Spectrum Allocation Research Articles

A Survey on Resource Management for 6G Heterogeneous Networks: Current Research, Future Trends, and Challenges

The sixth generation (6G) mobile communication system is expected to meet the different service needs of modern communication scenarios. Heterogeneous networks (HetNets) have received a lot of attention in recent years due to their potential as a novel structure for evolutionary networks. When compared to homogeneous networks, HetNets provide more potential for spatial spectrum reuse and higher quality of service (QoS). However, effective resource management (RM) solutions are essential to prevent interference and accomplish spectrum sharing due to mutual interference. This paper presents a comprehensive review of resource management in 6G HetNets. The study aims to give crucial background on HetNets to aid in the creation of more effective methods in this field of study. First, a detailed examination of recent work is presented in resource management aspects such as power allocation, user association, mode selection, and spectrum allocation. Second, we identify the most severe challenges associated with the current resource management methods and propose suitable solutions. Finally, several open issues and emerging areas of research are highlighted.

Comprehensive review on ML-based RIS-enhanced IoT systems: basics, research progress and future challenges

Sixth generation (6G) internet of things (IoT) networks will modernize the applications and satisfy user demands through implementing smart and automated systems. Intelligence-based infrastructure, also called reconfigurable intelligent surfaces (RISs), have been introduced as a potential technology striving to improve system performance in terms of data rate, latency, reliability, availability, and connectivity. A huge amount of cost-effective passive components are included in RISs to interact with the impinging electromagnetic waves in a smart way. However, there are still some challenges in RIS system, such as finding the optimal configurations for a large number of RIS components. In this paper, we first provide a complete outline of the advancement of RISs along with machine learning (ML) algorithms and overview the working regulations as well as spectrum allocation in intelligent IoT systems. Also, we discuss the integration of different ML techniques in the context of RIS, including deep reinforcement learning (DRL), federated learning (FL), and FL-deep deterministic policy gradient (FL-DDPG) techniques which are utilized to design the radio propagation atmosphere without using pilot signals or channel state information (CSI). Additionally, in dynamic intelligent IoT networks, the application of existing integrated ML solutions to technical issues like user movement and random variations of wireless channels are surveyed. Finally, we present the main challenges and future directions in integrating RISs and other prominent methods to be applied in upcoming IoT networks.

RL-IoT: Reinforcement Learning-Based Routing Approach for Cognitive Radio-Enabled IoT Communications

Internet of Things (IoT) devices are widely being used in various smart applications and being equipped with cognitive radio (CR) capabilities for dynamic spectrum allocation. Our objectives in this work are to achieve higher data rates and minimize end-to-end routing delays in CR-enabled IoT communication in order to maximize throughput. We propose a reinforcement learning (RL)-based routing approach in the cognitive radio network (CRN)-based IoT environment. The idea is to add the channel selection decision capability to the network layer in order to minimize packet collisions as well as end-to-end delay (EED). We perform a comprehensive performance evaluation of the proposed RL-IoT routing mechanism by simulating the cognitive radio-enabled Internet of Things (CR-IoT) communication environment in the cognitive radio cognitive network (CRCN) simulator and comparing the network performance achieved by our proposed mechanism with that of the recent AODV-based routing mechanism for IoT (AODV-IoT), ELD-CRN, and SpEED-IoT routing approaches. Our evaluation results show that the RL-IoT model performs better than existing approaches in terms of average data rate, throughput, packet collision, and EED.

Learning and Game Based Spectrum Allocation Model for Internet of Medical Things (IoMT) Platform

The Internet of Medical Things (IoMT) paradigm provides pervasive healthcare services in-home monitoring networks. Nowadays, these services play an imperative part in the life of human beings. However, excessive requirements of health services result in insufficient spectrum resources and service delays. In this study, a novel spectrum allocation scheme is proposed for the IoMT system platform. The main challenge of our scheme is to effectively share the limited spectrum resource while dynamically handling different service requests. To achieve a mutually desirable solution for multiple IoMT devices, our proposed scheme is designed as a bi-level control algorithm using the ideas of <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">multi-agent reinforcement learning (MARL)</i> and the <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">Balakrishnan-Gómez-Vohra (BGV)</i> solution. At the first level, each IoMT device selects its salient point according to the <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">MARL</i> model. At the second level, the spectrum resource is distributed through the <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">BGV</i> solution, which is implemented by considering the selected salient point of each device. Through the sequential interactions of intelligent devices, our bi-level control approach can effectively guide individual IoMT devices to choose cooperation strategies while optimizing the spectrum allocation process. Finally, numerical results show the effectiveness of our proposed scheme through the comparisons with benchmark protocols. We demonstrate the performance improvement of our method in terms of the normalized device payoff, IoMT system throughput and device fairness.

Research of the application of blockchain and smart contract technologies in spectrum management and trading in cognitive radio networks

The rapid growth of wireless communication technologies has led to an exponential increase in the demand for spectrum resources. Spectrum management, the process of allocation and control of limited radio frequency spectrum, is critical to the efficient and equitable distribution of this scarce resource. However, traditional methods of spectrum management face many problems, including inefficiency, lack of transparency and proneness to fraud. In recent years, blockchain technology has emerged as a potential solution to these problems. The emergence of smart contracts using blockchain technology has the potential to revolutionize spectrum trading and improve spectrum management practices. This paper explores the application of blockchain in spectrum management and discusses its potential to revolutionize the way spectrum resources are allocated and managed. Also, the concept of smart contracts and their application in spectrum trading are studied. It discusses the benefits of using smart contracts for transparent and efficient spectrum transactions, implementation challenges, and potential solutions. Through the use of smart contracts, a secure and decentralized market for spectrum resources can be created, facilitating dynamic and flexible allocation of spectrum.

A Survey of Resource Management in D2D Communication for B5G Networks

Due to the vast rise of users, the variety of application requirements, and the enormous necessity for ubiquitous connectivity anytime and anywhere, integration of new technologies and ideas are required. Therefore, Device to Device (D2D) communication technology has been regarded as a critical concern for Beyond Fifth Generation (B5G) networks. In the D2D technology, devices in close proximity may connect without using a Base Station (BS). Consequently, improving the capacity and performance of traditional networks. The major objectives are to accomplish a high geared data rate, increase the network coverage with low latency, and improve Energy Efficiency (EE) and Spectrum Efficiency (SE). However, the coexistence of D2D technology in the traditional network can pose many issues and challenges, such as power control, interference, spectrum access, security, and mode selection. Consequently, a comprehensive survey that includes an investigation of the current issues in D2D technology is required. In this survey, we present a new categorization of the most important aspects of resource management in B5G D2D communication, such as power allocation, spectrum allocation, interference management, and mode selection. Furthermore, we discuss the limitations and open issues of the current techniques and propose a model approach and future directions for D2D communication in B5G wireless networks. Our survey aims to assist researchers in exploring and improving D2D communication within the framework of forthcoming wireless networks.

A polyhedral study of a relaxation of the routing and spectrum allocation problem (Brief Announcement)

The routing and spectrum allocation (RSA) problem arises in the context of flexible grid optical networks, and consists in routing a set of demands through a network while simultaneously assigning a bandwidth to each demand, subject to non-overlapping constraints. One of the most effective integer programming formulations for RSA is the DR-AOV formulation, presented in a previous work. In this work we explore a relaxation of this formulation with a subset of variables from the original formulation, in order to identify valid inequalities that could be useful within a cutting-plane environment for tackling RSA. We present basic properties of this relaxed formulation, we identify several families of facet-inducing inequalities, and we show that they can be separated in polynomial time.

An Overview and Assessment of Today's U.S. Satellite Telemetry, Tracking and Telecommand (TT&amp;amp;C) Spectrum Allocation Needs

An Overview and Assessment of Today's U.S. Satellite Telemetry, Tracking and Telecommand (TT&amp;amp;C) Spectrum Allocation Needs

Maximum-Likelihood-Based Location, Orientation, Transmitted Power, and Antenna Beamwidth Estimator Utilizing the Angle-of-Arrival and Received Power for Spectrum Sharing at Millimeter Wave Bands

The fifth-generation (5G) system utilizing millimeter wave is available in Japan, but more bandwidth is demanded by the mobile phone operators to accommodate the demand for high data rate. Allocation of the vacant spectrum is impossible due to the past spectrum allocation to other radio systems. For example, 24.25-27.5 GHz has been allocated for the fixed wireless access (FWA) and license exempt small power data communication system in Japan. Although Japanese government is considering to license a part of the band for 5G under the spectrum sharing scheme, the coexistence design is quite challenging due to unavailable operation data of such systems in a form of database for the coexistence study. For this purpose, this paper introduces a maximum-likelihood estimator (MLE), which utilizes the angle-of-arrival (AoA) and received power of the line-of-sight (LOS)/diffraction paths as well as the map database to simultaneously estimate these unknown parameters via the spectrum sensing. It was applied to the measurement data obtained from a sub-urban environment where a commercial fixed wireless access system with similar operational parameters is under operation. Its performance was evaluated for a different number of receiver (Rx) points. The results showed that the parameter estimation accuracy is proportional to the number of receiver points. The distance and orientation errors of 45 m and 5 deg were achieved at 90th percentile with 40 Rx points. Furthermore, the prediction performance of the effective isotropic radiated power (EIRP) was decent, albeit a slight underestimation of the beamwidth.

Spectrum Options and Allocations for 6G: A Regulatory and Standardization Review

Spectrum Options and Allocations for 6G: A Regulatory and Standardization Review

Radio-Frequency Interference Considerations for Utility of the Galileo E6 Signal Based on Long-Term Monitoring by ARFIDAAS

<h3>Abstract</h3> The extent to which navigation signals in the E6 band may be impacted by shared spectrum allocations might be underappreciated. This paper presents top-level observations from a multi-year international radio frequency interference (RFI) monitoring project covering all L-band global navigation satellite system (GNSS) signals with specific focus on the challenges facing the E6 band. The context of this paper is the assumption that most users will be non-authorized and have access to only the open data-bearing signal component and not the encrypted pilot of the E6 Galileo signal. In virtually all locations where the Advanced RFI Detection, Analysis, and Alerting System (ARFIDAAS) monitoring stations were deployed, frequent disruption of the E6 band from systems such as radar installations or other authorized users of the spectrum was observed. In the presented paper, an effort is made to put the observations in the context of the expected use cases of the E6 signal.

Two Time-Scale Energy and Spectrum Allocation for MEC Networks with Hybrid Energy Supplies

Integrating power grid and renewable energy to charge mobile-edge computing (MEC) server is a promising solution for green networks, which can reduce the energy consumption of power grid and carbon emission. Intelligent management of energy and bandwidth resources in the two time-scale framework can increase the energy efficiency and network revenue in MEC networks with heterogeneous energy supplies and multiple mobile users (MUs). In this paper, we investigate the joint energy management (for workloads offloading and execution) and bandwidth allocation (for workloads transmission) problem that maximizes the network revenue in the MEC network with hybrid energy supplies. We formulate it into a challenging nonlinear optimization problem because of the data randomness and the temporal coupling effect. Based on the Lyapunov optimization approach with quick convergence speed and low complexity, we propose an online algorithm to overcome the obstacles by relaxing the battery constraints, allowing us to achieve close-to-optimal maximum network revenue with the stability of battery level and different throughput requirements of MUs. Extensive numeral simulations verify the theoretical analysis and demonstrate the superior performance of the proposed algorithm in different algorithms and scenarios.

Power Optimized Multiple-UAV Error-Free Network in Cognitive Environment

Many extensive UAV communication networks have used UAV cooperative control. Wireless networking services can be offered using unmanned aerial vehicles (UAVs) as aerial base stations. Not only is coverage maximization, but also better connectivity, a fundamental design challenge that must be solved. The number of applications for unmanned aerial vehicles (UAVs) operating in unlicensed bands is fast expanding as the Internet of Things (IoT) develops. Those bands, however, have become overcrowded as the number of systems that use them grows. Cognitive Radio (CR) and spectrum allocation approaches have emerged as a potential approach for resolving spectrum scarcity in wireless networks, and hence as technological solutions for future generations, from this perspective. As a result, combining CR with UAVs has the potential to give significant benefits for large-scale UAV deployment. The paper examines existing research on the subject of UAV covering and proposes a multi-UAV cognitive-based error-free model for energy-efficient communication. Coverage maximization, power control, and enhanced connection quality are the three steps of the proposed model. To satisfy the desired signal-to-noise ratio, the covering zone efficacy is investigated as a function of the distance among UAVs stationed in a specific geographic region depending on multiple deployment configurations like as rural, suburban, and urban macro deployment scenarios of the ITU-R M.2135 standard (SNR).

Contract-Theory-Based Secure Spectrum Sharing Framework in Internet of Vehicles

Internet of vehicles (IoV) enables a large number of applications for consumers by integrating massive electronics, which aggravates the spectrum scarcity and data security, and degrades the quality of services (QoS) and experiences of consumers obviously. Focusing on enhancing the security and improving spectrum utilization, this article proposes a contract-theory-based secure spectrum sharing framework, which adopts the consortium blockchain to guarantee security of spectrum sharing and utilizes the practical Byzantine fault tolerant consensus algorithm to achieve consensus in IoV. Smart contracts are then formed based on insurance contracts to promote primary users to share their idle spectrum resources. A preference-based spectrum allocation algorithm is designed to maximize the total utility of secondary users (SUs). Simulation results show that, compared with the existing schemes, the proposed scheme achieves better performance in terms of the utility of SUs and the spectrum utilization greatly, which provides more access opportunities to consumers in IoV.

Network dynamic routing and spectrum allocation algorithm based on blockchain technology

Network dynamic routing and spectrum allocation algorithm based on blockchain technology

DYNAMIC MULTICAST SPECTRUM (RE) ALLOCATION USING SHARED BACKUP PATH IN ELASTIC OPTICAL NETWORKS

The routing and spectrum allocation (RSA) problem in elastic optical networks (EON) is to first find a path for a connection request and then allocate a frequency slot to it. Due to spectrum continuity and contiguity constraints, some available frequency slots cannot be allocated to connection requests. In this case, one solution is to reassign slot to some existing connections to accept new ones. However, most current reallocation schemes are proposed for unprotected connections. With the very high throughput of connections in EONs (Tb/s per link), the requirement for connection protection is becoming standard. In this paper, we examine a frequency slot reallocation approach for protected multicast connections. The proposed approach allows the reallocation of available and already used slots by temporarily using the protection resources. The experimental results show an average blocking probability of 10% lower than reallocation with hoptuning and the conventional MC-RSA algorithm.

Performance analysis of NOMA in Rayleigh and Nakagami Fading channel

Cellular connectivity is expanding rapidly in the modern world. The multiple access strategy is one of the highly used methods for allocating the range of users in cellular network. Spectrum allocation is a crucial element to take into account since cellular communication is becoming more and more popular. NOMA is a channel access mechanism used in 5G mobile communication. It is also known as non-orthogonal multiple access. NOMA is a potential strategy for enhancing spectral efficiency and sum rate. Using the NOMA method, we evaluated the BER versus transmitted power of two users in rayleigh and nakagami fading channels. In this NOMA setup, a single antenna is shared by two users. Two users can accept the same frequency using 5G NOMA technology, but at different power levels. The results of the MATLAB simulation show that the two user NOMA in the Nakagami channel performs better than the Rayleigh channel in terms of Bit Error Rate vs. Transmitted Power.

Spectrum Sharing in the Sky and Space: A Survey

In order to achieve the vision of seamless wireless communication coverage, a space-air-ground integrated network is proposed as a key component of the sixth-generation (6G) mobile communication system. However, the spectrum used by aerial networks has become gradually crowded with the increase in wireless devices. Space networks are also in dire need of developing new bands to address spectrum shortages. As an effective way to solve the spectrum shortage problem, spectrum sharing between aerial/space networks and ground networks has been extensively studied. This article summarizes state-of-the-art studies on spectrum sharing between aerial/space networks and ground networks. First, this article provides an overview of aerial networks and space networks and introduces the main application scenarios of aerial networks and space networks. Then, this article summarizes the spectrum sharing techniques between aerial/space networks and ground networks, including existing spectrum utilization rules, spectrum sharing modes and key technologies. Finally, we summarize the challenges of spectrum sharing between aerial/space networks and ground networks. This article provides guidance for spectrum allocation and spectrum sharing of space-air-ground integrated networks.

Core and spectrum allocation to achieve graceful degradation of inter-core crosstalk with generalized hierarchical core prioritization on space-division multiplexing elastic optical networks

Space-division multiplexing elastic optical networks (SDM-EONs) with multi-core fibers (MCFs) are expected to meet the exponentially increasing demand for traffic. However, it is inevitable that inter-core crosstalk (IC-XT) will hinder efforts to increase capacity. Thus, attempts have been made to leverage SDM-EONs by solving the route, modulation format, core, and spectrum assignment problems, but the effects of IC-XT still need to be mitigated. Existing methods such as XT-Aware first fit and core prioritization methods are insufficient for addressing this issue. In this paper, we propose generalized hierarchical core prioritization for the graceful degradation of IC-XT in core and spectrum allocation. Simulation results establish that our approach mitigates adverse influences from IC-XT and has high flexibility in network environments. This study shows that the dynamic resource allocation method, despite being heuristic and computationally inexpensive, can sufficiently reduce the blocking probabilities in any SDM-EONs with hexagonal MCFs.

Predicting consecutive spectrum opportunities with hidden Markov models

SummaryThe increasing demands for wireless channels to accommodate the surge of internet of things devices and the associated services exacerbated the need for flexible channel allocation strategies. Opportunistic spectrum sharing is expected to provide a more reasonable use of the limited radio frequencies by allowing the coexistence of licensed users and unlicensed users in the same frequency. This arrangement is called opportunistic channel allocation, where unlicensed users explore the channel when the licensed user is not transmitting. The challenge in opportunistic spectrum allocation is to find transmission opportunities. Statistical and machine learning techniques have been used to forecast spectrum opportunities on time slotted channels based on acquired transmission data from licensed users. However, opportunity forecast is usually limited to one slot ahead of time. This work explores the use of Hidden Markov Model (HMM) training and predicting procedures to forecast a consecutive sequence of idle slots that can be explored for opportunistic transmission. To improve prediction accuracy and speedup the training process, the proposed training procedures reduce the classification alphabet, which is achieved by balancing the number of training sequences to limit the influence of outliers and provide opportunity forecast even when the training process is executed over a limited number of observed sequences. While an aggressive predictor may explore more opportunities as compared to a conservative predictor, the former may incur in higher collision rates while the latter may explore fewer opportunities. Hence, this work proposes a metric to assist in the process of selecting the best predictor's parameters to balance collision and opportunity rates. The proposed HMM predictors are evaluated on both synthetic and real traffic traces. The results on synthetic traffic shows that, for channel load from up to , the proposed predictors identified over of channel opportunities with the collision rates below . On real traffic traces, where the licensed users' transmissions have deterministic inter‐packet delays, the HMM predictors identified over of the channel opportunities with collision rates below .