Efficient Utilization Of Radio Resources Research Articles

Active RIS Assisted Spectrum Sharing: Able to Achieve Energy-Efficient Notable Detection Performance Gains

Efficient radio resource utilization is crucially important for future wireless communication systems. The advanced reconfigurable intelligent surface (RIS) has recently been incorporated into the spectrum sharing mechanism, allowing for both spectrum efficiency (SE) and energy efficiency (EE). Spectrum sensing is the cornerstone of spectrum sharing. Nevertheless, due to the <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">doubling fading</i> effect, the path loss experienced from the reflection link is thousands of times that from the direct link. Consequently, the detection performance gains obtained through passive RIS are negligible, especially in typical communication scenarios where the direct link is not weak. To address this issue, this paper proposes an active RIS assisted spectrum sensing mechanism that can notably improve the detection performance gains. Multiple active RISs are introduced and the closed-form expressions of the detection performance metrics are derived in this paper. More importantly, due to the extra energy consumption in amplifying reflected signals, it's necessary to investigate the EE maximization problem for the active RIS assisted mechanism by jointly optimizing the detection parameter and the reflecting precoding. Alternating optimization (AO) algorithm and quadratic transform (QT) method are utilized to decouple the multidimensional <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">fractional programming</i> (FP) problem. Then, the optimal detection parameter is obtained with a closed-form solution based on the convex optimization theory. The phase shift and amplification coefficient of each active element are optimized through the sequential rotation (SR) method and an improved differential evolutionary (DE) algorithm, respectively. Simulation results show that the proposed active-RIS mechanism could outperform the conventional passive-RIS mechanism in achieving notably enhanced detection performance gains and higher EE by optimizing the reflecting precoding.

LoRaWAN Meets ML: A Survey on Enhancing Performance with Machine Learning.

The Internet of Things is rapidly growing with the demand for low-power, long-range wireless communication technologies. Long Range Wide Area Network (LoRaWAN) is one such technology that has gained significant attention in recent years due to its ability to provide long-range communication with low power consumption. One of the main issues in LoRaWAN is the efficient utilization of radio resources (e.g., spreading factor and transmission power) by the end devices. To solve the resource allocation issue, machine learning (ML) methods have been used to improve the LoRaWAN network performance. The primary aim of this survey paper is to study and examine the issue of resource management in LoRaWAN that has been resolved through state-of-the-art ML methods. Further, this survey presents the publicly available LoRaWAN frameworks that could be utilized for dataset collection, discusses the required features for efficient resource management with suggested ML methods, and highlights the existing publicly available datasets. The survey also explores and evaluates the Network Simulator-3-based ML frameworks that can be leveraged for efficient resource management. Finally, future recommendations regarding the applicability of the ML applications for resource management in LoRaWAN are illustrated, providing a comprehensive guide for researchers and practitioners interested in applying ML to improve the performance of the LoRaWAN network.

Crowdsensing-Assisted Path Loss Estimation and Management of Dynamic Coverage in 3D Wireless Networks With Dense Small Cells

Emerging vertical applications enabled by connected devices and smart infrastructures have created an ever-increasing demand for high data rates over 5th-Generation (5G) and beyond wireless networks. Deployment of dense small cells (SCs) and millimeter wave (mmWave) communication systems have become inevitable in future wireless networks. Consequently, it is more accurate to model such networks in the 3D space due to the spatially distributed nature of the SCs, locations of the devices, radio resources and propagation environment. Accurate estimation of location-specific path loss parameters is then essential for efficient utilization of radio resources and management of dynamic coverage in 3D SC networks. In the paper, a framework for location-specific path loss estimation is developed for efficient radio resource management, based on the principle of crowdsensing together with Linear Algebra (LA) and machine learning (ML) techniques considering 2.5 GHz and 28 GHz bands. The corresponding procedure for capturing dynamic coverage of a SC base station (BS) serving to an arbitrary cluster is proposed and examined based on its 3D propagation characteristics. Results show that the accuracy of 3D channel parameter estimation using gradient descent ML techniques is superior compared to LA technique and can achieve over 98% estimation accuracy. It is shown that using the proposed process, parameters can be extrapolated for the slightly extended 3D communication distances from the cluster boundary for the worst-case locations of devices based on already estimated propagation parameters with accuracy over 74% for certain distances. Although numerical results are presented for a single amorphous 3D cell of a wireless network, the framework given in the paper can be extended to any arbitrary 3D wireless cellular network.

QoS‐Aware Optimal Radio Resource Allocation Method for Machine‐Type Communications in 5G LTE and beyond Cellular Networks

In this paper, we consider the saturation problem in the 3GPP LTE cellular system caused by the expected huge number of machine‐type communication (MTC) devices, leading to a significant impact on both machine‐to‐machine (M2M) and human‐to‐machine H2H traffic. M2M communications are expected to dominate traffic in LTE and beyond cellular networks. In order to address this problem, we proposed an advanced architecture designed for 5G LTE networks to enable the coexistence of H2H/M2M traffic, supported by different priority strategies to meet QoS for each traffic. The queuing strategy is implemented with an M2M gateway that manages four queues allocated to different types of MTC traffic. The optimal radio resource allocation method in LTE and beyond cellular networks was developed. This method is based on adaptive selection of channel bandwidth depending on the QoS requirements and priority traffic aggregation in the M2M gateway. Additionally, a new simulation model is proposed which can help in studying and analyzing the mutual impact between M2M and H2H traffic coexistence in 5G networks while considering high and low priority traffics for both M2M and H2H devices. This simulator automates the proposed method of optimal radio resource allocation between the M2M and H2H traffic to ensure the required QoS. Our simulation results proved that the proposed method improved the efficiency of radio resource utilization to 13% by optimizing the LTE frame formation process.

Green communication for cognitive radio networks based on game and utility-pricing theories.

The most crucial challenge in the functioning of the wireless networks is the efficient utilization of radio resources. A significant element of resource handling is power regulation. With increasing requirement of wireless data transmission services, it is essential to devise energy harvesting techniques for mobile devices. In this research, a new methodology has been proposed for distributed power regulation in cognitive radio, networks of CR are grounded on non-cooperation game phenomenon and pricing technique. QoS (Quality of service) of the user of CR is anticipated as a beneficial activity through pricing as well as dissemination of energy generating as an unbeneficial game wherein the consumers increase their overall efficacy. The price is defined as an actual function of transmission power to upraise the pricing of the most distant consumers. The proposed mathematical model shows that the proposed game model has a Nash equilibrium and is also unique. Furthermore, in order to make the proposed algorithm valid for green communication within the wireless network, the best response technique was proposed. Finally, simulation results showed that the proposed energy harvesting technique, grounded on a unique function of the utilization, reduces the consumption of transmission power and greatly improves the convergence speed; which are suitable for the vision of the 5G networks.

Access Point Association in Uplink Two-Hop Cellular IoT Networks With Data Aggregators

Node clustering and data aggregation help extend the coverage of cellular networks and increase the number of supported devices, while meeting the various service quality requirements and reducing energy consumption, making them suitable for enabling the future massive cellular Internet-of-Things (IoT) applications. Consequently, we propose to overlay the cellular network with a layer of data aggregators (DAs) to act as relays. DAs use cellular backhauling, and thus, the network provides both single- and two-hop routes; however, DAs share radio resources with single-hop devices, creating a dependency between the two routes. Thus, the proper design of the DA-enabled network becomes critical for cost effectiveness and efficient radio resource utilization. In this article, we formulate a joint access point association, resources utilization, and energy-efficient communication optimization problem that takes into account various networking factors, such as the number of devices, the number of DAs, the number of available resource units, interference, the transmission power limitation of the devices, DA transmission performance, and channel conditions. The objective is to show the usefulness of data aggregation and shed light on the importance of network design when the number of devices is massive. We propose a coalition game theory-based algorithm PAUSE to transform the optimization problem into a simpler form that can be successfully solved in polynomial time. Different network scenarios are simulated to showcase the effectiveness of PAUSE and to draw observations on cost-effective network design with DAs.

Performance Analysis of the Probabilistic Models of ISM Data Traffic in Cognitive Radio Enabled Radio Environments

Cognitive radio (CR) provides a way to utilize the radio resources in a smart manner. In order to exploit the benefits of CR technology either in CR systems or in CR enabled wireless sensor networks, CR based smart grids, CR based wireless body area networks (WBANs) and CR based Internet of Things (IOTs), it is necessary to characterize the radio traffic in a realistic way. In perspective of CR operating models, the radio traffic can be modeled either using conditional or unconditional modeling approaches. In unconditional modeling approach, observed data traffic is solely considered as interference while in conditional modeling approach the observed data traffic is first classified as signal and noise. Prior knowledge about the statistics of interference in unconditional modeling approach while signal (primary user) and noise (secondary user) characterization in conditional modeling framework will help in efficient utilization of radio resources within CR-enabled radio environment. Furthermore, performance analysis is carried out for both the unconditional and conditional models based on devised information theoretic criterion. Multivariate Gaussian mixture (MGM) as a special case of hidden markov random field (HMRF) and Gaussian mixture (GM) are suitable candidate models for the observed data traffic in ISM (Industrial, Scientific and Medical) band based on devised criterion in unconditional and conditional models, respectively.

Performance analysis of wireless-powered cognitive radio networks with ambient backscatter

Ambient backscatter is a promising wireless communication technique where low-power users communicate with each other without any dedicated power source. These communicating users transmit their information by reflecting ambient radio-frequency (RF) signals. In this paper, we propose an ambient backscatter communications-assisted wireless-powered underlay cognitive radio network (CRN). The proposed CRN consists of a single primary transmitter (PT) and multiple primary receivers (PRs), secondary transmitters (STs), and secondary receivers (SRs). For efficient utilization of radio resources, the STs in the proposed scheme dynamically adopt either harvest-then-transmit mode or backscatter mode. Furthermore, PRs cooperate with STs to select an appropriate mode for their communication with SRs. To evaluate the performance of our proposed scheme, we conduct system-level simulations. Numerical results show that the performance of the secondary system can be improved in terms of throughput with minimum effect on the communication of primary users.

NOMA-Based Resource Allocation and Mobility Enhancement Framework for IoT in Next Generation Cellular Networks

With the unprecedented technological advances witnessed in the last two decades, more devices are connected to the Internet, forming what is called the Internet of Things (IoT). The IoT devices with heterogeneous characteristics and the quality of experience (QoE) requirements may engage in the dynamic spectrum market due to the scarcity of radio resources. We propose a framework to efficiently quantify and supply radio resources to the IoT devices by developing intelligent systems. The primary goal of this paper is to study the characteristics of the next generation of cellular networks with non-orthogonal multiple access (NOMA) to enable connectivity to clustered IoT devices. First, we demonstrate how the distribution and QoE requirements of IoT devices impact the required number of radio resources in real time. Second, we prove that using an extended auction algorithm by implementing a series of complementary functions enhance the radio resource utilization efficiency. The results show a substantial reduction in the number of sub-carriers required when compared with conventional OMA and the intelligent clustering is scalable and adaptable to the cellular environment. Ability to move spectrum usages from one cluster to other clusters after borrowing when a cluster has fewer users or move out of the boundary is another soft feature that contributes to the reported radio resource utilization efficiency. Moreover, the proposed framework provides IoT service providers cost estimation to control their spectrum acquisition to achieve the required quality of service with a guaranteed bit rate (GBR) and non-GBR.

Optimal Channel and Relay Assignment in Ofdmbased Multi-Relay Multi-Pair Two-Way Communication Networks

Efficient utilization of radio resources in wireless networks is crucial and has been investigated extensively. This letter considers a wireless relay network where multiple user pairs conduct bidirectional communications via multiple relays based on orthogonal frequency-division multiplexing (OFDM) transmission. The joint optimization of channel and relay assignment, including subcarrier pairing, subcarrier allocation as well as relay selection, for total throughput maximization is formulated as a combinatorial optimization problem. Using a graph theoretical approach, we solve the problem optimally in polynomial time by transforming it into a maximum weighted bipartite matching (MWBM) problem. Simulation studies are carried out to evaluate the network total throughput versus transmit power per node and the number of relay nodes

A maximum throughput channel allocation protocol in multi-channel multi-user cognitive radio network

Channel allocation protocol plays a crucial role in ensuring that cognitive radio technology achieves more efficient radio resource utilization. To evaluate and compare the performance of different channel allocation protocols accurately, we need to model the cognitive radio system using various channel allocation protocols under one identical framework. Building such a framework is challenging. In this paper, we propose a method named distribution probability matrix (DPM) to describe the allocation results under different allocation protocols and a queueing analysis framework that uses DPM method. A framework that is capable of analyzing every individual secondary user (SU) in the system is developed. We also propose a maximum throughput (MT) channel allocation protocol aimed at achieving optimal throughput. Then we compare the MT protocol and existing protocols with the same objective using our proposed framework. The numerical results show that the MT protocol outperforms the existing protocols. Performance comparisons of different SUs are carried out to compare the protocols comprehensively. The convenience using our analytical framework of modeling different allocation protocols and evaluating the overall performances of the protocols is revealed throughout the analysis.

User Scheduling and Resource Allocation in HetNets With Hybrid Energy Supply: An Actor-Critic Reinforcement Learning Approach

Densely deployment of various small-cell base stations in cellular networks to increase capacity will lead to heterogeneous networks (HetNets), and meanwhile, embedding the energy harvesting capabilities in base stations as an alternative energy supply is becoming a reality. How to make efficient utilization of radio resource and renewable energy is a brand-new challenge. This paper investigates the optimal policy for user scheduling and resource allocation in HetNets powered by hybrid energy with the purpose of maximizing energy efficiency of the overall network. Since wireless channel conditions and renewable energy arrival rates have stochastic properties and the environment’s dynamics are unknown, the model-free reinforcement learning approach is used to learn the optimal policy through interactions with the environment. To solve our problem with continuous-valued state and action variables, a policy-gradient-based actor-critic algorithm is proposed. The actor part uses the Gaussian distribution as the parameterized policy to generate continuous stochastic actions, and the policy parameters are updated with the gradient ascent method. The critic part uses compatible function approximation to estimate the performance of the policy and helps the actor learn the gradient of the policy. The advantage function is used to further reduce the variance of the policy gradient. Using the numerical simulations, we demonstrate the convergence property of the proposed algorithm and analyze network energy efficiency.

Proactive Radio Resource Optimization With Margin Prediction: A Data Mining Approach

Driven by the exponential surge on high data rate services, network operators are facing the challenges of how to enhance the capacity and optimize the coverage in a cost-efficient approach. However, traditional network optimization technologies passively adjust the network configurations based on network's congestion ratio, drop-off rate, coverage holes, etc., leading to suboptimum user experiences. Therefore, the objective of this paper is to optimize the network configurations by obtaining the accurate network status, user demand, and application request distribution based on the real-time data. The data mining technique is introduced to predict the resource margin based on historical measurement statistics. To explore the dynamic distribution of user demand and application request, a weighted k-nearest neighbors model is proposed to predict periodic characteristics of network traffics, denoting different temporal and spatial patterns of radio resource margins. In contrast to the traditional passive network optimization approaches, the radio resources can be reconfigured actively to meet the dynamic patterns of traffic loads by using the proposed optimization algorithm. Results prove that the proposed data mining model can capture the dynamics of traffic loads to optimize the traffic load balance and increase the efficiency of radio resource utilization using the network statistic data.

A Joint SMSE Waveform Design and Blind Cyclostationary Based Sensing for Cognitive Radios

Cognitive Radio (CR) presents a lot of challenges with regard to developing the techniques and methodologies to dynamically utilize the unused or underused spectrum. The issue of efficient utilization of radio resources can be implemented unitedly through dynamic waveform design and an efficient spectrum sensing. In this paper, waveform design using spectrally modulated spectrally encoded (SMSE) framework is conjoined with the cyclostationary based sensing technique (CSS) for CR. To identify the free spectrum, an analytical expression of waveform design variables in terms of sensing parameters has been derived. Further, to find the channel occupancy/spectrum holes, a joint CSS and SMSE based test stat has been proposed. The joint test stat offers a better and clear decision by providing a large gap between test stat and the required threshold setting. Finally, to evaluate the receiver performance, receiver operating characteristic (ROC) curves have been plotted which show that the proposed joint scheme achieves a significant performance improvement at lower SNR values when compared with traditionally available detectors.

A Radio Resource Virtualization-Based RAT Selection Scheme in Heterogeneous Networks

In the future heterogeneous wireless networks, heterogeneity of radio resources from different radio access technologies (RATs) still exists. The heterogeneity, especially for networks with the coexistence of non-orthogonal and orthogonal resources, makes the radio resources difficult to be uniformly measured, and thus hinders the efficient utilization of radio resources. To overcome this limitation, this letter firstly proposes a radio resource virtualization approach in heterogeneous networks. Based on the accumulated historical data of resource utilization information, heterogeneous radio resources are virtualized into normalized resources using deep learning method. Secondly, the consumption difference of virtualized resources under different situations of network load and user demand is modeled. Moreover, aiming at efficiently utilizing radio resources and reducing access blocking rate, a RAT selection scheme based on the radio resource virtualization is proposed. Through simulation, the validity of the proposed scheme is evaluated.

이기종 무선망에서 에너지 효율 개선을 위한 망간 협력 기반 스케쥴링 기법

무선망에서 소모 전력, 전송률, 통신 반경 등 서비스 요구 사항에 따라 상호 다른 망의 발전이 진행되어 왔다. 최근 차세대 무선망 환경에서는 초고속, 초저지연, 저전력 등 서비스 요구 사항이 보다 다양해지고 높은 수준으로 설정되고 있으며, 이를 만족하기 위한 방안으로 이기종간 효과적인 연동에 대한 연구에 관심이 높아지고 있다. 본 논문에서는 이기종 무선망 환경에서 망간 연동을 통해 데이터 서비스가 이루어지는 상황에서 에너지 효율성을 반영하는 스케쥴링 기법을 제안한다. 특히 이기종 무선망 환경에서 사용자 형평성을 고려하면서 데이터 수율을 개선하는 비례균등 스케쥴링 방식을 기반으로 에너지 효율에 관한 요소를 반영하는 문제를 고려하고 에너지 효율을 개선함과 동시에 사용자 형평성, 데이터 수율을 모두 감안하는 에너지 효율적인 비례균등 스케쥴링 방식을 제안한다. 또한 모의실험을 통해 제안된 방식으로 비례균등 달성도를 유지하면서 에너지 효율이 개선됨을 보인다.

Performance Evaluation of Joint Admission and Eviction Controls of Secondary Users in Cognitive Radio Networks

In recent years, cognitive radio (CR) has been proposed to promote the efficient utilization of the spectrum by exploiting the spectrum holes. The fundamental issue in CR networks is the efficient utilization of radio resources. Call admission control (CAC), which controls the number of calls based on the available resources and bandwidth, is an important functionality to ensure the quality of service (QoS). Thus, in this paper, a new call admission and eviction control of secondary users (CAEC) is proposed. The proposed CAEC is a combination of three variant schemes. The main objective of this proposed scheme is to make admission and evictions decisions jointly in order to improve the spectrum utilization efficiency and ensure the QoS for secondary users (SUs) and to provide better balance between the required SU’s QoS and primary users’ protection. Performance evaluation of the proposed scheme with its variants is studied and compared with other two previous schemes, known as basic CAC (BCAC) and queue-based CAC (QCAC), in terms of the QoS requirements, such as useful utilization and loss probability. Results show that the proposed CAEC achieves higher admitted traffic for SUs than BCAC and QCAC. In addition, it ensures higher system utilization without degrading the primary users’ performance.

On the efficient utilization of radio resources in extremely dense wireless networks

The emergence of popular wireless technologies such as LTE and WiFi, and the exponential growth in the usage of these technologies, has led to extremely dense wireless networks. There are many proposals for coping with such densification. In particular, we evaluate the compound effect of inter-cell interference schemes and spectrum efficient intra-cell relay techniques, which have been individually proposed recently as separate solutions. We provide a jointly coordinated intracell and inter-cell resource allocation mechanism that opportunistically exploits network density as a resource. We show that intracell opportunistic relay, based on WiFi communications, reduces the complexity of Inter-Cell Interference Coordination (ICIC) and boosts the efficiency of ICIC in LTE. The superiority of the proposed solution to the legacy cellular network operation is proven via simulations.

모드 탐색 알고리즘을 이용한 측정치 기반의 전파 환경 시각화 기법

In this paper, we propose an algorithm to visualize radio wave environment based on the measured Received Signal Strength Indication(RSSI) and 3D geographic information. We estimate the source position using the circumcenter of the triangle and visualize the radio wave environment using the empirical propagation models. A mode seeking algorithm(mean-shift clustering) is used to seek the peak points and the center of gravity is utilized to reduce the estimation errors. Our approach finds its applications in the radio wave monitoring systems for the efficient utilization of radio resources.

Enhancing QoS Through Dynamic and Fare AP Selection in a Wireless LAN

The IEEE 802.11 WLAN is primarily used for web browsing which belongs to the category of nonreal time application. But the demand of real time applications like VOIP and video conferencing has become very much common to such WLAN. With IEEE 802.11e Mac protocol it is possible to improve the QoS for both real and non-real time traffic by service differentiation. To ensure efficient utilization of the radio resources and enhanced QoS the load imbalance should be resolved among APs from different BSSs. In large scale WLAN inter AP communication mechanism can be employed along side the current admission controller under EDCA. Beside service differentiation inter AP differentiation based QoS management can lead to efficient utilization of radio resources by moving STAs from heavily laded to a less loaded AP and ensure better QoS for all types of traffics. In this paper we propose a dynamic and fair AP selection mechanism to improve the QoS in a WLAN. The simulations have been carried out with NSv2.34. Index Terms — EDCA, QoS, Contention Window, VoIP, AC.