Robust Distributed Collaborative Beamforming for Wireless Sensor Networks with Channel Estimation Impairments

Abstract

We propose a new collaborative beamforming (CB) solution robust (i.e., RCB) against major channel estimation impairments over dual-hop transmissions through a wireless sensor network (WSN) of K nodes. The source first sends its signal to the WSN. Then, each node forwards its received signal after multiplying it by a properly selected beamforming weight. The latter aims to minimize the received noise power while maintaining the desired power equal to unity. These weights depend on some channel state information (CSI) parameters. Hence, they have to be estimated locally at each node, thereby, resulting in channel estimation errors that could severely hinder CB performance. Exploiting an efficient asymptotic approximation at large K, we develop alternative RCB solutions that adapt to different implementation scenarios and wireless propagation environments ranging from monochromatic (i.e., scattering-free) to polychromatic (i.e., scattered) ones. Besides, in contrast to existing techniques, our new RCB solutions are distributed (i.e., DCB) in that they do not require any information exchange among nodes, thereby dramatically improving both WSN spectral and power efficiencies. Simulation results confirm that the proposed robust DCB (RDCB) techniques are much more robust in terms of achieved signal-to-noise ratio (SNR) against channel estimation errors than best representative CB benchmarks.

Efficient Distributed Multi-Task Schemes for mmWave MIMO Channel Estimation

“…A thorough search of relevant literature shows that the trend towards distributed and cooperative schemes also emerges in the physical layer with some indicative, yet notable, examples being beamforming [37], [38] and spectrum sensing [39]. The present study for the first time focuses on the problem of fully-distributed channel estimation for mmWave MIMO systems and proposes novel and efficient schemes that enable devices to cooperate for estimating the involved channels.…”

“…When G t = N t , µ (D t ) = 0, and μ = 1. However, for G t ≫ N t , µ (D t ) attains values close to 1, making μ and thus the upper bound of the channel error in (38) on the order of L AoD .…”

“…In (38), ∥ ĥv k,r − hv k,r ∥ 2 2 represents the sparse channel vector estimation error of the SMV problem in (12). The proposed algorithm (WDiSOMP) aims to estimate the MIMO channel by exploiting the commonalities in the support sets of the sparse channel matrices of nearby devices.…”

“…where the last inequality is derived by using the definition of B r and (1−(L AoD −1)µ(Φ)) 2 by applying the Gershgorin circle theorem to λ min Φ H S k Φ S k . Combining (38) with (39), the upper bound is as…”

JSSA: Joint Sidelobe Suppression Approach for Collaborative Beamforming in Wireless Sensor Networks

“…Therefore, if the location of BS is changed, the performed CB array only needs to change the direction of mainlobe to the direction of this BS. Moreover, the communication radius R c is normalized to the wavelength and we assume R c = 1 which refers to several previous works [25], [37]. Thus, the actual CB radius can be extended to other values by using different wavelengths in real-life applications.…”