Joint Optimization for on-Demand Deployment of UAVs and Spectrum Allocation in UAVs-Assisted Communication

To improve the efficiency and fairness of the spectrum allocation for ground communication assisted by unmanned aerial vehicles(UAVs),a joint optimization method for on-demand deployment and spectrum allocation of UAVs is proposed,which is modeled as a mixed-integer non-convex optimization problem(MINCOP).An algorithm to estimate the minimum number of required UAVs is firstly proposed based on the pre-estimation and simulated annealing.The MINCOP is then decomposed into three sub-problems based on the block coordinate descent method,including the spectrum allocation of UAVs,the association between UAVs and ground users,and the deployment of UAVs.Specifically,the optimal spectrum allocation is derived based on the interference mitigation and channel reuse.The association between UAVs and ground users is optimized based on local iterated optimization.A particle-based optimization algorithm is proposed to resolve the subproblem of the UAVs deployment.Simulation results show that the proposed method could effectively improve the minimum transmission rate of UAVs as well as user fairness of spectrum allocation.

Non-dominated sorting quantum particle swarm optimization and its application in cognitive radio spectrum allocation

In order to solve discrete multi-objective optimization problems, a non-dominated sorting quantum particle swarm optimization (NSQPSO) based on non-dominated sorting and quantum particle swarm optimization is proposed, and the performance of the NSQPSO is evaluated through five classical benchmark functions. The quantum particle swarm optimization (QPSO) applies the quantum computing theory to particle swarm optimization, and thus has the advantages of both quantum computing theory and particle swarm optimization, so it has a faster convergence rate and a more accurate convergence value. Therefore, QPSO is used as the evolutionary method of the proposed NSQPSO. Also NSQPSO is used to solve cognitive radio spectrum allocation problem. The methods to complete spectrum allocation in previous literature only consider one objective, i.e. network utilization or fairness, but the proposed NSQPSO method, can consider both network utilization and fairness simultaneously through obtaining Pareto front solutions. Cognitive radio systems can select one solution from the Pareto front solutions according to the weight of network reward and fairness. If one weight is unit and the other is zero, then it becomes single objective optimization, so the proposed NSQPSO method has a much wider application range. The experimental research results show that the NSQPS can obtain the same non-dominated solutions as exhaustive search but takes much less time in small dimensions; while in large dimensions, where the problem cannot be solved by exhaustive search, the NSQPSO can still solve the problem, which proves the effectiveness of NSQPSO.

Centralized Dynamic Spectrum Allocation in Cognitive Radio Networks Based on Fuzzy Logic and Q-Learning

A novel centralized approach for Dynamic Spectrum Allocation (DSA) in the Cognitive Radio (CR) network is presented in this paper. Instead of giving the solution in terms of formulas modeling network environment such as linear programming or convex optimization, the new approach obtains the capability of iteratively on-line learning environment performance by using Reinforcement Learning (RL) algorithm after observing the variability and uncertainty of the heterogeneous wireless networks. Appropriate decision-making access actions can then be obtained by employing Fuzzy Inference System (FIS) which ensures the strategy being able to explore the possible status and exploit the experiences sufficiently. The new approach considers multi-objective such as spectrum efficiency and fairness between CR Access Points (AP) effectively. By interacting with the environment and accumulating comprehensive advantages, it can achieve the largest long-term reward expected on the desired objectives and implement the best action. Moreover, the present algorithm is relatively simple and does not require complex calculations. Simulation results show that the proposed approach can get better performance with respect to fixed frequency planning scheme or general dynamic spectrum allocation policy.

An Advanced Spectrum Allocation Algorithm for The Across-Cell D2D Communication in LTE Network with Higher Throughput

In the hybrid LTE cellular network with D2D(Device-to-Device) communication, D2D communication technologies can improve the spectral efficiency significantly. However, the D2D users have to reutilize the spectrum which is allocated to the cellular users. Therefore, the co-channel interference will be more complicated in the case of crosscell D2D communications. In this article, a novel spectrum allocation algorithm for inter-cell D2D communication considering the traffic load is proposed. The traffic load can be balanced by the proposed algorithm. Meanwhile D2D users can multiplex the spectrum allocated to a number of cellular users with a certain percentage to meet the requirements of Qo S of D2D communications and reduce the interference to cellular users. Finally, the simulation results demonstrate that the proposed algorithm can meet the needs of D2D users, balance the traffic load and improve the overall throughput of the system.

Best Effort Spectrum Allocation Scheme for Femtocell Networks in Dense Deployment

In orthogonal frequency division multiple access(OFDMA) based femtocell networks,the co-tier interference among femto base stations(FBS) becomes important in multiuser and densely deployed environment.In order to mitigate the co-tier interference and enhance the system total throughput,this paper proposed a best effort spectrum allocation scheme based on the extension of graph theory.In the scheme,a controller was proposed to collect the channel state information(CSI)of all femtocell user equipments(FUEs) in a certain range.Then,the controller evaluated the signal-to-interference Ratio(SIR) of each FUE and determined the set of its interference neighbors.By calculating the received power matrix(RPM) among FUEs and building interference graph matrix(IGM),different spectrum resource blocks(RBs) were assigned to the users with interference relation,while users without interference relation shared the same RBs,which could increase the spectrum efficiency.Simulation results show that the proposed algorithm can significantly improve the RB usage efficiency compared with the basic graph coloring theory,and more than 80% improvement can be acquired in dense deployment scenario.Besides,the throughput of both cell edge macro user equipments(MUEs) and cell edge FUEs is guaranteed on the premise of low interference.

Membrane-inspired quantum shuffled frog leaping algorithm for spectrum allocation

To solve discrete optimization difficulty of the spectrum allocation problem,a membrane-inspired quantum shuffled frog leaping（MQSFL） algorithm is proposed.The proposed MQSFL algorithm applies the theory of membrane computing and quantum computing to the shuffled frog leaping algorithm,which is an effective discrete optimization algorithm.Then the proposed MQSFL algorithm is used to solve the spectrum allocation problem of cognitive radio systems.By hybridizing the quantum frog colony optimization and membrane computing,the quantum state and observation state of the quantum frogs can be well evolved within the membrane structure.The novel spectrum allocation algorithm can search the global optimal solution within a reasonable computation time.Simulation results for three utility functions of a cognitive radio system are provided to show that the MQSFL spectrum allocation method is superior to some previous spectrum allocation algorithms based on intelligence computing.

Dynamic overlapped spectrum allocation based on potential game in cognitive radio networks

In order to make full use of wireless spectrum resources,the behavior of cognitive radio(CR)for dynamic spectrum allocation is analyzed based on the game theoretic framework.The traditional spectrum allocation schemes consider the spectrum allocation among independent frequency bands only,without taking into account mutually overlapped frequency bands.For this reason,an optimal allocation etiquette is defined to promote the cross characteristic of the frequency bands in a dynamic spectrum allocation model.New interference operator and interference temperature constraints are introduced in order to realize calculation of the interference,and the corresponding spectrum allocation scenario can be further formulated as a potential game.Based on the characteristic of dynamic selection using the game theory and the interference avoidance rule of interference temperature,the robustness of CR networks is increased and the scenario is more suitable for the dynamic changing of actual wireless communication and energy saving communication systems.Simulation results show that the signal to interference and noise ratio(SINR) level can be significantly improved through the optimal allocation of any available spectrum.The utilization rate of spectrum and throughput of overall CR networks are increased by fully utilizing the spectrum resources in the dynamic spectrum allocation model.

The Novel Spectrum Allocation Algorithm in Cognitive Radio System

In order to reduce the disturbance on an authorizing user and lower the competition between cognitive users, assure the normal communication of a cognitive radio system, reliability theory is applied to describe if a channel can be used by a cognitive user or not and the probability that the channel is continually used for a period. Three aspects including space, time domain and frequency domain are united for the research on the distribution of frequency spectrum. The simulation result shows that, in the space domain, time domain, frequency domain algorithm, the transmitted data volume and the total throughput of the system are superior to those in greedy algorithm and time domain—frequency domain algorithm, the novel algorithm is helpful to reduce the disturbance caused by a cognitive user to an authorizing user and lower the competition between cognitive users, this simulation result shows that the proposed algorithm is effective.

Spatio-Temporal Population Density and Spatial Dynamic Spectrum Allocation

A realistic population density distribution scenario in conjunction with the spatial dynamic spectrum allocation （DSA） is taken into account to mitigate the spectrum wastage in terms of extra guard bands. For the insertion of the extra guard bands, an efficient strategy based on self-assessment is applied to each victim cell individually and independently. Consequently, it is no more required to spread the extra guard band over the whole DSA region. Simulation results StlOW an improvement of 3% -4% in percentage of satisfied users for Universal Mobile Telecommunications System （UMTS） network and 4%-5% for Digital Video Broadcasting Terrestrial （DVB-T） network.

Spectrum Allocation for Cognitive Radio Networks with Non-Deterministic Bandwidth of Spectrum Hole

The spectrum allocation for cognitive radio networks(CRNs) has received considerable studies under the assumption that the bandwidth of spectrum holes is static. However, in practice, the bandwidth of spectrum holes is time-varied due to primary user/secondary user(PU/SU) activity and mobility, which result in non-determinacy. This paper studies the spectrum allocation for CRNs with non-deterministic bandwidth of spectrum holes. We present a novel probability density function(PDF) through order statistics as well as its simplified form to describe the statistical properties of spectrum holes, with which a statistical spectrum allocation model based on stochastic multiple knapsack problem(MKP) is formulated for spectrum allocation with non-deterministic bandwidth of spectrum holes. To reduce the computational complexity, we transform this stochastic programming problem into a constant MKP through exploiting the properties of cumulative distribution function(CDF), which can be solved via MTHG algorithm by using auxiliary variables. Simulation results illustrate that the proposed statistical spectrum allocation algorithm can achieve better performance compared with the existing algorithms when the bandwidth of spectrum holes is time-varied.

Spark Spectrum Allocation for D2D Communication in Cellular Networks

The device-to-device(D2D)technology performs explicit communication between the terminal and the base station(BS)terminal,so there is no need to transmit data through the BS system.The establishment of a short-distance D2D communication link can greatly reduce the burden on the BS server.At present,D2D is one of the key technologies in 5G technology and has been studied in depth.D2D communication reuses the resources of cellular users to improve system key parameters like utilization and throughput.However,repeated use of the spectrum and coexistence of cellular users can cause co-channel interference.Aiming at the interference problem under the constraint of fair resource allocation and improving the system throughput,this paper proposes an effective resource optimization scheme based on the firework method.The main idea is to expand the weighted sum rate and convert the allocated resource expression into fireworks to determine the correlation matrix.The simulation results show that,compared with the existing scheme,this scheme improves system performance by reducing interference.

Dynamic spectrum allocations in multi-cells and intra-cell of cognitive network to enhance system performance

In this study, we propose new dynamic spectrum allocations in multi-cells and intra-cell of cognitive network to enhance system performance in terms of decreasing probability of interruption and spectrum handoff of communication services in a cognitive system. The inter-cells of the spectrum allocation mechanism is designed to share the risk of vacating spectrum caused by licensed incumbents re-occupying the spectrum and minimize probability of service interruption in the cognitive network. This mechanism also can guarantee fairness among multi-cells. The intra-cell of the proposed spectrum allocation is based on a service data hierarchical model and establishes a mapping mechanism between layered data and the spectrum. It can reduce probability of spectrum handoff. Finally, simulation results are given and show that the new mechanism can reduce service interruption ratio and the probability of spectrum handoff caused by licensed incumbents with re-occupying the spectrum.

A parallel algorithm for statistical-fairness-based spectrum allocation of cognitive radios

A parallel algorithm for statistical-fairness-based spectrum allocation of cognitive radios is proposedin this paper. The key idea of the algorithm is to pursue the maximum total spectrum utilization of thesystem by adopting a parallel technique in every spectrum allocation, and to ensure the statistical fairnessrule by deploying a particular scheme during a series of allocations. The simulation results show that theproposed algorithm not only achieves a fairer and more efficient allocation of spectrum resources, but alsohas much shorter allocation duration than the color sensitive graph coloring (CSGC) algorithm.

GRAPH-THEORETIC ALGORITHM FOR SPECTRUM ALLOCATION IN IEEE 802.22 WIRELESS MESH NETWORK

The IEEE 802.22 standard based on wireless Cognitive Radio (CR) is an optimal solution to resolve the inefficient spectrum utility problem. In this paper, we focus on the spectrum allocation in IEEE 802.22 mesh networks and propose a new graph-theory algorithm. The algorithm aims at two objectives: one is the sum of the allocated channel bandwidth is maximum, and the other is the number of users can be active simultaneity is maximum. In this proposed algorithm, the topology of network was modeled as a general graph and could be transformed into a weighted complete bipartite-graph by three processes. The simulations show that the presented algorithm can improve the performance of spectrum allocation.

Genetic approach for Cell-by-Cell dynamic spectrum allocation in the heterogeneous scenario

In this paper,a genetic algorithm (GA) is investigated to deal with cell-by-cell dynamic spectrumallocation (DSA) in the heterogeneous scenario with temporal and spatial traffic demand changes,whichis also known as a difficult combinatorial optimization problem.A new two-dimensional chromosome encodingscheme is defined according to characteristics of the heterogeneous scenario,which prevents forminginvalid solutions during the genetic operation and enables much faster convergence.A novel randomcoloring gene generation function is presented which is the basic operation for initialization and mutationin the genetic algorithm.Simulative comparison demonstrates that the proposed GA-based cell-by-cellDSA outperforms the conventional contiguous DSA scheme both in terms of spectral efficiency gain andquality of service (QoS) satisfaction.

Extended Virtual Boundary and Its Application in Dynamic Spectrum Allocation

This paper presents an efficient dynamic spectrum allocation （DSA） scheme in a flexible spectrum licensing environment where multiple networks coexist and interfere with each other. In particular, an extension of virtual boundary concept in DSA is proposed, which is spectrally efficient than the previous virtual boundary concept applied to donor systems only. Here, the same technique is applied to both donor and rental systems so as to further reduce the occurrences where the insertion of guard bands is obligatory and as a result provides better spectral efficiency. The proposed extension improves the spectrum utilization without any compromise on interference and fairness issues.

Dynamic spectrum allocation for cell range extension in tiered Macro-Pico heterogeneous networks

Heterogeneous networks are employed in the next generation communication systems to enhance the area spectral efficiency （ASE）, where cell range extension （CRE） is a promising technique to improve the cell edge performance and utilize the low power node （LPN） resources more effectively. In this paper we propose a dynamic spectrum aUocation scheme for Macro-Pico scenario to mitigate both the co-tier and cross-tier interferences. The available system spectrum is divided into different parts by four steps, i.e. user set partition, service request collection, cross-tier occupation and CRE occupation decision, while the service request of each user is taken into consideration. During the process implementation, the reference signal receiving power （RSRP） threshold is derived by mathematical means to judge cell edge macro users when a predefined ratio is given. Simulation results show that the proposed scheme reaches almost the same cell edge performance with the best existing option, meanwhile provides higher overall system throughput and better spectral efficiency. Therefore, much better balance is achieved.

Spectrum allocation for wireless backhaul in heterogeneous ultra-dense networks

Wireless ultra-dense network （UDN） is one of the important technologies to solve the burst of throughput demand in the forthcoming fifth generation （SG） cellular networks. Reusing spectrum resource for the backhaul of small base stations （SBSs） is a hotspot research because of lower cost and rapid implementation with macro base stations （MBSs） in recent years. In heterogeneous UDN, the problem of spectrum allocation for wireless backhaul is investigated. In particular, two different spectrum resource reusing strategies for wireless backhaul are proposed in heterogeneous UDN with the limited bandwidth condition. Using a stochastic geometry-based heterogeneous UDN model, the success probabilities that mobile users communicate with SBSs or MBSs are derived under two different spectrum resource reusing strategies. In addition, the network throughput＇s analytical expressions and the optimal ratio of spectrum allocation are derived. Numeral results are provided to evaluate the performance of the proposed strategies at throughput. Thus, the effectiveness of the strategy that mobile users can only communicate with SBSs is validated.

Satellite Communications with 5G, B5G, and 6G: Challenges and Prospects

Satellite communications, pivotal for global connectivity, are increasingly converging with cutting-edge mobile networks, notably 5G, B5G, and 6G. This amalgamation heralds the promise of universal, high-velocity communication, yet it is not without its challenges. Paramount concerns encompass spectrum allocation, the harmonization of network architectures, and inherent latency issues in satellite transmissions. Potential mitigations, such as dynamic spectrum sharing and the deployment of edge computing, are explored as viable solutions. Looking ahead, the advent of quantum communications within satellite frameworks and the integration of AI spotlight promising research trajectories. These advancements aim to foster a seamless and synergistic coexistence between satellite communications and next-gen mobile networks.

Energy efficient resource allocation in non-cooperative multi-cell OFDMA systems

A non-cooperative game is proposed to perform the sub-carrier assignment and power allocation for the multi-cell orthogonal frequency division multiple access（OFDMA） system.The objective is to raise the spectral efficiency of the system and prolong the life time of user nodes.This paper defines a game player as a cell formed by the unique base station and the served users.The utility function considered here measures the user＇s achieved utility per power.Each individual cell＇s goal is to maximize the total utility of its users.To search the Nash equilibrium（NE） of the game,an iterative and distributed algorithm is presented.Since the NE is inefficient,the pricing of user＇s transmission power is introduced to improve the NE in the Pareto sense.Simulation results show the proposed game outperforms the water-filling algorithm in terms of fairness and energy efficiency.Moreover,through employing a liner pricing function,the energy efficiency could be further improved.