Spectrum Allocation Scheme Research Articles

A Reinforcement Learning based evolutionary multi-objective optimization algorithm for spectrum allocation in Cognitive Radio networks

To cope up with drastically increasing demand for radio resources lead to raise a challenge to the wireless community. The limited radio spectrum and fixed spectrum allocation strategy have become a bottleneck for various wireless communication. Cognitive Radio (CR) technology along with potential benefits of machine learning has attracted substantial research interest especially in the context of spectrum management. However, a variety of performance attributes as objectives draw attention during the technological preparations for spectrum management such as higher spectral efficiency, lower latency, higher network capacity, and better energy efficiency as these objectives are often conflicting with each other. Hence, this paper addresses the spectrum allocation problem concerning network capacity and spectrum efficiency as conflicting objectives and model the scenario as a multi-objective optimization problem in CR networks. An improved version of the Non-dominated Sorting Genetic Algorithm-II (NSGA-II) which combines the feature of evolutionary algorithm and machine learning called Non-dominated Sorting Genetic Algorithm based on Reinforcement Learning (NSGA-RL) is proposed which incorporates a self-tuning parameter approach to handle multiple conflicting objectives. The numerical findings validate the effectiveness of the proposed algorithm through the Pareto optimal set and obtain optimal solution efficiently to satisfy various requirements of spectrum allocation in CR networks.

Enhanced cooperative behavior and fair spectrum allocation for intelligent IoT devices in cognitive radio networks

The extensive utilization of Internet of Things (IoT) devices in a wide range of services prompts spectrum scarcity. Recently, Cognitive Radio Networks (CRNs) have shown promising results in addressing the spectrum scarcity problem. Therefore, CRNs are envisaged to play a crucial role in accommodating the massive number of emerging IoT devices. However, two dominant shortcomings hinder the coalescence of IoT and CRNs. First, the selfish behavior among Secondary Users (SUs) during the spectrum sensing process. Second, the inability to construct a convenient metric for fair spectrum assignment. The contribution of this paper is quadruple: (i) a Direct Strategy Selection (DSS) mechanism that satisfies Aspiration Equilibrium (AE) and enforces cooperation among SUs during the spectrum sensing process; (ii) a spectrum allocation scheme that takes cooperative behavior as a fairness parameter; (iii) studying the effects of three windowing techniques on the proposed allocation scheme and selecting the window that achieves maximum fairness; (iv) exploring the trade-off between the utility function and cooperation among SUs. The results show that DSS yields instant convergence and a threefold increase in the index of cooperation. Regarding spectrum allocation, the proposed scheme combined with the Reverse Attenuation Window (RAW) shows a 22% increase in the Average Satisfaction Ratio (ASR) for SUs. The simulations are conducted in the worst case scenario, where all SUs in the CRN are requesting to access the spectrum at the same time.

Decentralized Radio Resource Adaptation in D2D-U Networks

Unlike the conventional device-to-device (D2D) networks, the unlicensed D2D (D2D-U) pairs can not only reuse the licensed channels with the base station (BS) but also share the unlicensed channels with the WiFi stations. One challenge arises from the fact that the co-channel interference on licensed channels and the collision probability on unlicensed channels may cause extra power consumption at the terminals. Accordingly, we first propose a channel access method for the D2D-U pairs on unlicensed channels. Then, a decentralized joint spectrum and power allocation scheme is designed to minimize the power consumption at D2D-U pairs. Different from the existing distributed schemes, the proposed scheme can guarantee the global minimization of power consumption across the D2D-U pairs. Simulation results validate the theoretical analysis and verify the performance from the proposed scheme.

Network coding-based routing and spectrum allocation in elastic optical networks for enhanced physical layer security

In this work, an eavesdropping-aware routing and spectrum allocation approach is proposed utilizing network coding (NC) in elastic optical networks (EONs). To provide physical layer security in EONs and secure the confidential connections against eavesdropping attacks using NC, the signals of the confidential connections are combined (XOR-ed) with other signals at different nodes in their path, while transmitted through the network. The combination of signals through NC significantly increases the security of confidential connections, since an eavesdropper must access all individual signals, traversing different links, in order to decrypt the combined signal. A novel heuristic approach is proposed that solves the combined network coding and routing and spectrum allocation problem that also takes into account additional NC constraints that are required in order to consider a confidential connection as secure. Different routing and spectrum allocation strategies are proposed, aiming to maximize the level of security provided for the confidential demands, followed by an extensive performance evaluation of each approach in terms of the level of security provided, as well as the spectrum utilization and blocking probability, under different network conditions. Performance results demonstrate that the proposed approaches can provide efficient solutions in terms of network performance, while also providing the level of security required for each demand.

Elastic optical network with spectrum slicing for fragmented bandwidth allocation

Elastic Optical Networks (EONs) allow the channel spacing and the spectral width of an optical signal to be dynamically adjusted and hence have become an important paradigm in managing the heterogeneous bandwidth demands of optical backbone networks. The entire available optical spectrum is divided into some spectrum slots which define the smallest granularity of bandwidth and optical signals with variable bandwidths can occupy different number of such slots. The constraints imposed by the physical layer of an EON require that the slots occupied by an optical signal from source to destination have to be consecutive and contiguous in terms of their relative position in the optical spectrum. Furthermore, the same spectrum slots need to be reserved throughout the entire optical signal's path from its source to destination. The above constraints make the routing and spectrum allocation (RSA) in EONs very challenging because unavailability of enough spectrum slots that together equals the spectral width of the optical signal associated with an end-to-end request, will result in blocking of the request. Recent developments in the physical layer technologies have made all-optical ‘slicing’ of a request possible and make the request to be ‘fit’ into multiple non-consecutive spectral slots in an EON. But these all-optical ‘slicers’ employ complex technologies and can be very costly to employ. In this paper, we propose a spectrum allocation scheme for an EON node architecture with these ‘slicers’ and we also formulate a modified RSA scheme for EONs employing slicers, both as a mixed-integer linear programming (MILP) model and a heuristic algorithm. Our main aim is to analyze the tradeoff between the number of slicers that can be used per node versus the spectrum utilization and bandwidth blocking rate. The numerical results show that the proposed scheme with slicers can significantly improve bandwidth blocking rate, compared to the conventional scheme without slicer.

Research on Dynamic Allocation Strategy of KU Satellite Spectrum Based on Cognitive Radio Technology

Aiming at the problem of insufficient satellite spectrum resources in the KU band, combined with the most advanced cognitive radio technology in the communication field, this paper proposes a dynamic spectrum allocation strategy for KU satellites based on cognitive radio technology. By dynamically allocating frequency resources, the spectrum resources are greatly improved. Use efficiency, while improving the anti-interference ability of the satellite system.

Heterogeneous network spectrum allocation scheme based on three-phase bargaining game

The fifth generation (5G) networks are envisioned to support the exploding mobile data traffic demands while improving quality of service (QoS) provision. The heterogeneous network (HetNet) platform provides one possible solution for dealing with this traffic explosion problem. However, several technical issues and challenges are associated with the deployment of HetNets. In this paper, we aim to design a new spectrum allocation scheme to effectively use the limited HetNet spectrum resource. By employing the main ideas of relative utilitarian, equality averse and inequality averse bargaining solutions, our approach unfolds into three stages of spectrum allocation process while balancing the efficiency and fairness. Based on the main features of each bargaining solution, we hierarchically apply these three solutions to spectrum assignment among BSs, spectrum allocation among SBSs, and spectrum sharing among applications, respectively. According to the advantages of cooperative bargaining approach, the proposed scheme takes various benefits in a rational way. The numerical simulations demonstrate the effectiveness and potential gain of our proposed three-phase spectrum allocation mechanism over the existing protocols.

Modelling multi-packet multi-receiver spectrum sharing using D-OFDM in software-defined networks

Cognitive radio (CR) technology has been proposed to solve the inefficient spectrum utilization resulted from the traditional fixed spectrum allocation strategy used in wireless systems. Through dynamic and opportunistic spectrum access, CR users can significantly improve spectrum utilization. Most of existing spectrum access schemes designed for CR networks (CRNs) targeted either uni-cast transmission or multi-cast transmission paradigm. Such transmission paradigms allow a CR user to transmit the same data packet over multiple channels to a receiver or a group of receivers. However, existing designs cannot support simultaneous transmissions of multiple distinct packets to different receivers. This new transmission paradigm can be realised using the Discrete Orthogonal Frequency Division Multiplexing (D-OFDM) technology, where each CR transmitter can send multiple different packets to several (multiple) receivers over different channels. In this paper, we investigate the problem of assigning channels to CR transmitters with D-OFDM capabilities, each with multiple packets to be simultaneously sent to multiple receivers. Specifically, we formulate the channel assignment problem as an optimization problem with the goal of achieving the maximum possible network sum-rate. Then, we optimally solve the optimization problem based on standard linear programming methods. To realize our channel assignment in a distributed way, we propose an admission control spectrum access mechanism that allows communicating CR users to exchange their control information. Simulation experiments are carried out to reveal the effectiveness of the proposed protocol compared to previous protocols. The results reveal that significant performance improvement can be achieved by utilizing the D-OFDM technology, in which CR users are allowed to simultaneously transmit multiple packets to multiple receivers.

On Platform to Enable the Cognitive Radio Over 5G Networks

With the increase in the number of communication devices, the requirement for higher bandwidth is essential. To achieve this goal, research and industrial communities have both suggested that future wireless systems will take advantage of the numerous emerging technologies. Utilization of Cognitive Radio (CR) for the next-generation Fifth Generation (5G) communication technology is the major advancement for getting a higher bandwidth in a cellular communication network. In this paper, we present a comprehensive study of CR from the perspectives of spectrum allocation schemes, impact and role of MAC layer in spectrum sensing and sharing, CR application in multi-hop wireless networks, and challenges associated with channel selection and packet routing in multi-hop heterogeneous CR networks. This paper also presents the analysis, in literature, of a range of intelligent routing protocols that are considered viable for packets routing in CR networks. The need to address the issue of spectrum depletion and the apparent underutilization of available scarce spectrum resources in existing wireless networks is the primary motivation behind this study. Considering the fact that CR technology can potentially maximize the utilization of bulk of the unused communication spectrum bands for the future 5G of wireless network and beyond.

Enabling Virtual Radio Functions on Software Defined Radio for Future Wireless Networks

Today’s wired networks have become highly flexible, thanks to the fact that an increasing number of functionalities are realized by software rather than dedicated hardware. This trend is still in its early stages for wireless networks, but it has the potential to improve the network’s flexibility and resource utilization regarding both the abundant computational resources and the scarce radio spectrum resources. In this work we provide an overview of the enabling technologies for network reconfiguration, such as Network Function Virtualization, Software Defined Networking, and Software Defined Radio. We review frequently used terminology such as softwarization, virtualization, and orchestration, and how these concepts apply to wireless networks. We introduce the concept of Virtual Radio Function, and illustrate how softwarized/virtualized radio functions can be placed and initialized at runtime, allowing radio access technologies and spectrum allocation schemes to be formed dynamically. Finally we focus on embedded Software-Defined Radio as an end device, and illustrate how to realize the placement, initialization and configuration of virtual radio functions on such kind of devices.

D2D Enabled Cellular Network Spectrum Allocation Scheme Based on the Cooperative Bargaining Solution

In 5G cellular networks, device-to-device (D2D) communication has undoubtedly become a general trend to increase spectral efficiency while reducing communication delay. In particular, D2D technology is able to bear more and more services, which help user-centred and personalized services in future wireless networks. However, several technical issues and challenges are associated with the deployment of D2D communications. In this paper, we tackle the spectrum allocation problem of D2D enabled cellular networks. By employing the major ideas of cooperative game theory, we design a novel two-stage resource allocation protocol based on the weighted utilitarian and meta bargaining solutions. The purpose of bargaining solutions is to clarify what could be the best solution when game players share a surplus that they can jointly generate. According to the main advantages of step-by-step interactive bargaining mechanism, our proposed solution takes various benefits in a rational way. Some simulation results and numerical analysis are provided to confirm the effectiveness of our two-stage bargaining approach and validate the accuracy of the proposed spectrum allocation scheme. Finally, we address some challenges and identify research areas for the future study.

Co-Tier and Cross-Tier Spectrum Allocation Scheme Based on Interval Shapley and Banzhaf Values

Current evolution of 5G heterogeneous network (HetNet) leads to unprecedented traffic demands and user quality of service requirements. It is extremely challenging to meet these needs for future generation communications. Multi-flow carrier aggregation (MCA) is an emerging technique that is implemented to improve the capacity of cellular networks. In this paper, we present an effective spectrum allocation scheme for the HetNet platform under the MCA technology. For the joint co-tier and cross-tier collaborations in HetNets, we adopt the ideas of interval Shapley and Banzhaf values as solution concepts. To strike an appropriate performance balance while dynamically distributing the limited spectrum resource, our scheme is formulated as a coalition game model, and both interval values are applied for different type applications. Based on the main features of cooperative game theory, the proposed scheme takes various benefits in a rational way. Through extensive simulations, we compare the performance of the proposed scheme with existing state-of-the-art spectrum allocation protocols, and confirm the superiority of our coalitional game based approach. Finally, the conclusions are presented, and some research challenges are identified for the future study.

Spectrum Allocation Strategies Based on QoS in Cognitive Vehicle Networks

The vehicular ad hoc network (VANET) is an important part of modern intelligent transportation systems (ITS), and its emergence has provided support for improving traffic safety and driving experience. The problem of spectrum scarcity has become evident due to the increasing demand for various VANET services. Using cognitive radio (CR) technology in VANET to solve the problem of spectrum scarcity has become a research focus in recent years. The existing spectrum allocation mechanism cannot effectively solve problems, such as high delay, uncertain quality of service (QoS), and low throughput. In this study, we investigate the spectrum allocation strategies in CR for VANET(CR-VANET). For different network optimization indicators under different load networks, we divide CR-VANET into two scenarios: high-load CR-VANET (HCR-VANET) and low-load CR-VANET (LCR-VANET). In LCR-VANET, we establish a model for maximizing throughput with two constraints and propose a channel allocation scheme based on a greedy algorithm (CASGA) to maximize the network throughput. In HCR-VANET, application services are divided into safe application services(SAS) and unsafe application services. To improve the acceptance probability of SAS, we also propose an SMDP-based channel allocation scheme (SMDP-CAS) to maximize the acceptance probability of SAS. Simulation results prove that CASGA and SMDP-CAS greatly improve the throughput of the network and the acceptance probability of SAS.

Link State Aware Dynamic Routing and Spectrum Allocation Strategy in Elastic Optical Networks

Compared with traditional wavelength division optical network, elastic optical network (EON) divides the network spectrum into smaller spectrum slots to improve the spectrum utilization, but the high-quality spectrum division also complicates the routing and spectrum allocation (RSA) problem. Various strategies are proposed for reducing the RSA complexity and improving system traffic bearing capacity. However, previous RSA strategies do not consider the changing physical layer impairments that will also impact signal quality and even lead to violation of quality of transmission (QoT), the data cannot be transmitted correctly if the link state is degraded. Therefore, cross-layer optimization is desired, which means that different layer information is taken into account in the RSA strategy. In this paper, we propose a new link state-aware (LSA) RSA strategy to guarantee the QoT requirements under different link states. At first, the link state is evaluated based on chromatic dispersion (CD) and optical signal-to-noise ratio (OSNR), and a LightGBM model is exploited for CD and OSNR estimation. In LSA-RSA strategy, the link state is considered as a metric for qualified routing paths finding, and the link capacity is calculated based on the link state and used in spectrum allocation. Simulation results show that the average CD and OSNR estimation errors of the LightGBM model are 0.28ps/nm and 0.68dB, respectively. Under different link states and traffic loads, the LSA-RSA strategy can reduce traffic failure probability by more than 20%, and traffic load can increase 40Erlang when the bandwidth blocking probability equals 10%.

3D Fragmentation Metric and RCSA Scheme for Space Division Multiplexing Elastic Optical Networks

The space division multiplexing elastic optical networks (SDM-EONs) based on multi-core fiber (MCF) are an effective solution to improve transmission capacity. In SDM-EONs, spectrum resources take on three-dimensional (3D) properties (i.e., frequency, time, and space), which brings new spatial constraints to network resource allocation. Meanwhile, the spectrum fragmentation in the network also presents same characteristics with the setup and release of dynamic lightpath. To improve the success rate of transmission and the utilization of network resources, it is necessary to evaluate network fragmentation effectively from the three dimension. Therefore, this paper focuses on a new fragmentation measurement for SDM-EONs. Firstly, the 3D fragmentation metric is carried out considering the frequency-time-space domain. Then, routing, core, and spectrum allocation (RCSA) scheme is proposed to allocate the spectrum resource with minimal future fragmentation and core resource with maximal free resource reservation. Simulation results show the proposed scheme better in blocking probability, spectrum utilization, and fragmentation rate.

Deep Reinforcement Learning-Based Spectrum Allocation in Integrated Access and Backhaul Networks

We develop a framework based on deep reinforcement learning (DRL) to solve the spectrum allocation problem in the emerging integrated access and backhaul (IAB) architecture with large scale deployment and dynamic environment. The available spectrum is divided into several orthogonal sub-channels, and the donor base station (DBS) and all IAB nodes have the same spectrum resource for allocation, where a DBS utilizes those sub-channels for access links of associated user equipment (UE) as well as for backhaul links of associated IAB nodes, and an IAB node can utilize all for its associated UEs. This is one of key features in which 5G differs from traditional settings where the backhaul networks are designed independently from the access networks. With the goal of maximizing the sum log-rate of all UE groups, we formulate the spectrum allocation problem into a mix-integer and non-linear programming. However, it is intractable to find an optimal solution especially when the IAB network is large and time-varying. To tackle this problem, we propose to use the latest DRL method by integrating an actor-critic spectrum allocation (ACSA) scheme and deep neural network (DNN) to achieve real-time spectrum allocation in different scenarios. The proposed methods are evaluated through numerical simulations and show promising results compared with some baseline allocation policies.

Two-Level Game Based Spectrum Allocation Scheme for Multi-Flow Carrier Aggregation Technique

Nowadays, the number of mobile devices have increased rapidly, and require different amounts of spectrum with the limited carrier resource. As an emerging technique, multi-flow carrier aggregation (MCA) has been recently proposed to improve the capacity of 5G heterogeneous network systems. In this paper, we present an effective MCA control scheme for the future heterogeneous network (HetNet). With the MCA-enabled HetNet, we aim at maximizing the system throughput while considering each mobile device's benefit. To strike an appropriate performance balance between network operators and mobile users, our proposed scheme is designed as a two-level game model. The upper and lower level games are developed as different type game models, and work together interactively to reach a fair-efficient spectrum allocation solution. Our hierarchical game approach can achieve reciprocal advantages, and clarifies what is the best solution for the MCA-enabled system. Using extensive simulations, we compare the performance and confirm the superiority of our game-based approach. Finally, the conclusions are presented, and some research challenges are identified for the future study.

A Topology-Based Spectrum Assignment Solution for Static Elastic Optical Networks With Ring Topologies

On designing elastic optical networks, one of the main tasks to be solved is to allocate enough spectrum on the users’ paths to achieve communication to all network users, known as the Spectrum Assignment problem (SA). On a static network operation, the order in which the users’ spectrum demands are assigned is significant. However, standard approaches do not sort them previous to the spectrum assignment process. In this work, we first sort the users to maximize the spectrum usage based on their bandwidths and path length. Then, we propose a novel topology-based spectrum allocation strategy, which takes advantage of the ring network topology. We called it Spiral Fit, consisting of assigning the frequency spectrum to the users following a spiral (or a concentric rings) order. We compare our strategy’s performance and robustness with two optimization models, only in small network topologies (from 5 to 8 nodes) since optimization processes are too expensive on more significant networks. In more extensive networks, we compare the proposed method with the most referenced techniques. The results show that consistently our method obtains near-optimal results in small topologies and outperforms the heuristic solutions found in the literature in terms of network capacity (measured as a number of frequencies slot units and spectrum fragmentation).

Privacy-Preserving Spectrum Allocation in Cognitive Radio Networks Based on Truthful Online Double Auction Mechanism

Abstract Cognitive radio can improve spectrum utilization through spectrum sharing between primary and secondary users, thereby alleviating spectrum scarcity. Spectrum auction is one of the effective methods that can be utilized to achieve spectrum sharing. However, traditional spectrum auction is unable to guarantee the truth of the price given by bidders comprehensively, while neglecting the online characteristics of the buyers request arrival. Existing truthful online auction mechanisms do not consider the privacy leakage problem and fail to provide adequate privacy protection for bidders. In this paper, we propose a privacy-preserving spectrum allocation strategy based on a double online truthful auction mechanism. Specifically, we first propose a critical bid determination algorithm to guarantee the truth of the auction. Then, the channel allocation is modeled as a social welfare maximization optimization problem. Since the problem is NP-hard and it is difficult to solve in polynomial time. We propose a greedy algorithm that can effectively obtain a feasible solution to the problem. An online dynamic accessing mechanism is further designed by considering the arrival of requests of secondary users. Finally, differential privacy is introduced to encrypt sensitive information such as buyers location privacy and time dynamic. Experimental results validate our auction mechanism achieving higher social welfare and spectrum utilization.

Crosstalk-aware routing, spectrum, and core assignment in space-division multiplexing networks with bidirectional multicore fibers

Space-division multiplexing (SDM) has been proposed to increase fiber transmission capacity to cope with the increasing bandwidth requirements in the network. The use of multicore fiber (MCF) is considered to be a simple and feasible way to implement SDM technology. The intercore crosstalk (XT) in the MCF affects the performance of network transmission. Currently, bidirectional transmission of MCF has been proven to have lower XT than traditional unidirectional transmission of MCF. But there are few research studies on routing, core, and spectrum allocation strategies with bidirectional transmission. Thus, to get more XT reduction, we propose two different routing, core, and spectrum allocation strategies according to the traffic loads in the network based on the bidirectional transmission of MCF. The simulation results show that the proposed algorithm has lower blocking rate and higher resource utilization than the benchmark algorithm in the two stages of different traffic loads in the network.