In this paper, we propose a novel non-orthogonal multiple access (NOMA) scheme with beamwidth control for hybrid millimeter wave communication systems and study the resource allocation design to maximize the system energy efficiency. In particular, NOMA transmission allows more than one user to share a single radio frequency chain, which is beneficial to enhance the system energy efficiency. More importantly, the proposed beamwidth control can increase the number of served NOMA groups by widening the beamwidth that can further exploit the energy efficiency gain brought by NOMA.

Nonorthogonal multiple access (NOMA) is promising for increasing connectivity and capacity. But there has been little consideration on the quality of service of NOMA; let alone that in generic fading channels. This paper establishes closed-form upper bounds for the delay violation probability of downlink Nakagami- mand Rician NOMA channels, by exploiting stochastic network calculus (SNC).

The significant advances of cellular systems and mobile Internet services have yielded a variety of computation intensive applications, resulting in great challenge to mobile terminals (MTs) with limited computation resources. Mobile edge computing, which enables MTs to offload their computation tasks to edge servers located at cellular base stations (BSs), has provided a promising approach to address this challenging issue.

Non-orthogonal multiple access (NOMA) is one of the promising radio access techniques for next generation wireless networks. Opportunistic multi-user scheduling is necessary to fully exploit multiplexing gains in NOMA systems, but compared with traditional scheduling, inter-relations between users’ throughputs induced by multi-user interference poses new challenges in the design of NOMA schedulers. 

Given the recent surge in developments of deep learning, this paper provides a review of the state-of-the-art deep learning techniques for audio signal processing. Speech, music, and environmental sound processing are considered side-by-side, in order to point out similarities and differences between the domains, highlighting general methods, problems, key references, and potential for cross fertilization between areas.

We address voice activity detection in acoustic environments of transients and stationary noises, which often occur in real-life scenarios. We exploit unique spatial patterns of speech and non-speech audio frames by independently learning their underlying geometric structure. This process is done through a deep encoder-decoder-based neural network architecture.

Given the recent surge in developments of deep learning, this paper provides a review of the state-of-the-art deep learning techniques for audio signal processing. Speech, music, and environmental sound processing are considered side-by-side, in order to point out similarities and differences between the domains, highlighting general methods, problems, key references, and potential for cross fertilization between areas.