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The latency and control overhead of sending the preamble in synchronous communications can be excessive when transmitting short sensing/control messages. To reduce these overheads, this work proposes a preamble-free solution based on the framework of quickest change detection. Specific contributions include a joint decoding/demodulation scheme that is provably asymptotically optimal, and a more practical CuSum-like implementation. Numerical results show that the proposed scheme reduces the latency by 47%–79% when compared to the preamble-based solutions. The scheme is also inherently robust and automatically adapts to any unknown underlying SNRs. 

In this paper, we address the challenges of asynchronous gradient descent in distributed learning environments, particularly focusing on addressing the challenges of stale gradients and the need for extensive communication resources. We develop a novel communication efficient framework that incorporates a gradient evaluation algorithm to assess and utilize delayed gradients based on their quality, ensuring efficient and effective model updates while significantly reducing communication overhead. Our proposed algorithm requires agents to only send the norm of the gradients rather than the computed gradient. The server then decides whether to accept the gradient if the ratio between the norm of the gradient and the distance between the global model parameter and the local model parameter exceeds a certain threshold. With the proper choice of the threshold, we show that the convergence rate achieves the same order as the synchronous stochastic gradient without depending on the staleness value unlike most of the existing works. Given the computational complexity of the initial algorithm, we introduce a simplified variant that prioritizes the practical applicability without compromising on the convergence rates. Our simulations demonstrate that our proposed algorithms outperform existing state-of-the-art methods, offering improved convergence rates, stability, accuracy, and resource consumption. 

One of the most intriguing 6G vertical markets is precision agriculture, where communications, sensing, control, and robotics technologies are used to improve agricultural outputs and decrease environmental impact. Ambient IoT (A-IoT), which uses a network of devices that harvest ambient energy to enable communications, is expected to play an important role in agricultural use cases due to its low costs, simplicity, and battery-free (or battery-assisted) operation. In this article, we review the use cases of precision agriculture and discuss the challenges. We discuss how A-IoT can be used for precision agriculture and compare it with other ambient energy source technologies. We also discuss research directions related to both A-IoT and precision agriculture. 

This work generalizes the Age-of-Information (AoI) minimization problem of update-through-queue systems such that in addition to deciding the waiting time, the sender also chooses over which “channel” each update packet will be served. Different channels have different costs, delays, and quality characteristics that reflect the scheduler’s selections of routing, communications, and update modes. Instead of considering only two channels with restricted parameters as in the existing works, this work studies the general K-channel problem with arbitrary parameters. The results show that both the optimal waiting time and the optimal channel-selection policies admit an elegant water-filling structure, and can be efficiently computed by the proposed low-complexity fixed-point-based numerical method. 

Age-Of-Information (AoI) is a metric that focuses directly on the application-layer objectives, and a canonical AoI minimization problem is the update-through-queues models. Existing results in this direction fall into two categories: The open-loop setting for which the sender is oblivious of the packet departure time, versus the closed-loop setting for which the decision is based on instantaneous Acknowledgment (ACK). Neither setting perfectly reflects modern networked systems, which almost always rely on feedback that experiences some delay. Motivated by this observation, this work subjects the ACK traffic to a second queue so that the closed-loop decision is made based on delayed feedback. Near-optimal schedulers have been devised, which smoothly transition from the instantaneous-ACK to the open-loop schemes depending on how long the feedback delay is. The results quantify the benefits of delayed feedback for AoI minimization in the update-through-queues systems. 

We study reliable communication over point-to-point adversarial channels in which the adversary can observe the transmitted codeword via some function that takes the n-bit codeword as input and computes an r*n-bit output for some given r in [0,1]. We consider the scenario where the r*n -bit observation is computationally bounded - the adversary is free to choose an arbitrary observation function as long as the function can be computed using a polynomial amount of computational resources. This observation-based restriction differs from conventional channel-based computational limitations, where in the later case, the resource limitation applies to the computation of the (adversarial) channel error/corruption. For all r in [0,1−H(p)] where H(.) is the binary entropy function and p is the adversary’s error budget, we characterize the capacity of the above channel and find that the capacity is identical to the completely oblivious setting (r=0). This result can be viewed as a generalization of known results on myopic adversaries and on channels with active eavesdroppers for which the observation process depends on a fixed distribution and fixed-linear structure, respectively, that cannot be chosen arbitrarily by the adversary.