Mihai-Alin Badiu

The wealth of spectral resources in millimeter-wave (mmWave) and terahertz (THz) bands has the potential to support data transmission with Gbps, yet the narrow beams required at these frequencies introduce critical challenges in beam management. This paper investigates the application of integrated sensing and communications (ISAC) in the fifth-generation (5G) and beyond networks. By configuring synchronization signal blocks (SSB) and positioning reference signals (PRS), the proposed ISAC scheme enables intelligent inter-cell handovers and adaptive beam alignment for mobile terminals (MTs). With the tools from stochastic geometry, we first develop a unified analytical framework that yields tractable expressions for sensing and communication coverage probability and beam misalignment probability, explicitly capturing blockage process, beam dynamics, and multi-cell interference. Subsequently, we derive the optimal pilot design pattern that minimizes the beam misalignment probability. Taking into account both coverage performance and beam management overhead, the effective area spectral efficiency (ASE) metric is proposed to quantify the network-level benefits of ISAC. In addition, Monte Carlo simulation results are introduced to validate the theoretical analysis. Our study demonstrates that the proposed ISAC scheme can effectively enhance the ASE of the network and reduce the beam management overhead, especially in scenarios of dense base station (BS) deployments and large beam numbers. Furthermore, a fundamental beamwidth tradeoff is revealed: Although narrower beams improve the coverage for sensing, they can increase mobility-induced misalignment probabilities for communications. These insights provide concrete guidelines for practical ISAC-assisted beam management in future networks.

Internet of Things (IoT) is an emerging next-generation technology in the fourth industrial revolution. The industrial IoT is required to transmit the collected data in a timely manner to support real-time monitoring, control and automation. In such systems, the timeliness of information is very important, and meanwhile, different physical processes have different requirements on the accuracy of timeliness. However, existing performance metrics, such as the age of information (AoI), are unable to fully evaluate the timeliness of information with heterogeneous physical processes. Recently, we proposed an information-theoretic metric named the “value of information" (VoI) to measure the usefulness of information in the context of a heterogeneous and noisy environment. In this work, we study a joint path planning of the mobile robot and user scheduling optimisation problem in industrial IoT networks with the aim of maximising the minimum VoI among all users under mobility and communication constraints. We formulate this optimisation problem as a Markov decision process, and propose a reinforcement learning-based algorithm to find the VoI-aware mobility and communication strategy efficiently. Through numerical results, we show that the proposed method can capture the impact of data freshness, inherent correlation characteristics of underlying data sources and noise on the usefulness of information. Compared with the existing AoI-aware strategy, the proposed VoI-aware strategy achieves better performance by exploiting the heterogeneity of data sources especially when the wireless resource is limited.

The stochastic block model (SBM) is extensively used to model networks in which users belong to certain communities. In recent years, the study of information-theoretic compression of such networks has gained attention, with works primarily focusing on lossless compression. In this work, we address the lossy compression of SBM graphs by characterizing the rate-distortion function under a Hamming distortion constraint. Specifically, we derive the conditional rate-distortion function of the SBM with community membership as side information. We approach this problem as the classical Wyner-Ziv lossy problem by minimising mutual information of the graph and its reconstruction conditioned on community labels. Lastly, we also derive the rate-distortion function of the Erdős-Rényi (ER) random graph model.

Measuring the complexity of tree structures can be beneficial in areas that use tree data structures for storage, communication, and processing purposes. This complexity can then be used to compress tree data structures to their information-theoretic limit. Additionally, the lack of models for random generation of trees is very much felt in mathematical modeling of trees and graphs. In this paper, a number of existing tree generation models such as simply generated trees are discussed, and their information content is analysed by means of information theory and Shannon's entropy. Subsequently, a new model for generating trees based on practical appearances of trees is introduced, and an upper bound for its entropy is calculated. This model is based on selecting a random tree from possible spanning trees of graphs, which is what happens often in practice. Moving on to tree compression, we find approaches to universal tree compression of the discussed models. These approaches first transform a tree into a sequence of symbols, and then apply a dictionary-based compression method. Conditions for the universality of these method are then studied and analysed.

The Internet of Things (IoT) is an emerging next-generation technology in the fourth industrial revolution. In industrial IoT networks, sensing devices are largely deployed to monitor various types of physical processes. They are required to transmit the collected data in a timely manner to support real-time monitoring, control and automation. The timeliness of information is very important in such systems. Recently, an information-theoretic metric named the "value of information" (VoI) has been proposed to measure the usefulness of information. In this work, we consider an industrial IoT network with a set of heterogeneous sensing devices and an intelligent mobile entity. The concept of the value of information is applied to study a joint path planning and user scheduling optimisation problem. We aim to maximise the network-level VoI under mobility and communication constraints. We formulate this problem as a Markov decision process (MDP), and an efficient algorithm based on reinforcement learning is proposed to solve this problem. Through numerical results, we show that the proposed method is able to capture the usefulness of data from both time and space dimensions. By exploiting the correlation property of the data source, the proposed method is suitable for applications in resource-limited networks.

In this paper, we investigate the performance of a reconfigurable intelligent surface (RIS) assisted large-scale network by characterizing the coverage probability and the average achievable rate using stochastic geometry. Considering the spatial correlation between transmitters (TXs) and RISs, their locations are jointly modelled by a Gauss-Poisson process (GPP). Two association strategies, i.e., nearest association and fixed association, are both discussed. For the RIS-aided transmission, the signal power distribution with a direct link is approximated by a gamma random variable using a moment matching method, and the Laplace transform of the aggregate interference power is derived in closed form. Based on these expressions, we analyze the channel hardening effect in the RIS-assisted transmission, the coverage probability, and the average achievable rate of the typical user. We derive the coverage probability expressions for the fixed association strategy and the nearest association strategy in an interference-limited scenario in closed form. Numerical results are provided to validate the analysis and illustrate the effectiveness of RIS-assisted transmission with passive beamforming in improving the system performance. Furthermore, it is also unveiled that the system performance is independent of the density of TXs with the nearest association strategy in the interference-limited scenario.

Routing is one of the critical and ongoing challenges in Wireless Sensor Networks. The main challenge has always been to have a routing protocol that reduces the communication overhead, hence saving the energy of the sensors in the network. Hierarchical routing protocols are known to be the most energy-efficient routing protocols for Wireless Sensor Networks. In this paper, a more generalized hierarchical routing protocol is introduced for Wireless Sensor Network, which is based on tree data structures. The clustering in the proposed protocol has the format of a general tree, which is constructed in an adaptive manner based on the distance of the sensors. Results show that the proposed tree-based protocol introduces a significant benefit in energy consumption and lifetime of the network over the existing hierarchical approaches.

We study the richness of the ensemble of graphical structures (i.e., unlabeled graphs) of the one-dimensional random geometric graph model defined by $n$ nodes randomly scattered in [0, 1] that connect if they are within the connection range $r\in [{0,1}]$ . We provide bounds on the number of possible structures which give universal upper bounds on the structural entropy that hold for any $n$ , $r$ and distribution of the node locations. For fixed $r$ , the number of structures is $\Theta (a^{2n})$ with $a=a(r)=2 \cos {\left ({\frac {\pi }{\lceil 1/r \rceil +2}}\right)}$ , and therefore the structural entropy is upper bounded by $2n\log _{2} a(r) + O(1)$ . For large $n$ , we derive bounds on the structural entropy normalized by $n$ , and evaluate them for independent and uniformly distributed node locations. When the connection range $r_{n}$ is $O(1/n)$ , the obtained upper bound is given in terms of a function that increases with $n r_{n}$ and asymptotically attains 2 bits per node. If the connection range is bounded away from zero and one, the upper and lower bounds decrease linearly with $r$ , as $2(1-r)$ and $(1-r)\log _{2} e$ , respectively. When $r_{n}$ is vanishing but dominates $1/n$ (e.g., $r_{n} \propto \ln n / n$ ), the normalized entropy is between $\log _{2} e \approx 1.44$ and 2 bits per node. We also give a simple encoding scheme for random structures that requires 2 bits per node. The upper bounds in this paper easily extend to the entropy of the labeled random graph model, since this is given by the structural entropy plus a term that accounts for all the permutations of node labels that are possible for a given structure, which is no larger than $\log _{2}(n!) = n \log _{2} n {-} n + O(\log _{2} n)$ .

In this paper, we investigate the performance of a reconfigurable intelligent surface (RIS) assisted large-scale network by characterizing the coverage probability and the average achievable rate using stochastic geometry. Considering the spatial correlation between transmitters (TXs) and RISs, their locations are jointly modelled by a Gauss-Poisson process (GPP). Two association strategies, i.e., nearest association and fixed association, are both discussed. For the RIS-aided transmission, the signal power distribution with a direct link is approximated by a gamma random variable using a moment matching method, and the Laplace transform of the aggregate interference power is derived in closed form. Based on these expressions, we analyze the channel hardening effect in the RIS-assisted transmission, the coverage probability, and the average achievable rate of the typical user. We derive the coverage probability expressions for the fixed association strategy and the nearest association strategy in an interference-limited scenario in closed form. Numerical results are provided to validate the analysis and illustrate the effectiveness of RIS-assisted transmission with passive beamforming in improving the system performance. Furthermore, it is also unveiled that the system performance is independent of the density of TXs with the nearest association strategy in the interference-limited scenario.