Cooperative time-reversal communication in wireless sensor networks

Abstract

In this paper, we study the feasibility of using a technique called time reversal for cooperative communication on wireless sensor networks. An indoor environment containing multiple wireless sensors is used as an example in which to test and demonstrate this approach. Using numerical simulations, we study the behavior of the peak power received at a target sensor as a function of the number of cooperating transmitting sensors as well as the level of transmitted signal distortion and timing synchronization errors. The simulation results demonstrate that time reversal is an effective generalization of beamforming that provides an efficient basis for cooperative communication on broadband multipath channels.

Order-Optimal Data Aggregation in Wireless Sensor Networks Using Cooperative Time-Reversal Communication

“…In two recent publications [2,3], we have studied some aspects of the application of TRC to cooperative communication in wireless networks. In this paper, we investigate the utility of cooperative TRC for improving the achievable data rate in wireless sensor networks.…”

“…In cases when node u lies on the diagonals of the square area, we tilt the line D uO slightly to avoid intersections with the Voronoi cells at the grid points 3. Neighboring nodes are those that are separated by unit Manhattan distance.…”

Performance of Cooperative Time-Reversal Communication in a Mobile Wireless Environment

“…Then, the instantaneous power output from the receiver node at an arbitrary time t=t 0 relative to time t=0 at the transmitter node, subject to an energy constraint, is maximized by transmitting an appropriate multiple of the signal . Furthermore, the received signal r(t) corresponding to the transmission of the signal s(t)= is given by the autocorrelation function R h (t) of the channel impulse response; that is, The proof of this lemma, which is quite straightforward, is given in [18] along with a discussion of the implications of the lemma; however, it should be clear that the lemma and the principle of superposition together imply that if perfect impulses are used in the training procedure, and if the resulting pulse estimation and synchronized time-reversed retransmission are performed perfectly, all of the transmitted waveforms from group A will converge at the receiver node to produce an impulsive waveform (equal to the sum of the autocorrelation functions of the various channel impulse responses) that maximizes the peak power output from the channel at the desired time. Hence, under perfect conditions, we expect to observe a waveform at the receiver node with maximum possible peak output power that increases with the number of transmitting nodes.…”

“…In practice, of course, neither the training pulse shape, the pulse estimation, nor the transmission synchronization will be perfect, and system performance will suffer as a result. In [18], the performance of cooperative TRC was studied as a function of the number of transmitting nodes under theoretically perfect conditions and in the presence of both pulse distortion and timing errors. In this paper, we summarize the earlier results and compare them with the performance degradation associated with movement of the receiver node from the position corresponding to transmission of the training information.…”

“…Various signal processing techniques based on time-reversal (TR) processing have been proposed and studied previously for many applications, including acoustical imaging [11], electromagnetic imaging [12], underwater acoustic communication [13], and wireless communication channels [14][15][16]. In two recent publications [17,18], the application of TRC to cooperative communication in wireless networks has been introduced and studied by several of the authors.…”