The Delay-Bounded Quality Of Service Guaranteed Resource Allocation For New Wireless Communications Networks

With the urgent demand for different services of wireless communication in the fields of living,working,leisure and transportation,the highly intelligent 5G mobile communication system has become a hot research topic.However,the highly intelligent 5G mobile terminal is often powered by the battery with limited lifetime.It is very difficult for charging and costly for the replacement,which has a direct impact on the future of wireless communication network service life,thus unable to meet the demands of users.Energy harvesting technology,which can harvest energy from energy sources,i.e.,the nature and the new energy,can prolong the lifetime of wireless communication terminals.Due to the random distribution and mobility of harvested energy powered wireless communications nodes,the energy harvesting often intermittently occurs.As methods of energy harvesting,wireless power transfer(WPT)and simultaneous wireless information and power transfer(SWIPT)use the dedicated radio frequency(RF)radiation to maintain reliable energy harvesting based communications for wireless communications networks.On the other hand,in order to improve the efficiency of wireless communication networks,it is necessary to develop new high spectral efficiency wireless communication networks.Combining device-to-device(D2D)communications with cognitive radio networks can improve spectrum efficiency of wireless communication network in time domain,frequency domain and spatial domain.Meanwhile,how to guarantee the quality of service(QoS)for delay-sensitive services in wireless communication networks has not been well solved.Since different delay-sensitive services have different QoS requirements,how to provide the delay QoS provisioning while improving the spectrum efficiency is very challenging.Traditionally,the delay QoS guarantee is deterministic.However,the parameters of the wireless channel are constantly changing with time,frequency,location and etc.Thus,the statistical delay-bounded QoS provisioning,which is based on large deviation theory,is more practical.The optimal resource allocation can be applied to guarantee the delay QoS and improve the spectrum efficiency or energy efficiency for wireless communication networks.Based on the delay-bounded QoS requirements and the energy arrival process,we allocate the wireless resources,such as transmission power,transmission time,energy,wireless channel,and etc,to maximize the spectrum efficiency and energy efficiency.We focus on four wireless communications networks:the single channel energy harvesting wireless sensor networks,the multi-channel wireless powered sensor networks,the SWIPT based relay networks,and D2D communications based cognitive radio networks.In these wireless networks,we study the resource allocation to maximize the spectrum efficiency or energy efficiency while guaranteeing the statistical delay-bounded QoS.The main contribution of this paper is presented as follows.1.We develop the statistical delay-bounded QoS-driven power control policies to maximize the effective energy efficiency(EEE)for energy harvesting based wireless sensor networks with infinite battery and finite battery,respectively.First,we build the long-term energy harvesting constraints to formulate the EEE maximization problems for the battery-infinite and battery-finite energy harvesting based wireless sensor networks,respectively.Second,we derive the closed-form expressions of the statistical delay-bounded QoS-driven power control policies.For the battery-infinite wireless sensor networks,our developed QoS-driven power control policy converges to the Energy harvesting Water Filling(E-WF)scheme and the Energy harvesting Channel Inversion(E-CI)scheme under the very loose and stringent QoS constraints,respectively.For the battery-finite wireless sensor networks,our developed QoS-driven power control policy becomes the Truncated energy harvesting Water Filling(T-WF)scheme and the Truncated energy harvesting Channel Inversion(T-CI)scheme under the very loose and stringent QoS constraints,respectively.Specifically,we evaluate the impact of battery capacity limitation on the optimal power control policy for battery-finite energy harvesting based wireless sensor networks.Third,we analyze the outage probabilities and validate the feasibility of our developed power control polices.Furthermore,we evaluate the outage probabilities to theoretically analyze the performance of our developed QoS-driven power control policies.The obtained numerical results validate our analysis and show that our developed optimal power control policies can optimize the EEE over energy harvesting based wireless sensor networks.2.We propose the joint downlink energy assignment and uplink power control schemes with heterogeneous statistical QoS provisioning for multi-channel wireless powered sensor networks.In particular,we consider a network model where downlink transferred energy are emitted by a hybrid access point(HAP)to replenish sensor nodes and enable the uplink information transmission back to the HAP.First,we build up the downlink energy transfer and the uplink information transmission models,respectively.To support the heterogeneous statistical QoS for uplink information transmission,we give the aggregate effective capacity(AEC),which is defined as the aggregate throughput under the delay-bounded QoS constraints.Then,based on our proposed system model,we address the uplink AEC maximization problems by decentralized and centralized resource allocation,respectively.For decentralized resource allocation,the HAP makes the energy assignment for each downlink based on uplink QoS requirements while the sensor nodes allocate the uplink transmit power based on the QoS requirements,the instantaneous channel state information(CSI),and the energy assigned by the HAP,which yields the optimal joint downlink energy assignment and uplink information transmission power control scheme.For centralized resource allocation,we develop the joint time allocation,downlink energy assignment,and uplink power control scheme.To derive this scheme,we propose the iterative algorithm based on Lagrange-Dual method and sub-gradient algorithm.Finally,we conduct extensive numerical simulations to demonstrate the effect of heterogeneous statistical QoS requirements on our proposed resource allocation schemes.3.We develop the statistical delay-bounded QoS provisioning resource allocation for SWIPT based relay communications networks.To further improve the spectrum efficiency for SWIPT based relay communications networks,we maximize the effective capacity for half duplex and full duplex transmissions,respectively.Specifically,first,dividing the wireless information and energy transmission process by a power splitting ratio,we build up the relay network model,describe the full duplex communication self interference problem by a self interference cancellation coefficient and formulate the half/full duplex communication transmission models.Then,in the half duplex communication and full duplex transmission modes,respectively,we formulate the effective capacity maximization problems under the statistical delay-bounded QoS provisioning.Employing the Karush-Kuhn-Tucker(KKT)conditions,we derive the corresponding closed-form expressions of the joint optimal power allocation and power split ratio allocation for half/full duplex in statistical QoS guaranteed SWIPT based relay networks.Finally,the simulation experiment is conducted to verify the statistical delay-bounded QoS guaranteed optimal joint power allocation and power split ratio allocation for the SWIPT based wireless relay networks.4.We propose the joint channel selection and power control scheme for video streaming over D2D communications based cognitive radio networks.In order to evaluate the delays experienced by various streaming traffics,we build up the physical queue and virtual queue models based on "M/G/1 queue" and "M/G/l queue with vacations" theories,respectively.Aiming to minimize the video distortion,we formulate an optimization problem subject to the rate constraint,maximum power constraint,the tolerant interference constraint and the minimum rate requirement constraint.The problem is a mixed integer nonlinear programming(MINLP),which is non-convex NP-hard.To solve this problem,the Lagrange-Dual method is applied to reformulate the problem and sub-gradient algorithm is employed to iteratively obtain a relaxed solution.Then,the Branch and Bound method is used to recover the relaxed solution to the integer solution for the MINLP.The extensive simulation results obtained validate our proposed joint channel selection and power control scheme and show the better performance than the existing research works.