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There have been some rapid advances on the design of full duplex (FD) transceivers in recent years. Although the benefits of FD have been studied for single-hop wireless communications, its potential on throughput performance in a multi-hop wireless network remains unclear. As for multi-hop networks, a fundamental problem is to compute the achievable end-to-end throughput for one or multiple communication sessions. The goal of this paper is to offer some fundamental understanding on end-to-end throughput performance limits of FD in a multi-hop wireless network. We show that through a rigorous mathematical formulation, we can cast the multi-hop throughput performance problem into a formal optimization problem. Through numerical results, we show that in many cases, the end-to-end session throughput in a FD network can exceed $2 \times$ of that in a half duplex (HD) network. Our finding can be explained by the much larger design space for scheduling that is offered by removing HD constraints in throughput maximization problem. The results in this paper offer some new understandings on the potential benefits of FD for end-to-end session throughput in a multi-hop wireless network. [ABSTRACT FROM AUTHOR]

The optimum designs of multihop networks operating in an interference-limited environment are studied in this paper. The objective is to identify the optimum operation parameters that can maximize the end-to-end throughput of a multihop link in the network. The optimum designs are performed with respect to two parameters, the number of hops, and the signal-to-interference ratio (SIR) threshold for successful transmission. Under a fixed total transmission power, more hops means shorter distance for each hop, but also less power per hop and longer delays. A higher SIR threshold can achieve a higher throughput by employing high-order modulations, but at the cost of a higher outage probability. We capture these tradeoff relationships by deriving the throughput, which is expressed as an explicit function of a number of system parameters. It is shown through theoretical analysis that the throughput is quasi-concave in the number of hops or the SIR threshold, and the optimum design parameters that can maximize the throughput are obtained analytically and verified through simulations. [ABSTRACT FROM AUTHOR]

Being readily available in most of 802.11 radios, multirate capability appears to be useful as WiFi networks are getting more prevalent and crowded. More specifically, it would be helpful in high-density scenarios because internode distance is short enough to employ high data rates. However, communication at high data rates mandates a large number of hops for a given node pair in a multihop network and thus, can easily be depreciated as per-hop overhead at several layers of network protocol is aggregated over the increased number of hops. This paper presents a novel multihop, multirate adaptation mechanism, called multihop transmission opportunity (MTOP), that allows a frame to be forwarded a number of hops consecutively to minimize the MAC-layer overhead between hops. This seemingly collision-prone nonstop forwarding is proved to be safe via analysis and USRP/GNU Radio-based experiment in this paper. The idea of MTOP is in clear contrast to the conventional opportunistic transmission mechanism, known as TXOP, where a node transmits multiple frames back-to-back when it gets an opportunity in a single-hop WLAN. We conducted an extensive simulation study via OPNET, demonstrating the performance advantage of MTOP under a wide range of network scenarios. [ABSTRACT FROM PUBLISHER]

Previous works on multicast capacity mainly focus on deriving asymptotic order results in large-scale wireless networks. While they can explore the general scaling laws of throughput capacity, it is also of great interest for practical concern to predict the exact achievable throughput in networks with an arbitrary finite number of nodes. In this paper, we investigate the nonasymptotic throughput and delay of multihop wireless networks for multicast applications wherein for each source node, $k$ nodes are randomly selected as receivers. It is challenging for the exact performance analysis since multicast transmission has a dynamic nature due to the following factors: 1) random distribution of nodes; 2) bursty traffic arrivals; and 3) different timescales for transient analysis. To tackle the problem, we propose an explicit analytical model and develop a multicast routing scheme, which accounts for these aspects. With our proposed model, we derive lower bound (LB) and upper bound (UB) on nonasymptotic multicast throughput and delay using stochastic network calculus. We show that the performance results hold for all timescales and network sizes and are strongly correlated to data burstiness and the number of receivers. While we investigate from a nonasymptotic point of view, our results can also cover the asymptotic scaling laws. Simulations are conducted to further verify the accuracy of the analytical bounds. [ABSTRACT FROM AUTHOR]

The capacity scaling laws of two overlaid networks are investigated, which are located in the same area sharing the same wireless resources with different priorities. The primary network can be regarded as an existing communication system operated in a licensed band and, therefore, is assumed to operate in an order-optimal fashion to achieve its standalone capacity scaling law. The secondary cognitive network must keep its interference to the primary network below a certain threshold while at the same time maximizing its own throughput scaling law based on cognition information. The existing scaling results for cognitive networks inherently assume multihop communication, which is a restricted coding model. By contrast, in this paper, a general coding model is considered without any specific physical layer coding assumptions. The capacity scaling exponents for both networks are analyzed when the numbers of primary nodes n , primary base stations l , which support the communication between primary nodes, and secondary nodes m increase with the relations , \beta >1 , and l=n^\gamma , 0\leq \gamma \leq 1 . For the extended network model, the capacity scaling exponents are completely characterized as \max \2-\alpha /2,1/2,\gamma \ and \max \2-\alpha /2,1/2\ for the primary and secondary networks respectively, where \alpha >2 denotes the path-loss exponent. That is, the capacity scaling laws for the primary and secondary networks are represented, respectively, by n^{\max \{2-\alpha /2,1/2,\gamma \}\pm \epsilon } and m^{\max \{2-\alpha /2,1/2\}\pm \epsilon }$ for \epsilon >0$ arbitrarily small. For the dense network model, when the primary network achieves its standalone capacity scaling exponent of 1, the secondary network is shown to achieve a scaling exponent of 1-{1}/({2\beta }) , which improves the previous scaling exponent of 1/2 achieved by multihop. For both models, it turns out that the conventional multihop approach is in general quite suboptimal. [ABSTRACT FROM AUTHOR]