Publication | Closed Access
Resource Allocation and Inter-Cell Interference Management for Dual-Access Small Cells
74
Citations
24
References
2015
Year
EngineeringInterference CancellationInterference ManagementInternet Of ThingsMulti-access NetworkMobile Data OffloadingElectrical EngineeringComputer EngineeringMobile ComputingWireless AccessSmall CellSmall CellsCell CommunicationSpectrum ManagementEdge ComputingBusinessHeterogeneous NetworkChannel Access MethodResource AllocationWifi Interface
Small cells have proven popular for cell coverage enhancement and traffic offloading from macrocells. The paper proposes a resource‑allocation method for small cells that jointly optimizes licensed and unlicensed band usage to maximize user rates, ensure fairness, and limit interference to neighboring macrocells. They formulate a linear‑programming optimization that jointly allocates licensed and unlicensed resources, incorporates SUE QoS requirements, and controls inter‑cell interference to neighboring macrocells. The linear‑programming formulation is efficient and low‑complexity, can be adapted to maximize operator revenue, and outperforms several existing solutions.
In this paper, we present a method for resource allocation for small cells that integrate licensed and unlicensed RF operations motivated by the widespread WiFi hotspots and the common inclusion of WiFi interface in most cellular terminals. Small cells have proven popular for cell coverage enhancement and traffic offloading from macrocells. We formulate an optimization problem that jointly allocates resources over both licensed and unlicensed bands with the goal of maximizing sum small cell user equipment (SUE) rate while achieving fairness among these user equipments and controlling inter-cell interference to neighboring macrocell users. The proposed solution further considers the quality of service (QoS) requirement of SUE traffics to be distributed over both licensed and unlicensed bands. We show the formulation of the proposed optimization problem as an efficient and low complexity linear programming. We further show that our problem formulation can be modified to maximize the revenue of mobile network operators. Our proposed solution achieves better performance than several existing solutions.
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