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Muhammad Azeem Khan

Publications and source records attributed to Muhammad Azeem Khan.

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Goal-Oriented Communication and Control Co-Design via Semantic Push-Pull in Industrial IoT

Emerging 6G industrial IoT architectures require wireless networked control systems capable of stabilizing diverse control loops over tightly constrained radio resources. Conventional periodic and Age-of-Information (AoI) based scheduling guarantees bounded staleness at the cost of persistent channel saturation. Conversely, pure event-triggered (PureET) strategies minimize transmissions but risk catastrophic silent deterioration when local sensor-side thresholds fail to reflect critical state evolution. To bridge this gap, we propose a communication-control co-design framework governed by a 6G Semantic Layer that independently arbitrates uplink and downlink resources. Instead of relying on freshness, our architecture evaluates the actual control impact of a packet using the state-to-error ratio (SER). We unify this control confidence with channel reliability in terms of signal-to-noise ratio (SNR) to orchestrate a threshold-based sensor push and a state-aware controller pull mechanism. To ensure equitable resource allocation across dynamically heterogeneous plants, the proposed framework explicitly scales actuation deadbands according to local plant dynamics. Simulations over Rayleigh-faded channels demonstrate that this approach fundamentally shifts the Pareto frontier between transmission rate and control quality. The proposed scheme achieves tracking accuracy comparable to periodic schedulers at a reduced communication overhead, while mitigating the estimation errors characteristic of PureET.

cs.IT

Value of Information and Timing-aware Scheduling for Federated Learning

Data possesses significant value as it fuels advancements in AI. However, protecting the privacy of the data generated by end-user devices has become crucial. Federated Learning (FL) offers a solution by preserving data privacy during training. FL brings the model directly to User Equipments (UEs) for local training by an access point (AP). The AP periodically aggregates trained parameters from UEs, enhancing the model and sending it back to them. However, due to communication constraints, only a subset of UEs can update parameters during each global aggregation. Consequently, developing innovative scheduling algorithms is vital to enable complete FL implementation and enhance FL convergence. In this paper, we present a scheduling policy combining Age of Update (AoU) concepts and data Shapley metrics. This policy considers the freshness and value of received parameter updates from individual data sources and real-time channel conditions to enhance FL's operational efficiency. The proposed algorithm is simple, and its effectiveness is demonstrated through simulations.

cs.NI