3D placement of unmanned aerial vehicles and partially overlapped channel assignment for throughput maximization

This paper investigates a wireless system with multi-Unmanned Aerial Vehicles(UAVs)for improving the overall throughput.In contrast to previous studies that optimize the locations of UAVs and channel assignment separately,this paper considers the two issues jointly by exploiting Partially Overlapped Channels(POCs).The optimization problem of maximizing network throughput is formulated as a non-convex and non-linear problem.In order to find a practical solution,the problem is decomposed into two subproblems,which are iteratively optimized.First,the optimal locations of UAVs are determined under a fixed channel assignment scheme by solving the mixed-integer second-order cone problem.Second,an efficient POC allocation scheme is determined via the proposed channel assignment algorithm.Simulation results show that the proposed approach not only significantly improves system throughput and service reliability compared with the cases in which only orthogonal channels and stationary UAVs are considered,but also achieves similar performance using the exhaustive search algorithm with lower time complexity.

Throughput Enhancement for NOMA Systems Using Intelligent Reflecting Surfaces

In this article,we optimize the powers associated to Non Orthogonal Multiple Access(NOMA)users,sensing and harvesting duration for Cognitive Radio Networks(CRN).The secondary source harvests energy from node A signal.Then,it senses the channel to detect primary source.Then,the secondary source transmits a signal that is reflected by Intelligent Reflecting Surfaces(IRS)so that all reflections have a zero phase at any user.A set Ii of reflectors are associated to user Ui.The use ofM=Mi=512,256,128,64,32,16,8 reflectors per user offers 45,42,39,36,33,30,27 dB gain vs.the absence of IRS.We also suggest the use of IRS in energy harvesting.The use P=8 reflectors for energy harvesting and M=Mi=8 reflectors per user for data communications offers 7 and 38 dB gain vs.one IRSM=Mi=8 and the absence of IRS.The use of P=16 reflectors for energy harvesting and M=Mi=8 reflectors per user for data communications offers 9 and 42 dB gain vs.one IRS M=Mi=8 and the absence of IRS.

Resource Allocation for Throughput Maximization in Cognitive Radio Network with NOMA

Spectrum resources are the precious and limited natural resources.In order to improve the utilization of spectrum resources and maximize the network throughput,this paper studies the resource allocation of the downlink cognitive radio network with non-orthogonalmultiple access(CRN-NOMA).NOMA,as the key technology of the fifth-generation communication(5G),can effectively increase the capacity of 5G networks.The optimization problem proposed in this paper aims to maximize the number of secondary users(SUs)accessing the system and the total throughput in the CRN-NOMA.Under the constraints of total power,minimum rate,interference and SINR,CRN-NOMA throughput is maximized by allocating optimal transmission power.First,for the situation of multiple sub-users,an adaptive optimization method is proposed to reduce the complexity of the optimization solution.Secondly,for the optimization problem of nonlinear programming,a maximization throughput optimization algorithm based on Chebyshev and convex(MTCC)for CRN-NOMA is proposed,which converts multi-objective optimization problem into single-objective optimization problem to solve.At the same time,the convergence and time complexity of the algorithm are verified.Theoretical analysis and simulation results show that the algorithm can effectively improve the system throughput.In terms of interference and throughput,the performance of the sub-optimal solution is better than that of orthogonal-frequency-division-multiple-access(OFDMA).This paper provides important insights for the research and application of NOMA in future communications.

Load-Aware Offloading Strategy in Two-Tier Heterogeneous Network

Although small cell offloading technology can alleviate the congestion in macrocell, aggressively offloading data traffic from macrocell to small cell can also degrade the performance of small cell due to the heavy load. Because of collision and backoff, the degradation is significant especially in network with contention-based channel access, and finally decreases throughput of the whole network. To find an optimal fraction of traffic to be offloaded in heterogeneous network, we combine Markov chain with the Poisson point process model to analyze contention-based throughput in irregularly deployment networks. Then we derive the close-form solution of the throughput and find that it is a function of the transmit power and density of base stations.Based on this, we propose the load-aware offloading strategies via power control and base station density adjustment. The numerical results verify our analysis and show a great performance gain compared with non-load-aware offloading.

A TABU SEARCH APPROACH TOWARDS CONGESTION AND TOTAL FLOW MINIMIZATION IN OPTICAL NETWORKS

This paper considers rearrangeable multihop lightwave networks whereby each network node is equipped with a number p of transmitters and receivers, and a spectrum of wavelengths is accessible by, and shared among, all nodes by using the Wavelength Division Multiplexing (WDM). Depending on input traffic flow, nodal transmitters and receivers can be re-tuned to create virtual connectivity best suited with respect to a given optimization criterion. We present an efficient heuristic algorithm that combines two criteria for optimization: throughput maximization, as well as total flow minimization. Throughput maximization criterion is equivalent to congestion minimization, while minimizing total flow under the assumption of having links with equal lengths implies minimization of the average number of hops. Taking into account lengths of the links (i.e. link costs proportional with distances), the total flow minimization becomes equivalent to the total delay minimization. Tabu search is implemented as a two-ph