In this paper, we develop an approach to the design of beamformers with small-spacing uniform linear microphone arrays by incorporating spareness constraints for attenuating scattered interference incident from some pre-specified ranges of directions of arrival. The design process is formulated as a constrained LASSO problem. By adjusting the value of a tuning parameter, the proposed method can make compromises among three important yet conflicting (especially at low frequencies) performance measures of small-spacing microphone arrays, i.e., the directivity factor (DF), which quantifies the array spatial gain, the white noise gain (WNG), which evaluates the robustness of the beamformer, and the signal-to-interference-ratio (SIR) gain with respect to scattered interference. Simulation results illustrate the properties of the developed approach.

The development of mobile networks has led to the emergence of challenges such as high delays in storage, computing and traffic management. To deal with these challenges, fifth-generation networks emphasize the use of technologies such as mobile cloud computing and mobile edge computing. Mobile Edge Cloud Computing (MECC) is an emerging distributed computing model that provides access to cloud computing services at the edge of the network and near mobile users. With offloading tasks at the edge of the network instead of transferring them to a remote cloud, MECC can realize flexibility and real-time processing. During computation offloading, the requirements of Internet of Things (IoT) applications may change at different stages, which is ignored in existing works. With this motivation, we propose a task offloading method under dynamic resource requirements during the use of IoT applications, which focuses on the problem of workload fluctuations. The proposed method uses a learning automata-based offload decision-maker to offload requests to the edge layer. An auto-scaling strategy is then developed using a long short-term memory network which can estimate the expected number of future requests. Finally, an Asynchronous Advantage Actor-Critic algorithm as a deep reinforcement learning-based approach decides to scale down or scale up. The effectiveness of the proposed method has been confirmed through extensive experiments using the iFogSim simulator. The numerical results show that the proposed method has better scalability and performance in terms of delay and energy consumption than the existing state-of-the-art methods.

The differential microphone array, or differential beamformer, has attracted much attention for its frequency-invariant beampattern, high directivity factor and compact size. In this work, the design of differential beamformers with small inter-element spacing planar microphone arrays is concerned. In order to exactly control the main lobe beamwidth and sidelobe level and obtain minimum main lobe beamwidth with a given sidelobe level, we design the desired beampattern by applying the Chebyshev polynomials at first, via exploiting the structure of the frequency-independent beampattern of a theoretical Nth-order differential beamformer. Next, the so-called null constrained and least square beamformers, which can obtain approximately frequency-invariant beampattern at relatively low frequencies and can be steered to any direction without beampattern distortion, are proposed based on planar microphone arrays to approximate the designed desired beampatterns. Then, for dealing with the white noise amplification at low-frequency bands and beampattern divergence problems at high-frequency bands of the null constrained and least square beamformers, the so-called minimum norm and combined solutions are deduced, which can compromise among the white noise gain, directivity factor and beampattern distortion flexibly. Preliminary simulation results illustrate the properties and advantages of the proposed differential beamformers.

In this paper, the design of multichannel noise reduction filters in the short-time-Fourier-transform (STFT) domain is addressed. By investigating the structure of the linear filter, a set of multichannel iterative noise reduction filters are developed based on the Kronecker product decomposition. Instead of computing a long noise reduction filter, we compute three much shorter sub-filters which are separately applied in the spatial, temporal and frequency dimensions. Consequently, compared with the traditional multichannel STFT-domain noise reduction filters, the proposed approaches have two advantages: 1) significantly lower computational complexity; 2) less past observations are needed to construct the iterative filters, which leads to better tracking ability for the temporal/spatial signal nonstationarity. Experimental results demonstrate the advantages of the developed iterative filters over the traditional ones.