In this paper, we investigate how to utilize renewable energy harvested by small cells to increase network capacity. Specifically, we try to maximize the average capacity of a small cell with the harvested energy from the environments (e.g., solar and wind) with constraints on energy causality and battery capacity. With the coverage preserved by the umbrella cells, we investigate the potential of capacity improvement by dynamic on-off operations of small cells. We propose a heuristic polynomial-time near-optimal algorithm for joint power control and sleep-awake scheduling of this mixed-integer optimization problem. The capacity obtained by the proposed heuristic algorithm can approach the maximal capacity as long as the small cell can be equipped with a battery with large enough capacity. In the simulations, we demonstrate that our proposed algorithm can increase system capacity by 25%. We find that always attempting to keep a small cell in active state may not be always a good strategy for capacity maximization even when the optimal transmit power allocation is applied.

Energy-efficient base station (BS) operation is a key design goal in green cellular networks. An effective way for energy conservation of BSs is to switch BSs on/off according to the traffic profile. However, such operations may create coverage holes in the network. In this paper, we aim to minimize the total power consumption of the network by switching BSs on/off adaptively while maintaining the network coverage. We find that the BS activation problem for minimal network power consumption with full network coverage preservation is an NP-hard problem. To address the problem, we first derive the optimal cell size for minimizing BS power consumption per unit coverage area and propose a polynomial-time algorithm for energy-efficient BS activation. The simulation results show that our algorithm can approach the minimum network power consumption and adapt to network traffic load under non-uniform traffic load distributions. More importantly, we demonstrate that network densification with small cells for bursting throughput in hot spot areas can also be beneficial in saving network energy during the low traffic load period.

Energy-efficient base station (BS) operation is a key design goal in green cellular networks. The most effective way for energy conservation of BSs is to switch on/off BS adaptively. However, such operations may induce the problem of creating coverage holes in the network. In this paper, we attempt to minimize the total power consumption of the network by switching on and off BSs adaptively while maintaining the network coverage. Specifically, we derive the optimal cell size and determine the optimal number of active BSs for power consumption minimization with network coverage preservation. We also propose a near-optimal polynomial-time heuristic algorithm for energy-efficient BS activation with network coverage preservation. The simulation results show that our algorithm can approach the performance upper bound with only approximately 3dB performance loss in overall network power consumption for a cellular network with various deployment strategies.

Minimization of power consumption is a critical design goal for wireless-relay networks comprised of battery-powered sensor devices. Traditionally, wireless communication is optimized to minimize the total transmission power. In the paper, we further include the detection power and the reception power in order to minimize the overall network power consumption for information exchange in wireless-relay sensor networks (WSNs). Specifically, after the physical attributes of relay nodes are given, we wish to maximize the network lifetime by a proper selection of the transmission range, the sleep period, and the participation density. We propose a random gossip network (RGN) as a framework for our analysis. We find simple rules govern the optimal settings of these three parameters for all-to-all broadcast in the RGN. One key relationship is that the optimal number of nodes broadcasting messages in a time epoch within the transmission range depends only on the path loss exponent and the network dimensionality. This optimal value does not depend on factors such as the physical network size, the reception cost, and the message origination rate. Moreover, it is shown that neither increasing nor decreasing the physical network size will affect the optimal value of these design parameters. The optimal setting for power consumption minimization is a scalable solution. Finally, we demonstrate that our results can be well applied to predict the optimal value of operational parameters for information exchange as well as information dissemination in actual WSNs.

While the coverage-driven functional verification is getting popular, a fast and convenient coverage measurement tool is necessary. In this paper, we propose a novel approach for functional coverage measurement based on the VCD files produced by the simulators. The usage flow of the proposed dumpfile-based coverage analysis is much easier and smoother than that of existing instrumentation-based coverage tools. No pre-processing tool is required and no extra code will be inserted into the source code. Most importantly, the flexibility in choosing coverage metrics and measured code regions is increased. Only one simulation run is needed for any kind of coverage reports. By conducting some experiments on real examples, it shows very promising results in terms of the performance and the accuracy of coverage reports.