In this paper, we propose a distributed-robust sound source localization (D-R-SSL) method in wireless acoustic sensor networks (WASNs). Specifically, the space is first divided into small equal-sized grids, and the local probability of each grid containing the source is calculated by using intra-node time difference of arrivals (TDoAs). Next, we allocate weights for local probabilities from different nodes, and develop a distributed weight calculation strategy which is robust against the TDoA outliers. Then, the source position is derived from the weighted summation of local probabilities. In order to improve the SSL efficiency, we further present a fast implementation approach (named as F-D-R-SSL). Finally, we also analyse the computational complexity and communication loads of the proposed methods. Simulation and real-world experiments demonstrate that the F-D-R-SSL is as powerful or even stronger than state-of-the-art non-grid SSLs.

Cell-free massive Multiple Input Multiple Output (MIMO) systems, which can be applied to deploy a large number of geographically dispersed access points within an area, have been playing an essential role in the operation of next-generation mobile communication networks due to their better performance in the flexibility of network deployment. The key to improving network service is to choose the appropriate APs and effectively control their transmission power for reduced co-channel interference and improved overall performance. To address co-channel interference and reduce computational complexity, a pilot allocation-based access point selection algorithm is proposed in this study that is used to iteratively allocate pilots, with access points selected to significantly reduce pilot contamination and serve user. However, considering the complexity of max-min power control, the particle swarm optimization (PSO) algorithm is applied in this paper as the solution. To prevent the algorithm from falling into local optima, the PSO algorithm is further improved by adjusting the inertia weight flexibly, thereby making the algorithm more efficient in searching for the global optimum. Simulation results are obtained to validate the proposed algorithm, which significantly enhances the overall spectral efficiency of the users while ensuring a consistent quality of services delivered to the users.

We are often surrounded by intelligent devices with one or more acoustic sensors, which constitute a wireless acoustic sensor network (WASN) and can be exploited for various audio/speech processing tasks. As the captured audio signals are inevitably corrupted by ambient noises, signal enhancement is vital in WASNs. To this end, this paper proposes a distributed-robust and energy-efficient MVDR beamformer (BF) for WASNs. Specifically, a distributed MVDR BF is first derived by recursively updating the inverse of the noise correlation matrix, which requires fewer data transmission without sacrificing performance. Then, its robust version is further designed to alleviate the adverse effects of parameter mismatches. Finally, an energy-efficient network topology control (EENTC) is carried out to reduce the energy consumption, by optimizing the weight matrix of the distributed averaging process. Since the proposed EENTC strategy involves non-convex programming, we transform it into a convex one and solve it via the Dinkelbach algorithm. Unlike the centralized BFs, the proposed method works without an additional central processor. Moreover, it is robust against parameter mismatch during beamforming and can reduce a large amount of data transmission. Simulation and real-world experimental results confirm the validity of the proposed method.

In distributed acoustic sensor networks, only a few nodes make a significant contribution to speech enhancement tasks. Using these most informative nodes instead of the entire network not only avoids unnecessary energy consumption but also prolongs the lifetime of sensors. To this end, a sensor selection method for distributed speech enhancement is proposed. The best subset of microphone nodes is determined by maximizing the signal-to-noise ratio (SNR), while keeping the activated nodes connected with each other. The above criterion involves an integer and non-linear programming, which is linearized with multiple base-3 sub-optimization problems, and each of them is solved by a state-of-the-art steepest descent (SD) algorithm. In addition, a greedy searching strategy is presented to select sensors rapidly. Finally, a distributed SD algorithm is further derived, which is more suitable for distributed sensor networks. The proposed method can obtain the optimal subnetwork in noisy and reverberant environments. Unlike the existing approaches, it can select nodes from a microphone network with arbitrary communication graphs. Moreover, it requires only local communications among nodes without an external central processor. Experimental results confirm the validity of the proposed method.