cs.IT

arXiv:2501.00909v1 Announce Type: new Abstract: This paper considers reconfigurable intelligent surface (RIS)-aided integrated sensing and communication (ISAC) systems under dual-polarized (DP) channels. Unlike the existing ISAC systems, which ignored polarization of electromagnetic waves, this study adopts DP base station (BS) and DP RIS to serve users with a pair of DP antennas. The achievable sum rate is maximized through jointly optimizing the beamforming matrix at the DP BS, and the reflecting coefficients at the DP RIS. To address this problem, we first utilize the weighted minimum mean-square error (WMMSE) method to transform the objective function into a more tractable form, and then an alternating optimization (AO) method is employed to decouple the original problem into two subproblems. Due to the constant modulus constraint, the DP RIS reflection matrix optimization problem is addressed by the majorization-minimization (MM) method. For the DP beamforming matrix, we propose a penalty-based algorithm that can obtain a low-complexity closed-form solution. Simulation results validate the advantage of deploying DP transmit array and DP RIS in the considered ISAC systems.

arXiv:2301.10414v3 Announce Type: replace Abstract: Today, the vast majority of the world's digital information is represented using the fundamental assumption, introduced by Claude Shannon in 1948, that ``...the semantic aspects of communication are irrelevant to the engineering problem (of the design of communication systems)...''. Consider, nonetheless, the observation that we often combine a message with other information in order to deduce new facts, thereby expanding the value of such a message. It is noteworthy that to-date, no rigorous theory of communication has been put forth which postulates the existence of deductive capabilities on the receiver's side. The purpose of this paper is to present such a theory. We formally model such deductive capabilities using logic reasoning, and present a rigorous theory which covers the following generic scenario: Alice and Bob each have knowledge of some logic sentence, and they wish to communicate as efficiently as possible with the shared goal that, following their communication, Bob should be able to deduce a particular logic sentence that Alice knows to be true, but that Bob currently cannot prove. Many variants of this general setup are considered in this article; in all cases we are able to provide sharp upper and lower bounds. Our contribution includes the identification of the most fundamental requirements that we place on a logic and associated logical language for all of our results to apply. Practical algorithms that are in some cases asymptotically optimal are provided, and we illustrate the potential practical value of the design of communication systems that incorporate the assumption of deductive capabilities at the receiver using experimental results that suggest significant possible gains compared to classical systems.

arXiv:2501.00549v1 Announce Type: new Abstract: In this paper, we address the problem of timely delivery of status update packets in a real-time communication system, where a transmitter sends status updates generated by a source to a receiver over an unreliable channel. The timestamps of transmitted and received packets are measured using separate clocks located at the transmitter and receiver, respectively. To account for possible clock drift between these two clocks, we consider both deterministic and probabilistic drift scenarios. We analyze the system's performance regarding the Age of Information (AoI) and derive closed-form expressions for the distribution and the average AoI under both clock drift models. Additionally, we explore the impact of key system parameters on the average AoI through analytical and numerical results.

arXiv:2501.00824v1 Announce Type: new Abstract: The complexity of neural networks and inference tasks, coupled with demands for computational efficiency and real-time feedback, poses significant challenges for resource-constrained edge devices. Collaborative inference mitigates this by assigning shallow feature extraction to edge devices and offloading features to the cloud for further inference, reducing computational load. However, transmitted features remain susceptible to model inversion attacks (MIAs), which can reconstruct original input data. Current defenses, such as perturbation and information bottleneck techniques, offer explainable protection but face limitations, including the lack of standardized criteria for assessing MIA difficulty, challenges in mutual information estimation, and trade-offs among usability, privacy, and deployability. To address these challenges, we introduce the first criterion to evaluate MIA difficulty in collaborative inference, supported by theoretical analysis of existing attacks and defenses, validated using experiments with the Mutual Information Neural Estimator (MINE). Based on these findings, we propose SiftFunnel, a privacy-preserving framework for collaborative inference. The edge model is trained with linear and non-linear correlation constraints to reduce redundant information in transmitted features, enhancing privacy protection. Label smoothing and a cloud-based upsampling module are added to balance usability and privacy. To improve deployability, the edge model incorporates a funnel-shaped structure and attention mechanisms, preserving both privacy and usability. Extensive experiments demonstrate that SiftFunnel outperforms state-of-the-art defenses against MIAs, achieving superior privacy protection with less than 3% accuracy loss and striking an optimal balance among usability, privacy, and practicality.

arXiv:2501.00201v1 Announce Type: cross Abstract: We investigate the joint admission control and discrete-phase multicast beamforming design for integrated sensing and communications (ISAC) systems, where sensing and communications functionalities have different hierarchies. Specifically, the ISAC system first allocates resources to the higher-hierarchy functionality and opportunistically uses the remaining resources to support the lower-hierarchy one. This resource allocation problem is a nonconvex mixed-integer nonlinear program (MINLP). We propose an exact mixed-integer linear program (MILP) reformulation, leading to a globally optimal solution. In addition, we implemented three baselines for comparison, which our proposed method outperforms by more than 39%.

arXiv:2501.01383v1 Announce Type: cross Abstract: A classic problem in data analysis is studying the systems of subsets defined by either a similarity or a dissimilarity function on $X$ which is either observed directly or derived from a data set. For an electrical network there are two functions on the set of the nodes defined by the resistance matrix and the response matrix either of which defines the network completely. We argue that these functions should be viewed as a similarity and a dissimilarity function on the set of the nodes moreover they are related via the covariance mapping also known as the Farris transform or the Gromov product. We will explore the properties of electrical networks from this point of view. It has been known for a while that the resistance matrix defines a metric on the nodes of the electrical networks. Moreover for a circular electrical network this metric obeys the Kalmanson property as it was shown recently. We will call such a metric an electrical Kalmanson metric. The main results of this paper is a complete description of the electrical Kalmanson metrics in the set of all Kalmanson metrics in terms of the geometry of the positive Isotropic Grassmannian whose connection to the theory of electrical networks was discovered earlier. One important area of applications where Kalmanson metrics are actively used is the theory of phylogenetic networks which are a generalization of phylogenetic trees. Our results allow us to use in phylogenetics the powerful methods of reconstruction of the minimal graphs of electrical networks and possibly open the door into data analysis for the methods of the theory of cluster algebras.

arXiv:2501.00399v1 Announce Type: new Abstract: In this letter, we propose a novel Movable Superdirective Pairs (MSP) approach that combines movable antennas with superdirective pair arrays to enhance the performance of millimeter-wave (mmWave) communications on the user side. By controlling the rotation angles and positions of superdirective antenna pairs, the proposed MSP approach maximizes the received signal-to-noise ratio (SNR) of multipath signals without relying on phase shifters or attenuators. This approach addresses the limitations of traditional superdirective antennas, which are typically restricted to the endfire direction and suffer from reduced scanning bandwidth and increased complexity. An efficient algorithm based on alternating optimization and the gradient projection method is developed to solve the non-convex optimization problem of antennas' joint rotating positioning. Simulation results demonstrate that the MSP approach achieves significant performance gains over fixed-position array (FPA) employing Maximum Ratio Combining (MRC), while reducing system complexity and hardware costs.

arXiv:2501.00546v1 Announce Type: new Abstract: Simultaneously transmitting and reflecting reconfigurable intelligent surface (STAR-RIS)-aided cell-free massive multiple-input multiple-output (CF-mMIMO) systems are investigated under spatially correlated fading channels using realistic imperfect hardware. Specifically, the transceiver distortions, \textcolor{black}{time-varying phase noise, and RIS phase shift errors} are considered. Upon considering imperfect hardware and pilot contamination, we derive a linear minimum mean-square error (MMSE) criterion-based cascaded channel estimator. Moreover, a closed-form expression of the downlink ergodic spectral efficiency (SE) is derived based on maximum ratio (MR) based transmit precoding and channel statistics, where both a finite number of access points (APs) and STAR-RIS elements as well as imperfect hardware are considered. Furthermore, by exploiting the ergodic signal-to-interference-plus-noise ratios (SINRs) among user equipment (UE), a max-min fairness problem is formulated for the joint optimization of the passive transmitting and reflecting beamforming (BF) at the STAR-RIS as well as of the power control coefficients. An alternating optimization (AO) algorithm is proposed for solving the resultant problems, where iterative adaptive particle swarm optimization (APSO) and bisection methods are proposed for circumventing the non-convexity of the RIS passive BF and the quasi-concave power control sub-problems, respectively. Our simulation results illustrate that the STAR-RIS-aided CF-mMIMO system attains higher SE than its RIS-aided counterpart. The performance of different hardware parameters is also evaluated. Additionally, it is demonstrated that the SE of the worst UE can be significantly improved by exploiting the proposed AO-based algorithm compared to conventional solutions associated with random passive BF and equal-power scenarios.

arXiv:2501.00612v1 Announce Type: new Abstract: Information theory has provided foundations for the theories of several application areas critical for modern society, including communications, computer storage, and AI. A key aspect of Shannon's 1948 theory is a sharp lower bound on the number of bits needed to encode and communicate a string of symbols. When he introduced the theory, Shannon famously excluded any notion of semantics behind the symbols being communicated. This semantics-free notion went on to have massive impact on communication and computing technologies, even as multiple proposals for reintroducing semantics in a theory of information were being made, notably one where Carnap and Bar-Hillel used logic and reasoning to capture semantics. In this paper we present, for the first time, a Shannon-style analysis of a communication system equipped with a deductive reasoning capability, implemented using logical inference. We use some of the most important techniques developed in information theory to demonstrate significant and sometimes surprising gains in communication efficiency availed to us through such capability, demonstrated also through practical codes. We thus argue that proposals for a semantic information theory should include the power of deductive reasoning to magnify the value of transmitted bits as we strive to fully unlock the inherent potential of semantics.

arXiv:2501.00677v1 Announce Type: new Abstract: Robust matrix completion (RMC) is a widely used machine learning tool that simultaneously tackles two critical issues in low-rank data analysis: missing data entries and extreme outliers. This paper proposes a novel scalable and learnable non-convex approach, coined Learned Robust Matrix Completion (LRMC), for large-scale RMC problems. LRMC enjoys low computational complexity with linear convergence. Motivated by the proposed theorem, the free parameters of LRMC can be effectively learned via deep unfolding to achieve optimum performance. Furthermore, this paper proposes a flexible feedforward-recurrent-mixed neural network framework that extends deep unfolding from fix-number iterations to infinite iterations. The superior empirical performance of LRMC is verified with extensive experiments against state-of-the-art on synthetic datasets and real applications, including video background subtraction, ultrasound imaging, face modeling, and cloud removal from satellite imagery.

arXiv:2501.01138v1 Announce Type: new Abstract: Joint source-channel coding (JSCC) offers a promising avenue for enhancing transmission efficiency by jointly incorporating source and channel statistics into the system design. A key advancement in this area is the deep joint source and channel coding (DeepJSCC) technique that designs a direct mapping of input signals to channel symbols parameterized by a neural network, which can be trained for arbitrary channel models and semantic quality metrics. This paper advances the DeepJSCC framework toward a semantics-aligned, high-fidelity transmission approach, called semantics-guided diffusion DeepJSCC (SGD-JSCC). Existing schemes that integrate diffusion models (DMs) with JSCC face challenges in transforming random generation into accurate reconstruction and adapting to varying channel conditions. SGD-JSCC incorporates two key innovations: (1) utilizing some inherent information that contributes to the semantics of an image, such as text description or edge map, to guide the diffusion denoising process; and (2) enabling seamless adaptability to varying channel conditions with the help of a semantics-guided DM for channel denoising. The DM is guided by diverse semantic information and integrates seamlessly with DeepJSCC. In a slow fading channel, SGD-JSCC dynamically adapts to the instantaneous signal-to-noise ratio (SNR) directly estimated from the channel output, thereby eliminating the need for additional pilot transmissions for channel estimation. In a fast fading channel, we introduce a training-free denoising strategy, allowing SGD-JSCC to effectively adjust to fluctuations in channel gains. Numerical results demonstrate that, guided by semantic information and leveraging the powerful DM, our method outperforms existing DeepJSCC schemes, delivering satisfactory reconstruction performance even at extremely poor channel conditions.

arXiv:2501.01411v1 Announce Type: new Abstract: We investigate the coboundary expansion property of product codes called product expansion, which plays an important role in the recent constructions of good quantum LDPC codes and classical locally testable codes. Prior research revealed that this property is equivalent to agreement testability and robust testability for products of two codes of linear distance. However, for products of more than two codes, product expansion is a strictly stronger property. In this paper, we prove that the collection of random codes over a sufficiently large field has good product expansion. We believe that in the case of four codes, these ideas can be used to construct good quantum locally testable codes in a way similar to the current constructions using only products of two codes.

arXiv:2501.00641v1 Announce Type: cross Abstract: In this paper, we rethink delay Doppler channels (also called doubly selective channels). We prove that no modulation schemes can compensate a non-trivial Doppler spread well. This means that the current active OTFS (that is the same as VOFDM) cannot compensate a non-trivial Doppler spread. We then discuss some of the existing methods to deal with time-varying channels, in particular time-frequency (TF) coding in an OFDM system. TF coding is equivalent to space-time coding in the math part. We also summarize state of the art on space-time coding that was an active research topic over 10 years ago.

arXiv:2501.01353v1 Announce Type: cross Abstract: We investigate an uplink MIMO-OFDM localization scenario where a legitimate base station (BS) aims to localize a user equipment (UE) using pilot signals transmitted by the UE, while an unauthorized BS attempts to localize the UE by eavesdropping on these pilots, posing a risk to the UE's location privacy. To enhance legitimate localization performance while protecting the UE's privacy, we formulate an optimization problem regarding the beamformers at the UE, aiming to minimize the Cram\'er-Rao bound (CRB) for legitimate localization while constraining the CRB for unauthorized localization above a threshold. A penalty dual decomposition optimization framework is employed to solve the problem, leading to a novel beamforming approach for location privacy preservation. Numerical results confirm the effectiveness of the proposed approach and demonstrate its superiority over existing benchmarks.

arXiv:2501.00379v1 Announce Type: new Abstract: Federated Dropout is an efficient technique to overcome both communication and computation bottlenecks for deploying federated learning at the network edge. In each training round, an edge device only needs to update and transmit a sub-model, which is generated by the typical method of dropout in deep learning, and thus effectively reduces the per-round latency. \textcolor{blue}{However, the theoretical convergence analysis for Federated Dropout is still lacking in the literature, particularly regarding the quantitative influence of dropout rate on convergence}. To address this issue, by using the Taylor expansion method, we mathematically show that the gradient variance increases with a scaling factor of $\gamma/(1-\gamma)$, with $\gamma \in [0, \theta)$ denoting the dropout rate and $\theta$ being the maximum dropout rate ensuring the loss function reduction. Based on the above approximation, we provide the convergence analysis for Federated Dropout. Specifically, it is shown that a larger dropout rate of each device leads to a slower convergence rate. This provides a theoretical foundation for reducing the convergence latency by making a tradeoff between the per-round latency and the overall rounds till convergence. Moreover, a low-complexity algorithm is proposed to jointly optimize the dropout rate and the bandwidth allocation for minimizing the loss function in all rounds under a given per-round latency and limited network resources. Finally, numerical results are provided to verify the effectiveness of the proposed algorithm.

arXiv:2501.00193v1 Announce Type: new Abstract: Pseudo-random number generators (PRNGs) are essential in a wide range of applications, from cryptography to statistical simulations and optimization algorithms. While uniform randomness is crucial for security-critical areas like cryptography, many domains, such as simulated annealing and CMOS-based Ising Machines, benefit from controlled or non-uniform randomness to enhance solution exploration and optimize performance. This paper presents a hardware PRNG that can simultaneously generate multiple uncorrelated sequences with programmable statistics tailored to specific application needs. Designed in 65nm process, the PRNG occupies an area of approximately 0.0013mm^2 and has an energy consumption of 0.57pJ/bit. Simulations confirm the PRNG's effectiveness in modulating the statistical distribution while demonstrating high-quality randomness properties.

arXiv:2501.00214v1 Announce Type: new Abstract: In this work, we propose an error-free, information-theoretically secure, asynchronous multi-valued validated Byzantine agreement (MVBA) protocol, called OciorMVBA. This protocol achieves MVBA consensus on a message $\boldsymbol{w}$ with expected $O(n |\boldsymbol{w}|\log n + n^2 \log q)$ communication bits, expected $O(n^2)$ messages, expected $O(\log n)$ rounds, and expected $O(\log n)$ common coins, under optimal resilience $n \geq 3t + 1$ in an $n$-node network, where up to $t$ nodes may be dishonest. Here, $q$ denotes the alphabet size of the error correction code used in the protocol. When error correction codes with a constant alphabet size (e.g., Expander Codes) are used, $q$ becomes a constant. An MVBA protocol that guarantees all required properties without relying on any cryptographic assumptions, such as signatures or hashing, except for the common coin assumption, is said to be information-theoretically secure (IT secure). Under the common coin assumption, an MVBA protocol that guarantees all required properties in all executions is said to be error-free. We also propose another error-free, IT-secure, asynchronous MVBA protocol, called OciorMVBArr. This protocol achieves MVBA consensus with expected $O(n |\boldsymbol{w}| + n^2 \log n)$ communication bits, expected $O(1)$ rounds, and expected $O(1)$ common coins, under a relaxed resilience (RR) of $n \geq 5t + 1$. Additionally, we propose a hash-based asynchronous MVBA protocol, called OciorMVBAh. This protocol achieves MVBA consensus with expected $O(n |\boldsymbol{w}| + n^3)$ bits, expected $O(1)$ rounds, and expected $O(1)$ common coins, under optimal resilience $n \geq 3t + 1$.

arXiv:2501.00281v1 Announce Type: new Abstract: Noisy channels are valuable resources for cryptography, enabling information-theoretically secure protocols for cryptographic primitives like bit commitment and oblivious transfer. While existing work has primarily considered memoryless channels, we consider more flexible channel resources that a dishonest player can configure arbitrarily within some constraints on their min-entropy. We present a protocol for string commitment over such channels that is complete, hiding, and binding, and derive its achievable commitment rate, demonstrating the possibility of string commitment in noisy channels with a stronger adversarial model. The asymptotic commitment rate coincides with previous results when the adversarial channels are the same binary symmetric channel as in the honest case.

arXiv:2501.00371v1 Announce Type: new Abstract: Our work addresses the well-known open problem of distributed computing of bilinear functions of two correlated sources ${\bf A}$ and ${\bf B}$. In a setting with two nodes, with the first node having access to ${\bf A}$ and the second to ${\bf B}$, we establish bounds on the optimal sum-rate that allows a receiver to compute an important class of non-linear functions, and in particular bilinear functions, including dot products $\langle {\bf A},{\bf B}\rangle$, and general matrix products ${\bf A}^{\intercal}{\bf B}$ over finite fields. The bounds are tight, for large field sizes, for which case we can derive the exact fundamental performance limits for all problem dimensions and a large class of sources. Our achievability scheme involves the design of non-linear transformations of ${\bf A}$ and ${\bf B}$, which are carefully calibrated to work synergistically with the structured linear encoding scheme by K\"orner and Marton. The subsequent converse derived here, calibrates the Han-Kobayashi approach to yield a relatively tight converse on the sum rate. We also demonstrate unbounded compression gains over Slepian-Wolf coding, depending on the source correlations. In the end, our work derives fundamental limits for distributed computing of a crucial class of functions, succinctly capturing the computation structures and source correlations. Our findings are subsequently applied to the practical master-workers-receiver framework, where each of $N$ distributed workers has a bounded memory reflecting a bounded computational capability. By combining the above scheme with the polynomial code framework, we design novel structured polynomial codes for distributed matrix multiplication, and show that our codes can surpass the performance of the existing state of art, while also adapting these new codes to support chain matrix multiplications and information-theoretically secure computations.

arXiv:2404.17973v2 Announce Type: replace Abstract: The 6G mobile networks feature two new usage scenarios -- distributed sensing and edge artificial intelligence (AI). Their natural integration, termed integrated sensing and edge AI (ISEA), promises to create a platform that enables intelligent environment perception for wide-ranging applications. A basic operation in ISEA is for a fusion center to acquire and fuse features of spatial sensing data distributed at many edge devices (known as agents), which is confronted by a communication bottleneck due to multiple access over hostile wireless channels. To address this issue, we propose a novel framework, called Spatial Over-the-Air Fusion (Spatial AirFusion), which exploits radio waveform superposition to aggregate spatially sparse features over the air and thereby enables simultaneous access. The framework supports simultaneous aggregation over multiple voxels, which partition the 3D sensing region, and across multiple subcarriers. It exploits both spatial feature sparsity with channel diversity to pair voxel-level aggregation tasks and subcarriers to maximize the minimum receive signal-to-noise ratio among voxels. Optimally solving the resultant mixed-integer problem of Voxel-Carrier Pairing and Power Allocation (VoCa-PPA) is a focus of this work. The proposed approach hinges on derivations of optimal power allocation as a closed-form function of voxel-carrier pairing and a useful property of VoCa-PPA that allows dramatic solution space reduction. Both a low-complexity greedy algorithm and an optimal tree-search algorithm are then designed for VoCa-PPA. The latter is accelerated with a customised compact search tree, node pruning and agent ordering. Extensive simulations using real datasets demonstrate that Spatial AirFusion significantly reduces computation errors and improves sensing accuracy compared with conventional over-the-air computation without awareness of spatial sparsity.