Radio Resource Allocation For Cloud-based Satellite And Terrestrial Spectrum Shared Networks

Next generation mobile communication networks will integrate different types of radio access technologies as a heterogeneous system adaptively offering services to emerging applications with diverse requirements.Currently,satellite communications and terrestrial communications have achieved business-level interconnection and interoperability through gateway connections.Based on the urgent needs of massive users for intelligent connection and access,existing networks are facing huge challenges such as low integration of satellites and ground in terms of spectrum,interface and terminals,scarcity of spectrum and inflexible resource management.In the future,heterogeneous cloud access networks can achieve efficient network scheduling and resource management,which is essential to meet future scalability,flexibility,and integration with the entire telecommunications ecosystem.It can realize spectrum integration and interference management between heterogeneous networks,and effectively solve the common problem of spectrum scarcity.Therefore,cloud-based satellite and terrestrial spectrum shared networks(CB-STSSN)combines the triple advantages of efficient and flexible network management of heterogeneous cloud access(HCRAN),vast coverage of satellite networks,and good communication quality of terrestrial networks and can provide anytime and anywhere seamless communication service.Despite many advantages over traditional networks brought by high integration of CB-STSSN,there exist some technical challenges remaining to be solved.The most challenging issue is the complex co-channel interference,which results from spectrum sharing between satellite and ground component aiming to achieve spectrum reuse gain.In this situation,resource management will have an important effect on the interference among different users.Therefore,to fully take advantages of CB-STSSN,it is critical to limit the co-channel interference when allocating wireless resources in terms of spectrum and power such that severe interference can be avoided and the overall system performance can be improved.Towards this end,this thesis studies efficient multi-dimensional(spectrum and power)resource management methods for CB-STSSN.Furthermore,based on the cloud-based network architecture,the analytical model,derivations and numerical results are provided for the analysis of coverage and interference of CB-STSSN.The main research objectives of this thesis can be summarized as follows.First,a general challenge faced by both satellite and terrestrial networks is the problem of spectrum scarcity.While spectrum sharing has the potential to solve this problem,it brings severe co-channel interference.The network coverage and interference are first modelled and analyzed.On the other hand,a dynamic spectrum allocation strategy based on the radio map is proposed for CBSTSSN.It has been observed that the distribution of traffic demands among different satellite beams is usually not uniform and changes with time.In this case,the static spectrum planning method currently adopted has many limitations as it assigns fixed resources to different beams and fails to adapt to the dynamic traffic demands.To tackle this problem,a radio map is used for the predictions of traffic demands and spectrum utilization conditions in different beams.Leveraging on the prediction results,the proposed method allocates spectrum resources according to a Joint Non-preemptive and Preemptive based scheme,which can realize priorized resource allocation and proportional fairness among users of same priority.Second,in view of the limited onboard power of satellites,energy efficiency has become a valuable performance metric for next generation networks.This thesis first modularizes the power consumption of CB-STSSN and the hybrid cochannel interference model is constructed.For the co-channel interference problem,the interference from satellite to terrestrial network is limited by a threshold and this threshold is converted to the constraint of the transmit power of satellites by using the interference path model.The limited onboard power of satellites is saved by constraining the second order difference between the allocated bandwidth and the actual required bandwidth of satellite spot beam.Furthermore,the network delay is considered by transferring the spot beam delay constraint to the lower limit of allocated capacity of the spot beam.An optimization problem is formalized with the aim to maximize the energy efficiency.The problem is solved using dual theory,sub-gradient method,binomial approximations,and diminishing step strategy.And the complexity is analyzed.Simulation results indicate that the proposed algorithm can improve system throughput and energy efficiency at the cost of a small increase of complexity.Third,the joint resource optimization of satellite,terrestrial macro cell and micro cell in the baseband resource pool can suppress the hybrid co-channel interference among multi-layer networks.This thesis proposes a high energyefficient joint satellite and terrestrial power optimization algorithm to maximize the energy efficiency with constraints of hybrid co-channel interference.The objective function is a strong coupled function of three variables.Therefore,it cannot be solved through traditional fractional programming.The optimization variables are normalized to efficiently improve the convergence and accuracy of the algorithm.Based on the analysis of the hybrid co-channel interference,this thesis proposes a method to suppress the interference generated from the main interferers.The complexity and baseband power consumption are decreased by sparsifying matrices,improving the energy efficiency.Based on this,this thesis proposes a high energy-efficient joint satellite and terrestrial power optimization algorithm.The algorithm improves the convergence by iterating between the water-filling of normalized sparse matrix and convex optimization with interference threshold.Simulation results indicate that the energy efficiency can be improved significantly at the cost of only a small decrease of the system throughput.