Dynamic Spectrum Allocation Algorithm Research Articles

Dynamic Routing and Spectrum Allocation Algorithms in Elastic Optical Networks for Satellite Internet (Invited)

Dynamic Routing and Spectrum Allocation Algorithms in Elastic Optical Networks for Satellite Internet (Invited)

Network dynamic routing and spectrum allocation algorithm based on blockchain technology

Network dynamic routing and spectrum allocation algorithm based on blockchain technology

Research on Dynamic Spectrum Allocation Algorithm Based on Cyclic Neural Network

Due to the wide application of cognitive wireless network, the network structure is becoming more and more complex. It is difficult to establish the corresponding mathematical model to simulate the high complexity network environment. The algorithm based on recurrent neural network in deep reinforcement learning can effectively solve this problem. In addition, with the rise of deep learning in recent years, the combination of reinforcement learning and deep learning shows excellent ability in dealing with complex problems and data operation. This paper is aimed at studying dynamic spectrum allocation based on cyclic neural network. This paper briefly introduces MATLAB, sets up the network environment of algorithm simulation, then analyzes the overall performance of the improved genetic algorithm, and explores the influence of genetic algorithm-related parameters and network environment-related parameters on the performance of the algorithm. The results show the improved genetic algorithm. The network efficiency can be improved by about 2%, but the spectrum switching frequency can be reduced by 69%. When the number of primary users in the network is large, the network benefit of improving the genetic algorithm is superior to the other two algorithms. In addition, when the crossover probability is 0.6 and 0.1, the fitness value is higher than the crossover probability of 0.9 and 0.5; the interference of authorized users in the network initially has less impact on the secondary user.

Effect of Dynamic Spectrum Aggregation and Allocation Algorithms in Cognitive Radio Networks

With rapid proliferation of radio technology, discontinuous spectrum aggregation and access in a single radio has become conceivable. When the bandwidth of the spectrum fragment is narrow, then spectrum aggregation can be utilized to bundle multiple small fragments in one radio. This can positively improve the efficiency of the spectrum utilization. Though, aggregation of fragmented segments leads to improved spectrum utilization it results in further spectrum fragmentation leading to practical hardware constraints. As a solution, we use the popular memory allocation techniques to perform spectrum aggregation so that further fragmentation can be reduced. We derive at an efficient Spectrum Aggregation Algorithm based on Memory Allocation techniques.

Dynamic Crosstalk-Aware Lightpath Provisioning in Spectrally–Spatially Flexible Optical Networks

We focus on the problem of crosstalk-aware lightpath provisioning in a dynamic, spectrally and spatially flexible optical network, in which spectral super-channels are carried over multi-core fibers with distance-adaptive transmission. The problem involves establishing a lightpath for a connection such that the inter-core crosstalk (XT) between the new lightpath and any exiting lightpaths does not cause the quality of transmission of any of these light-paths to be below an acceptable level. To this end, we analyze and compare two broad classes of schemes that aim to ensure acceptable XT levels in the network. The first is based on static/worst-case XT, whereas the second uses dynamic/ precise XT estimation. These methods are implemented in a dynamic XT-aware routing, spatial mode, and spectrum allocation algorithm that solves the lightpath-provisioning problem efficiently with the goal of minimizing bandwidth blocking. Extensive simulation experiments performed in realistic network scenarios indicate that the utilized XT estimation methodology has a significant impact on network performance, with the precise XT approach favored over the worst-case XT strategy.

Two-Stage Stable Matching Based Dynamic Spectrum Allocation in Heterogeneous Networks

This paper addresses the resource allocation problem for small cells in the two-tier heterogeneous networks (HetNets) with non-independent resource blocks (RBs) usability, which is prevalent in HetNets application. To formulate the correlations of different RBs to facilitate the resource allocation, a normalized pointwise mutual information (NPMI)-based coefficient is proposed, which is easy to compute and adapt based on the historical information about RBs usability. To obtain a fast allocation solution, a two-stage stable matching-based dynamic spectrum allocation algorithm (TSSM) is proposed, which takes both network throughput and user performance as optimization metrics. Specifically, through robust preferable stable matching in the first stage and the iterative matchings with NPMI-based coefficient as the preference profiles in the second stage, the efficient matching solution can be obtained with low complexity and thus is well adapted to the requirement of the resource allocation in HetNets. The validity of the proposed coefficient in modeling the relevance of RBs usability and the benefit of its usage in the allocation algorithm is verified with typical spectrum usability related data. The extensive simulation results on various spectrum environment show the average probability of no RB to use following the proposed TSSM are reduced by more than 13.2%, and the robust requirement for users can be guaranteed better at the same time. Furthermore, the accumulated throughput of all users in the network increases by more than 19.4% on an average under different RBs usability environment.

Dynamic Spectrum Allocation Algorithms for Industrial Cognitive Radio Networks

Irregular spectrum usage and spectrum scarcity in emergency situations is a common problem in industrial wireless networks. To enhance the dynamic spectrum usage, cognitive radio network (CRN) is introduced in various automotive industrial wireless applications named as industrial cognitive radio network (ICRN). However, establishing the control channel by using channel hopping mechanism in ICRN is a challenging problem. In order to achieve reliable performance in ICRN, efficient channel hopping protocols need to be designed. In this paper, two channel hopping protocols are designed for the ICRN with or without the global clock synchronization to maximize the degree of rendezvous within the shortest time, minimize the inter rendezvous intervals and to reduce the maximum time to rendezvous by two secondary users. Performance evaluation of our protocols outperform in terms of throughput, percentage of rendezvous and average time to rendezvous over existing CRN protocols.

A Dynamic Spectrum Allocation Algorithm for a Maritime Cognitive Radio Communication System Based on a Queuing Model

With the rapid development of maritime digital communication, the demand for spectrum resources is increasing, and building a maritime cognitive radio communication system is an effective solution. In this paper, the problem of how to effectively allocate the spectrum for secondary users (SUs) with different priorities in a maritime cognitive radio communication system is studied. According to the characteristics of a maritime cognitive radio and existing research about cognitive radio systems, this paper establishes a centralized maritime cognitive radio communication model and creates a simplified queuing model with two queues for the communication model. In the view of the behaviors of SUs and primary users (PUs), we propose a dynamic spectrum allocation (DSA) algorithm based on the system status, and analyze it with a two-dimensional Markov chain. Simulation results show that, when different types of SUs have similar arrival rates, the algorithm can vary the priority factor according to the change of users’ status in the system, so as to adjust the channel allocation, decreasing system congestion. The improvement of the algorithm is about 7–26%, and the specific improvement is negatively correlated with the SU arrival rate.

An Enhanced Dynamic Spectrum Allocation Algorithm Based on Cournot Game in Maritime Cognitive Radio Communication System

The recent development of maritime transport has resulted in the demand for a wider communication bandwidth being more intense. Cognitive radios can dynamically manage resources in a spectrum. Thus, building a new type of maritime cognitive radio communication system (MCRCS) is an effective solution. In this paper, the enhanced dynamic spectrum allocation algorithm (EDSAA) is proposed, which is based on the Cournot game model. In EDSAA, the decision-making center (DC) sets the weights according to the detection capability of the secondary user (SU), before adding these weighting coefficients in the price function. Furthermore, the willingness of the SU will reduce after meeting their basic communication needs when it continues to increase the leasable spectrum by adding the elastic model in the SU’s revenue function. On this basis, the profit function is established. The simulation results show that the EDSAA has Nash equilibrium and conforms to the actual situation. It shows that the results of spectrum allocation are fair, efficient and reasonable.

Dynamic Spectrum, Subcarrier and Power Allocation in Heterogeneous Cellular Networks Based on Interference and Small Cells’ Backhaul Limitations

Heterogeneous cellular networks (HCN) consist of different layers with shared spectrum; hence, resource management algorithms would play a key role in improving the network efficiency. Almost every present resource allocation algorithm in HCN assumes that the spectrum which is dedicated to each layer is fixed. In this paper, we propose a dynamic spectrum and multicell logarithmic resource allocation algorithm to select the number of shared channels between macros and small cells along with subcarrier and power allocation. Besides, backhaul bandwidth limit in multicell scenario is also suggested, which can be considered as another contribution of this paper. In order to achieve maximum downlink network throughput, we formulate the problem with two primary considerations: interference management and small cell backhaul capacity limitation. This involves the mixed continuous, nonlinear integer and a non-convex problem which in turn is broken to smaller parts. Then by using the upper bounds, a near-optimal solution is found which satisfies the Karush–Kuhn–Tucker conditions. Based on numerical analysis, the network throughput is improved up to 61 % compared to fixed spectrum allocation techniques which use half of the spectrum for shared channels.

Spectrum allocation algorithms for wireless cellular networks supporting mobile IPTV

Spectrum allocation algorithms for supporting mobile IPTV services over wireless cellular networks are proposed in this work. The algorithms are designed to offer mobile IPTV services with better performance while preserving the quality of conventional voice services. In the proposed algorithms, a higher frequency allocation priority is assigned for voice services to maintain their QoS. Additionally, the soft frequency reuse scheme is utilized to enhance the spectral efficiency and reduce inter-cell interference among neighboring cells. To further improve the spectral efficiency, the dynamic spectrum allocation algorithm that can coordinate the frequency allocation among cell-edge bands of neighboring cells is designed as well. Simulation results demonstrate that the proposed spectrum allocation algorithms significantly improve the performance of mobile IPTV services without compromising the quality of voice services.

Fairness enhanced dynamic routing and spectrum allocation in elastic optical networks

The finer spectrum granularity unit in elastic optical networks makes wider spectrum services more likely to be restricted by the constraints of spectrum continuity and spectrum contiguity (2SC), leading to higher blocking probability (BP). Obviously, it would deteriorate the fairness among multi-granularity services. Therefore, a fair dynamic routing and spectrum allocation algorithm is proposed. By virtualising the network resources as a ‘resource pool’, a two-dimensional rectangle packing model in combination with service holding-time is introduced to satisfy the 2SC constraints and to optimise the allocation of the ‘resource pool’ for dynamic multi-granularity services. Besides multi-granularity service fairness is achieved by tackling heterogeneous-size services together. Simulation results show that the benefit in terms of fairness can be reaped. Meanwhile, the authors also investigate the proposed algorithm's impact on performance metrics: namely, spectrum resource utilisation and BP. The results indicate that the proposed algorithm can achieve lower BP and higher spectrum efficiency.

Potential of dynamic spectrum allocation in LTE macro networks

Abstract. In recent years Mobile Network Operators (MNOs) worldwide are extensively deploying LTE networks in different spectrum bands and utilising different bandwidth configurations. Initially, the deployment is coverage oriented with macro cells using the lower LTE spectrum bands. As the offered traffic (i.e. the requested traffic from the users) increases the LTE deployment evolves with macro cells expanded with additional capacity boosting LTE carriers in higher frequency bands complemented with micro or small cells in traffic hotspot areas. For MNOs it is crucial to use the LTE spectrum assets, as well as the installed network infrastructure, in the most cost efficient way. The dynamic spectrum allocation (DSA) aims at (de)activating the available LTE frequency carriers according to the temporal and spatial traffic variations in order to increase the overall LTE system performance in terms of total network capacity by reducing the interference. This paper evaluates the DSA potential of achieving the envisaged performance improvement and identifying in which system and traffic conditions the DSA should be deployed. A self-optimised network (SON) DSA algorithm is also proposed and evaluated. The evaluations have been carried out in a hexagonal and a realistic site-specific urban macro layout assuming a central traffic hotspot area surrounded with an area of lower traffic with a total size of approximately 8 × 8 km2. The results show that up to 47 % and up to 40 % possible DSA gains are achievable with regards to the carried system load (i.e. used resources) for homogenous traffic distribution with hexagonal layout and for realistic site-specific urban macro layout, respectively. The SON DSA algorithm evaluation in a realistic site-specific urban macro cell deployment scenario including realistic non-uniform spatial traffic distribution shows insignificant cell throughput (i.e. served traffic) performance gains. Nevertheless, in the SON DSA investigations, a gain of up to 25 % has been observed when analysing the resource utilisation in the non-hotspot cells.

Dynamic Spectrum Allocation Using Q-Learning in Cognitive Radio Systems

In this paper we present an improved dynamic spectrum allocation algorithm based on the intelligence of Q-learning. The state space, action space and reward function of the algorithm are built, and, the agents are guided to perform actions through designing the reward function. Numerical simulation results show that the proposed algorithm can improve system throughput efficiently compared to other algorithms. Facing the status of spectrum resources is tension and spectrum utilization is low, it can also boost the spectrum using condition in the future.

Nonconvex dynamic spectrum allocation for cognitive radio networks via particle swarm optimization and simulated annealing

Dynamic spectrum access is a promising technique designed to meet the challenge of rapidly growing demands for broadband access in cognitive radio networks. By utilizing the allocated spectrum, cognitive radio devices can provide high throughput and low latency communications. This paper introduces an efficient dynamic spectrum allocation algorithm in cognitive radio networks based on the network utility maximization framework. The objective function in this optimization problem is always nonconvex, which makes the problem difficult to solve. Prior works on network resource optimization always transformed the nonconvex optimization problem into a convex one under some strict assumptions, which do not meet the actual networks. We solve the nonconvex optimization problem directly using an improved particle swarm optimization (PSO) method. Simulated annealing (SA), combined with PSO to form the PSOSA algorithm, overcomes the inherent defects and disadvantages of these two individual components. Simulations show that the proposed solution achieves significant throughput compared with existing approaches, and it is efficient in solving the nonconvex optimization problem.

Tabu search for dynamic spectrum allocation in cellular networks

In this paper, we present and analyse a Tabu Search algorithm for Dynamic Spectrum Allocation in cellular networks. We study a case where an operator is providing packet services to the end users. The objective of the cellular operator is to maximise its reward while taking into account the trade-off between the spectrum cost and the revenues obtained from end users. These revenues are modelled here as an increasing function of the achieved throughput. The cost is proportional to the bandwidth of the spectrum leased to the regulator or some spectrum broker. Results show that the algorithm allows the operator to increase its reward by taking advantage of the spatial and temporal heterogeneities of the traffic in the network, rather than assuming homogeneous traffic for its radio resource allocation. Our Tabu Search-based Dynamic Spectrum Allocation algorithm is efficient in terms of the required memory space and convergence speed. Results show that the algorithm is fast enough to suit a dynamic context. Copyright © 2012 John Wiley & Sons, Ltd.

Dynamic Spectrum Allocation Algorithm with Interference Management in Co-Existing Networks

This work addresses analytically the impact of multi-cell, multi-operator interference on the overall radio resources when multiple operators co-exist and share a common pool of spectrum. We propose a centralised dynamic spectrum allocation (DSA) scheme that is able to measure the interference level and interact dynamically to minimise interference and enhance spectrum utilisation while maintaining a satisfactory level of QoS. Furthermore, a concise system model and framework able to describe the interaction among different operators is presented.

Dynamic Spectrum Allocation Based on MEG Algorithm

Dynamic spectrum allocation (DSA) based on secondary spectrum market is considered a promising technology to improve spectrum utilization efficiency and to relieve the wireless spectrum shortage problem. We propose a dynamic spectrum allocation algorithm named market equilibrium and game (MEG), and construct a complete secondary spectrum market. The market based on the MEG algorithm consists of two submarkets: multiple primary services providers (PSPs) and a dynamic spectrum allocation server (DSAS) form the high submarket, while the low submarket is composed of the DSAS and a number of secondary users. In the low submarket, the MEG algorithm provides a game type selection strategy. By this strategy, the DSAS can win more payoffs with lower unit spectrum price, which encourages secondary users to use more spectrum. A secondary user can also choose its preferable game type between dynamic game and Nash bargaining flexibly. On the other hand, a bargaining procedure in the high submarket is designed in the MEG algorithm to ensure that market equilibrium is quickly reached. A performance analysis shows that the strategy of game type selection is fair and feasible for both the DSAS and the secondary users. Moreover, the bargaining procedure is better than the existing algorithm which adjusts price step by step in the high submarket. Simulation results also demonstrate that the market fluctuation in the low submarket is passed to the high submarket by way of the DSAS. The MEG algorithm can effectively satisfy the highly-fluctuating demands from the secondary users. In addition, the MEG algorithm can improve the payoffs of all players and increase spectrum utilization efficiency.

Cross-Layer Routing and Dynamic Spectrum Allocation in Cognitive Radio Ad Hoc Networks

Throughput maximization is one of the main challenges in cognitive radio <i xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">ad hoc</i> networks, where the availability of local spectrum resources may change from time to time and hop by hop. For this reason, a cross-layer opportunistic spectrum access and dynamic routing algorithm for cognitive radio networks is proposed, which is called the routing and dynamic spectrum-allocation (ROSA) algorithm. Through local control actions, ROSA aims to maximize the network throughput by performing joint routing, dynamic spectrum allocation, scheduling, and transmit power control. Specifically, the algorithm dynamically allocates spectrum resources to maximize the capacity of links without generating harmful interference to other users while guaranteeing a bounded bit error rate (BER) for the receiver. In addition, the algorithm aims to maximize the weighted sum of differential backlogs to stabilize the system by giving priority to higher capacity links with a high differential backlog. The proposed algorithm is distributed, computationally efficient, and has bounded BER guarantees. ROSA is shown through numerical model-based evaluation and discrete-event packet-level simulations to outperform baseline solutions, leading to a high throughput, low delay, and fair bandwidth allocation.

Decentralized Dynamic Spectrum Allocation Based on Adaptive Antenna Array Interference Mitigation Diversity

A new class of decentralized dynamic spectrum allocation (DSA) algorithms that exploit adaptive antenna array interference mitigation (IM) diversity at the receiver, is proposed for interference-limited environments with high level of frequency reuse. The system model consists of base stations (BS) that can optimize uplink frequency allocation to their subscriber stations (SS) to achieve the least impact of IM on the useful signal, assuming no control over band allocation of other BSs sharing the same bands. It is demonstrated that when the number of SSs that share the same frequency approaches the number of antenna elements at a BS, the potential performance gain is most significant for the IM-based DSA compared to the random frequency allocation. A ?good neighbor? decentralized DSA strategy is introduced. The convergence and convergence rate of IM-based DSA are investigated by means of the theory of absorbing Markov chains and statistical simulations. It is shown that the ?good neighbor? strategy leads to much better convergence properties of the entire system compared with the conventional ?selfish? approach. This suggests that the ?good neighbor? IM-based DSA techniques may have a practical perspective even in the scenarios, where global convergence with probability one cannot be established.