Efficient Radio Resource Allocation Research Articles

Efficient improvement of energy detection technique in cognitive radio networks using K-nearest neighbour (KNN) algorithm

With the birth of the IoT era, it is evident that the existing number of devices is going to rise exponentially. Any two devices will communicate with each other using the same frequency band with limited availability. Therefore, it is of vital importance that this frequency band used for communication be used efficiently to accommodate the maximum number of devices with the available radio resources. Cognitive radio (CR) technology serves this exact purpose. The stated one is an intelligent radio that is made to automatically identify the optimal wireless channel in the available wireless spectrum at a given instant. An important functionality of CR is spectrum sensing. Energy detection is a very popular algorithm used for spectrum sensing in CR technology for efficient allocation of radio resources to the devices intended to communicate with each other. Energy detection detects the presence of a primary user (PU) signal by continuously monitoring a selected frequency bandwidth. The conventional energy detection technique is known to perform poorly in lower SNR ranges. This paper works towards the improvement of the energy detection algorithm with the help of machine learning (ML). The ML model uses the general properties of the signal as training data and classifies between a PU signal and noise at very low SNR ranges (− 25 to − 10 dB). In this research, a K-nearest neighbours (KNN) model is selected for its versatility and simplicity. Upon testing the model with an out-of-sample dataset, the KNN model produced a detection accuracy of 94.5%.

Resource Slicing for eMBB and URLLC Services in Radio Access Network Using Hierarchical Deep Learning

Network slicing is a promising technique for wireless service providers to support enhanced mobile broadband (eMBB) and ultra-reliable low-latency communication (URLLC) services in a shared radio access network (RAN) infrastructure. In this paper, we apply numerology, mini-slot based transmission, and punctured scheduling techniques to support eMBB and URLLC network slices. For efficient allocation of radio resources (e.g., physical resource blocks, transmit power) to the users, we formulate RAN slicing problem as a multi-timescale problem. To solve this problem and address the dynamics of the traffic, we propose a hierarchical deep learning framework. Specifically, in each long time slot, the service provider employs a deep reinforcement learning (DRL) algorithm to determine the slice configuration parameters. The eMBB and URLLC schedulers use their own attention-based deep neural network (DNN) algorithm to allocate radio resources to their corresponding users in each short and mini time slot, respectively. Simulation results show that the proposed framework can achieve a higher aggregate throughput and a higher service level agreement (SLA) satisfaction ratio compared to some other RAN slicing approaches, including the resource proportional placement algorithm, decomposition and relaxation based resource allocation algorithm, and distributed bandwidth optimization algorithm.

SARAS: Secure resource allocation scheme for NOMA‐based device‐to‐device communication using coalition game and cognitive radio

SummaryDevice‐to‐device (D2D) is a fifth‐generation (5G) network's key technology which allows devices in proximity to communicate one‐to‐one without a base station (BS). Moreover, it ameliorates channel gain, communication latency, energy efficiency, and spectral efficiency. However, mitigating interference (D2D and cellular mobile users [CMUs]) and eavesdropper effect (at D2D links) is a prime concern in D2D communication. These issues can be resolved with an efficient allocation of radio resources. Cognitive radio (CR) and non‐orthogonal multiple access (NOMA) are viable solutions to resolve the aforementioned issues. Motivated by the aforementioned discussion, in this paper, we propose a joint CR and NOMA‐based scheme, that is, SARAS for secure resource allocation in D2D communication. It aims to maximize the overall sum rate and secrecy capacity of D2D users with assured quality of service (QoS) and quality of experience (QoE). CR is used for pairing between strong and weak D2D users (i.e., D2D pair) and NOMA's successive interference cancelation (SIC) technique helps to reduce the interference effect between cellular and D2D users. We used a coalition game for efficient resource utilization, which transfers D2D users from one coalition to another based on the preference order and channel conditions. This landed D2D users in the best coalition having favorable channel conditions. We have executed the proposed SARAS scheme over the MATLAB simulation tool, considering a varying number of D2D and CMUs. Simulation results show that the proposed scheme outperforms the traditional OFDMA, random, and nearest approaches concerning parameters such as sum rate and secrecy capacity.

Efficient Allocation of the Amount of Radio Resources in 5G NR to Efficient Allocation of Radio Resources in 5G NR

Efficient Allocation of the Amount of Radio Resources in 5G NR to Efficient Allocation of Radio Resources in 5G NR

A Context-Aware Radio Resource Management in Heterogeneous Virtual RANs

New-generation wireless networks are designed to support a wide range of services with diverse key performance indicators (KPIs) requirements. A fundamental component of such networks, and a pivotal factor to the fulfillment of the target KPIs, is the virtual radio access network (vRAN), which allows high flexibility on the control of the radio link. However, to fully exploit the potentiality of vRANs, an efficient mapping of the rapidly varying context to radio control decisions is not only essential, but also challenging owing to the interdependence of user traffic demand, channel conditions, and resource allocation. Here, we propose CAREM, a reinforcement learning framework for dynamic radio resource allocation in heterogeneous vRANs, which selects the best available link and transmission parameters for packet transfer, so as to meet the KPI requirements. To show its effectiveness, we develop a testbed for proof-of-concept. Experimental results demonstrate that CAREM enables an efficient radio resource allocation under different settings and traffic demand. Also, compared to the closest existing scheme based on neural network and the standard LTE, CAREM exhibits an improvement of one order of magnitude in packet loss and latency, while it provides a 65% latency improvement relatively to the contextual bandit approach.

Efficient radio resource allocation scheme for 5G networks with device-to-device communication

<p>A vital technology in the next-generation cellular network is device-to-device (D2D) communication. Cellular user enabled with D2D communication provides high spectral efficiency and further increases the coverage area of the cell, especially for the end-cell users and blind spot areas. However, the implementation of D2D communication increases interference among the cellular and D2D users. In this paper, we proposed a radio resource allocation (RRA) algorithm to manage the interference using fractional frequency reuse (FFR) scheme and Hungarian algorithm. The proposed algorithm is divided into three parts. First, the FFR scheme allocates different frequency bands among the cell (inner and outer region) for both the cellular and the D2D users to reduce the interference. Second, the Hungarian weighted bipartite matching algorithm is used to allocate the resources to D2D users with the minimum total system interference, while maintaining the total system sum rate. The cellular users share the resources with more than one D2D pair. Lastly, the local search technique of swapping is used for further allocation to minimize the interference. We implemented two types of assignments, fair multiple assignment, and restricted multiple assignment. We compared our results with existing algorithms which verified that our proposed algorithm provides outstanding results in aspects like interference reduction and system sum rate. For restricted multiple assignment, 60-70% of the D2D users are allocated in average cases.</p>

Wireless Edge Machine Learning: Resource Allocation and Trade-Offs

The aim of this paper is to propose a resource allocation strategy for dynamic training and inference of machine learning tasks at the edge of the wireless network, with the goal of exploring the trade-off between energy, delay and learning accuracy. The scenario of interest is composed of a set of devices sending a continuous flow of data to an edge server that extracts relevant information running online learning algorithms, within the emerging framework known as Edge Machine Learning (EML). Taking into account the limitations of the edge servers, with respect to a cloud, and the scarcity of resources of mobile devices, we focus on the efficient allocation of radio (e.g., data rate, quantization) and computation (e.g., CPU scheduling) resources, to strike the best trade-off between energy consumption and quality of the EML service, including service end-to-end (E2E) delay and accuracy of the learning task. To this aim, we propose two different dynamic strategies: (i) The first method aims to minimize the system energy consumption, under constraints on E2E service delay and accuracy; (ii) the second method aims to optimize the learning accuracy, while guaranteeing an E2E delay and a bounded average energy consumption. Then, we present a dynamic resource allocation framework for EML based on stochastic Lyapunov optimization. Our low-complexity algorithms do not require any prior knowledge on the statistics of wireless channels, data arrivals, and data probability distributions. Furthermore, our strategies can incorporate prior knowledge regarding the model underlying the observed data, or can work in a totally data-driven fashion. Several numerical results on synthetic and real data assess the performance of the proposed approach.

Characterization of sparse WLAN data traffic in opportunistic indoor environments as a prior for coexistence scenarios of modern wireless technologies

CR enabled radio environment provides the seamless operating framework to cope with the requirements of next generation wireless systems like higher data rates, massive machine time communication and interference free coexistence scenarios of wireless technologies. Traffic characterization studies assist to optimize coexistence framework by providing necessary information about the usage patterns of wireless services in observed radio bands. Prior knowledge about the observed sparse wireless local area network (WLAN) data traffic is a key for opportunistic radio resource allocation and utilization in the given coexistence scenarios of WLAN either with cellular systems (including 5G-NR/4G Long Term Evolution (LTE)) or with low power wireless systems (including Bluetooth and ZigBee). Parametric methodology is adapted to model the sparse WLAN data traffic observed in 2.4 GHz Industrial, Scientific and Medical (ISM) band as spectral and temporal activity. The multivariate Gaussian mixture model (MGMM) is proposed in both scenarios where either dependency is considered between the observed WLAN data traffic or assuming that the observed traffic is independent and identically distributed (i.i.d). It is to be validated that in order to have an efficient characterization of sparse WLAN data traffic, the dependency (correlation) between neighbored frequency subbands must be considered. By considering the dependency either between neighbored frequency subbands or neighbored time domain signals actually help to characterize the sparse WLAN data traffic in more realistic way which could not be possible by assuming them i.i.d. Such statistics to characterize sparse data traffic really help as user activity for efficient allocation and utilization of radio resources in CR enabled coexistence scenarios of wireless technologies where WLAN acitivity is considered nominal either as secondary user or classify it grey spectrum as primary user.

Efficient resource allocation algorithms for high energy efficiency with fairness among users in OFDMA networks

In this article, we devise two efficient radio resource allocation algorithms in order to increase fair energy efficiency among users of OFDMA networks. The objective function is considered to be the well-known max-min function in order for allocating radio resources fairly in terms of energy efficiency, by taking the maximum transmit power, minimum rate requirements, and subchannel assignment constraints into account. The resulting problem is a nonconvex mixed-integer nonlinear problem for which two algorithms are proposed. In the first proposed algorithm, by reallocating the transmit power on subchannels, we obtain a suitable subchannel assignment with high fair energy efficiency. In the second proposed algorithm, by applying generalized fractional programming, mathematically modifying the objective function and constraints to a continuous and convex form and employing sequential convex programming, we allocate power and subchannel jointly. The proposed algorithms efficiently utilize radio resources and therefore, yield a far better performance than available solutions.

Throughput and Fairness Optimal Scheduling for High Speed Cell Edge Users in LTE Networks

Guaranteeing Quality of Service (QoS) to mobile users is the primary aim of cellular broadband system like Long Term Evolution (LTE). Radio resource allocation and scheduling are two important functions in the LTE networks to enhance the quality of service. For increasing the generally user experience, an efficient radio resource allocation and Scheduling algorithm should be used. However, this became a non-trivial task as the demands and requirements of user data changes day-to-day. In these situations, with the limited radio resources, maximum system capacity can be obtained on expense of unfair share of the resources. In this work, high speed cell edge users are considered as they experience poor signal strength and their quality of service degrades when they move away from Evolved-Nodes (e-Nodes). Here, a novel scheduling algorithm has been introduced to extend the cell edge throughput amid during high mobility scenarios. The proposed scheduling scheme will be compared with the conventional schemes like best CQI, RR and PF in terms of throughput and fairness. It is presented that the proposed scheme gives better performance against the conventional ones in the chosen scenario.

Throughput Enhancement Based on Optimized Regularized Particle Filter for H-CRAN

The fifth-generation technology is emerging with various real-time applications that demand high data rate. The number of connected devices is expected to increase. However, spectral and energy resources are limited. Therefore, it is challenging to meet users satisfaction. Thus, the necessity for a structure that can sustain real-time performance of wireless communication systems is pressing. This paper presents an efficient radio resource allocation scheme using regularized particle filter with optimized kernel size and kernel bandwidth, and Monte Carlo Markov chain move step based on metropolis algorithm is used to estimate next state of channel. Channel state is calculated from the temporal channel correlation using maximum likelihood estimation. Throughput for the next user state is predicted using posterior probability with normalized weights. The main aim of this paper is to observe the user throughput. The problem is modeled as throughput maximization subject to channel state. The optimization problem is formulated as a non convex optimization and solved using branch and bound method. It has been shown in the simulation results that the average throughput is increased by ${\text{5.94}\%}{\text{--52.98}\%}$ compared to sequential importance sampling with resampling (SISR) method. Therefore, the RPF scheme has proved to be efficient by its remarkable increase in user throughput compared to the SISR particle filter algorithm.

Game theoretic efficient radio resource allocation in 5G resilient networks: A data driven approach

AbstractIn recent years, 5G resilient networks have gained significant attention in the wireless industry. The prime concern of commercial networks is to maximize network capacity to increase their revenue. However, in disaster situations during outages when cell sites are down, instead of capacity, coverage becomes predominant. In this paper, we propose a game theory–based optimal resource allocation scheme, while aiming to maximize the sum rate and coverage probability for the uplink transmissions in disaster situations. The proposed hierarchical game theoretical framework optimizes the uplink performance in multitier heterogeneous network with pico base stations and femto access points overlaid under a macro base station. The test simulations are based on a real‐time data set obtained for a predefined amount of time. The data statistics are then manipulated to create practical disaster situations. The solution for the noncooperative game has been obtained by using pure strategy Nash equilibrium. We perform simulations with different failure rates and the results show that the proposed scheme improves the sum rate and outage probability by significant margin with or without disaster scenario.

Efficient radio resource allocation in SDN/NFV based mobile cellular networks under the complete sharing policy

Novel networking paradigms, such as software-defined networking (SDN) and network function virtualisation (NFV), introduce new opportunities in the design of next-generation mobile networks. The present work investigates the benefits of the emerging SDN and NFV technologies on the radio resource management (RRM) in mobile cellular networks. In particular, the aim of the proposed RRM scheme is to enable an efficient and flexible radio resource allocation in order to assure quality of experience of mobile users. The authors consider the orthogonal frequency division multiple access scheme and the complete radio resource sharing policy. To enable time- and space-efficient resource allocation, the authors investigate the applicability of the well-known Kaufman–Roberts recursion in the context of new architectural and functional changes of SDN/NFV based mobile environments. Finally, they discuss the applicability of the proposed approach for more complicated resource sharing policies.

On the performance of LTE downlink scheduling algorithms: A case study on edge throughput

Radio resource allocation is a crucial task in the LTE networks. To increase the overall user experience, an efficient radio resource allocation algorithm should be utilized. In this work, a new scheduling algorithm has been proposed to increase the edge throughput without sacrificing system throughput. Comparative performance results indicate that the proposed scheduler increases the edge throughput and fairness while limiting degradation in the cell throughput between 0 to 2 percent with respect to the other schedulers.

Content Centric Cross-Layer Scheduling for Industrial IoT Applications Using 6TiSCH

The latest 6TiSCH standard enables highly reliable industrial monitoring and control applications through deterministic wireless communications and efficient allocation of radio resources among wireless nodes. However, much of these benefits are tied to the ability of the scheduler in orchestrating communication over individual links in an efficient and a non-conflicting manner. The state-of-the-art 6TiSCH scheduling algorithms only tackle the scheduling problem at the MAC layer with an assumption of having an optimal routing layer. Moreover, to the best of our knowledge, no solution so far has taken content-specific scheduling into account. This paper proposes CONCISE, which is a content-centric cross-layer scheduling solution. CONCISE creates content-independent routing topologies and schedulers, resulting in an overlaid routing structure and multiple content-based schedules. Further, CONCISE addresses in-network processing and data aggregation with the objective of reducing network traffic. Performance evaluation demonstrates that CONCISE can effectively reduce inner layer network traffic by up to 65%. As a result, it can provide up to 50% delay reduction and achieve significantly higher packet delivery ratio, compared with existing scheduling solutions. Initial proof of concept is implemented and evaluated in Contiki OS.

Interference Aware Resource Allocation with QoS Guarantees in OFDMA/SC-FDMA

Efficient Radio Resource Allocation (RRA) is of utmost importance for achieving maximum capacity in mobile networks. However, the performance assessment should take into account the main constraints of these networks. This letter presents important enhancements to RRA algorithms proposed in [1]. Prior work [1] ignores some important system constraints such as the impact of inter-cell interference and granularity of frequency allocation blocks. Here we show the performance degradation when these system constraints are assumed on the algorithms in [1] as well as propose some improvements on these algorithms in order to achieve better performance.

Noise Power Estimators in ISM Radio Environments: Performance Comparison and Enhancement Using a Novel Samples Separation Technique

Noise power estimation is central to efficient radio resource allocation in modern wireless communication systems. In the literature, there exist many noise power estimation methods that can be classified based on underlying theoretical principle; the most common are spectral averaging, eigenvalues of sample covariance matrix, information theory, and statistical signal analysis. However, how these estimation methods compare against each other in terms of accuracy, stability, and complexity is not well studied, and the focus instead remains on the enhancement of individual methods. In this paper, we adopt a common simulation methodology to perform a detailed performance evaluation of the prominent estimation techniques. The basis of our comparison is the signal-to-noise ratio estimation in the simulated industrial, scientific and medical band transmission, while the reference noise signal is acquired from an industrial production plant using a software-defined radio platform, USRP-2932. In addition, we analyze the impact of different techniques for noise-samples’ separation on the estimation process. As a response to defects in the existing techniques, we propose a novel noise-samples’ separation algorithm based on the adaptation of rank-order filtering. Our analysis shows that the proposed solution, apart from its low complexity, has a very good root-mean-squared error of 0.5 dB and smaller than 0.1-dB resolution, thus achieving a performance comparable with the methods exploiting information theory concepts.

Adaptive Cross-Layer-Based Packet Scheduling and Dynamic Resource Allocation for LTE-Advanced Relaying Cellular Communications

In 4G, LTE-Advanced specifies the key objective that the gigabit data rate for real-time video streaming can be transmitted under a high mobility scenario; thus, packet scheduling and radio resource management become the critical techniques that should be addressed effectively. Additionally, the mechanisms of relaying and the OFDMA coding have been certainly adopted in LTE-Advanced and IEEE 802.16 j/m for the purposes of increasing the wireless service coverage and improving signal quality of UEs nearing the cell boundaries. Several studies propose some improvements for the relaying-based packet scheduling, but suffer from long packet delay, high packet dropping probability moderate system capacity, and degrading QoS of real-time packet transmissions. This paper thus proposes an Adaptive LTE-Advanced cross-layer packet Scheduling (ALS) to guarantee real-time high-speed packet service for LTE-Advanced. ALS consists of two mechanisms: (1) Adaptive Reward Priority Scheduling: the cross layer-based adaptive packet scheduling at the MAC layer and (2) Dynamic Resource Allocation algorithm: the efficient radio resource allocation at the PHY layer. Numerical results demonstrate ALS outperforms the compared approaches in system capacity, packet dropping probability, average packet delay, etc.

Fast and Efficient Radio Resource Allocation in Dynamic Ultra-Dense Heterogeneous Networks

Ultra-dense network (UDN) is considered as a promising technology in 5G wireless networks. In an UDN network, dynamic traffic patterns can lead to a high computational complexity and an excessive communications overhead with traditional resource allocation schemes. In this paper, a new resource allocation scheme presenting a low computational overhead and a low subband handoff rate in a dynamic ultra-dense heterogeneous network is presented. The scheme first defines a new interference estimation method that constructs network interference state map, based on which a radio resource allocation scheme is proposed. The resource allocation problem is a MAX-K cut problem and can be solved through a graph-theoretical approach. System level simulations reveal that the proposed scheme decreases the subband handoff rate by 30% with less than 3.2% network throughput degradation.

Socially aware cluster formation and radio resource allocation in D2D networks

Due to the popularity of mobile user devices, D2D communications become a key technology to improve local services in the next generation cellular networks. However, the benefits count on efficient radio resource allocation between cellular links and D2D links, which poses critical challenges. By exploiting the network knowledge extracted from underlying mobile social networks (MSNs), this article proposes a socially aware D2D communication scheme to improve the spectrum and energy efficiency in cellular networks. First, by jointly considering the social and network knowledge, we propose a cluster formation scheme to categorize a group of UEs into multiple D2D clusters. Within each cluster, highly spectrum-efficient D2D multicasting (from the cluster head, i.e., a D2D transmitter UE to multiple cluster member UEs) is enabled. Next, a half-duplex scheme and a full-duplex scheme are proposed to coordinate the channel sharing between the cellular links and the D2D links. Simulation results show that the proposed schemes can form D2D clusters rationally among a group of UEs, and therefore largely increase the spectrum and energy efficiency of the network.