System-Level Simulation and Radio Resource Management for Distributed Antenna

The performance of cellular networks will experience a considerable improvement by the use of newtechnologies such as distributed antenna systems (DASs), multi-cell cooperation (MCC), and cognitive radio (CR).However, several issues remain open in the system-level evaluation, radio resource management (RRM), and particularlyin the design of billing/licensing schemes for these types of system. This paper proposes a system-level simulator(SLS) that will help us address these issues. An advanced RRM solution is also proposed for a multi-cell DAS in adense urban Manhattan scenario with two levels of cooperation: inside the cell (intra-cell) to coordinate the transmissionof distributed nodes controlled by the base station of the cell, and between cells of a cluster (inter-cell) toadapt cell transmissions according to updated intercell interference measurements. The RRM solution blends networkand financial metrics using the theory of multi-objective and financial portfolio optimization. In this paper each network/spectrum resource is considered as a financial asset whose allocation has to be optimized based on economic metrics such as return and risk (i.e., variation of the return). The core of the intra-cell RRM algorithm is based on an iterative weighted least squares (WLS) optimization scheme where power levels and beam-forming vectors are jointlydesigned to comply with a target instantaneous SINR (signal-to-interference-plus-noise ratio) threshold for each transmission.This instantaneous SINR threshold ensures the transmission of the selected modulation and coding scheme(MCS) with a given value of BLER (block error rate) and spectral efficiency. The WLS scheme allows for a smoothintegration of scheduling and adaptive modulation and coding (AMC) schemes with the underlying space division multiplexing(SDM) physical layer. Convergence speed is improved by reusing the outcome of previous WLS iterations.The weight coefficients of the WLS optimization contain network metrics such as queue length and fairness, as well aseconomic metrics such as return and risk. This process is complemented with a multi-objective and financial portfoliooptimization stage for joint spectrum selection and resource (chunk) allocation that attempts to maximize return and minimize risk. Cells within a cluster exchange the results of their optimization processes for purposes of rejectinginter-cell interference, thereby achieving MCC. All resource allocation schemes use an imperfect copy of channel and queueing state information, which is the result of inaccurate measurements, imperfect feedback, or sensing errors.