Despite extensive research on Quality-of-Services (QoS) analysis of 5G networks, practical applications remain limited. This gap may be attributed to the lack of available tools and the complexity of developing or configuring technical systems to implement theoretical approaches. To address this, we developed an edge control system within a private 5G base station, utilizing Precision Time Protocol (PTP) to enable accurate Age of Information (AoI) measurements. Through analysis using AoI and Age of Actuation (AoA) metrics, we demonstrate the accomplishment of comparable results in an emulated Local Area Network (LAN), which offers greater accessibility and reduced complexity. We also test the reliability of network emulation application on system performance analysis.

We study the real-time remote tracking of a two-state Markov process powered by an energy harvesting source. The source dynamically decides whether to transmit over an unreliable channel based on the state of the system. This problem is formulated as a Markov decision process (MDP) to determine the optimal transmission policy that minimizes the average Version Innovation Age (VIA) as a key performance metric. We demonstrate that the optimal transmission policy is threshold-based, determined by the battery level, source state, and VIA value. We numerically validate the analytical structure of the optimal policy and compare its performance against two baseline policies under various system parameters, establishing the superior performance of our approach.

Full-duplex (FD) communication improves spectral efficiency by allowing simultaneous transmission and reception on the same frequency band. This paper analyzes a two-tier network consisting of macrocells and picocells, where base stations (BSs) operate in FD mode while users operate in half-duplex (HD) mode. However, interference remains a major challenge in these systems, significantly impacting coverage probability. To enhance network performance, we incorporate user offloading and resource partitioning. Offloading redistributes users from macrocells to picocells to balance the network load. However, offloaded users often experience lower SINR compared to those connected to macrocells. Resource partitioning is applied to mitigate this SINR degradation and reduce interference. This method allocates a fraction of time or frequency resources where macrocells remain inactive, allowing picocells to serve users with less interference. We derive analytical expressions for the downlink coverage probability, considering the effects of offloading, resource partitioning, and key network parameters. Our results indicate that load balancing by itself is insufficient; however, when resource partitioning is combined with offloading, DL coverage probability is significantly improved.

We consider the problem of real-time remote monitoring of a two-state Markov process, where a sensor observes the source state and decides whether to transmit updates over an unreliable channel. We introduce a change-aware randomized stationary policy, in which the source is sampled probabilistically whenever its state changes, and a semantics-aware randomized stationary policy, in which sampling is performed probabilistically based on the current source state and whether the system was in sync in the previous time slot. We then propose two new performance metrics:the Version Innovation Age (VIA), which measures significant changes in content between versions, andthe Age of Incorrect Version (AoIV), which quantifies the outdated versions at the receiver compared to the source when the system is in an incorrect state. We analyze their performance under the proposed and other state-of-the-art sampling policies. Specifically, we derive closed-form expressions for the distributions and averages of VIA, AoIV, and the Age of Incorrect Information (AoII), and formulate three constrained optimization problems to minimize them while accounting for constraints on the time-averaged sampling cost and the reconstruction error. Finally, we compare various sampling and transmission policies and identify the conditions under which each policy performs best.

In this work, we study the real-time tracking and reconstruction of an information source with the purpose of actuation. A device monitors the state of the information source and transmits status updates to a receiver over a wireless erasure channel. We consider two models for the source, namely anN-state Markov chain and anN-state Birth-Death Markov process. We investigate several joint sampling and transmission policies, including a semantics-aware one, and we study their performance for a set of metrics. Specifically, we investigate the real-time reconstruction error and its variance, the cost of actuation error, the consecutive error, and the cost of memory error. These metrics capture different characteristics of the system performance, such as the impact of erroneous actions and the timing of errors. In addition, we propose a randomized stationary sampling and transmission policy and we derive closed-form expressions for the aforementioned metrics. We then formulate two optimization problems. The first optimization problem aims to minimize the time-averaged reconstruction error subject to time-averaged sampling cost constraint. Then, we compare the optimal randomized stationary policy with uniform, change-aware, and semantics-aware sampling policies. Our results show that in the scenario of constrained sampling generation, the optimal randomized stationary policy outperforms all other sampling policies when the source is rapidly evolving. Otherwise, the semantics-aware policy performs the best. The objective of the second optimization problem is to obtain an optimal sampling policy that minimizes the average consecutive error with a constraint on the time-averaged sampling cost. Based on this, we propose await-then-generatesampling policy which is simple to implement.

Abstract This paper presents an exact statistical analysis for the best relay location in a random two‐way relay network whose channels are subject to multi‐slope path loss and Rayleigh fading. In this network, relays are distributed randomly as a two‐dimensional Poisson point process, and the best relay is selected according to a max–min criterion. The midpoint between the two sources is adopted as a reference point and the relays are indexed such that the closer to the reference point, the smaller is the index. An exact analytical expression is derived for the probability that the th nearest relay to the reference point is the best relay. Then, a detailed statistical analysis for the location of the best relay with respect to the reference point is presented. In each case, a low‐complexity approximation is also derived and its accuracy is verified by numerical simulation. The proposed analysis demonstrates the counter‐intuitive fact that the best relay in a random two‐way relay network is quite unlikely to be located on, or very close to the reference point. Numerical results also suggest that the proposed analysis can be used to restrict the search region for finding the best relay.