Collision Codes: Decoding Superimposed BPSK Modulated Wireless Transmissions

The introduction of physical layer network coding gives rise to the concept of turning a collision of transmissions on a wireless channel useful. In the idea of physical layer network coding, two synchronized simultaneous packet transmissions are carefully encoded such that the superimposed transmission can be decoded to produce a packet which is identical to the bitwise binary sum of the two transmitted packets. This paper explores the decoding of superimposed transmission resulted by multiple synchronized simultaneous transmissions. We devise a coding scheme that achieves the identification of individual transmission from the synchronized superimposed transmission. A mathematical proof for the existence of such a coding scheme is given

Cooperative Retransmissions Through Collisions

Interference in wireless networks is one of the key capacity-limiting factors. Recently developed interference-embracing techniques show promising performance on turning collisions into useful transmissions. However, the interference-embracing techniques are hard to apply in practical applications due to their strict requirements. In this paper, we consider utilising the interference-embracing techniques in a common scenario of two interfering sender-receiver pairs. By employing opportunistic listening and analog network coding (ANC), we show that compared to traditional ARQ retransmission, a higher retransmission throughput can be achieved by allowing two interfering senders to cooperatively retransmit selected lost packets at the same time. This simultaneous retransmission is facilitated by a simple handshaking procedure without introducing additional overhead. Simulation results demonstrate the superior performance of the proposed cooperative retransmission.Comment: IEEE ICC 2011, Kyoto, Japan. 5 pages, 5 figures, 2 tables. Analog Network Coding, Retransmission, Access Point, WLAN, interference, collision, capacity, packet los

Maximum Multipath Routing Throughput in Multirate Wireless Mesh Networks

In this paper, we consider the problem of finding the maximum routing throughput between any pair of nodes in an arbitrary multirate wireless mesh network (WMN) using multiple paths. Multipath routing is an efficient technique to maximize routing throughput in WMN, however maximizing multipath routing throughput is a NP-complete problem due to the shared medium for electromagnetic wave transmission in wireless channel, inducing collision-free scheduling as part of the optimization problem. In this work, we first provide problem formulation that incorporates collision-free schedule, and then based on this formulation we design an algorithm with search pruning that jointly optimizes paths and transmission schedule. Though suboptimal, compared to the known optimal single path flow, we demonstrate that an efficient multipath routing scheme can increase the routing throughput by up to 100% for simple WMNs.Comment: This paper has been accepted for publication in IEEE 80th Vehicular Technology Conference, VTC-Fall 201

An Efficient Network Coding based Retransmission Algorithm for Wireless Multicasts

Retransmission based on packet acknowledgement (ACK/NAK) is a fundamental error control technique employed in IEEE 802.11-2007 unicast network. However the 802.11-2007 standard falls short of proposing a reliable MAC-level recovery protocol for multicast frames. In this paper we propose a latency and bandwidth efficient coding algorithm based on the principles of network coding for retransmitting lost packets in a singlehop wireless multicast network and demonstrate its effectiveness over previously proposed network coding based retransmission algorithms.Comment: 5 pages, 5 figure

Large-scale Heteroscedastic Regression via Gaussian Process

Heteroscedastic regression considering the varying noises among observations has many applications in the fields like machine learning and statistics. Here we focus on the heteroscedastic Gaussian process (HGP) regression which integrates the latent function and the noise function together in a unified non-parametric Bayesian framework. Though showing remarkable performance, HGP suffers from the cubic time complexity, which strictly limits its application to big data. To improve the scalability, we first develop a variational sparse inference algorithm, named VSHGP, to handle large-scale datasets. Furthermore, two variants are developed to improve the scalability and capability of VSHGP. The first is stochastic VSHGP (SVSHGP) which derives a factorized evidence lower bound, thus enhancing efficient stochastic variational inference. The second is distributed VSHGP (DVSHGP) which (i) follows the Bayesian committee machine formalism to distribute computations over multiple local VSHGP experts with many inducing points; and (ii) adopts hybrid parameters for experts to guard against over-fitting and capture local variety. The superiority of DVSHGP and SVSHGP as compared to existing scalable heteroscedastic/homoscedastic GPs is then extensively verified on various datasets.Comment: 14 pages, 15 figure