By introducing the relay capability, the IEEE 802.16j standard is developed to improve the WiMAX performance. Under the transparent mode, existing studies aim at improving network throughput by increasing the transmission rates of mobile stations (MSs). However, we show that using higher rates will let MSs consume more energy. In the paper, we define an energy-conserved uplink resource allocation (EURA) problem in 802.16j networks under the transparent mode, which asks how to arrange the uplink resource to 1) satisfy MSs' requests and 2) minimize their energy consumption. Objective 1 is necessary while objective 2 should be achieved when objective 1 is met. The above bi-objective problem is especially important when the network is non-saturated. The EURA problem is NP-hard and we propose a heuristic with two key designs. First, we exploit relay stations to allow more concurrent uplink transmissions to fully use the frame space. Second, we reduce MSs' transmission powers by adjusting their rates and paths. Simulation results show that our heuristic can save up to 80% of MSs' energy as compared with existing work.

With the significant advancement of statistical timing and yield analysis algorithms, there is a strong need for accurate and analytical spatial correlation models. In this paper, we propose a novel spatial correlation modeling method that can not only capture the general spatial correlation relationship but also can generate highly accurate and analytical models. Our method, based on singular value decomposition, can generate sequences of polynomial weighted by the singular values. Experimental results from foundry measurement data show that our proposed approach is 3x accuracy improvement over several distance based spatial correlation modeling methods.

With the significant advancement of statistical timing and yield analysis algorithms, there is a strong need for accurate and analytical spatial correlation models. In this paper, we propose a novel spatial correlation modeling method not only can capture the general spatial correlation relationship but also can generate highly accurate and analytical models. Our method, based on Singular Value Decomposition (SVD), can generate sequences of polynomial weighted by the singular values. Experimental results from foundry measurement data show that our proposed approach is 5X accuracy improvement over several distance based spatial correlation modeling methods.