Power control and channel allocation optimization game algorithm with low energy consumption for wireless sensor network

In a wireless sensor network （WSN）, the energy of nodes is limited and cannot be charged. Hence, it is necessary to reduce energy consumption. Both the transmission power of nodes and the interference among nodes influence energy consumption. In this paper, we design a power control and channel allocation game model with low energy consumption （PCCAGM）. This model contains transmission power, node interference, and residual energy. Besides, the interaction between power and channel is considered. The Nash equilibrium has been proved to exist. Based on this model, a power control and channel allocation optimization algorithm with low energy consumption （PCCAA） is proposed. Theoretical analysis shows that PCCAA can converge to the Pareto Optimal. Simulation results demonstrate that this algorithm can reduce transmission power and interference effectively. Therefore, this algorithm can reduce energy consumption and prolong the network lifetime.

Random Channel Allocation Scheme for SDMA Systems

A random allocation scheme for SDMA systems is proposed with a goal of more efficient dynamic allocation. Based on theoretical analysis and derivation, the blocking probability of the proposed scheme is calculated and compared with those of the ftrst duplicate （FD） and duplicate last （DL） schemes with different state-independent probabilities （p,） of acquring a dupicate channel suecessfully and 5 resources; moreover, a more realistic performance analysis of the random scheme is made with state-dependent ps in the SDMA/CDMA environment. The results show that the random scheme has a similar allocation pefformace to the FD and DL schemes, but is simpler than them in computation and scheduling.

Dynamic Channel Allocation in Sectored Cellular Systems

It is known that dynamic channel assignment(D CA ) strategy outperforms the fixed channel assignment(FCA) strategy in omni-direc tional antenna cellular systems. One of the most important methods used in DCA w as channel borrowing. But with the emergence of cell sectorization and spatial d ivision multiple access(SDMA) which are used to increase the capacity of cel lular systems, the channel assignment faces a series of new problems. In this pa per, a dynamic channel allocation scheme based on sectored cellular systems is p roposed. By introducing intra-cell channel borrowing (borrowing channels from n eighboring sectors) and inter-cell channel borrowing (borrowing channels from n eighboring cells) methods, previous DCA strategies, including compact pattern ba sed channel borrowing(CPCB) and greedy based dynamic channel assignment(GDCA) schemes proposed by the author, are improved significantly. The computer simu lation shows that either intra-cell borrowing scheme or inter-cell borrowing s cheme is efficient enough to uniform and non-uniform traffic service distributi ons.

A New Dynamic Channel Allocation Scheme for Microcellular Systems

A new Distributed Dynamic Channel Allocation (DDCA) algorithm named Combined DDCA is proposed in this paper.In this algorithm, each base station selects channels by learning through past experience of channel usage together with CIR measurement. Computer simulations are used to evaluate system performances. Performances are evaluated in two criteria, blocking probability and interaction probability. Comparisons with other DDCA algorithms have been carried out to validate the proposed algorithm.

Joint channel and power allocation scheme for cognitive wireless networks

Cognitive radio （CR） is a promising technology deemed to improve the efficiency of spectrum utilization. This paper considers a spectrum underlay cognitive radio network, in which the cognitive users （CUs） are allowed to use the radio spectrum concurrently with the primary users （PUs） under the interference temperature constraint. We investigate the system performance by using the proposed joint channel and power allocation scheme under two transmit strategies to achieve higher data rates and performance diversity gain respectively. Simulation results show that the proposed scheme provides a significant improvement on the bit error rate （BER） performance and spectrum efficiency of a cognitive wireless network.

Traffic Modeling and Analysis of Cellular Networks with Dynamic Channel Allocation

Dynamic channel allocation can reduce the blocking probability of cellular networks. This paper aims atestimating the blocking probability for cellular networki with dynamic channel allocation. Traffic analysis modelsare presented to evaluate performance of cellular networkS, The blocking probabilities versus traffic offered to eachcell are analyzed and simulated Comparisons between analysis and simulation results are made.

DYNAMIC RESOURCE ALLOCATION SCHEME UNDER TRAFFIC CONDITION IN SATELLITE SYSTEMS

As the traffic distribution in China mainland is far from uniform, a new traffic model in China mainland is presented on the basis of per-capita Gross Domestic Product (GDP) and density of population. Based on this characteristic traffic model, a new Traffic Dependent Dynamic Channel Allocation and Reservation (TDDCAR) technique is proposed, the simulation model is built, and the strategies' performance is evaluated through computer simulation. The simulation results show that, compared to the conventional Fixed Channel Allocation (FCA), TDDCAR estimates the traffic conditions in every spot beam and frequently adjusts the traffic according to current traffic conditions. It has achieved a significant improvement in new call blocking probability, handover blocking probability, and fair index, particularly, in heavy traffic conditions. The building of traffic model in China mainland and the analysis of the simulation results has been a key foundation for the study of resource allocation schemes in the future.

Resource allocation for cognitive WLAN over fiber

In this paper,a novel WLAN system,Cognitive WLAN over Fiber (CWLANoF),is introduced in the first place.Moreover,when CWLANoF has more channels than STAs,a new channel allocation scheme is proposed using the Hungarian algorithm,which is demonstrated to be the optimal one.Furthermore,when CWLANoF has fewer channels than STAs,it is possible for more than one STA to share the same channel simultaneously based on the new features of CWLANoF.And the power control scheme is proposed for this kind of sharing,considering efficiency and fairness.Finally,extensive simulation results illustrate the significant performance improvement of the proposed channel allocation scheme and power control scheme.

Secrecy Rate Maximization for 6G Cognitive Satellite-UAV Networks

To cover remote areas where terrestrial cellular networks may not be available,non-terrestrial infrastructures such as satellites and unmanned aerial vehicles(UAVs)can be utilized in the upcoming sixth-generation(6G)era.Considering the spectrum scarcity problem,satellites and UAVs need to share the spectrum to save costs,leading to a cognitive satellite-UAV network.Due to the openness of both satellite links and UAV links,communication security has become a major concern in cognitive satelliteUAV networks.In this paper,we safeguard a cognitive satellite-UAV network from a physical layer security(PLS)perspective.Using only the slowlyvarying large-scale channel state information(CSI),we jointly allocate the transmission power and subchannels to maximize the secrecy sum rate of UAV users.The optimization problem is a mixed integer nonlinear programming(MINLP)problem with coupling constraints.We propose a heuristic algorithm which relaxes the coupling constraints by the penalty method and obtains a sub-optimal low-complexity solution by utilizing random matrix theory,the max-min optimization tool,and the bipartite graph matching algorithm.The simulation results corroborate the superiority of our proposed scheme.

Optimization of Cognitive Femtocell Network via Oppositional Beetle Swarm Optimization Algorithm

In past decades,cellular networks have raised the usage of spectrum resources due to the victory of mobile broadband services.Mobile devices create massive data than ever before,facing the way cellular networks are installed pre-sently for satisfying the increased traffic requirements.The development of a new exclusive spectrum offered to meet up the traffic requirements is challenging as spectrum resources are limited,hence costly.Cognitive radio technology is pre-sented to increase the pool of existing spectrum resources for mobile users via Femtocells,placed on the top of the available macrocell network for sharing the same spectrum.Nevertheless,the concurrent reuse of spectrum resources from Femto networks poses destructive interference on macro networks.To resolve this issue,this paper introduces an optimal channel allocation model using the Oppo-sitional Beetle Swarm Optimization Algorithm(OBSOA)to allocate the channel with interference avoidance.A new OBSOA is derived in this paper by the inclu-sion of opposition-based learning(OBL)in BSOA.This algorithm allocates the channels used by PUs(PUs)to the secondary users(SUs)in such a way that inter-ference is minimized.This proposed approach is implemented in the MATrix LABoratory(MATLAB)platform.The performance of this proposed approach is evaluated in terms of several measures and the experimental outcome verified the superior nature of the OBSOA-based channel allocation model.OBSOA mod-el has resulted in a maximum signal-to-interference-plus-noise ratio value of 86.42 dB.

AN ENERGY-EFFICIENT OPTIMIZATION DEPLOYMENT SCHEME FOR WIRELESS SENSOR NETWORKS

Most of the current deployment schemes for Wireless Sensor Networks (WSNs) do not take the network coverage and connectivity features into account, as well as the energy consumption. This paper introduces topology control into the optimization deployment scheme, establishes the mathe-matical model with the minimum sum of the sensing radius of each sensors, and uses the genetic al-gorithm to solve the model to get the optimal coverage solution. In the optimal coverage deployment, the communication and channel allocation are further studied. Then the energy consumption model of the coverage scheme is built to analyze the performance of the scheme. Finally, the scheme is simulated through the network simulator NS-2. The results show the scheme can not only save 36% energy av-eragely, but also achieve 99.8% coverage rate under the condition of 45 sensors being deployed after 80 iterations. Besides, the scheme can reduce the five times interference among channels.

Lifetime Enhancement of Cluster Based Wireless Sensor Network through Energy Efficient MAC Protocol

The efficient use of energy is an important performance metric to extend the lifetime of wireless sensor networks. Since major energy consumption of node is due to its transceiver, the design of MAC protocol plays a vital role in sensor network design. In cluster based sensor networks, due to the different functions of member node and cluster head node, the usage of common MAC protocol results increased energy consumption. To overcome this problem, a novel energy efficient hybrid MAC protocol (EEHMAC) for cluster based wireless sensor networks is proposed in this paper. The proposed EEHMAC protocol uses E-TDMA (Energy efficient TDMA) for intra-cluster communication and FDMA (Frequency Division Multiple Access) for inter-cluster communication. IDS (Iterative Deepening Search) based Scheduling algorithm is used for assigning time slot and frequency slot to nodes. Nodes in EEHMAC follow the periodic duty cycle, which reduces the idle listening, and overhearing problems. Simulation results reveal that an average of 18% energy saving is achieved compared to LEACH (Low Energy Adaptive Clustered Hierarchy) protocol and 10% energy is saved in comparison with GH-MAC (Graph theory based Hybrid MAC) protocol. It is evident that delay of EEHMAC is reduced by 17% and throughput is increased by 15% under all traffic conditions. These results demonstrate that EEHMAC performs better than existing MAC protocols in terms of energy efficiency, delay and throughput.

Multi-UAV Collaborative Edge Computing Algorithm for Joint Task Offloading and Channel Resource Allocation

Unmanned aerial vehicle (UAV)-based edge computing is an emerging technology that provides fast task processing for a wider area. To address the issues of limited computation resource of a single UAV and finite communication resource in multi-UAV networks, this paper joints consideration of task offloading and wireless channel allocation on a collaborative multi-UAV computing network, where a high altitude platform station (HAPS)is adopted as the relay device for communication between UAV clusters consisting of UAV cluster heads (ch-UAVs) and mission UAVs (m-UAVs). We propose an algorithm, jointing task offloading and wireless channel allocation to maximize the average service success rate (ASSR)of a period time. In particular,the simulated annealing(SA)algorithm with random perturbations is used for optimal channel allocation,aiming to reduce interference and minimize transmission delay.A multi-agent deep deterministic policy gradient (MADDPG) is proposed to get the best task offloading strategy. Simulation results demonstrate the effectiveness of the SA algorithm in channel allocation. Meanwhile,when jointly considering computation and channel resources,the proposed scheme effectively enhances the ASSR in comparison to other benchmark algorithms.