Transmit Power Minimization in Small Cell Networks Under Time Average QoS Constraints

TL;DR

This paper develops a stochastic optimization framework for small cell networks to minimize transmit power while satisfying time average QoS constraints, leveraging Lyapunov optimization for dynamic resource allocation.

Contribution

It introduces a Lyapunov-based algorithm for power minimization under time average QoS, considering practical factors like delay and CSI feedback constraints.

Findings

Time average QoS constraints lead to significant power savings.

The proposed algorithm effectively balances QoS satisfaction and power expenditure.

Performance bounds demonstrate robustness under practical network constraints.

Abstract

We consider a small cell network (SCN) consisting of N cells, with the small cell base stations (SCBSs) equipped with Nt \geq 1 antennas each, serving K single antenna user terminals (UTs) per cell. Under this set up, we address the following question: given certain time average quality of service (QoS) targets for the UTs, what is the minimum transmit power expenditure with which they can be met? Our motivation to consider time average QoS constraint comes from the fact that modern wireless applications such as file sharing, multi-media etc. allow some flexibility in terms of their delay tolerance. Time average QoS constraints can lead to greater transmit power savings as compared to instantaneous QoS constraints since it provides the flexibility to dynamically allocate resources over the fading channel states. We formulate the problem as a stochastic optimization problem whose solution is the design of the downlink beamforming vectors during each time slot. We solve this problem using the approach of Lyapunov optimization and characterize the performance of the proposed algorithm. With this algorithm as the reference, we present two main contributions that incorporate practical design considerations in SCNs. First, we analyze the impact of delays incurred in information exchange between the SCBSs. Second, we impose channel state information (CSI) feedback constraints, and formulate a joint CSI feedback and beamforming strategy. In both cases, we provide performance bounds of the algorithm in terms of satisfying the QoS constraints and the time average power expenditure. Our simulation results show that solving the problem with time average QoS constraints provide greater savings in the transmit power as compared to the instantaneous QoS constraints.