AoI-Delay Tradeoff in Mobile Edge Caching: A Mixed-Order Drift-Plus-Penalty Algorithm

We consider a scheduling problem in a Mobile Edge Caching (MEC) network, where a base station (BS) uploads messages from multiple source nodes (SNs) and transmits them to mobile users (MUs) via downlinks, aiming to jointly optimize the average service Age of Information (AoI) and service delay over MUs. This problem is formulated as a difficult sequential decision making problem with discrete-valued and linearly-constrained design variables. To solve this problem, we first approximate its achievable region by characterizing its superset and subset. The superset is derived based on the rate stability theorem, while the subset is obtained using a novel stochastic policy. We also validate that this subset is substantially identical to the achievable region when the number of schedule resources is large. Additionally, we propose a sufficient condition to check the existence of the solution to the problem. Then, we propose the mixed-order drift-plus-penalty algorithm that uses a dynamic programming (DP) method to optimize the summation over a linear and quadratic Lyapunov drift and a penalty term, to handle the product term over different queue backlogs in the objective function. Finally, by associating the proposed algorithm with the stochastic policy, we demonstrate that it achieves an $O(1/V)$ versus $O(V)$ tradeoff for the average AoI and average delay

Deciphering Charging Status, Absolute Quantum Efficiency, and Absorption Cross Section of MultiCarrier States in Single Colloidal Quantum Dot

Upon photo- or electrical-excitation, colloidal quantum dots (QDs) are often found in multi-carrier states due to multi-photon absorption and photo-charging of the QDs. While many of these multi-carrier states are observed in single-dot spectroscopy, their properties are not well studied due to random charging/discharging, emission intensity intermittency, and uncontrolled surface defects of single QD. Here we report in-situ deciphering the charging status, and precisely assessing the absorption cross section, and determining the absolute emission quantum yield of mono-exciton and biexciton states for neutral, positively-charged, and negatively-charged single core/shell CdSe/CdS QD. We uncover very different photon statistics of the three charge states in single QD and unambiguously identify their charge sign together with the information of their photoluminescence decay dynamics. We then show their distinct photoluminescence saturation behaviors and evaluated the absolute values of absorption cross sections and quantum efficiencies of monoexcitons and biexcitons. We demonstrate that addition of an extra hole or electron in a QD changes not only its emission properties but also varies its absorption cross section

Joint Task and Data Oriented Semantic Communications: A Deep Separate Source-channel Coding Scheme

Semantic communications are expected to accomplish various semantic tasks with relatively less spectrum resource by exploiting the semantic feature of source data. To simultaneously serve both the data transmission and semantic tasks, joint data compression and semantic analysis has become pivotal issue in semantic communications. This paper proposes a deep separate source-channel coding (DSSCC) framework for the joint task and data oriented semantic communications (JTD-SC) and utilizes the variational autoencoder approach to solve the rate-distortion problem with semantic distortion. First, by analyzing the Bayesian model of the DSSCC framework, we derive a novel rate-distortion optimization problem via the Bayesian inference approach for general data distributions and semantic tasks. Next, for a typical application of joint image transmission and classification, we combine the variational autoencoder approach with a forward adaption scheme to effectively extract image features and adaptively learn the density information of the obtained features. Finally, an iterative training algorithm is proposed to tackle the overfitting issue of deep learning models. Simulation results reveal that the proposed scheme achieves better coding gain as well as data recovery and classification performance in most scenarios, compared to the classical compression schemes and the emerging deep joint source-channel schemes

Performance Analysis of MDMA-Based Cooperative MRC Networks with Relays in Dissimilar Rayleigh Fading Channels

Multiple access technology is a key technology in various generations of wireless communication systems. As a potential multiple access technology for the next generation wireless communication systems, model division multiple access (MDMA) technology improves spectrum efficiency and feasibility regions. This implies that the MDMA scheme can achieve greater performance gains compared to traditional schemes. Relayassisted cooperative networks, as a infrastructure of wireless communication, can effectively utilize resources and improve performance when MDMA is applied. In this paper, a communication relay cooperative network based on MDMA in dissimilar rayleigh fading channels is proposed, which consists of two source nodes, any number of decode-and-forward (DF) relay nodes, and one destination node, as well as using the maximal ratio combining (MRC) at the destination to combine the signals received from the source and relays. By applying the state transition matrix (STM) and moment generating function (MGF), closed-form analytical solutions for outage probability and resource utilization efficiency are derived. Theoretical and simulation results are conducted to verify the validity of the theoretical analysis.Comment: 6 pages, 4 figures, conferenc

NeurJSCC Enabled Semantic Communications: Paradigms, Applications, and Potentials

Recent advances in deep learning have led to increased interest in solving high-efficiency end-to-end transmission problems using methods that employ the nonlinear property of neural networks. These techniques, we call neural joint source-channel coding (NeurJSCC), extract latent semantic features of the source signal across space and time, and design corresponding variable-length NeurJSCC approaches to transmit latent features over wireless communication channels. Rapid progress has led to numerous research papers, but a consolidation of the discovered knowledge has not yet emerged. In this article, we gather diverse ideas to categorize the expansive aspects on NeurJSCC as two paradigms, i.e., explicit and implicit NeurJSCC. We first focus on those two paradigms of NeurJSCC by identifying their common and different components in building end-to-end communication systems. We then focus on typical applications of NeurJSCC to various communication tasks. Our article highlights the improved quality, flexibility, and capability brought by NeurJSCC, and we also point out future directions

Communication Beyond Transmitting Bits: Semantics-Guided Source and Channel Coding

Classical communication paradigms focus on accurately transmitting bits over a noisy channel, and Shannon theory provides a fundamental theoretical limit on the rate of reliable communications. In this approach, bits are treated equally, and the communication system is oblivious to what meaning these bits convey or how they would be used. Future communications towards intelligence and conciseness will predictably play a dominant role, and the proliferation of connected intelligent agents requires a radical rethinking of coded transmission paradigm to support the new communication morphology on the horizon. The recent concept of "semantic communications" offers a promising research direction. Injecting semantic guidance into the coded transmission design to achieve semantics-aware communications shows great potential for further breakthrough in effectiveness and reliability. This article sheds light on semantics-guided source and channel coding as a transmission paradigm of semantic communications, which exploits both data semantics diversity and wireless channel diversity together to boost the whole system performance. We present the general system architecture and key techniques, and indicate some open issues on this topic.Comment: IEEE Wireless Communications, text overlap with arXiv:2112.0309

Improved Nonlinear Transform Source-Channel Coding to Catalyze Semantic Communications

Recent deep learning methods have led to increased interest in solving high-efficiency end-to-end transmission problems. These methods, we call nonlinear transform source-channel coding (NTSCC), extract the semantic latent features of source signal, and learn entropy model to guide the joint source-channel coding with variable rate to transmit latent features over wireless channels. In this paper, we propose a comprehensive framework for improving NTSCC, thereby higher system coding gain, better model versatility, and more flexible adaptation strategy aligned with semantic guidance are all achieved. This new sophisticated NTSCC model is now ready to support large-size data interaction in emerging XR, which catalyzes the application of semantic communications. Specifically, we propose three useful improvement approaches. First, we introduce a contextual entropy model to better capture the spatial correlations among the semantic latent features, thereby more accurate rate allocation and contextual joint source-channel coding are developed accordingly to enable higher coding gain. On that basis, we further propose response network architectures to formulate versatile NTSCC, i.e., once-trained model supports various rates and channel states that benefits the practical deployment. Following this, we propose an online latent feature editing method to enable more flexible coding rate control aligned with some specific semantic guidance. By comprehensively applying the above three improvement methods for NTSCC, a deployment-friendly semantic coded transmission system stands out finally. Our improved NTSCC system has been experimentally verified to achieve considerable bandwidth saving versus the state-of-the-art engineered VTM + 5G LDPC coded transmission system with lower processing latency