Outage Performance of Two-Hop OFDM Systems with Spatially Random Decode-and-Forward Relays

In this paper, we analyze the outage performance of different multicarrier relay selection schemes for two-hop orthogonal frequency-division multiplexing (OFDM) systems in a Poisson field of relays. In particular, special emphasis is placed on decode-and-forward (DF) relay systems, equipped with bulk and per-subcarrier selection schemes, respectively. The exact expressions for outage probability are derived in integrals for general cases. In addition, asymptotic expressions for outage probability in the high signal-to-noise ratio (SNR) region in the finite circle relay distribution region are determined in closed forms for both relay selection schemes. Also, the outage probabilities for free space in the infinite relay distribution region are derived in closed forms. Meanwhile, a series of important properties related to cooperative systems in random networks are investigated, including diversity, outage probability ratio of two selection schemes and optimization of the number of subcarriers in terms of system throughput. All analysis is numerically verified by simulations. Finally, a framework for analyzing the outage performance of OFDM systems with spatially random relays is constructed, which can be easily modified to analyze other similar cases with different forwarding protocols, location distributions and/or channel conditions

Adaptive OFDM Index Modulation for Two-Hop Relay-Assisted Networks

In this paper, we propose an adaptive orthogonal frequency-division multiplexing (OFDM) index modulation (IM) scheme for two-hop relay networks. In contrast to the traditional OFDM IM scheme with a deterministic and fixed mapping scheme, in this proposed adaptive OFDM IM scheme, the mapping schemes between a bit stream and indices of active subcarriers for the first and second hops are adaptively selected by a certain criterion. As a result, the active subcarriers for the same bit stream in the first and second hops can be varied in order to combat slow frequency-selective fading. In this way, the system reliability can be enhanced. Additionally, considering the fact that a relay device is normally a simple node, which may not always be able to perform mapping scheme selection due to limited processing capability, we also propose an alternative adaptive methodology in which the mapping scheme selection is only performed at the source and the relay will simply utilize the selected mapping scheme without changing it. The analyses of average outage probability, network capacity and symbol error rate (SER) are given in closed form for decode-and-forward (DF) relaying networks and are substantiated by numerical results generated by Monte Carlo simulations.Comment: 30 page

Outage Performance Analysis of Multicarrier Relay Selection for Cooperative Networks

In this paper, we analyze the outage performance of two multicarrier relay selection schemes, i.e. bulk and per-subcarrier selections, for two-hop orthogonal frequency-division multiplexing (OFDM) systems. To provide a comprehensive analysis, three forwarding protocols: decode-and-forward (DF), fixed-gain (FG) amplify-and-forward (AF) and variable-gain (VG) AF relay systems are considered. We obtain closed-form approximations for the outage probability and closed-form expressions for the asymptotic outage probability in the high signal-to-noise ratio (SNR) region for all cases. Our analysis is verified by Monte Carlo simulations, and provides an analytical framework for multicarrier systems with relay selection

Price Matching Policies and Consumer Loyalty

I examine the relationship between brick-and-mortar retailers offering price-matching guarantees and the atmosphere of their stores. In an extension of the Hotelling model, if a retailer improves the atmosphere of its store, then it raises its price. However, with a price-match guarantee and a competitor with a lower-quality store and a lower price, some of the retailer’s consumers may demand a price match. I identify the circumstances under which a price-match guarantee prevents a retailer from earning increased revenues that may result from improving its in-store atmosphere. In my model, the results depend on the share of consumers who seek price matches and the share of consumers who are loyal to the retailer

Performance analysis of RIS-assisted full-duplex communication over correlated Nakagami-m fading channel

In this paper, we investigate the performance of a reconfigurable intelligent surface (RIS) assisted full-duplex (FD) communication network, where each user is facilitated by a specific RIS in the network. The correlated Nakagami- m fading channel is first considered in a RIS system, which is a general channel model that can capture the spatial correlation effect inherent in the RIS-assisted communication system. Using the two-dimensional Laplace transform and its inverse, the closed form expressions of the mean and variance of the signal power distribution are obtained. Then, the outage probability and average achievable rates of the uplink and downlink users are derived in closed form. Furthermore, the impact of the residual self-interference (SI) on FD communication performance is discussed. It is demonstrated that FD communication outperforms HD communication when the residual SI is below a threshold, and the threshold is derived in closed form. Simulation results are presented to confirm the accuracy of the theoretical analysis and show the negative impact of channel correlation on the system performance. Moreover, it is illustrated that the outage probability and the average achievable rate of the uplink user will converge to a constant when the residual SI is linearly dependent on the transmit power

SAFARI: Versatile and Efficient Evaluations for Robustness of Interpretability

Interpretability of Deep Learning (DL) is a barrier to trustworthy AI. Despite great efforts made by the Explainable AI (XAI) community, explanations lack robustness -- indistinguishable input perturbations may lead to different XAI results. Thus, it is vital to assess how robust DL interpretability is, given an XAI method. In this paper, we identify several challenges that the state-of-the-art is unable to cope with collectively: i) existing metrics are not comprehensive; ii) XAI techniques are highly heterogeneous; iii) misinterpretations are normally rare events. To tackle these challenges, we introduce two black-box evaluation methods, concerning the worst-case interpretation discrepancy and a probabilistic notion of how robust in general, respectively. Genetic Algorithm (GA) with bespoke fitness function is used to solve constrained optimisation for efficient worst-case evaluation. Subset Simulation (SS), dedicated to estimate rare event probabilities, is used for evaluating overall robustness. Experiments show that the accuracy, sensitivity, and efficiency of our methods outperform the state-of-the-arts. Finally, we demonstrate two applications of our methods: ranking robust XAI methods and selecting training schemes to improve both classification and interpretation robustness.Comment: Accepted by the IEEE/CVF International Conference on Computer Vision 2023 (ICCV'23

Privacy Protection and Utility Trade-Off for Social Graph Embedding

In graph embedding protection, deleting the embedding vector of a node does not completelydisrupt its structural relationships. The embedding model must be retrained over the networkwithout sensitive nodes, which incurs a waste of computation and offers no protection forordinary users. Meanwhile, the edge perturbations do not guarantee good utility. This workproposed a new privacy protection and utility trade-off method without retraining. Firstly, sinceembedding distance reflects the closeness of nodes, we label and group user nodes into sensitive,near-sensitive, and ordinary regions to perform different strengths of privacy protection. Thenear-sensitive region can reduce the leaking risk of neighboring nodes connecting to sensitivenodes without sacrificing all of their utility. Secondly, we use mutual information to measureprivacy and utility while adapting a single model-based mutual information neural estimatorto vector pairs to reduce modeling and computational complexity. Thirdly, by keeping addingdifferent noise to the divided regions and reestimating the mutual information between theoriginal and noise-perturbed embeddings, our framework achieves a good trade-off betweenprivacy and utility. Simulation results show that the proposed framework is superior to state-of-the-art baselines like LPPGE and DPNE

Study of relay selection in a multi-cell cognitive network

This paper studies best relay selection in a multi-cell cognitive network with amplify-and-forward (AF) relays. We derive the analytical integral-form expression of the cumulative distribution function (CDF) for the received signal-to-noise-plus-interference-ratio (SINR) at the destination node, based on which the closed-form of the outage probability is obtained. Analysis shows that the proposed relay selection scheme achieves the best SINR at the destination node with interference to the primary user being limited by a pre-defined level. Simulation results are also presented to verify the analysis. The proposed relay selection approach is an attractive way to obtain diversity gain in a multi-cell cognitive network