Rene L. Cruz

Downlink optimization of indoor wireless networks using multiple antenna systems

We compare the performance of two multiple antenna systems to be used in quality of service (QoS) supported indoor wireless networks. While a conventional array antenna system (AAS) has collocated, closely spaced antenna elements, a distributed antenna system (DAS) has largely spaced antennas over the entire area of radio coverage. To support multimedia applications requiring high bandwidth and on time delivery, we propose a set of highly spectrum efficient radio resource management algorithms. We focus on the optimization of downlink since many kinds of Internet traffic show the downlink dominance in their traffic asymmetry. To maximize the downlink throughput, we present a new transmit beamforming algorithm which can be equally applied to both DAS and AAS. The beamforming algorithm is integrated with a link scheduling algorithm that exploits the space division multiplexing (SDM) capability of multiple antenna systems to meet the QoS requirements of all terminals. Numerical examples conducted for a line of sight (LOS) environment demonstrate that a network with DAS outperforms one with AAS in terms of signal coverage and provides 40-160% higher capacity.

Joint beamforming, power control and scheduling for multihop wireless networks

In this paper, we propose a joint beamforming, power control and link scheduling algorithm for multihop wireless networks. We consider a multihop wireless network of nodes where each node is equipped with a transmit antenna array and a receive antenna array. A prespecified average data rate requirement is given for each link, as well as the channel matrix between each transmit array and receive array. The objective is to meet the required data rates while minimizing the average transmission power expended, subject to a peak transmission power constraint per node. While beamforming, power control and link scheduling need to be jointly optimized to achieve this goal, our proposed duality approach enables us to decouple these mechanisms. A simple, link-optimal, multiple-input and multiple-output (MIMO) beamforming algorithm is derived. Also, it is demonstrated that a simple 2 level (On-Off) power control suffices for the most energy efficient networking. Numerical examples suggests that while a TDMA scheduling is optimal for a low traffic demand, we need to schedule more simultaneous transmissions as the traffic load increases.