2 research outputs found
Interference Regime Enforcing Rate Maximization for Non-Orthogonal Multiple Access (NOMA)
An interference regime enforcing rate maximization scheme is proposed to
maximize the achievable ergodic sum-rate of the parallel Gaussian interference
channels by enforcing very strong interference at receivers through power
allocation whenever strong interference is observed. Applying successive
interference cancellation (SIC) at the receivers, very strong interference can
be completely eliminated in this case that enables non-orthogonal multiple
access (NOMA). An optimization problem is formulated for ergodic rate
maximization, where the conditions for creating very strong interference are
included as additional constraints. Then, a generalized iterative waterfilling
algorithm is derived to solve this optimization problem. The resulting power
allocation scheme is compared in terms of the sum-rate with two other schemes
when the interference is treated as noise with optimal or sub-optimal power
allocation. The results show that the interference regime enforcing scheme
provides major improvement to the sum-rate of parallel Gaussian interference
channels and enables NOMA
Deep Learning for Wireless Communications
Existing communication systems exhibit inherent limitations in translating
theory to practice when handling the complexity of optimization for emerging
wireless applications with high degrees of freedom. Deep learning has a strong
potential to overcome this challenge via data-driven solutions and improve the
performance of wireless systems in utilizing limited spectrum resources. In
this chapter, we first describe how deep learning is used to design an
end-to-end communication system using autoencoders. This flexible design
effectively captures channel impairments and optimizes transmitter and receiver
operations jointly in single-antenna, multiple-antenna, and multiuser
communications. Next, we present the benefits of deep learning in spectrum
situation awareness ranging from channel modeling and estimation to signal
detection and classification tasks. Deep learning improves the performance when
the model-based methods fail. Finally, we discuss how deep learning applies to
wireless communication security. In this context, adversarial machine learning
provides novel means to launch and defend against wireless attacks. These
applications demonstrate the power of deep learning in providing novel means to
design, optimize, adapt, and secure wireless communications