Improving throughput in SWIPT-based wireless multirelay networks with relay selection and rateless codes

This paper studies a dual-hop Simultaneous Wireless Information and Power Transfer(SWIPT)-based multi-relay network with a direct link.To achieve high throughput in the network,a novel protocol is first developed,in which the network can switch between a direct transmission mode and a Single-Relay-Selection-based Cooperative Transmission(SRS-CT)mode that employs dynamic decode-and-forward relaying accomplished with Rateless Codes(RCs).Then,under this protocol,an optimization problem is formulated to jointly optimize the network operation mode and the resource allocation in the SRS-CT mode.The formulated problem is difficult to solve because not only does the noncausal Channel State Information(CSI)cause the problem to be stochastic,but also the energy state evolution at each relay is complicated by network operation mode decision and resource allocation.Assuming that noncausal CSI is available,the stochastic optimization issue is first to be addressed by solving an involved deterministic optimization problem via dynamic programming,where the complicated energy state evolution issue is addressed by a layered optimization method.Then,based on a finite-state Markov channel model and assuming that CSI statistical properties are known,the stochastic optimization problem is solved by extending the result derived for the noncausal CSI case to the causal CSI case.Finally,a myopic strategy is proposed to achieve a tradeoff between complexity and performance without the knowledge of CSI statistical properties.The simulation results verify that our proposed SRS-and-RC-based design can achieve a maximum of approximately 40%throughput gain over a simple SRS-and-RC-based baseline scheme in SWIPT-based multi-relay networks.

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.

Special issue on 5G wireless communication systems and technologies

The explosive growth of data traffic due to ubiquitous smart devices has accelerated the evolution of mobile communication from 4G to 5G. To achieve an extremely high performance, 5G mobile communication system is facing challenges in the design of new network architectures, new radio access techniques, and new transmission waveforms. Motivated by this observation, FITEE organizes this special issue on 5G Wireless Communication Systems and Technologies, to advance the studies of this field.

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-phase strategy dealing with diversification as well as intensification of search. Computational experiments include consecutive runs with different sets of weights associated with the two criteria. Results for a benchmark set of problems are presented.