Lexicographic Codebook Design for OFDM with Index Modulation

In this paper, we propose a novel codebook design scheme for orthogonal frequency-division multiplexing with index modulation (OFDM-IM) to improve system performance. The optimization process can be implemented efficiently by the lexicographic ordering principle. By applying the proposed codebook design, all subcarrier activation patterns with a fixed number of active subcarriers will be explored. Furthermore, as the number of active subcarriers is fixed, the computational complexity for estimation at the receiver is reduced and the zero-active subcarrier dilemma is solved without involving complex higher layer transmission protocols. It is found that the codebook design can potentially provide a trade-off between diversity and transmission rate. We investigate the diversity mechanism and formulate three diversity-rate optimization problems for the proposed OFDMIM system. Based on the genetic algorithm (GA), the method of solving these formulated optimization problems is provided and verified to be effective. Then, we analyze the average block error rate (BLER) and bit error rate (BER) of OFDM-IM systems applying the codebook design. Finally, all analyses are numerically verified by Monte Carlo simulations. In addition, a series of comparisons are provided, by which the superiority of the codebook design is thereby confirmed

Delay Constrained Buffer-Aided Relay Selection in the Internet of Things with Decision-Assisted Reinforcement Learning

This paper investigates the reinforcement learning for the relay selection in the delay-constrained buffer-aided networks. The buffer-aided relay selection significantly improves the outage performance but often at the price of higher latency. On the other hand, modern communication systems such as the Internet of Things often have strict requirement on the latency. It is thus necessary to find relay selection policies to achieve good throughput performance in the buffer-aided relay network while stratifying the delay constraint. With the buffers employed at the relays and delay constraints imposed on the data transmission, obtaining the best relay selection becomes a complicated high-dimensional problem, making it hard for the reinforcement learning to converge. In this paper, we propose the novel decision-assisted deep reinforcement learning to improve the convergence. This is achieved by exploring the a-priori information from the buffer-aided relay system. The proposed approaches can achieve high throughput subject to delay constraints. Extensive simulation results are provided to verify the proposed algorithms

Updating of FE models for forced response analysis of mistuned bladed discs in wide frequency ranges

A model updating approach is proposed in this paper to describe blade disc mistuning by adding point mass elements in each blade model. This approach uses concepts from test planning and model updating methods to select the locations and the values of mistuning elements to match experimental natural frequencies of several blade-alone modes. The case study presented in the paper demonstrates that the updated bladed disc model, assembled from the updated models of the individual blades, provides the capability to predict forced vibration response over a frequency range covering several families of modes of the bladed dis

Domain of attraction of hysteresis-series based chaotic attractors

This paper discusses the domain of attraction and its sensitivity for a class of chaotic attractors generated by using second-order linear systems with hysteresis-series. It is found that the domain of attraction of the chaotic attractors is determined by an unstable limit cycle. The chaotic dynamical behaviors are demonstrated by using the Poincaré map. The sensitivity of the domain of attraction with respect to the system parameters is studied and some simulation results are presented

Time-delayed chaos control with repetitive learning

Abstract - In this paper, a time-delayed chaos control method based on repetitive learning is proposed. The integration of the repetitive learning control principle and the time delayed chaos control technique enables adaptive learning of appropriate control actions from learning cycles. In contrast to conventional repetitive learning control, no exact knowledge (analytic representation) of the target periodic orbits is needed, except for the time delay constant, which can be identified via either experiments or adaptive learning methods. To facilitate the discussion, the typical chaotic Duffing system is used as an example for illustration of the general methodology. Simulation result is provided to show the effectiveness of the proposed approach

Hybrid Multicast/Unicast Design in NOMA-based Vehicular Caching System

In this paper, we investigate a hybrid multicast/unicast scheme for a multiple-input single-output cache-aided non-orthogonal multiple access (NOMA) vehicular scenario in the face of rapidly fluctuating vehicular wireless channels. Considering a more practical situation, imperfect channel state information is taking into account. In this paper, we formulate an optimization problem to maximize the unicast sum rate under the constraints of the peak power, the peak backhaul, the minimum unicast rate, and the maximum multicast outage probability. To solve the formulated non-convex problem, a lower bound relaxation method is proposed, which enables a division of the original problem into two convex sub-problems. Computer simulations show that the proposed caching-aided NOMA is superior to the orthogonal multiple access counterpart

Enhanced Secrecy Performance of Multihop IoT Networks with Cooperative Hybrid-Duplex Jamming

© 2005-2012 IEEE. As the number of connected devices is exponentially increasing, security in Internet of Things (IoT) networks presents a major challenge. Accordingly, in this work we investigate the secrecy performance of multihop IoT networks assuming that each node is equipped with only two antennas, and can operate in both Half-Duplex (HD) and Full-Duplex (FD) modes. Moreover, we propose an FD Cooperative Jamming (CJ) scheme to provide higher security against randomly located eavesdroppers, where each information symbol is protected with two jamming signals by its two neighbouring nodes, one of which is the FD receiver. We demonstrate that under a total power constraint, the proposed FD-CJ scheme significantly outperforms the conventional FD Single Jamming (FD-SJ) approach, where only the receiving node acts as a jammer, especially when the number of hops is larger than two. Moreover, when the Channel State Information (CSI) is available at the transmitter, and transmit beamforming is applied, our results demonstrate that at low Signal-to-Noise Ratio (SNR), higher secrecy performance is obtained if the receiving node operates in HD and allocates both antennas for data reception, leaving only a single jammer active; while at high SNR, a significant secrecy enhancement can be achieved with FD jamming. Our proposed FD-CJ scheme is found to demonstrate a great resilience over multihop networks, as only a marginal performance loss is experienced as the number of hops increases. For each case, an integral closed-form expression is derived for the secrecy outage probability, and verified by Monte Carlo simulations

Performance analysis of multi-antenna selection policies using the golden code in multiple-input multiple-output systems

In multiple-input multiple-output (MIMO) systems, multiple-antenna selection has been proposed as a practical scheme for improving the signal transmission quality as well as reducing realisation cost because of minimising the number of radio-frequency chains. In this study, the authors investigate transmit antenna selection for MIMO systems with the Golden Code. Two antenna selection schemes are considered: max-min and max-sum approaches. The outage and pairwise error probability performance of the proposed approaches are analysed. Simulations are also given to verify the analysis. The results show the proposed methods provide useful schemes for antenna selection.</div

Ergodic Secrecy Rate of RIS-Assisted Communication Systems in the Presence of Discrete Phase Shifts and Multiple Eavesdroppers

This letter investigates the ergodic secrecy rate (ESR) of a reconfigurable intelligent surface (RIS)-assisted communication system in the presence multiple eavesdroppers (Eves), and by assuming discrete phase shifts at the RIS. In particular, a closed-form approximation of the ESR is derived for both non-colluding and colluding Eves. The analytical results are shown to be accurate when the number of reflecting elements of the RIS N is large. Asymptotic analysis is provided to investigate the impact of N on the ESR, and it is proved that the ESR scales with log2N for both non-colluding and colluding Eves. Numerical results are provided to verify the analytical results and the obtained scaling laws

A hybrid relay and intelligent reflecting surface network and its ergodic performance analysis

This letter proposes a novel hybrid relay and Intelligent Reflecting Surface (IRS) assisted system for future wireless networks. We demonstrate that for practical scenarios where the amount of radiated power and/or the number of reflecting elements are/is limited, the performance of an IRS-supported system can be significantly enhanced by utilizing a simple Decode-and-Forward (DF) relay. Tight upper bounds for the ergodic capacity are derived for the proposed scheme under different channel environments, and shown to closely match Monte-Carlo simulations