Scheduling, Proportional Fair, LTE,

Editorial Energy Efficiency of Machine-Learning-Based Designs for Future Wireless Systems and Networks

While 5G standards are being developed, research is moving toward designing the next generation of communications (e.g., 5.5G and 6G) which are expected to provide data rates of the order of 1 Tb/s using frequency bands in the range of 100 GHz to 3 THz. In addition to providing massive capacity and connectivity by exploiting new network architectures (e.g., cell-free massive MIMO, integrated terrestrial-aerial-underwater networks), radio transmission technologies (e.g., THz communications) and resource management techniques (e.g., end-to-end network resource slicing), future networks will support new context-aware applications and services (e.g., those based on joint communications and sensing) and provide connected intelligence in the era of Internet-of-Everything.

Efficient Fronthaul and Backhaul Connectivity for IoT Traffic in Rural Areas

In this article, internet of things (IoT) connectivity in rural areas is investigated. Both fronthaul and backhaul considerations are studied. First, intelligent radio resource management (RRM) and network planning techniques are discussed for IoT access/fronthaul networks. The proposed RRM scheduling approach was shown to lead to good performance in scheduling IoT devices. Then, several backhauling techniques for providing connectivity to rural areas are investigated and their cost efficiency is analyzed. Techniques based on free space optics with solar powered devices are found to be a suitable backhaul solution.

Heuristic Radio Resource Management for Massive MIMO in Satellite Broadband Communication Networks

The advent of technologies allowing the implementation of flexible satellite payloads is opening up great opportunities to efficiently cope with non-uniform traffic conditions. The latter is one of the “crux” of modern satellite broadband access service economic provision. In this paper we derive affordable (linear) complexity radio resource management (RRM) techniques for satellite multi-beam broadband communication networks that are closely approaching the optimum solution which has an exponential complexity with the number of users. The proposed heuristic RRM solution is applied to the most demanding Massive Multiple Input Multiple Output (M-MIMO) case. It is shown that the low-complexity payload pragmatic M-MIMO solution combined with heuristic RRM is able to efficiently cope with very different traffic conditions with a small penalty in terms of throughput compared to the much more complex ideal Minimum Mean Square Error (MMSE) M-MIMO adopting the same RRM algorithm. Instead, the pragmatic M-MIMO with heuristic RRM is outperforming MMSE M-MIMO exploiting simple RRM.

Enhanced Adaptive Call Admission Control Scheme With Bandwidth Reservation for LTE Networks

Call admission control (CAC) is one of the radio resource management techniques that regulates and provide resources for new or ongoing calls in the network. The existing CAC schemes wastes bandwidth due to its failure to check before degrading admitted real-time calls and it also increases the call dropping probability (CBP) and calling blocking probability (CBP) of real-time calls due to the delay incurred when bandwidth is degraded from them. This paper proposed an enhanced adaptive call admission control (EA-CAC) scheme with bandwidth reservation. The scheme employs a prior-check mechanism that ensured bandwidth to be degraded will be enough to admit the new call request. It further incorporates an adaptive degradation mechanism that degrades non-real time calls before degrading the RT calls. The performance of the EA-CAC scheme was evaluated against two existing schemes using Vienna LTE system level simulator. The EA-CAC scheme exhibits better performance compared to the two schemes in terms of throughput, CBP, and CDP of RT calls without sacrificing the performance of NRT calls.

A Review on Ultra Reliable and Low Latency Communications (PHY and MAC Layer Perspectives)

Fifth Generation (5G) wireless network plays major attention in the communication scenario due to the advanced features in data transmission. 5G is designed to overcome the issues, such as data rate, reliability, throughput, latency and capacity in the wireless network domains. The 5G wire-less services aim to increase the data rate through massive communication services. In this survey, well known research papers based on low latency communication at the Physical layer (PHY) and Medium Access Control layer (MAC) are reviewed based on the Ultra-Reliable Low Latency Communication (URLLC) perspective. Here, the communication techniques are categorized as, structure-based model, resource reuse-based techniques, radio resource management techniques, scheduling based approach, diversity-based techniques and network slicing model based on the purpose of data communication. The issues and challenges faced by the existing research works are reviewed and discussed in the research gaps and issues section. Moreover, the analysis carried out in this survey is based on the evaluation parameter, network layer, network parameters, system, purpose and simulation toolsets. The future work of the URLLC research is based on the issues collected from the recent research papers reviewed in this study.

Resource Management in Cloud Radio Access Network: Conventional and New Approaches.

Cloud radio access network (C-RAN) is a promising mobile wireless sensor network architecture to address the challenges of ever-increasing mobile data traffic and network costs. C-RAN is a practical solution to the strict energy-constrained wireless sensor nodes, often found in Internet of Things (IoT) applications. Although this architecture can provide energy efficiency and reduce cost, it is a challenging task in C-RAN to utilize the resources efficiently, considering the dynamic real-time environment. Several research works have proposed different methodologies for effective resource management in C-RAN. This study performs a comprehensive survey on the state-of-the-art resource management techniques that have been proposed recently for this architecture. The resource management techniques are categorized into computational resource management (CRM) and radio resource management (RRM) techniques. Then both of the techniques are further classified and analyzed based on the strategies used in the studies. Remote radio head (RRH) clustering schemes used in CRM techniques are discussed extensively. In this research work, the investigated performance metrics and their validation techniques are critically analyzed. Moreover, other important challenges and open research issues for efficient resource management in C-RAN are highlighted to provide future research direction.

Power Control Schemes for Interference Management in LTE-Advanced Heterogeneous Networks

The issue of inter-cell interference with the co-tier and cross-tier arises in LTE-A Heterogeneous networks when thelow power base station deployed in a huge number in the ultra-densities area. This problem is managed by RRM (Radio Resource Management)technique. RRM uses different algorithms, application, and parameters to avoid and mitigate co-channel and cross-channel interference. The main objective of the Radio Resource Management is to maximize the use of limited resources. It manages all the resources like power, frequency and time. The power control Radio Resource Management scheme is used to control the interference between Femto to Femtocells and Femto to Macrocells. These resources are regulated separately or in combination,the approach is called hybrid Radio Resource Management. In this paper various Power schemebased on Fixed, Constant Femto Base Station and Targeted SINR is used. These Powers control Scheme are applied to the networktopology with increment in the size of femto cell range and number of femto and macro userin increment order. The performance is analyzed on SINR, pathloss and throughput. The comparison results of in between Fixed, Constant Femto Base Station and Targeted SINR power schemeare shown in graphs. The result reflects that constant FBS scheme has outperformed in user increment scenario and range increment scenario.

Time-domain ICIC and optimized designs for 5G and beyond: a survey

Time-domain enhanced inter-cell interference coordination (eICIC) is an effective technique to reduce the cross-tier inter-cell interference (ICI) in long term evolution (LTE)-based heterogeneous small cell networks (HetSCNs). This paper first clarifies two main communication scenarios in HetSCNs, i.e., macrocells deployed with femtocells (macro-femto) and with picocells (macro-pico). Then, the main challenges in HetSCNs, particularly the severe cross-tier ICI in macro-femto caused by femtocells with closed subscribe group (CSG) access or in macro-pico caused by picocells with range expansion are analyzed. Based on the prominent feature of dominant interference in HetSCNs, the main idea of time-domain interference coordination and two basic schemes in the eICIC standardization, i.e., almost blank subframe (ABS) and orthogonal frequency division multiplexing symbol shift are presented, with a systematic introduction to the interactions of these techniques with other network functions. Then, given macro-femto and macro-pico HetSCNs, an overview is provided on the advanced designs of ABS-based eICIC, including self-optimized designs with regard to key parameters such as ABS muting ratio, and joint optimized designs of ABS-based eICIC and other radio resource management techniques, such as user association and power control. Finally, the open issues and future research directions are discussed.

A Mass-Processing Simulation Framework for Resource Management in Dense 5G-IoT Scenarios

Because of the increment in network scale and test expenditure, simulators gradually become main tools for research on key problems of wireless networking, such as radio resource management (RRM) techniques. However, existing simulators are generally event-driven, causing unacceptably large simulation time owing to the tremendous number of events handled during a simulation. In this article, a mass-processing framework for RRM simulations is proposed for the scenarios with a massive amount of terminals of Internet of Things accessing 5G communication systems, which divides the time axis into RRM periods and each period into a number of mini-slots. Transmissions within the coverage of each access point are arranged into mini-slots based on the simulated RRM schemes, and mini-slots are almost fully occupied in dense scenarios. Because the sizes of matrices during this process are only decided by the fixed number of mini-slots in a period, the time expended for performance calculation is not affected by the number of terminals or packets. Therefore, by avoiding the event-driven process, the proposal can simulate dense scenarios in a quite limited time. By comparing with a classical event-driven simulator, NS2, we show the significant merits of our proposal on low time and memory costs.

Theory of Algorithm Suitability on Managing Radio Resources in Next Generation Mobile Networks

Beyond 2020, wireless networking model will be radically changed and oriented to business-driven concept as foreseen by the next generation mobile network (NGMN) alliance. As the available spectrum granted to a given operator is physically limited, new radio resource management techniques are required to ensure massive connectivity for wireless devices. Given this situation, we investigate in this paper how the key network functionalities as self-optimizing network (SON) must be thought to meet NGMN requirements. We propose therefore, algorithm suitability theory (AST) combined to the notion of network operator infrastructure convergence. The approach is based on software-defined networking (SDN) principle that allows an adaptability of the load balance algorithm to the dynamic network status. Besides, we use the concept of network function virtualization (NFV) that alleviates the constraint of confining the wireless devices to their home network operator only. Relying on these two technologies, we build AST through a lexicographic optimality criterion based on SPC (Status, Performance, and Complexity) order. Numerical results demonstrate a better network coverage verified by the improvement of metrics such as call blocking rate, spectrum efficiency, energy efficiency and load balance index.

Fuzzy logic-based call admission control in 5G cloud radio access networks with preemption

Fifth generation (5G) cellular networks will be comprised of millions of connected devices like wearable devices, Androids, iPhones, tablets, and the Internet of Things (IoT) with a plethora of applications generating requests to the network. The 5G cellular networks need to cope with such sky-rocketing traffic requests from these devices to avoid network congestion. As such, cloud radio access networks (C-RAN) has been considered as a paradigm shift for 5G in which requests from mobile devices are processed in the cloud with shared baseband processing. Despite call admission control (CAC) being one of radio resource management techniques to avoid the network congestion, it has recently been overlooked by the community. The CAC technique in 5G C-RAN has a direct impact on the quality of service (QoS) for individual connections and overall system efficiency. In this paper, a novel fuzzy logic-based CAC scheme with preemption in C-RAN is proposed. In this scheme, cloud bursting technique is proposed to be used during congestion, where some delay tolerant low-priority connections are preempted and outsourced to a public cloud with a penalty charge. Simulation results show that the proposed scheme has low blocking probability below 5%, high throughput, low energy consumption, and up to 95% of return on revenue.

Radio resource management with QoS guarantees for LTE-A systems: a review focused on employing the multi-objective optimization techniques

The increasing number of subscribers’ demand has led to the evolution of future wireless networks that support multimedia applications and require ensuring the quality of services it provides. As the radio resource is becoming scarce, it is turning out to be a vital issue that how should the demands for higher data rates with limited resources is met for the evolving long term evolution-advanced (LTE-A) systems. Moreover, the efficiency and performance of resource management can be further improved by autonomously assigning and managing resources among various users and applications. We have surveyed various radio resource management (RRM) techniques being used for resource sharing in LTE-A networks that focus on the potential of multi-objective optimization algorithms for achieving desired QoS in LTE-A system. In this paper, we present a comprehensive review of RRM techniques, scheduling, and QoS along with a focus on implementing the multi-objective optimization techniques for efficient resource allocation.

A Review and Survey of Radio Resource Management Techniques in LTE-A Network: Challenges and Perspectives

A Review and Survey of Radio Resource Management Techniques in LTE-A Network: Challenges and Perspectives