This paper addresses query scheduling for goaloriented semantic communication in pull-based status update systems. We consider a system where multiple sensing agents (SAs) observe a source characterized by various attributes and provide updates to multiple actuation agents (AAs), which act upon the received information to fulfill their heterogeneous goals at the endpoint. A hub serves as an intermediary, querying the SAs for updates on observed attributes and maintaining a knowledge base, which is then broadcast to the AAs. The AAs leverage the knowledge to perform their actions effectively. To quantify the semantic value of updates, we introduce agrade of effectiveness(GoE) metric. Furthermore, we integrate cumulative perspective theory(CPT) into the long-term effectiveness analysis to account for risk awareness and loss aversion in the system. Leveraging this framework, we compute effect-aware scheduling policies aimed at maximizing the expected discounted sum of CPT-based total GoE provided by the transmitted updates while complying with a given query cost constraint. To achieve this, we propose amodel-basedsolution based on dynamic programming andmodel-freesolutions employing state-of-the-art deep reinforcement learning (DRL) algorithms. Our findings demonstrate that effect-aware scheduling significantly enhances the effectiveness of communicated updates compared to benchmark scheduling methods, particularly in settings with stringent cost constraints where optimal query scheduling is vital for system performance and overall effectiveness.

This paper studies decision-making for goal-oriented effective communication. We consider an end-to-end status update system where a sensing agent (SA) observes a source, generates and transmits updates to an actuation agent (AA), while the AA takes actions to accomplish a goal at the endpoint. We integrate the push- and pull-based update communication models to obtain apush-and-pullmodel, which allows the transmission controller at the SA to decide whether to push an update to the AA and the query controller at the AA to pull updates by initiating queries at specific time instants. To gauge effectiveness, we utilize agrade of effectiveness(GoE) metric incorporating the updates’ freshness, usefulness, and the timeliness of actions as qualitative attributes. We then derive effect-aware policies to maximize the expected discounted sum of the updates’ effectiveness subject to induced costs. The effect-aware policy at the SA considers the potential effectiveness of communicated updates at the endpoint, while at the AA, it accounts for the probabilistic evolution of the source and importance of the generated updates. Our results show the proposed push-and-pull model outperforms models solely based on push- or pull-based updates both in terms of efficiency and effectiveness. Additionally, using effect-aware policies at both agents enhances effectiveness compared to periodic and/or probabilistic, effect-agnostic policies at either or both agents.

We study a pull-based communication system where a sensing agent updates an actuation agent using a query control policy, which is adjusted in the evolution of an observed information source and the usefulness of each update for achieving a specific goal. For that, a controller decides whether to pull an update at each slot, predicting what is probably occurring at the source and how much effective impact that update could have at the endpoint. Thus, temporal changes in the source evolution could modify the query arrivals to capture important updates. The amount of impact is determined by a grade of effectiveness (GoE) metric, which incorporates both freshness and usefulness attributes of the communicated updates. Applying an iterative algorithm, we derive query decisions that maximize the longterm average GoE for the communicated packets, subject to cost constraints. Our analytical and numerical results show that the proposed query policy exhibits higher effectiveness than existing periodic and probabilistic query policies for a wide range of query arrival rates.

The problem of goal-oriented semantic filtering and timely source coding in multiuser communication systems is considered here. We study a distributed monitoring system in which multiple information sources, each observing a physical process, provide status update packets to multiple monitors having heterogeneous goals. Two semantic filtering schemes are first proposed as a means to admit or drop arrival packets based on their goal-dependent importance, which is a function of the intrinsic and extrinsic attributes of information and the probability of occurrence of each realization. Admitted packets at each sensor are then encoded and transmitted over block-fading wireless channels so that served monitors can timely fulfill their goals. A truncated error control scheme is derived, which allows transmitters to drop or retransmit undelivered packets based on their significance. Then, we formulate the timely source encoding optimization problem and analytically derive the optimal codeword lengths assigned to the admitted packets which maximize a weighted sum of semantic utility functions for all pairs of communicating sensors and monitors. Our analytical and numerical results provide the optimal design parameters for different arrival rates and highlight the improvement in timely status update delivery using the proposed semantic filtering, source coding, and error control schemes.

Cell-free massive MIMO (CF-mMIMO) network and its user-centric (UC) version are considered as promising techniques for the next generations of wireless networks. However, fronthaul and backhaul assignments are challenging issues in these networks. In this paper, energy efficiencies of uplink transmission for the CF- and UC-mMIMO networks are studied, wherein access points (APs) are connected to aggregation nodes (ANs) through radio frequency (RF) and/or free-space optic (FSO) fronthauls, and the ANs are connected to a central processing unit via fiber backhauls. The achievable data rates are derived by taking into account the effects of hardware non-ideality at the APs and ANs, FSO alignment and weather conditions. To have a robust and energy-efficient network, especially in the presence of FSO misalignment and adverse weather conditions, first, a cognitive RF–FSO fronthaul assignment algorithm is proposed at the cost of sharing the available RF bandwidth between the access and fronthaul links. Then, optimal power allocations at the users and APs are investigated, and two analytical approaches are proposed to solve the non-convex optimization problem. Through numerical results, we have discussed how utilizing the cognitive RF–FSO fronthaul assignment achieves higher energy efficiency compared to that of FSO-only, RF-only, or simultaneously using RF and FSO fronthaul links, e.g., achieving up to$198\%$higher energy efficiency under unfavorable conditions. Moreover, the effects of FSO misalignment, weather conditions, and power allocations on the networks’ performances are discussed.

We study a multiuser system in which an information source provides status updates to two monitors with heterogeneous goals. Semantic filtering is first performed to select the most useful realizations for each monitor. Packets are then encoded and sent so that each monitor can timely fulfill its goal. In this regard, some realizations are important for both monitors, while every other realization is informative for only one monitor. We determine the optimal real codeword lengths assigned to the selected packet arrivals in the sense of maximizing a weighted sum of semantics-aware utility functions for the two monitors. Our analytical and numerical results provide the optimal design parameters for different arrival rates and highlight the improvement in timely status update delivery using semantic filtering and source coding.