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We present a distributed stable protocol for local broadcast in multi-hop wireless networks, where packets are injected to the nodes continuously, and each node needs to quickly disseminate the injected packets to all its neighbors within a given communication range $R$. We investigate the maximum packet injection rate and the minimum packet latency that can be achieved in a stable protocol. This paper assumes the signal-to-interference-plus-noise-ratio (SINR) interference model, which reflects more accurately the physical characteristics of the wireless interference, such as fading and signal accumulation, than conventional local interference models, e.g., graph-based models. More specifically, we present a stable protocol that can handle both stochastic and adversarial injection patterns. The protocol is asymptotically optimal in terms of both injection rate and packet latency. To the best of our knowledge, this paper is the first one studying the properties of stable protocols for the basic primitive of local broadcast in a multi-hop setting under SINR. Our proposed protocol utilizes a static local broadcast algorithm as a subroutine. This static algorithm is of independent interest, and it closes the $O(\log n)$ gap between the upper and lower bounds for static local broadcast. Simulation results indicate that our proposed algorithms can perform well in realistic environments. [ABSTRACT FROM AUTHOR]

Recently, multiple concurrent transmission techniques have been proposed for smartly exploiting interference to improve network throughput. This paper considers zero-forcing (ZF) precoding that allows two interfering links to concurrently transmit under certain situations. This paper focuses on identifying appropriate schedule schemes that efficiently utilize the ZF precoding opportunities to improve network throughput. With this motivation, we define novel link models to formulate our concerned problem. We also develop a distributed scheduling algorithm by using belief propagation. Finally, we conduct extensive simulation experiments to evaluate the efficiency of ZF in improving network throughput in multihop wireless networks. Our simulations confirm that intelligently utilizing ZF in multihop networks is very effective in improving throughput. [ABSTRACT FROM PUBLISHER]

Interference neutralization (IN) is regarded as a promising interference management techniques for multi-hop wireless networks. Yet most existing results of IN are limited to two-hop networks such as the relay-aided cellular network. Little progress has been made so far in the exploration of IN in generic multi-hop (more than two hops) networks. This paper aims to bridge this gap by developing an optimization framework for IN in a generic multi-hop network with the objective of maximizing the end-to-end throughput of multiple coexisting communication sessions. We first derive a mathematical model for IN in a special one-hop network to characterize the capability of IN, and then generalize this model to a multi-hop network. Based on the IN model, we develop a cross-layer optimization framework for a multi-hop network with the objective of fully translating the benefits of IN to the end-to-end throughput of the multi-hop sessions. To evaluate the performance of IN in multi-hop networks, we compare its performance against the case where IN is not employed. Simulation results show that the use of IN can significantly (more than 50%) increase the session throughput and, more notably, the throughput gain of IN increases with the node density and traffic intensity in the network. [ABSTRACT FROM PUBLISHER]

Interference alignment (IA) is widely regarded as a promising interference management technique for wireless networks and its potential is most profound in interference-intensive environments. This motivates us to study IA for multicast communications in multihop MIMO networks, which are rich in interference by nature. We develop a set of linear constraints that can characterize a feasible design space of IA for multicast communications. The set of linear constraints constitutes a simple mathematical model of IA that allows us to conduct cross-layer multicast throughput optimization in multihop MIMO networks, but without getting involved into the onerous signal design at the physical layer. Based on the mathematical model of IA, we formulate a multicast throughput maximization problem and develop an approximation solution that can achieve $(1-\varepsilon)$ -optimality. Simulation results show that the use of IA can significantly increase the multicast throughput in multihop MIMO networks and the throughput gain increases with the volume of multicast traffic and the number of antennas. [ABSTRACT FROM AUTHOR]