23,290 research outputs found
Spatial Noise-Field Control With Online Secondary Path Modeling: A Wave-Domain Approach
Due to strong interchannel interference in multichannel active noise control (ANC), there are fundamental problems associated with the filter adaptation and online secondary path modeling remains a major challenge. This paper proposes a wave-domain adaptation algorithm for multichannel ANC with online secondary path modelling to cancel tonal noise over an extended region of two-dimensional plane in a reverberant room. The design is based on exploiting the diagonal-dominance property of the secondary path in the wave domain. The proposed wave-domain secondary path model is applicable to both concentric and nonconcentric circular loudspeakers and microphone array placement, and is also robust against array positioning errors. Normalized least mean squares-type algorithms are adopted for adaptive feedback control. Computational complexity is analyzed and compared with the conventional time-domain and frequency-domain multichannel ANCs. Through simulation-based verification in comparison with existing methods, the proposed algorithm demonstrates more efficient adaptation with low-level auxiliary noise.DP14010341
A New Variable Regularized QR Decomposition-Based Recursive Least M-Estimate Algorithm-Performance Analysis and Acoustic Applications
published_or_final_versio
Cognitive and Energy Harvesting-Based D2D Communication in Cellular Networks: Stochastic Geometry Modeling and Analysis
While cognitive radio enables spectrum-efficient wireless communication,
radio frequency (RF) energy harvesting from ambient interference is an enabler
for energy-efficient wireless communication. In this paper, we model and
analyze cognitive and energy harvesting-based D2D communication in cellular
networks. The cognitive D2D transmitters harvest energy from ambient
interference and use one of the channels allocated to cellular users (in uplink
or downlink), which is referred to as the D2D channel, to communicate with the
corresponding receivers. We investigate two spectrum access policies for
cellular communication in the uplink or downlink, namely, random spectrum
access (RSA) policy and prioritized spectrum access (PSA) policy. In RSA, any
of the available channels including the channel used by the D2D transmitters
can be selected randomly for cellular communication, while in PSA the D2D
channel is used only when all of the other channels are occupied. A D2D
transmitter can communicate successfully with its receiver only when it
harvests enough energy to perform channel inversion toward the receiver, the
D2D channel is free, and the at the receiver is above the
required threshold; otherwise, an outage occurs for the D2D communication. We
use tools from stochastic geometry to evaluate the performance of the proposed
communication system model with general path-loss exponent in terms of outage
probability for D2D and cellular users. We show that energy harvesting can be a
reliable alternative to power cognitive D2D transmitters while achieving
acceptable performance. Under the same outage requirements as
for the non-cognitive case, cognitive channel access improves the outage
probability for D2D users for both the spectrum access policies.Comment: IEEE Transactions on Communications, to appea
A Comprehensive Survey of Potential Game Approaches to Wireless Networks
Potential games form a class of non-cooperative games where unilateral
improvement dynamics are guaranteed to converge in many practical cases. The
potential game approach has been applied to a wide range of wireless network
problems, particularly to a variety of channel assignment problems. In this
paper, the properties of potential games are introduced, and games in wireless
networks that have been proven to be potential games are comprehensively
discussed.Comment: 44 pages, 6 figures, to appear in IEICE Transactions on
Communications, vol. E98-B, no. 9, Sept. 201
A Tutorial on Bayesian Optimization of Expensive Cost Functions, with Application to Active User Modeling and Hierarchical Reinforcement Learning
We present a tutorial on Bayesian optimization, a method of finding the
maximum of expensive cost functions. Bayesian optimization employs the Bayesian
technique of setting a prior over the objective function and combining it with
evidence to get a posterior function. This permits a utility-based selection of
the next observation to make on the objective function, which must take into
account both exploration (sampling from areas of high uncertainty) and
exploitation (sampling areas likely to offer improvement over the current best
observation). We also present two detailed extensions of Bayesian optimization,
with experiments---active user modelling with preferences, and hierarchical
reinforcement learning---and a discussion of the pros and cons of Bayesian
optimization based on our experiences
A Novel Multiobjective Cell Switch-Off Framework for Cellular Networks
Cell Switch-Off (CSO) is recognized as a promising approach to reduce the
energy consumption in next-generation cellular networks. However, CSO poses
serious challenges not only from the resource allocation perspective but also
from the implementation point of view. Indeed, CSO represents a difficult
optimization problem due to its NP-complete nature. Moreover, there are a
number of important practical limitations in the implementation of CSO schemes,
such as the need for minimizing the real-time complexity and the number of
on-off/off-on transitions and CSO-induced handovers. This article introduces a
novel approach to CSO based on multiobjective optimization that makes use of
the statistical description of the service demand (known by operators). In
addition, downlink and uplink coverage criteria are included and a comparative
analysis between different models to characterize intercell interference is
also presented to shed light on their impact on CSO. The framework
distinguishes itself from other proposals in two ways: 1) The number of
on-off/off-on transitions as well as handovers are minimized, and 2) the
computationally-heavy part of the algorithm is executed offline, which makes
its implementation feasible. The results show that the proposed scheme achieves
substantial energy savings in small cell deployments where service demand is
not uniformly distributed, without compromising the Quality-of-Service (QoS) or
requiring heavy real-time processing
Proximal Multitask Learning over Networks with Sparsity-inducing Coregularization
In this work, we consider multitask learning problems where clusters of nodes
are interested in estimating their own parameter vector. Cooperation among
clusters is beneficial when the optimal models of adjacent clusters have a good
number of similar entries. We propose a fully distributed algorithm for solving
this problem. The approach relies on minimizing a global mean-square error
criterion regularized by non-differentiable terms to promote cooperation among
neighboring clusters. A general diffusion forward-backward splitting strategy
is introduced. Then, it is specialized to the case of sparsity promoting
regularizers. A closed-form expression for the proximal operator of a weighted
sum of -norms is derived to achieve higher efficiency. We also provide
conditions on the step-sizes that ensure convergence of the algorithm in the
mean and mean-square error sense. Simulations are conducted to illustrate the
effectiveness of the strategy
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