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We prove a characterization of the structural conditions on matrices of sign-rank 3 and unit disk graphs (UDGs) which permit constant-cost public-coin randomized communication protocols. Therefore, under these conditions, these graphs also admit implicit representations. The sign-rank of a matrix $M \in \{\pm 1\}^{N \times N}$ is the smallest rank of a matrix $R$ such that $M_{i,j} = \mathrm{sign}(R_{i,j})$ for all $i,j \in [N]$; equivalently, it is the smallest dimension $d$ in which $M$ can be represented as a point-halfspace incidence matrix with halfspaces through the origin, and it is essentially equivalent to the unbounded-error communication complexity. Matrices of sign-rank 3 can achieve the maximum possible bounded-error randomized communication complexity $\Theta(\log N)$, and meanwhile the existence of implicit representations for graphs of bounded sign-rank (including UDGs, which have sign-rank 4) has been open since at least 2003. We prove that matrices of sign-rank 3, and UDGs, have constant randomized communication complexity if and only if they do not encode arbitrarily large instances of the Greater-Than communication problem, or, equivalently, if they do not contain arbitrarily large half-graphs as semi-induced subgraphs. This also establishes the existence of implicit representations for these graphs under the same conditions.

Terahertz (THz) integrated sensing and communication (ISAC) enables simultaneous data transmission with Terabit-per-second (Tbps) rate and millimeter-level accurate sensing. To realize such a blueprint, ultra-massive antenna arrays with directional beamforming are used to compensate for severe path loss in the THz band. In this paper, the time-frequency-space transmit design is investigated for THz ISAC to generate time-varying scanning sensing beams and stable communication beams. Specifically, with the dynamic array-of-subarray (DAoSA) hybrid beamforming architecture and multi-carrier modulation, two ISAC hybrid precoding algorithms are proposed, namely, a vectorization (VEC) based algorithm that outperforms existing ISAC hybrid precoding methods and a low-complexity sensing codebook assisted (SCA) approach. Meanwhile, coupled with the transmit design, parameter estimation algorithms are proposed to realize high-accuracy sensing, including a wideband DAoSA MUSIC (W-DAoSA-MUSIC) method for angle estimation and a sum-DFT-GSS (S-DFT-GSS) approach for range and velocity estimation. Numerical results indicate that the proposed algorithms can realize centi-degree-level angle estimation accuracy and millimeter-level range estimation accuracy, which are one or two orders of magnitudes better than the methods in the millimeter-wave band. In addition, to overcome the cyclic prefix limitation and Doppler effects in the THz band, an inter-symbol interference- and inter-carrier interference-tackled sensing algorithm is developed to refine sensing capabilities for THz ISAC.

With the rapid proliferation of Internet of Things (IoT) devices and the growing concern for data privacy among the public, Federated Learning (FL) has gained significant attention as a privacy-preserving machine learning paradigm. FL enables the training of a global model among clients without exposing local data. However, when a federated learning system runs on wireless communication networks, limited wireless resources, heterogeneity of clients, and network transmission failures affect its performance and accuracy. In this study, we propose a novel dynamic cross-tier FL scheme, named FedDCT to increase training accuracy and performance in wireless communication networks. We utilize a tiering algorithm that dynamically divides clients into different tiers according to specific indicators and assigns specific timeout thresholds to each tier to reduce the training time required. To improve the accuracy of the model without increasing the training time, we introduce a cross-tier client selection algorithm that can effectively select the tiers and participants. Simulation experiments show that our scheme can make the model converge faster and achieve a higher accuracy in wireless communication networks.

Developers interrupting their participation in a project might slowly forget critical information about the code, such as its intended purpose, structure, the impact of external dependencies, and the approach used for implementation. Forgetting the implementation details can have detrimental effects on software maintenance, comprehension, knowledge sharing, and developer productivity, resulting in bugs, and other issues that can negatively influence the software development process. Therefore, it is crucial to ensure that developers have a clear understanding of the codebase and can work efficiently and effectively even after long interruptions. This registered report proposes an empirical study aimed at investigating the impact of the developer's activity breaks duration and different code quality properties. In particular, we aim at understanding if the amount of activity in a project impact the code quality, and if developers with different activity profiles show different impacts on code quality. The results might be useful to understand if it is beneficial to promote the practice of developing multiple projects in parallel, or if it is more beneficial to reduce the number of projects each developer contributes.

We analytically decide whether the broadcast transmission scheme or the unicast transmission scheme achieves the optimal age of information (AoI) performance of a multiuser system where a base station (BS) generates and transmits status updates to multiple user equipments (UEs). In the broadcast transmission scheme, the status update for all UEs is jointly encoded into a packet for transmission, while in the unicast transmission scheme, the status update for each UE is encoded individually and transmitted by following the round robin policy. For both transmission schemes, we examine three packet management strategies, namely the non-preemption strategy, the preemption in buffer strategy, and the preemption in serving strategy. We first derive new closed-form expressions for the average AoI achieved by two transmission schemes with three packet management strategies. Based on them, we compare the AoI performance of two transmission schemes in two systems, namely, the remote control system and the dynamic system. Aided by simulation results, we verify our analysis and investigate the impact of system parameters on the average AoI. For example, the unicast transmission scheme is more appropriate for the system with a large number UEs. Otherwise, the broadcast transmission scheme is more appropriate.

Semantic communication has gained significant attention from researchers as a promising technique to replace conventional communication in the next generation of communication systems, primarily due to its ability to reduce communication costs. However, little literature has studied its effectiveness in multi-user scenarios, particularly when there are variations in the model architectures used by users and their computing capacities. To address this issue, we explore a semantic communication system that caters to multiple users with different model architectures by using a multi-purpose transmitter at the base station (BS). Specifically, the BS in the proposed framework employs semantic and channel encoders to encode the image for transmission, while the receiver utilizes its local channel and semantic decoder to reconstruct the original image. Our joint source-channel encoder at the BS can effectively extract and compress semantic features for specific users by considering the signal-to-noise ratio (SNR) and computing capacity of the user. Based on the network status, the joint source-channel encoder at the BS can adaptively adjust the length of the transmitted signal. A longer signal ensures more information for high-quality image reconstruction for the user, while a shorter signal helps avoid network congestion. In addition, we propose a hybrid loss function for training, which enhances the perceptual details of reconstructed images. Finally, we conduct a series of extensive evaluations and ablation studies to validate the effectiveness of the proposed system.

In wireless security, cognitive adversaries are known to inject jamming energy on the victim's frequency band and monitor the same band for countermeasures thereby trapping the victim. Under the class of cognitive adversaries, we propose a new threat model wherein the adversary, upon executing the jamming attack, measures the long-term statistic of Kullback-Leibler Divergence (KLD) between its observations over each of the network frequencies before and after the jamming attack. To mitigate this adversary, we propose a new cooperative strategy wherein the victim takes the assistance for a helper node in the network to reliably communicate its message to the destination. The underlying idea is to appropriately split their energy and time resources such that their messages are reliably communicated without disturbing the statistical distribution of the samples in the network. We present rigorous analyses on the reliability and the covertness metrics at the destination and the adversary, respectively, and then synthesize tractable algorithms to obtain near-optimal division of resources between the victim and the helper. Finally, we show that the obtained near-optimal division of energy facilitates in deceiving the adversary with a KLD estimator.

The envisioned robotic aerial base station (RABS) concept is expected to bring further flexibility to integrated sensing and communication (ISAC) systems. In this letter, characterizing the spatial traffic distribution on a grid-based model, the RABS-assisted ISAC system is formulated as a robust optimization problem to maximize the minimum satisfaction rate (SR) under a cardinality constrained uncertainty set. The problem is reformulated as a mixed-integer linear programming (MILP) and solved approximately by the iterative linear programming rounding algorithm. Numerical investigations show that the minimum SR can be improved by 28.61% on average compared to fixed small cells.

Cloud-edge serverless applications or serverless deployments spanning multiple regions introduce the need to govern the scheduling of functions to satisfy their functional constraints or avoid performance degradation. For instance, functions may require to be allocated to specific private (edge) nodes that have access to specialised resources or to nodes with low latency to access a certain database to decrease the overall latency of the application. State-of-the-art serverless platforms do not support directly the implementation of topological constraints on the scheduling of functions. We address this problem by presenting a declarative language for defining topology-aware, function-specific serverless scheduling policies, called tAPP. Given a tAPP script, a compatible serverless scheduler can enforce different, co-existing topological constraints without requiring ad-hoc platform deployments. We prove our approach feasible by implementing a tAPP-based serverless platform as an extension of the Apache OpenWhisk serverless platform. We show that, compared to vanilla OpenWhisk, our extension does not negatively impact the performance of generic, non-topology-bound serverless scenarios, while it increases the performance of topology-bound ones.

Maritime activities represent a major domain of economic growth with several emerging maritime Internet of Things use cases, such as smart ports, autonomous navigation, and ocean monitoring systems. The major enabler for this exciting ecosystem is the provision of broadband, low-delay, and reliable wireless coverage to the ever-increasing number of vessels, buoys, platforms, sensors, and actuators. Towards this end, the integration of unmanned aerial vehicles (UAVs) in maritime communications introduces an aerial dimension to wireless connectivity going above and beyond current deployments, which are mainly relying on shore-based base stations with limited coverage and satellite links with high latency. Considering the potential of UAV-aided wireless communications, this survey presents the state-of-the-art in UAV-aided maritime communications, which, in general, are based on both conventional optimization and machine-learning-aided approaches. More specifically, relevant UAV-based network architectures are discussed together with the role of their building blocks. Then, physical-layer, resource management, and cloud/edge computing and caching UAV-aided solutions in maritime environments are discussed and grouped based on their performance targets. Moreover, as UAVs are characterized by flexible deployment with high re-positioning capabilities, studies on UAV trajectory optimization for maritime applications are thoroughly discussed. In addition, aiming at shedding light on the current status of real-world deployments, experimental studies on UAV-aided maritime communications are presented and implementation details are given. Finally, several important open issues in the area of UAV-aided maritime communications are given, related to the integration of sixth generation (6G) advancements.