626 research outputs found
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
An enhanced OFDM light weight physical layer encryption scheme
The broadcast nature of wireless networks makes them susceptible to attacks by eavesdroppers than wired networks. Any untrusted node can eavesdrop on the medium, listen to transmissions and obtain sensitive information within the wireless network. In this paper, we propose a new mechanism which combines the advantages of two techniques namely iJam and OFDM phase encryption. Our modified mechanism makes iJam more bandwidth efficient by using Alamouti scheme to take advantage of the repetition inherent in its implementation. The adversary model is extended to the active adversary case, which has not been done in the original work of iJam and OFDM phase encryption. We propose, through a max min optimization model, a framework that maximizes the secrecy rate by means of a friendly jammer. We formulate a Zero-Sum game that captures the strategic decision making between the transmitter receiver pair and the adversary. We apply the fictitious play (FP) algorithm to reach the Nash equilibria (NE) of the game. Our simulation results show a significant improvement in terms of the ability of the eavesdropper to benefit from the received information over the traditional schemes, i.e. iJam or OFDM phase encryption
Trust Dynamics in WSNs: An Evolutionary Game-Theoretic Approach
A sensor node (SN) in Wireless Sensor Networks (WSNs) can decide whether to collaborate with others based on a trust management system (TMS) by making a trust decision. In this paper, we study the trust decision and its dynamics that play a key role to stabilize the whole network using evolutionary game theory. When SNs are making their decisions to select action Trust or Mistrust, a WSNs trust game is created to reflect their utilities. An incentive mechanism bound with one SN’s trust degree is incorporated into this trust game and effectively promotes SNs to select action Trust. The replicator dynamics of SNs’ trust evolution, illustrating the evolutionary process of SNs selecting their actions, are given. We then propose and prove the theorems indicating that evolutionarily stable strategies can be attained under different parameter values, which supply theoretical foundations to devise a TMS for WSNs. Moreover, we can find out the conditions that will lead SNs to choose action Trust as their final behavior. In this manner, we can assure WSNs’ security and stability by introducing a trust mechanism to satisfy these conditions. Experimental results have confirmed the proposed theorems and the effects of the incentive mechanism
Optimization and Applications of Modern Wireless Networks and Symmetry
Due to the future demands of wireless communications, this book focuses on channel coding, multi-access, network protocol, and the related techniques for IoT/5G. Channel coding is widely used to enhance reliability and spectral efficiency. In particular, low-density parity check (LDPC) codes and polar codes are optimized for next wireless standard. Moreover, advanced network protocol is developed to improve wireless throughput. This invokes a great deal of attention on modern communications
Machine Learning for Metasurfaces Design and Their Applications
Metasurfaces (MTSs) are increasingly emerging as enabling technologies to
meet the demands for multi-functional, small form-factor, efficient,
reconfigurable, tunable, and low-cost radio-frequency (RF) components because
of their ability to manipulate waves in a sub-wavelength thickness through
modified boundary conditions. They enable the design of reconfigurable
intelligent surfaces (RISs) for adaptable wireless channels and smart radio
environments, wherein the inherently stochastic nature of the wireless
environment is transformed into a programmable propagation channel. In
particular, space-limited RF applications, such as communications and radar,
that have strict radiation requirements are currently being investigated for
potential RIS deployment. The RIS comprises sub-wavelength units or meta-atoms,
which are independently controlled and whose geometry and material determine
the spectral response of the RIS. Conventionally, designing RIS to yield the
desired EM response requires trial and error by iteratively investigating a
large possibility of various geometries and materials through thousands of
full-wave EM simulations. In this context, machine/deep learning (ML/DL)
techniques are proving critical in reducing the computational cost and time of
RIS inverse design. Instead of explicitly solving Maxwell's equations, DL
models learn physics-based relationships through supervised training data. The
ML/DL techniques also aid in RIS deployment for numerous wireless applications,
which requires dealing with multiple channel links between the base station
(BS) and the users. As a result, the BS and RIS beamformers require a joint
design, wherein the RIS elements must be rapidly reconfigured. This chapter
provides a synopsis of DL techniques for both inverse RIS design and
RIS-assisted wireless systems.Comment: Book chapter, 70 pages, 12 figures, 2 tables. arXiv admin note:
substantial text overlap with arXiv:2101.09131, arXiv:2009.0254
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