Optimal Resource Scheduling In Wireless Caching Networks

The rapid proliferation of smart devices has triggered an unprecedented growth of the global mobile data traffic.Meanwhile,the demand for wireless communication services has been shifting from the conventional connection-centric communications such as phone calls and messaging,to the emerging content-centric communications such as video streaming and mobile TV.Thus,it is of great importance to handle the “data tsunami” and the demand shift of users by fully exploiting the scarce radio resources and optimizing the wireless communication services.Recently,buffer-aided wireless networks and cache-enabled wireless networks have received increasing interest from academia: buffering can provide more flexible data transmissions and hence can better support the cross-layer design;caching can bring contents closer to users and hence can relieve the network congestion and improve user-perceived experience.In this thesis,we focus on optimal resource scheduling in wireless caching networks.We deeply study buffer-aided cooperative communication systems and cache-enabled content-centric wireless networks,analyse the structural properties of the optimal policies,design structure-aware low-complexity optimal and suboptimal algorithms,and provide design insights for practical systems.The main contributions of this thesis are summarized as follows.Firstly,we consider a two-hop half-duplex relaying system,where the relay is equipped with a finite buffer.We focus on stochastic link selection and transmission rate control to maximize the average system throughput subject to a half-duplex constraint.We formulate this stochastic optimization problem as an infinite horizon average cost Markov decision process(MDP).By using sample-path analysis and exploiting the specific problem structure,we first obtain an equivalent Bellman equation with reduced state and action spaces.By analysing the monotonicity and supermodularity properties of the value function of the MDP,we show that the optimal policy has a threshold-based structure.Based on the threshold-based structure and Markov chain theory,we further simplify the original complex stochastic optimization problem to a static optimization problem,and propose a low-complexity algorithm to solve the simplified static optimization problem by making use of its special structure.Furthermore,we obtain the closed-form optimal threshold for the symmetric case.Secondly,we study optimal dynamic multicast scheduling for cache-enabled content-centric wireless networks.Given cache status,we consider dynamic content multicast scheduling to jointly minimize the average delay,power,and fetching costs.We formulate this stochastic optimization problem as an infinite horizon average cost MDP.By using relative value iteration algorithm(RVIA)and special structures of the request queue dynamics,we analyze the properties of the value function and the state-action cost function of the MDP for both the uniform and nonuniform channel cases.Based on these properties,we show that the optimal policy has a switch structure in the uniform case and a partial switch structure in the nonuniform case.Moreover,in the uniform case with two contents,we show that the switch curve is monotonically nondecreasing.Then,we propose two structure-aware optimal algorithms to obtain the optimal policy.Motivated by the switch structures of the optimal policy,we propose a low-complexity suboptimal policy,which exhibits similar switch structures to the optimal policy,and design a low-complexity algorithm to compute this policy.Furthermore,we extend the analytical results to the Markov-modulated request arrival model and the multicell model.Thirdly,we study optimal content delivery in cache-enabled heterogeneous cellular networks(HetNets)by taking into account the inherent multicast capability of wireless medium.For given cache status at base stations,we consider stochastic content multicast scheduling to jointly minimize the average network delay and power costs under a multiple access constraint.We establish a content-centric request queue model and formulate this stochastic optimization problem as an infinite horizon average cost MDP.By using RVIA and special properties of the request queue dynamics and HetNets,we characterize some properties of the value function of the MDP.Based on these properties,we show that the optimal multicast scheduling policy is of threshold type.Then,we propose a structure-aware optimal algorithm to obtain the optimal policy.To further reduce the complexity,we also propose a low-complexity suboptimal policy,which possesses similar structural properties to the optimal policy,and develop a low-complexity structure-aware algorithm to obtain this policy.Finally,we investigate optimal cooperative caching for cache-enabled cognitive radio networks.By cooperative caching,we mean that the unlicensed secondary base station(SBS)can cache certain primary contents to serve primary users,in exchange for the opportunities to access the licensed spectrum.We consider the joint optimization of caching and scheduling of the SBS to maximize the weighted average number of satisfied secondary requests under the average available time constraint and the cache capacity constraint.This problem is a mixed-integer bilinear programming.By exploring the special structure of the problem,we first show that the optimal caching satisfies a cache-split structure and the optimal scheduling satisfies a rateratio structure.Then,based on these optimality properties,we transform the original problem into a simplified joint cache splitting and secondary user partitioning optimization problem,and propose an efficient algorithm to solve it optimally.Moreover,we investigate the impacts of the primary content popularity distribution on the system performance.