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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]

To improve the efficiency of wireless data communications, new physical-layer transmission methods based on known-interference cancelation (KIC) have been developed. These methods share the common idea that interference can be canceled when its content is known. Existing work on KIC mainly focuses on single-hop or two-hop networks, with physical-layer network coding and full-duplex communications as typical examples. This paper extends the idea of KIC to general multihop networks and proposes an end-to-end KIC (E2E-KIC) transmission method, together with its medium access control (MAC) design. With E2E-KIC, multiple nodes in a flow passing through a few nodes in an arbitrary topology can simultaneously transmit and receive on the same channel. We first present a theoretical analysis on the effectiveness of E2E-KIC in an idealized case. Then, to support E2E-KIC in multihop networks with an arbitrary topology, we propose an E2E-KIC-supported MAC protocol (E2E-KIC MAC), which is based on an extension of the Request-to-Send/Clear-to-Send mechanism in the IEEE 802.11 MAC. We also analytically analyze the performance of the proposed E2E-KIC MAC in the presence of hidden terminals. Simulation results illustrate that the proposed E2E-KIC MAC can improve network throughput and reduce end-to-end delay. [ABSTRACT FROM PUBLISHER]

In this paper, we propose two cross-layer algorithms, namely, the Power-optimal Scheduling Algorithm (PSA) and the Throughput-optimal Scheduling Algorithm (TSA), to minimize energy consumption and to maximize throughput, respectively, in multihop wireless networks. Our algorithms guarantee a flow-based minimum data rate and jointly integrate congestion control, power allocation, routing, and link rate scheduling. Different from traditional algorithms that assume infinite buffers, the proposed algorithms deterministically upper bound the flow-based packet queue length and thus can be employed in multihop networks with finite buffers. In addition, the algorithms achieve a power expenditure/throughput “\epsilon-close” to the optimal value, with a tradeoff of order O(\1/\epsilon) in buffer size. The average end-to-end delay upper bound can also be derived from the finite buffer property. Finally, numerical results are presented to show the performance of the two algorithms with different system parameters. [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]