Shannon's rate-distortion theory treats source symbols as unstructured labels. When the source is a knowledge base equipped with a logical proof system, a natural fidelity criterion is closure fidelity: a reconstruction is acceptable if it preserves the deductive closure of the original. This paper develops a rate-distortion theory under this criterion. Central to the theory is the irredundant core-a canonical generating set extracted by a fixed-order deletion procedure, from which the full deductive closure can be rederived. We prove that the zero-distortion semantic rate equals a quantity that is strictly below the classical entropy rate whenever the knowledge base contains redundant states. More generally, the full semantic rate-distortion function depends only on the core; redundant states are invisible to both rate and distortion. We derive a semantic source-channel separation theorem showing a semantic leverage phenomenon: under closure fidelity, the required source rate is reduced by an asymptotic leverage factor greater than one, allowing the same knowledge base to be communicated with proportionally fewer channel uses-not by violating Shannon capacity, but because redundant states become free. We also prove a strengthened Fano inequality that exploits core structure. For heterogeneous multi-agent communication, an overlap decomposition gives necessary and sufficient conditions for closure-reliable transmission and identifies a semantic bottleneck in broadcast settings that persists even over noiseless channels. All results are verified on Datalog instances with up to 24,000 base facts.
Haixia Cui, Bo Xie, Hongjiang Wang, Victor C. M. Leung
Wireless channel modeling is critical to understanding and optimizing signal transmission. Wireless channels are influenced by many factors, including path loss, reflection, fading, and interference, making them complex and difficult to model and predict. Although some traditional wireless channel modeling methods are effective, they have limitations in handling complex multipath effects and nonlinear characteristics. Generative artificial intelligence (GAI) has the ability to generate robust data and therefore can be potentially used to model realistic wireless channel characteristics. However, there exist some challenges in GAI for wireless channel modeling, including physical layer network security issues, limited generalization ability, and the bandwidth consumption of centralized training. This article introduces a GAI-based wireless channel modeling framework, which leverages blockchain and federated learning to address efficiency, network security, and data privacy concerns in GAI channel modeling. Blockchain technology, through distributed ledgers and smart contracts, enables resource sharing and efficient utilization, enhancing computational efficiency and reducing network security risks. Federated learning allocates training tasks to different devices and nodes, thus allowing model training without centralizing data, protecting user privacy, and reducing network bandwidth usage. This article demonstrates the performance and effectiveness of the proposed framework through numerical results.
In wireless sensor networks (WSN), a large share of the energy demand arises from wireless communication, especially in wide area networks where transmission distances are at the scale of kilometers.Ensuring reliability of communication links while optimizing energy demand requires heterogeneous radio configurations throughout the network demanding for an automated process for identifying suitable transceiver settings in order to mitigate the effort of manual configuration during deployment.Furthermore, wireless links are susceptible to dynamic influences such as environmental conditions and interference from concurrent channel usage, rendering static radio configuration impractical.Therefore, autonomous organization and self-configuration of wireless communication networks, such as transmission parameter optimization, drastically reduce cost and effort for installation and maintenance of large-scale sensor systems.Such dynamic adaptive behavior can be achieved by local execution of decentralized methods that enable decision-making at the network edge, while also inherently offering advantages such as enhanced system robustness and scalability.In this work, we present a method that exemplifies this approach and experimentally evaluate its performance on real hardware.The adaptive algorithm optimizes the transmitter configuration of a LoRa transceiver by employing a model-free reinforcement learning approach based on an actor-critic setup using a parameterized stochastic policy and state-value function approximation.Experimental results show that the approach surpasses a standard approach in terms of long-term energy demand.Furthermore, the method's capability of adapting to dynamic wireless channels is demonstrated.
The future of communication technology is moving from 5G to 6G with new innovations. Blockchain (BC) is a such immersive technology that significantly impacts the betterment of communication technology. BC-based spectrum-sharing solutions can be used in Dynamic Spectrum Access (DSA) systems to fulfill the need for secure and efficient communication. With the invention of cognitive radio networks, DSA became a popular topic for the scientific community. Spectrum misuse/violations can occur due to the rapid growth of spectrum sharing. As the system is open to malicious attacks, licensed spectrum owners must be identified and verified. However, the existing BC-based DSA solutions are more expensive, non-optimized, and lack spectrum misuse detection. This paper proposes a novel consensus algorithm called “Proof of Equation” for spectrum misuse detection. The core of the proposed algorithm is a consensus score calculation based on a numerical equation with three parameters rather than using cryptographic calculations. The performance of the proposed algorithm is studied using Python simulations, and simulation results show that the proposed algorithm outperforms the Proof of Work (PoW) and Proof of Stake (PoS) consensus algorithms in terms of block production time.
A key challenge in 4G and emerging 5G systems is that of reliably detecting the uplink transmissions of users close to the edge between cells. These users are subject to significant signal attenuation due to path loss, and frequent hand-off from one cell to the other, making channel estimation very challenging. Even multiuser detection using base station cooperation often fails to detect such users, due to channel estimation errors and the sensitivity of multiuser detection to near-far power imbalance. Is it even possible to reliably decode the cell-edge users' signals under these circumstances? This paper shows, perhaps surprisingly, that with a suitable base station `interferometry' strategy, the cell-edge users' signals can be reliably decoded at low SNR under mild conditions. Exploiting the fact that cell-edge users' signals are weak but common to both base stations, while users close to a base station are unique to that base station, reliable detection is enabled by Canonical Correlation Analysis (CCA) - a machine learning technique that reliably estimates a common subspace, even in the presence of strong individual interference. Free from cell-center interference, the resulting mixture of cell-edge signals can then be unraveled using well-known algebraic signal processing techniques. Simulations demonstrate that the proposed detector achieves order of magnitude BER improvement compared to an `oracle' zero-forcing with successive interference cancellation that assumes perfect knowledge of all channels. The paper also includes proof of common subspace identifiability for the assumed generative model, which was curiously missing from the machine learning / CCA literature.
We present an efficient simultaneous broadcast protocol ν-SimCast that allows n players to announce independently chosen values, even if up to t < n players are corrupt. Independence is guaranteed in the partially syn-2 chronous communication model, where communication is structured into rounds, while each round is asynchronous. The ν-SimCast protocol is more efficient than previous constructions. For repeated executions, we reduce the communication and computation complexity by a factor O(n). Combined with a deterministic extractor, ν-SimCast provides a particularly efficient solution for distributed coin-flipping. The protocol does not require any zero-knowledge proofs and is shown to be secure in the standard model under the Decisional Diffie Hellman assumption.