TTCM-aided rate-adaptive distributed source coding for Rayleigh fading channels

Adaptive turbo-trellis-coded modulation (TTCM)-aided asymmetric distributed source coding (DSC) is proposed, where two correlated sources are transmitted to a destination node. The first source sequence is TTCM encoded and is further compressed before it is transmitted through a Rayleigh fading channel, whereas the second source signal is assumed to be perfectly decoded and, hence, to be flawlessly shown at the destination for exploitation as side information for improving the decoding performance of the first source. The proposed scheme is capable of reliable communications within 0.80 dB of the Slepian-Wolf/Shannon (SW/S) theoretical limit at a bit error rate (BER) of 10-5. Furthermore, its encoder is capable of accommodating time-variant short-term correlation between the two sources

Distributed source-channel coding using reduced-complexity syndrome-based TTCM

In the context of distributed joint source-channel coding, we conceive reduced-complexity turbo trellis coded modulation (TTCM)-aided syndrome-based block decoding for estimating the cross-over probability pe of the binary symmetric channel, which models the correlation between a pair of sources. Our joint decoder achieves an accurate correlation estimation for varying correlation coefficients at 3 dB lower SNR, than conventional TTCM decoder, despite its considerable complexity reduction

Detection of Lying Electrical Vehicles in Charging Coordination Application Using Deep Learning

The simultaneous charging of many electric vehicles (EVs) stresses the distribution system and may cause grid instability in severe cases. The best way to avoid this problem is by charging coordination. The idea is that the EVs should report data (such as state-of-charge (SoC) of the battery) to run a mechanism to prioritize the charging requests and select the EVs that should charge during this time slot and defer other requests to future time slots. However, EVs may lie and send false data to receive high charging priority illegally. In this paper, we first study this attack to evaluate the gains of the lying EVs and how their behavior impacts the honest EVs and the performance of charging coordination mechanism. Our evaluations indicate that lying EVs have a greater chance to get charged comparing to honest EVs and they degrade the performance of the charging coordination mechanism. Then, an anomaly based detector that is using deep neural networks (DNN) is devised to identify the lying EVs. To do that, we first create an honest dataset for charging coordination application using real driving traces and information revealed by EV manufacturers, and then we also propose a number of attacks to create malicious data. We trained and evaluated two models, which are the multi-layer perceptron (MLP) and the gated recurrent unit (GRU) using this dataset and the GRU detector gives better results. Our evaluations indicate that our detector can detect lying EVs with high accuracy and low false positive rate

Quantum Long Short-Term Memory (QLSTM) vs Classical LSTM in Time Series Forecasting: A Comparative Study in Solar Power Forecasting

Accurately forecasting solar power generation is crucial in the global progression towards sustainable energy systems. In this study, we conduct a meticulous comparison between Quantum Long Short-Term Memory (QLSTM) and classical Long Short-Term Memory (LSTM) models for solar power production forecasting. Our controlled experiments reveal promising advantages of QLSTMs, including accelerated training convergence and substantially reduced test loss within the initial epoch compared to classical LSTMs. These empirical findings demonstrate QLSTM's potential to swiftly assimilate complex time series relationships, enabled by quantum phenomena like superposition. However, realizing QLSTM's full capabilities necessitates further research into model validation across diverse conditions, systematic hyperparameter optimization, hardware noise resilience, and applications to correlated renewable forecasting problems. With continued progress, quantum machine learning can offer a paradigm shift in renewable energy time series prediction. This pioneering work provides initial evidence substantiating quantum advantages over classical LSTM, while acknowledging present limitations. Through rigorous benchmarking grounded in real-world data, our study elucidates a promising trajectory for quantum learning in renewable forecasting. Additional research and development can further actualize this potential to achieve unprecedented accuracy and reliability in predicting solar power generation worldwide.Comment: 17 pages, 8 figure

Statistical Beamforming for Multi-Set Space–Time Shift-Keying-Based Full-Duplex Millimeter Wave Communications

Full-duplex (FD) communication has been shown to provide an increased achievable rate, while millimeter wave (mmWave) communications benefit from a large available bandwidth that further improves the achievable rate. On the other hand, the concept of multi-set space-time shift keying (MS-STSK) has been proposed to provide a flexible design trade-off between throughput and performance. Hence, in this work, we consider the design of an FD-aided MS-STSK transceiver for millimeter wave communications. However, a major challenge is that channel reciprocity is not valid in mmWave communications due to shorter channel coherence time. Thus, the uplink (UL) pilots cannot be utilized to estimate the downlink (DL) channel. To overcome this challenge, we propose a beamforming technique based on channel statistics without assuming channel reciprocity. For this purpose, a closed-form expression for the outage probability of the system is derived by employing the characterization of the ratio of the Indefinite Quadratic Form (IQF). The derived analytical expression is then utilized to design optimum beamforming weights using the Sequential Quadratic Programming (SQP)-based heuristic method. Moreover, an Iterative Statistical Method (ISM) of joint transmit and receive beamforming algorithm is also developed by utilizing Principle Eigenvector (PE) and Generalized Rayleigh Quotient (G-RQ) optimization techniques. Finally, we verify our simulation results with the theoretical analysis

Joint source and turbo trellis coded hierarchical modulation for context-aware medical image transmission

An iterative Joint Source and Turbo Trellis Coded Hierarchical Modulation is introduced for robust context-aware medical image transmission. Lossless source compression as well as Quality of Service (QoS) might be considered as the main constraints in the telemedicine field. Our proposed scheme advocated was design to exploit both the joint source-and-channel iterative decoding and the cooperative structure in order for tackling these requirements. The Source Node (SN) is constituted by a lossless Variable Length Code (VLC) and Turbo Trellis-Coded Modulation (TTCM) which relies on Hierarchical Modulation (HM). The Relay Node (RN) is used to support the transmission of the most important content of the image. Our proposed scheme exhibits a robustness performance over a realistic uncorrelated Rayleigh fading channel, while it outperforms the non-cooperative scheme by 3 dB at asymptotic (error-free) Peak Signal to Noise Ratio (PSNR) value

Statistical Beamforming Techniques for Power Domain NOMA System

Power-domain non-orthogonal multiple access (NOMA) assigns different power levels for near and far users in order to discriminate their signals by employing successive interference cancellation (SIC) at the near user. In this context, multiple-input-single-output NOMA (MISO-NOMA), where the base station (BS) is equipped with multiple antennas while each mobile user has a single antenna receiver, is shown to have a better overall performance by using the knowledge of instantaneous channel state information (CSI). However, this requires prior estimation of CSI using pilot transmission, which increases the transmission overhead. Moreover, its performance is severely degraded in the presence of CSI estimation errors. In this work, we provide statistical beamforming solutions for downlink power-domain NOMA that utilize only knowledge of statistical CSI, thus reducing the transmission overhead significantly. First, we derive the outage probabilities for both near and far users in the multi-user NOMA system without imposing strong assumptions, such as Gaussian or Chi-square distribution. This is done by employing the exact characterization of the ratio of indefinite quadratic form (IQF). Second, this work proposes two techniques to obtain the optimal solution for beam vectors which rely on the derived outage probabilities. Specifically, these two methods are based on (1) minimization of total beam power while constraining the outage probabilities to the QoS threshold, and (2) minimization of outage probabilities while constraining the total beam power. These proposed methods are non-convex function of beam vectors and, hence, are solved using numerical optimization via sequential quadratic programming (SQP). Since the proposed methods do not require pilot transmission for channel estimation, they inherit better spectral efficiency. Our results validate the theoretical findings and prove the supremacy of the proposed method

Distributed joint source-channel coding and modulation for wireless communications

Distributed Source Coding (DSC) schemes rely on separate encoding but joint decoding of statistically dependent sources, which exhibit correlation. DSC has numerous promising applications ranging from reduced-complexity handheld video communications to onboard hyperspectral image coding under computational limitations. The concept of separate encoding at the first sight compromises the attainable encoding performance. However, DSC theory proves that independent encoding can in fact be designed as efficiently as joint encoding, as long as joint decoding is allowed. More specifically, Distributed Joint Source-Channel coding (DJSC) is associated with the scenario, where the correlated source signals are transmitted through a noisy channel. A series of Turbo Trellis-Coded Modulation (TTCM) aided DJSC-based cooperative transmission schemes are proposed.An iterative Joint Source-coding, Channel-coding and Modulation (JSCM) scheme relying on the intrinsic amalgamation of Variable Length Code (VLC) and TTCMwas proposed for two-wayaided transmission. The system advocated was designed for improving the attainable throughput, reliability and coverage area compared to that of conventional one-way relaying. Briefly, a pair of users exchange their information with the aid of a twin-antenna aided Relay Node (RN). We quantify the Discrete-input Continuous-output Memoryless Channel (DCMC) capacity of the corresponding two-way relay channel. The semi-analytical EXtrinsic Information Transfer Characteristics (EXIT) charts are employed for investigating the decoding convergence of the joint source and channel decoder as well as for assisting the overall system design. Furthermore, our iterative scheme employs a novel low-complexity source coding technique that significantly reduces the number of states in the bit-based trellis before invoking it for robust image and video transmission.Then, an adaptive DJSC scheme is conceived for the transmission of a pair of correlated sources to a Destination Node (DN). The first source sequence is TTCMencoded and then it is compressed before it is transmitted both over a Rayleigh fading and Nakagami-m fading channels, where the second source signal is assumed to be perfectly decoded side-information at the DN for the sake of improving the achievable decoding performance of the first source. The proposed scheme is capable of performing reliable communications for various levels of correlation near to the theoretical Slepian-Wolf/Shannon (SW/S) limit. Additionally, its encoder is capable of accommodating arbitrary time-variant short-term correlation between the two sources.Pursuing our objective of designing practical DJSC schemes, we further extended the abovementioned arrangement to a more realistic cooperative communication system, where the pair of correlated sources are transmitted to a DN with the aid of a RN. Explicitly, the two correlated source sequences are TTCMencoded and compressed before transmission over a Rayleigh fading Multiple Access Channel (MAC). The RN transmits both users’ signal with the aid of a powerful Superposition Modulation (SPM) technique that judiciously allocates the transmit power between the two signals. The correlation is beneficially exploited at both the RN and the DN using our powerful iterative joint decoder, which is optimised using EXIT charts. We further conceive a so-called Block Syndrome Decoding (BSD) approach for our DJSC scheme, which reduces the decoding complexity, whilst additionally providing an accurate correlation estimate.As a further new cooperative technique, our DJSC scheme invokes RN-aided Network Coding (NC) which is capable of improving the overall throughput without increasing the energy dissipation. To investigate our DJSC in the context of diverse environments, our NC-based schemes are also appraised in the context of slow fading effects that might be imposed by obstacles blocking the line-of-sight transmission links. Our proposed scheme is shown to achieve substantial performance gains over its conventional non-cooperative counterpart