A scalable architecture for distributed receive beamforming: analysis and experimental demonstration

We propose, analyze and demonstrate an architecture for scalable cooperative reception. In a cluster of N + 1 receive nodes, one node is designated as the final receiver, and the N other nodes act as amplify-and-forward relays which adapt their phases such that the relayed signals add up constructively at the designated receiver. This yields received SNR scaling linearly with N, while avoiding the linear increase in overhead incurred by a direct approach in which received signals are separately quantized and transmitted for centralized processing. By transforming the task of long-distance distributed receive beamforming into one of local distributed transmit beamforming, we can leverage a scalable one-bit feedback algorithm for phase synchronization. We show that time division between the long-distance and local links eliminates the need for explicit frequency synchronization. We provide an analytical framework, whose results closely match Monte Carlo simulations, to evaluate the impact of phase noise due to relaying delay on the performance of the one-bit feedback algorithm. Experimental results from our prototype implementation on software-defined radios demonstrate the expected gains in received signal strength despite significant oscillator drift, and are consistent with results from our analytical framework.Comment: submitted to IEEE Transactions on Wireless Communication

Some aspects of physical prototyping in Pervasive Computing

This document summarises the results of several research campaigns over the past seven years. The main connecting theme is the physical layer of widely deployed sensors in Pervasive Computing domains. In particular, we have focused on the RF-channel or on ambient audio. The initial problem from which we started this work was that of distributed adaptive transmit beamforming. We have been looking for a simple method to align the phases of jointly transmitting nodes (e.g. sensor or IoT nodes). The algorithmic solution to this problem was to implement a distributed random optimisation method on the participating nodes in which the transmitters and the receiver follow an iterative question-and-answer scheme. We have been able to derive sharp asymptotic bounds on the expected optimisation time of an evolutionary random optimiser and presented an asymptotically optimal approach. One thing that we have learned from the work on these physical layer algorithms was that the signals we work on are fragile and perceptive to physical environmental changes. These could be obstacles such as furniture, opened or closed windows or doors as well as movement of individuals. This observation motivated us to view the wireless interface as a sensor for environmental changes in Pervasive Computing environments. Another use of physical layer RF-signals is for security applications. We are currently working to further push these mentioned directions and novel fields of physical prototyping. In particular, the calculation of mathematical operations on the wireless channel at the time of transmission appears to contain good potential for gains in efficiency for communication and computation in Pervasive Computing domains