Spectrum Resource Allocation Research Articles

Joint Cooperative Spectrum Sensing and Resource Allocation in Dynamic Wireless Energy Harvesting Enabled Cognitive Sensor Networks

Joint Cooperative Spectrum Sensing and Resource Allocation in Dynamic Wireless Energy Harvesting Enabled Cognitive Sensor Networks

Collaborative Cloud-Edge-End Task Offloading in MEC-Based Small Cell Networks With Distributed Wireless Backhaul

Collaborative cloud-edge-end computing is a promising solution to support computation-intensive and latency-sensitive tasks by utilizing rich computing resources of cloud datacenters and low access delay of mobile edge computing (MEC) servers. Compared with traditional cloud computing and MEC, the cloud-edge environment has a stronger heterogeneity of servers and networks, resulting in significant differences between servers in the computation speed and access delay. However, few studies on cloud-edge-end task offloading focused on the characteristic of 5G heterogeneous networks in the cloud-edge environment. In this paper, we study the task offloading problem for collaborative cloud-edge-end computing in MEC-enabled small cell networks with low-cost distributed wireless backhaul. We aim to minimize the energy consumption of all user devices (UDs) via jointly optimizing the offloading decision, UDs’ transmission power, and the allocation of spectrum and computation resources. To solve the non-convex problem, we decouple the original problem into three subproblems, and design an efficient method with solving these three subproblems iteratively to obtain a high-quality solution. The simulation results indicate that our proposed method can lead to significant reduction in the energy consumption of all UDs compared with other conventional methods.

Lightpath provisioning model considering crosstalk-derived fragmentation in spectrally-spatially elastic optical networks

Recently, multi-core fiber (MCF) has been incorporated with elastic optical networks (EONs) to enhance the fiber capacity, which forms spectrally-spatially EONs (SS-EONs). In MCF, inter-core crosstalk (XT) occurs and degrades the signal quality. Fragmentation is one of the major issues since it leads to inefficient utilization of spectrum resources. Fragmentation metric-aware spectrum resource allocation is one of the solutions to suppress fragmentation. In SS-EONs, several studies have been conducted to deal with inter-core XT and fragmentation. The conventional fragmentation-aware model considering XT calculates the fragmentation metric by distinguishing spectrum slots as vacant or not. Some vacant slots are unavailable due to inter-core XT, and the unavailable vacant slots can cause fragmentation. This paper proposes a fragmentation-aware lightpath provisioning model, which suppresses the fragmentation caused by allocating spectrum slots to lightpaths and due to inter-core XT. The proposed model classifies vacant slots into available ones and unavailable ones. An optimization problem is formulated as an integer linear programming (ILP) problem. A heuristic algorithm is introduced when the ILP problem is not tractable. Numerical results observe that the proposed model suppresses the blocking probability compared to a benchmark model based on the literature.

Radio Resource Management Satellite Communication Network MCDM Method

Worldwide deployment of heterogeneous wireless networks is growing as a result of consumer demand for connectivity at all times and in all places. These customers' interest in multimedia apps like video streaming and VoIP, which demand tight Quality of Service (QoS) support, is growing at the same time. With such limitations, provisioning network resources is a difficult undertaking. In fact, it might be challenging for a network operator to identify trustworthy criteria to choose the optimum network that ensures user happiness while maximising network utilisation, given the availability of numerous access technologies (WiFi, WiMAX, or cellular networks). To solve this problem, each eNB just needs to learn the traffi c conditions or patterns of its owncell in our proposal. Wireless communication systems depend heavily on radio resource management (RRM). To ensure the efficient and successful operation of wireless networks, it involves the allocation and control of radio frequency spectrum, power, and other resources. RRM is significant because it can use scarce radio resources as efficiently as possible, enhancing capacity, lowering interference, and improving service quality. Successful deployment and operation of wireless communication systems like cellular networks, Wi-Fi, and Bluetooth depend on effective RRM approaches. The need for wireless communication is growing, and new technologies and standards are constantly being developed. The methodology of radio resource management (RRM) involves a variety of techniques and algorithms designed to allocate radio resources in a way that maximizes network performance while minimizing interference. Taken as alternate parameter is Laser communication, optical networks, satellite optical communication, vibrations, satellite networks. Taken as is solar radiation power, thermal bending, micro meteorite impact, solar and lunar gravity, earth oblations method. satellite optical communication has reached near 2000 data set compare other data set. The operation of wireless communication networks depends on radio resource management (RRM). Wireless networks would have interference, congestion, and a lacklustre level of service if effective RRM procedures weren't used. RRM is therefore a key component in ensuring that wireless communication systems can provide users with dependable and high-quality services.

Energy-Aware Mode Selection for D2D Resource Allocation in 5G Networks

This paper proposes an energy-aware mode-selection mechanism for device-to-device (D2D) resource allocation in the coexisting 5G network environment of D2D users and traditional cellular users by taking into consideration the mechanisms of mode selection, transmit power, and spectrum resource allocation simultaneously. In the proposed approach, all spectrum resources are fully shared by traditional cellular users and D2D users. We first determine the communication mode of all users after considering the transmission opportunity of traditional cellular users and analyzing the interference degree of different D2D communication modes. Next, we divide all users into three tiers according to the distances between users and the base station to solve the interference problem generated by excessing transmit power in uplink transmission. Afterward, based on the different communication modes, corresponding spectrum resource allocation mechanisms using the Hungarian algorithm are developed. We simulate a single cell environment using Python language and perform several simulations for different pairs of traditional cellular users and D2D users. The simulation results reveal that the proposed approach outperforms the proposed approach by Hou et al. in system throughput. With power control, the energy efficiency of the proposed approach could be enhanced by 20%.

Lifting Wavelet-Assisted EM Joint Estimation and Detection in Cooperative Spectrum Sensing.

Spectrum sensing in Cognitive radio (CR) is a way to improve spectrum utilization by detecting spectral holes to achieve a dynamic allocation of spectrum resources. As it is often difficult to obtain accurate wireless environment information in real-world scenarios, the detection performance is limited. Signal-to-noise ratio (SNR), noise variance, and channel prior occupancy rate are critical parameters in wireless spectrum sensing. However, obtaining these parameter values in advance is challenging in practical scenarios. A lifting wavelet-assisted Expectation-Maximization (EM) joint estimation and detection method is proposed to estimate multiple parameters and achieve full-blind detection, which uses lifting wavelet in noise variance estimation to improve detection probability and convergence speed. Moreover, a stream learning strategy is used in estimating SNR and channel prior occupancy rate to fit the scenario where the SU has mobility. The simulation results demonstrate that the proposed method can achieve comparable detection performance to the semi-blind EM method.

Dynamic multiple access based on deep reinforcement learning for Internet of Things

With the development of wireless communication technology, the rational use of spectrum resources utilizing efficient multiple access technology has become a research hotspot. The channel access strategy in dynamic spectrum access technology will directly affect the system’s spectrum utilization. Deep reinforcement learning is proposed to train users in the network to learn optimal channel access strategy to achieve reasonable allocation of spectrum resources on the channel. At the same time, the multiple access scheme adopted by users who have access to the channel will also affect the throughput of the system. Traditional multiple access schemes include Orthogonal Multiple Access scheme and Non-orthogonal Multiple Access (NOMA) scheme. In a dynamic environment, the multiple access scheme suitable for users on the channel is also dynamic. Therefore, DRL is introduced to help users learn the multiple access scheme selection strategy to maximize user throughput. Finally, we propose a dynamic multiple access algorithm based on deep reinforcement learning, which optimizes the channel access strategy and scheme selection strategy respectively, and improves the performance. The simulation results show that the proposed scheme is superior to other schemes in various scenarios, especially, the average throughput of the proposed scheme is about 0.8 Mbps and 0.5 Mbps higher than that of the FDMA scheme and NOMA scheme, respectively.

Resource Scheduling of Time-Sensitive Services for B5G/6G Connected Automated Vehicles

Due to the shortage of spectrum resources in the Internet of vehicles, the diversified time-sensitive services for B5G/6G connected automated vehicles cannot be effectively transmitted and the existing researches lack efficient and intelligent resource scheduling methods. Therefore, this paper proposes a spectrum resource scheduling model for time-sensitive services, and designs a two-tier joint resource scheduling method based on the age of information and bandwidth requirements under macro base station (MBS) and Road Side Unit (RSU). In the first layer, we propose a Vickery-Clarke-Groves (VCG) enabled auction model to solve the time-sensitive services resource scheduling problem, in order to guarantee the authenticity of vehicle users (VUEs) bidding. In addition, the lagrange relaxation algorithm is utilized to obtain the optimal solution of spectrum resource allocation to ensure the VUEs service delay and achieve low complexity resource scheduling. In the second layer, we design a federated learning based MBS auxiliary communication method to alleviate RSU communication pressure. The communication links of differentiated time-sensitive services are predicted to improve the quality of perception services. Simulation results verify that the proposed algorithms can enhance the VUEs experience effectively compared with the conventional methods, in terms of the delay, throughput and packet loss ratio.

On three shades of partial protection in elastic optical networks

Gone have been the days when the allocation of spectrum resources for a demand is over-provisioned due to worst-case based designs and the optical transmission technologies are based on the fixed spectral grid, modulation format and/or line-rate schemes. Elastic optical networks technologies featuring adaptive spectrum allocation mechanisms have been becoming the architecture and technology of choice for future core networks to meet explosive traffic growths in a cost and energy-efficient manner. Partial protection strategies based on the observation that in failure events, a service can tolerate a certain amount of degradation and therefore by reducing the protection traffic in the network, better spectrum utilization could be attained. Such concept has been widely studied in the traditional wavelength division multiplexing (WDM) context and yet has been somehow faded due to the fact that fixed transmission technologies provide limited room for spectral improvements. This paper aims to renew the interest in partial protection and re-adapt it to the context of elastic optical networks. The evolutionary perspective we lay out in this paper identifies a new route for achieving greater spectral efficiency in a pragmatic way by simply differentiating the protection services for each demand rather than the uniform treatment for all demands. As such, we present a new research problem entitled, routing, modulation level, spectrum and protection service assignment which is essentially an extension of the well-established one, that is, routing, modulation level, and spectrum assignment as the (partial) protection service for each demand is taken into account and optimized. Three variants of that problem reflecting shades of applying partial protection are covered in details. Specifically, the first one considers the intuitive case as the amount of protection traffic for each demand is provided as the input to the network planning process while the second one is dedicated to the special case of enforcing the same level of partial protection for all demands. More interesting case emerges in the third variant where given the service level agreement for the total protected network traffic, we provide the optimal solution that determines the protection service for each individual demand so as to minimize the spectral occupancy. Extensive numerical simulations have been conducted in the realistic COST239 network topology and it has been revealed that a remarkable spectral saving could be achieved thanks to introducing partial protection services. Moreover, our results have demonstrated that the demand-wise approach far outperforms the one-size-fits-all one and the difference could be up to 25% in our studied cases.

A Deep Reinforcement Learning Scheme for Spectrum Sensing and Resource Allocation in ITS

In recent years, the Internet of Vehicles (IoV) has been found to be of huge potential value in the promotion of the development of intelligent transportation systems (ITSs) and smart cities. However, the traditional scheme in IoV has difficulty in dealing with an uncertain environment, while reinforcement learning has the advantage of being able to deal with an uncertain environment. Spectrum resource allocation in IoV faces the uncertain environment in most cases. Therefore, this paper investigates the spectrum resource allocation problem by deep reinforcement learning after using spectrum sensing technology in the ITS, including the vehicle-to-infrastructure (V2I) link and the vehicle-to-vehicle (V2V) link. The spectrum resource allocation is modeled as a reinforcement learning-based multi-agent problem which is solved by using the soft actor critic (SAC) algorithm. Considered an agent, each V2V link interacts with the vehicle environment and makes a joint action. After that, each agent receives different observations as well as the same reward, and updates networks through the experiences from the memory. Therefore, during a certain time, each V2V link can optimize its spectrum allocation scheme to maximize the V2I capacity as well as increase the V2V payload delivery transmission rate. However, the number of SAC networks increases linearly as the number of V2V links increases, which means that the networks may have a problem in terms of convergence when there are an excessive number of V2V links. Consequently, a new algorithm, namely parameter sharing soft actor critic (PSSAC), is proposed to reduce the complexity for which the model is easier to converge. The simulation results show that both SAC and PSSAC can improve the V2I capacity and increase the V2V payload transmission success probability within a certain time. Specifically, these novel schemes have a 10 percent performance improvement compared with the existing scheme in the vehicular environment. Additionally, PSSAC has a lower complexity.

Low complexity joint spectrum resource and power allocation for ultra dense networks

In this paper, we propose a low complexity spectrum resource allocation scheme cross the access points (APs) for the ultra dense networks (UDNs), in which all the APs are divided into several AP groups (APGs) and the total bandwidth is divided into several narrow band spectrum resources and each spectrum resource is allocated to APGs independently to decrease the interference among the cells. Furthermore, we investigate the joint spectrum and power allocation problem in UDNs to maximize the overall throughput. The problem is formulated as a mixed-integer nonconvex optimization (MINCP) problem which is difficult to solve in general. The joint optimization problem is decomposed into two subproblems in terms of the spectrum allocation and power allocation respectively. For the spectrum allocation, we model it as a auction problem and a combinatorial auction approach is proposed to tackle it. In addition, the DC programming method is adopted to optimize the power allocation subproblem. To decrease the signaling and computational overhead, we propose a distributed algorithm based on the Lagrangian dual method. Simulation results illustrate that the proposed algorithm can effectively improve the system throughput.

Research on the Best Way to Allocate Spectrum Resources: Perspective from Auction

The context of this paper's research is spectrum auctions, and talk in detail about the spectrum auction that took place in the UK in the spring of 2000, as well as other ways of allocating spectrum, comparing them, and classifying the advantages and disadvantages. The reason why this study spectrum auctions is that to compare other methods and choose the best way to allocate spectrum so that both firms and the government can benefit the most. The research method is a case study, by reading the literature and investigating the data, we will analyze the research problem from an objective point of view. Finally, by analyzing several advantages of spectrum auctions, this article find that auctions are indeed the most efficient way to allocate spectrum rather than beauty pageants. The significance of the research is that the allocation of the spectrum can make a large amount of money for the government and at the same time, firms will pay a large amount of money, so the goal of the research in this paper is to find a method that can satisfy both firms and government needs.

Joint Resource Allocation Algorithm for Energy Harvest-Based D2D Communication Underlaying Cellular Networks Considering Fairness

In this paper, we aim to maximize the minimum data volume for the energy harvest (EH)-based D2D communication underlaying cellular network. The formulated problem is a mixed integer nonlinear programming (MINLP), which we split into two sub-problems to solve separately. We first employ the Successive Convex Approximation (SCA) method to solve the time and power allocation, while putting the spectrum multiplexed pairs as fixed. Then, we develop a fair allocation mechanism to allocate spectrum resources. Numerical results show that our developed algorithm outperforms other benchmark schemes in terms of the minimum and overall data volumes.

A survivable multipath resource allocation strategy based on fragmentation-sensitive fragmentation-aware in space division multiplexing elastic optical networks

The space division multiplexing elastic optical network (SDM-EONs) can effectively supply the large-capacity for the current optical network. If a link failure occurs, it will bring huge data losses or disruptions for the operator. We propose a survivable multipath fragmentation-sensitive fragmentation-aware routing, core and spectrum assignment (SM-FSFA-RSCA) scheme to solving the problem of survivability of the request in SDM-EONs and spectrum fragmentation that caused by the dynamic allocation and release of spectrum resources. We design a fragmentation-sensitive fragmentation metric (FSFM) to estimate the spectrum fragmentation in the established lightpaths, and design a dynamic routing and core selection strategy for the request to control spectrum fragmentation in the core according to FSFM. We consider survivable multipath routing strategy for saving spectrum resources, each path of the multipath can be used as a working path or a protection path. In addition, for spectrum allocation, a survivable multipath with shared spectrum allocation strategy is adopted to maximize the utilization of frequency resources. The simulation results show that the algorithm proposed in this paper significantly reduces the bandwidth blocking probability,fragmentation ratio and also improves the spectrum utilization.

Multi-Operator Dynamic Spectrum Sharing for Wireless Communications: A Consortium Blockchain Enabled Framework

To enable secure and efficient dynamic spectrum sharing (DSS) with guaranteed revenue and quality of service (QoS) in future wireless communications, we present a consortium blockchain based DSS framework, where the regulators supervise the whole process of DSS, and thus the revenue of each participant can be guaranteed. Each mobile network operator (MNO) on the chain can adaptively act as a spectrum provider or spectrum requestor based on their demand, and the spectrum resource allocation is recorded on the chain with a smart contract. The optimal spectrum pricing and buying strategies are solved based on a multi-leader multi-follower (MLMF) Stackelberg game model, and the equilibrium is solved with the proposed algorithm. We then build a prototype with Hyperledger Fabric consortium blockchain, and the average latency is evaluated. Simulations and prototype evaluations validate the feasibility of blockchain based DSS and show that the average latency increase with the participants, which provides useful insights for real applications.

Practical Performance Analysis of Interference in DSS System

The 5G network is developing rapidly. However, due to spectrum resource limitation, it is expected to use the 5G network to ensure high resource utilization and network efficiency, while keeping part of 4G in the same band for existing 4G users. Dynamic spectrum-sharing (DSS) technology enables 4G/5G wireless networks to coexist in scarce spectrum resources and dynamically allocates spectrum resources in the same band. 4G/5G DSS has been successfully commercialized in some countries such as Germany and Brazil. However, complex 4G/5G DSS networks will introduce intra-frequency interference in the inter-system, which will affect network performance. Therefore, we innovatively proposed two interference mitigation schemes: buffer setting and rate matching. Furthermore, we have verified the practical performance of both schemes in a commercial network for the first time to determine the feasibility of the schemes. From theory, simulation, and practical analysis, both schemes can effectively mitigate the interference of the inter-system introduced by DSS: increasing the network rate by 60% in the interference environment and improving the user experience in the DSS architecture.

Performance Analysis of Directional Ultra-Dense Networks with Dynamic Spectrum Partition Strategy

Intercell interference coordination (ICIC) plays a significant role in strengthening ultra-dense network (UDN) downlink coverage. From a statistical average perspective, a user is primarily interfered by its adjacent base station (BS), especially the second nearest BS. By modeling BSs equipped with directional antennas as a Poisson point process (PPP), this paper proposes a dynamic spectrum resource allocation strategy mainly about users’ service BS and its nearest interference BS, where the subchannel assigned by the typical (served) user is interlaced from the channel simultaneously occupied by users within the effective radiation range of its second nearest BS. To fully explore this scheme for directional networks, we develop analytical expressions in terms of success probability and ergodic rate for the typical user based on the techniques of stochastic geometry, taking into account the fading of directional BS radiation angle. Then, we derive the meta distribution of the signal-to-interference ratio (SIR) for capturing individual link performance changes of users. Simulations verify the correctness of numerical results, and it is revealed that this strategy is in favor of users alleviating interference from their second nearest BSs and the performance advantages of the proposed ICIC strategy are better than those of the traditional directional UDNs.

Temporal prediction for spectrum environment maps with moving radiation sources

AbstractSpectrum resources are becoming harder to come by for wireless communications. The spectrum environment map (SEM), which depicts the electromagnetic environment's current state and future trend, is a valuable technique for managing and allocating spectrum resources. Most SEM construction approaches only take static SEMs into account and cannot forecast time‐domain changes and trends of SEMs in dynamic scenes. In this paper, a brand‐new temporal SEM prediction method for the high dynamic spectrum environment is proposed. This method is based on knowledge of radiation source and the optical flow driven by propagation channel models. First, a novel radiation source localization strategy is designed to obtain the radiation source movement information. Then, the optical flow field of the available SEMs is combined with the information regarding radiation source movement. In order to forecast future SEMs, a propagation model driven reconstruction technique is developed. Simulation findings demonstrate how well the suggested strategy is tailored to capture the spatiotemporal correlation of SEMs. This technique performs better than the state‐of‐the‐art in terms of single‐ and multiple‐step SEM predictions.

SPECTRUMNET: Cooperative Spectrum Monitoring Using Deep Neural Networks

Spectrum monitoring is one of the significant tasks required during the spectrum sharing process in cognitive radio networks (CRNs). Although spectrum monitoring is widely used to monitor the usage of allocated spectrum resources, this work focuses on detecting a primary user (PU) in the presence of secondary user (SU) signals. For signal classification, existing methods, including cooperative, noncooperative, and neural network-based models, are frequently used, but they are still inconsistent because they lack sensitivity and accuracy. A deep neural network model for intelligent wireless signal identification to perform spectrum monitoring is proposed to perform efficient sensing at low SNR (signal to noise ratio) and preserve hyperspectral image features. A hybrid deep learning model called SPECTRUMNET (spectrum sensing using deep neural network) is presented. It can quickly and accurately monitor the spectrum from spectrogram images by utilizing cyclostationary features and convolutional neural networks (CNN). The class imbalance issue is solved by uniformly spreading the samples throughout the classes using the oversampling method known as SMOTE (Synthetic Minority Oversampling Technique). The proposed model achieves a classification accuracy of 94.46% at a low SNR of −15 dB, which is an improvement over existing CNN models with minor trainable parameters.

Nonlinear Analysis of the Car-Following Model considering Delay under the V2X Environment

This paper proposes a model that analyzes the delay effect in V2X communication. V2X technology in the intelligent transportation system can realize the exchange of information between vehicles, people, and the environment. Still, more V2X links occupy many channel resources, and the occupation of channel resources leads to longer communication delays. Therefore, based on the optimal velocity of the car-following model, we model the channel delay in the V2X environment. The stability analysis results show that delay increases lead to system instability and traffic congestion. However, if more links are established, that is, the number of vehicles in the vehicle group ahead increases, traffic jams are less likely to occur. The numerical simulation results show that the longer the delay, the more serious the traffic congestion. In addition, we find an apparent phase transition region in the numerical simulation results, which is consistent with the theoretical analysis results. Therefore, in the intelligent transportation system, the delay caused by the complex electromagnetic environment is a factor that cannot be ignored, and thus the allocation of spectrum resources needs to be reasonably arranged. Otherwise, the V2X technology leads to traffic congestion.