Goal-oriented Tensor: Beyond Age of Information Towards Semantics-Empowered Goal-Oriented Communications

Optimizations premised on open-loop metrics such as Age of Information (AoI) indirectly enhance the system's decision-making utility. We therefore propose a novel closed-loop metric named Goal-oriented Tensor (GoT) to directly quantify the impact of semantic mismatches on goal-oriented decision-making utility. Leveraging the GoT, we consider a sampler & decision-maker pair that works collaboratively and distributively to achieve a shared goal of communications. We formulate a two-agent infinite-horizon Decentralized Partially Observable Markov Decision Process (Dec-POMDP) to conjointly deduce the optimal deterministic sampling policy and decision-making policy. To circumvent the curse of dimensionality in obtaining an optimal deterministic joint policy through Brute-Force-Search, a sub-optimal yet computationally efficient algorithm is developed. This algorithm is predicated on the search for a Nash Equilibrium between the sampler and the decision-maker. Simulation results reveal that the proposed sampler & decision-maker co-design surpasses the current literature on AoI and its variants in terms of both goal achievement utility and sparse sampling rate, signifying progress in the semantics-conscious, goal-driven sparse sampling design.

Deep Reinforcement Learning-Assisted Age-optimal Transmission Policy for HARQ-aided NOMA Networks

The recent interweaving of AI-6G technologies has sparked extensive research interest in further enhancing reliable and timely communications. \emph{Age of Information} (AoI), as a novel and integrated metric implying the intricate trade-offs among reliability, latency, and update frequency, has been well-researched since its conception. This paper contributes new results in this area by employing a Deep Reinforcement Learning (DRL) approach to intelligently decide how to allocate power resources and when to retransmit in a \emph{freshness-sensitive} downlink multi-user Hybrid Automatic Repeat reQuest with Chase Combining (HARQ-CC) aided Non-Orthogonal Multiple Access (NOMA) network. Specifically, an AoI minimization problem is formulated as a Markov Decision Process (MDP) problem. Then, to achieve deterministic, age-optimal, and intelligent power allocations and retransmission decisions, the Double-Dueling-Deep Q Network (DQN) is adopted. Furthermore, a more flexible retransmission scheme, referred to as Retransmit-At-Will scheme, is proposed to further facilitate the timeliness of the HARQ-aided NOMA network. Simulation results verify the superiority of the proposed intelligent scheme and demonstrate the threshold structure of the retransmission policy. Also, answers to whether user pairing is necessary are discussed by extensive simulation results.

Spinal Codes Optimization: Error Probability Analysis and Transmission Scheme Design

Spinal codes are known to be capacity achieving over both the additive white Gaussian noise (AWGN) channel and the binary symmetric channel (BSC). Over wireless channels, Spinal encoding can also be regarded as an adaptive-coded-modulation (ACM) technique due to its rateless property, which fits it with mobile communications. Due to lack of tight analysis on error probability of Spinal codes, optimization of transmission scheme using Spinal codes has not been fully explored. In this work, we firstly derive new tight upper bounds of the frame error rate (FER) of Spinal codes for both the AWGN channel and the BSC in the finite block-length (FBL) regime. Based on the derived upper bounds, we then design the optimal transmission scheme. Specifically, we formulate a rate maximization problem as a nonlinear integer programming problem, and solve it by an iterative algorithm for its dual problem. As the optimal solution exhibits an incremental-tail-transmission pattern, we propose an improved transmission scheme for Spinal codes. Moreover, we develop a bubble decoding with memory (BD-M) algorithm to reduce the decoding time complexity without loss of rate performance. The improved transmission scheme at the transmitter and the BD-M algorithm at the receiver jointly constitute an "encoding-decoding" system of Spinal codes. Simulation results demonstrate that it can improve both the rate performance and the decoding throughput of Spinal codes.