Abstract-Full-duplex communication has the potential to substantially increase the throughput in wireless networks. However, the benefits of full-duplex are still not well understood. In this paper, we characterize the full-duplex rate gains in both singlechannel and multi-channel use cases. For the single-channel case, we quantify the rate gain as a function of the remaining self-interference and SNR values. We also provide a sufficient condition under which the sum of uplink and downlink rates on a full-duplex channel is concave in the transmission power levels. Building on these results, we consider the multi-channel case. For that case, we introduce a new realistic model of a compact (e.g., smartphone) full-duplex receiver and demonstrate its accuracy via measurements. We study the problem of jointly allocating power levels to different channels and selecting the frequency of maximum self-interference suppression, where the objective is maximizing the sum of the rates over uplink and downlink OFDM channels. We develop a polynomial time algorithm which is nearly optimal in practice under very mild restrictions. To reduce the running time, we develop an efficient nearly-optimal algorithm under the high SINR approximation. Finally, we demonstrate via numerical evaluations the capacity gains in the different use cases and obtain insights into the impact of the remaining selfinterference and wireless channel states on the performance.
Dynamic Spectrum Access systems exploit temporarily available spectrum ('white spaces') and can spread transmissions over a number of non-contiguous sub-channels. Such methods are highly beneficial in terms of spectrum utilization. However, excessive fragmentation degrades performance and hence off-sets the benefits. Thus, there is a need to study these processes so as to determine how to ensure acceptable levels of fragmentation. Hence, we present experimental and analytical results derived from a mathematical model. We model a system operating at capacity serving requests for bandwidth by assigning a collection of gaps (sub-channels) with no limitations on the fragment size. Our main theoretical result shows that even if fragments can be arbitrarily small, the system does not degrade with time. Namely, the average total number of fragments remains bounded. Within the very difficult class of dynamic fragmentation models (including models of storage fragmentation), this result appears to be the first of its kind. Extensive experimental results describe behavior, at times unexpected, of fragmentation under different algorithms. Our model also applies to dynamic linked-list storage allocation, and provides a novel analysis in that domain. We prove that, interestingly, the 50% rule of the classical (non-fragmented) allocation model carries over to our model. Overall, the paper provides insights into the potential behavior of practical fragmentation algorithms.
Numerous energy harvesting wireless devices that will serve as building blocks for the Internet of Things (IoT) are currently under development. However, there is still only limited understanding of the properties of various energy sources and their impact on energy harvesting adaptive algorithms. Hence, we focus on characterizing the kinetic (motion) energy that can be harvested by a wireless node with an IoT form factor and on developing energy allocation algorithms for such nodes. In this paper, we describe methods for estimating harvested energy from acceleration traces. To characterize the energy availability associated with specific human activities (e.g., relaxing, walking, cycling), we analyze a motion dataset with over 40 participants. Based on acceleration measurements that we collected for over 200 hours, we study energy generation processes associated with day-long human routines. We also briefly summarize our experiments with moving objects. We develop energy allocation algorithms that take into account practical IoT node design considerations, and evaluate the algorithms using the collected measurements. Our observations provide insights into the design of motion energy harvesters, IoT nodes, and energy harvesting adaptive algorithms.
We present a control plane architecture to accelerate multicast and incast traffic delivery for data-intensive applications in cluster-computing interconnection networks. The architecture is experimentally examined by enabling physical layer optical multicasting on-demand for the application layer to achieve non-blocking performance.
This paper focuses on cascading line failures in the transmission system of the power grid. Such a cascade may have a devastating effect not only on the power grid but also on the interconnected communication networks. Recent large-scale power outages demonstrated the limitations of epidemic- and percolation-based tools in modeling the cascade evolution. Hence, based on a linearized power flow model (that substantially differs from the classical packet flow models), we obtain results regarding the various properties of a cascade. Specifically, we consider performance metrics such as the the distance between failures, the length of the cascade, and the fraction of demand (load) satisfied after the cascade. We show, for example, that due to the unique properties of the model: (i) the distance between subsequent failures can be arbitrarily large and the cascade may be arbitrarily long, (ii) a large set of initial line failures may have a smaller effect than a failure of one of the lines in the set, and (iii) minor changes to the network parameters may have a significant impact. Moreover, we show that finding the set of lines whose removal has the most significant impact (under various metrics) is NP-Hard. Moreover, we develop a fast algorithm to recompute the flows at each step of the cascade. The results can provide insight into the design of smart grid measurement and control algorithms that can mitigate a cascade.
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