SON-Aided Intelligent Energy-Efficient Network Operation And Resource Allocation In Wireless Networks

With the exponential growth of mobile traffic and the increasing number of mobile terminals,the wireless network infrastructure has evolved as a heterogeneous and increasingly complex network,making the network operation management and optimization very costly,more vulnerable and time-consuming with the traditionally manual and semi-automatical approaches.Hence,the future wireless network is expected to realize intelligent and efficient self-management in managing and controlling the network with an autonomic manner,dealing with network problems(outage,performance degradation),increasing the quality of service(Qo S)and reducing energy consumption cost.The self-organizing network(SON)with different SON functions which is envisioned as a powerful tool,is densely-deployed to realize self-configuration,self-healing and self-optimization in the wireless network.However,in such highly dynamic network scenarios,especially for dense small cell networks,a comprehensive self-healing solution becomes necessary to identify performance issues(Key Parameter Indicators(KPI))and to reduce the Operation Expenditure(OPEX)and management cost with the increasing cell density.In addition,different SON functions can lead to parametric and objective conflicts resulting in network performance oscillation when operate simultaneously in the same area,so a theoretical scheme of a SON coordination problem and of the solution should be addressed.Furthermore,considering the highly dynamics of the traffic arrival and wireless channel states with the limited network resources(available bandwidth and transmission powers),efficient network management is also urgently needed to reduce the energy consumption cost and improve the network efficiency to realize fully sustainable network management for future wireless networks.Hence,in this thesis,we mainly focus the works as follows,1.we investigate the self-healing(SH)problem in SON-based ultra dense small cell networks,where KPI information of some small cells may not be available.We propose a comprehensive self-healing scheme including both small cell outage detection mechanism and small cell outage compensation mechanism,and is capable of dealing with partial KPI situations.Specifically,the proposed SH scheme includes small cell outage detection(SCOD)stage and small cell outage compensation(SCOC)stage,where in SCOD stage the outage is detected,followed by the SCOC stage to compensate the users in the outage zones.For the SCOD stage,we propose a SCOD algorithm through machine learning approach based on partial KPI statistics.An effective Support Vector Data Description(SVDD)algorithm with low computational complexity is developed to detect and locate the outaged small cells with the context of KPIs and user position information.For the SCOC stage,to compensate the users in detected outage zone,we propose a novel distributed resource allocation algorithm that aims to optimize load balancing of the detected outage small cells area.The SCOC scheme is designed to allocate resources of neighboring small cells to the outage users,considering the dynamics and density of small cell environments.Specifically,we formulate resource allocation as a mixed integer optimization problem,solved by Lagrangian dual theory to guarantee both load balancing and Qo S requirements of users.2.we investigate a novel multiple time-scale coordination scheme(MTCS)for SON functions to ensure steady wireless network operation.Specifically,the self-organizing mechanisms in SON functions are modeled as Markov control loops and the stability conditions of control loops with different time scales are derived.Furthermore,we devise an analytical model,multiple time-scale Markov decision process(MMDP)for hierarchically M SON coordination decision making processes,where SON coordination decisions in each level(each time-scale)are made in a specific time-scale,and the Q-learning algorithm is developed to solve the proposed model,considering the dynamics of wireless network environments.Then,in order to improve energy efficiency of the wireless networks,we study a MLB-ESM use case with two typical SON functions(MLB(mobility load balancing)and ESM(energy saving management))with designed network utility in ultra-dense small cell networks.At last,the coordination capabilities of the proposed MTCS scheme is evaluated by the simulation results.3.we investigate the resource allocation for hybrid energy supplied OFDMA cellular networks where the base station is powered by renewable energy and electric grid.With the aim of exploiting the harvested energy,we propose an energy-aware resource allocation(EARA)algorithm for maximizing the network utility,which represents the tradeoff between the network throughput and the grid power consumption.In particular,the EARA algorithm only needs the current network states without a relevant priori distribution knowledge.Hence,it is applicable with unpredictable dynamics of wireless channel states,renewable energy arrivals and stochastic traffics in practical OFDMA cellular networks.In addition,we character the performance of EARA algorithm theoretically considering the complexity and the overhead of communications.Furthermore,we propose an implementation architecture for the EARA algorithm,and analyze the low implementation costs such as the low computational complexity.The simulation results verify the theoretical analysis and show that the proposed algorithm is effective in the wireless networks.4.we investigate the resource-on-demand(Ro D)allocation strategy for multiple radio access technologies(multi-RAT)wireless networks,which are simultaneously powered by both harvested energy and grid power.By considering the dynamics of randomly varying harvested energy,dynamic traffic arrival and time-varying wireless channels,the flexible energy scheduling is formulated as stochastic optimization problem to minimize grid power consumption cost.Specifically,under the sporadic availability and discontinuity of the harvested energy,a dynamic network energy queue model is proposed to provide the enduring operation for resource optimization in multi-RAT wireless networks with renewable energy.Following the Lyapunov optimization framework,the stochastic optimization problem is transformed into a sequence of optimization subproblems,including the network flow control subproblem(NFCS),the network energy management subproblem(NEMS)and the network resource allocation subproblem(NRAS).By solving these subproblems,an optimal resource-on-demand algorithm for multi-RAT wireless networks is developed in the dynamic environment.We propose a Ro D strategy for flexible energy scheduling in multi-RATs with heterogeneous energy sources,where the harvested energy is effectively shared between RATs to satisfy varying energy demand dynamically.The grid power consumption can be effectively reduced with efficient utilization of harvested energy,where a priori distribution knowledge of the wireless channel and data arrival state is not required,which is very practical in real systems.The tradeoff between grid power consumption and network delay is derived,where the increase of delay is approximately linear in V and the decrease of grid power consumption is at the speed of 1/V with the control parameter V.It can provide guidelines for dynamic resource allocation in multi-RAT networks with heterogenous energy sources.