Since Shannon's pioneering masterpiece which established the foundation of modern information theory, the design target of communications has long been promising bit-level message reconstruction and achieving Shannon capacity. However, this focus neglects the semantics and effectiveness aspects of information. Recently, the development of wireless technologies and the spurt of deep learning (DL) techniques allow us to reclaim the meaning/usefulness aspect in the design of 6G communication paradigms, where goal-oriented communication is becoming a trend. Age of information (AoI), a well-known metric that captures the significance of information by recording the time elapsed from the generation time slot, has been extended to various variants, such as value of information (VoI), urgency of information (UoI), age of incorrect information (AoII), and so on. While each of them proposes novel ways to measure the semantics/effectiveness aspect of information, there is not yet a unified framework encompassing all of them. To this end, we propose a novel tensor-based approach, the goal-oriented tensor (GoT), to unify them, which allows more flexible and fine-grained goal characterizations. Following the proposed GoT, we architect a holistic goal-oriented semantic communication framework, in which semantics perception, dissemination, and control-plane decision making collaboratively work toward realizing specific goals. Finally, we outline several open challenges to fulfill the vision of the GoT framework.

Delivering timely status updates in a timeliness-critical communication system is of paramount importance to assist accurate and efficient decision making. Therefore, the topic of analyzing Age of Information (AoI) has aroused new research interest. This paper contributes to new results in this area by systematically analyzing the AoI of two types of Hybrid Automatic Repeat reQuest (HARQ) techniques that have been newly standardized in the Release-16 5G New Radio (NR) specifications, namely reactive HARQ and proactive HARQ. Under a code-based status update system with non-trivial coding delay, transmission delay, propagation delay, decoding delay, and feedback delay, we derive unified closed-form average AoI and average Peak AoI expressions for reactive HARQ and proactive HARQ, respectively. Based on the obtained explicit expressions, we formulate an AoI minimization problem to investigate the age-optimal codeblock assignment strategy in the finite block-length (FBL) regime. Through case studies and analytical results, we provide comparative insights between reactive HARQ and proactive HARQ from a perspective of freshness of information. The numerical results and optimization solutions show that proactive HARQ draws its strength from both age performance and system robustness, thus enabling the potential to provide new system advancement of a freshness-critical status update system.

In this paper, we investigate the timeliness performance of a downlink wireless communication system with non-orthogonal multiple access (NOMA). The timeliness of the system is characterized by Age of Information (AoI). To efficiently utilize the time-frequency resource and achieve a tradeoff between timeliness and reliability, we propose an adaptive transmission policy under hybrid automatic repeat request with chase combining (HARQ-CC) aided NOMA systems. In particular, the BS can adaptively adjust the power allocation and decide whether to transmit old or new packets to users in the NOMA system, based on the current AoI status and the positive/negative acknowledgement (ACK/NACK) feedback signal.We first analyze the BLER under such adaptive systems, and then formulate an AoI minimization problem based on the derived BLER. By transforming the objective function to a Markov Decision Process (MDP) problem, an optimal policy is obtained to minimize the average AoI of the system. Considering the high complexity of the MDP, we further divise an alternative near-optimal policy based on Lyapunov Drift function. Furthermore, we consider the fairness of users and propose a greedy policy to minimize the maximal expected AoI of users. Based on extensive simulations, it has been found that NOMA can outperform OMA on both an overall and a user-level basis when operating with adaptive retransmission and power allocation strategies.

With the development of space-air-ground integrated network (SAGIN), the Low Earth Orbit Satellite Communication (LEO SatCom) network has great potential to provide high rate communication services for aeronautical traffic. To overcome the frequency handovers between aircraft and LEO satellites, we generated a global aeronautical traffic demand map and utilized the graph-based handover (GBH) framework and the multi-attribute decision making (MADM) algorithm jointly to optimize the overall throughput. Moreover, we further propose a new parameter, called channel reservation order (CRO), for MADM algorithm. Simulation results indicate our strategy improves the performance in terms of overall throughput and access failure probability.

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.

The age of information (AoI) is a new metric to measure the timeliness of various status update systems, and hybrid automatic repeat request (HARQ) transmission scheme is usually applied to ensure higher timeliness. However, little research considers encoding delay, propagation delay, decoding delay and feedback delay in the HARQ-based coded status update system. To the best of our knowledge, in this paper, the HARQ-based Spinal coded timely status update system with all the practical delay elements is considered for the first time. We derive the average AoI expression of the system and analyze the monotony of the AoI expression to give an average AoI upper bound. Then we optimize the HARQ transmission scheme to minimize the AoI. To decrease the complexity of the optimization algorithm, we separate it into two steps. First, we optimize the puncturing pattern of Spinal codes and propose a transmission scheme under incremental tail transmission puncturing (ITTP) pattern. Second, we optimize the number of symbols in each round under the ITTP transmission scheme, and propose the optimal transmission scheme under the coarse-grained ITTP pattern. Simulation results show that the proposed transmission scheme can significantly decrease the AoI compared to the baseline transmission schemes.