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With its growing number of deployed devices and applications, the Internet of Things (IoT) raises significant challenges for network maintenance procedures. In this work, we address a problem of active fault detection in an IoT scenario, whereby a monitor can probe a remote device to acquire fresh information and facilitate fault detection. However, probing could significantly impact the system’s energy and communication resources. To this end, we utilize the Age of Information to measure the freshness of information at the monitor and adopt a semantics-aware communication approach between the monitor and the remote device. In semantics-aware communications, the processes of generating and transmitting information are treated jointly to consider the importance of information and the purpose of communication. We formulate the problem as a Partially Observable Markov Decision Process and show analytically that the optimal policy is of a threshold type. Finally, we use a computationally efficient stochastic approximation algorithm to approximate the optimal policy and present numerical results that exhibit the advantage of our approach compared to a conventional delay-based probing policy.

As the process of envisioning, researching, and strategizing for 6G communications has commenced, we present our vision on the role of semantic communications (SemCom) in the beyond 5G era. In the following discourse, we delve into the factors that may impede the progress of research and implementation of 6G with the objective of catering to the semantically enriched communication requirements of the 2030s. In addition, we proceed to establish the fundamental attributes of SemCom and engage in an examination of the requisite technological modalities. In order to bolster this overarching vision, we introduce a semantic networking architecture that encapsulates and characterizes various application scenarios of SemCom. Expanding on the concept of point-to-point SemCom systems the semantic networking architecture focuses on extracting and filtering goal specific semantic information at the source before transmitting signals. It also involves decoding and post processing semantics at the destination. What sets this networking architecture apart is its transformation into a multi-user distributed, edge-to-cloud network with deadline constraint traffic. Given its complexity, implementing such an architecture necessitates frameworks for extracting and representing knowledge theoretical models to predict and manage multiple time varying deadline/delay constrained traffic flows that may cause significant congestion in the network as well, as innovative metrics infused with semantics to measure performance while encapsulating its inherent relevance. Finally, we provide research considerations and future directions towards the integration of semantics in the forthcoming 6G wireless systems.

One of the fundamental challenges in 5G and beyond technologies is to support short packet transmissions while ensuring ultra-reliable communication. Due to the distributed nature of the networks, such as machine-to-machine (M2M) communications, interference is unavoidable. The impact of interference on the system’s performance must be better understood when users are constrained to transmit short packets. In addition, users’ traffic is bursty. Thus, they may not always have data to send. This work considers a two-user Z-interference channel (Z-IC) under Rayleigh fading. The work characterizes the stability region corresponding to prominent interference mitigation schemes such as treating interference as noise, successive interference cancellation, and joint decoding schemes using the finite block-length information theory framework. The developed results consider the packet length, rate, and underlying channel model. Evaluating stability region involves determining the probability of successful decoding for the various interference mitigation techniques. The different probabilities of successful decoding are characterized for various interference mitigation techniques. These results are not explored in the existing literature in the context of Z-IC. The developed results also help to explore the impact of interference on average delay and the average age of information for various interference mitigation techniques.

This paper considers the motion energy minimization problem for a robot that uses millimeter-wave (mm-wave) communications assisted by an intelligent reflective surface (IRS). The robot must perform tasks within given deadlines, and it is subject to quality of service (QoS) constraints. This problem is crucial for fully automated factories governed by the binomial of autonomous robots and new generations of mobile communications, i.e., 5G and 6G. In this new context, robot energy efficiency and communication reliability remain fundamental problems that couple in optimizing robot trajectory and communication QoS. More precisely, to account for the mutual dependency between robot position and communication QoS, robot trajectory and beamforming at the IRS and access point all need to be optimized. We present a solution that can decouple the two problems by exploiting mm-wave channel characteristics. Then, a closed-form solution is obtained for the beamforming optimization problem, whereas the trajectory is optimized by a novel successive-convex optimization-based algorithm that can deal with abrupt line-of-sight (LOS) to non-line-of-sight (NLOS) transitions. Specifically, the algorithm uses a radio map to avoid obstacles and poorly covered areas. We prove that the algorithm can converge to a solution satisfying the Karush-Kuhn-Tucker conditions. The simulation results show a fast convergence rate of the algorithm and a dramatic reduction of the motion energy consumption with respect to methods that aim to find maximum-rate trajectories. Moreover, we show that passive IRSs represent a powerful solution to improve the radio coverage and motion energy efficiency of robots.

Buffer-aided (BA) relaying improves the diversity of cooperative networks often at the cost of increasing end-to-end packet delays. This characteristic renders BA relaying unsuitable for delay-sensitive applications. However, the increased diversity makes BA relaying appealing for ultra-reliable communications. Towards enabling ultra-reliable low-latency communication (URLLC), we aim at enhancing BA relaying for supporting delay-sensitive applications. In this paper, reliable full-duplex (FD) network operation is targeted and for this purpose, hybrid relay selection algorithms are formulated, combining BA successive relaying (SuR) and delay- and diversity-aware (DDA) half-duplex (HD) algorithms. In this context, a hybrid FD DDA algorithm is presented, namely LoLa4SOR, switching between SuR and HD operation. Additionally, a low-complexity distributed version is given, namely d-LoLa4SOR, providing a trade-off among channel state information requirements and performance. The theoretical analysis shows that the diversity of LoLa4SOR equals to two times the number of available relays $K$ , i.e., $2K$ , when the buffer size $L$ is greater than or equal to 3. Comparisons with other HD, SuR and hybrid algorithms reveal that LoLa4SOR offers superior outage and throughput performance while, the average delay is reduced due to SuR-based FD operation and the consideration of buffer state information for relay-pair selection. d-LoLa4SOR, as one of the few distributed algorithms in the literature, has a reasonable performance that makes it a more practical approach.

In this work, we study age-optimal scheduling with stability constraints in a multiple access channel with two heterogeneous source nodes transmitting to a common destination. The first node is connected to a power grid and it has randomly arriving data packets. Another energy harvesting (EH) sensor monitors a stochastic process and sends status updates to the destination. We formulate an optimization problem that aims at minimizing the average age of information (AoI) of the EH node subject to the queue stability condition of the grid-connected node. First, we consider a Probabilistic Random Access (PRA) policy where both nodes make independent transmission decisions based on some fixed probability distributions. We show that with this policy, the average AoI is equal to the average peak AoI, if the EH node only sends freshly generated samples. In addition, we derive the optimal solution in closed form, which reveals some interesting properties of the considered system. Furthermore, we consider a Drift-Plus-Penalty (DPP) policy and develop AoI-optimal and peak-AoI-optimal scheduling algorithms using the Lyapunov optimization theory. Simulation results show that the DPP policy outperforms the PRA policy in various scenarios, especially when the destination node has low multi-packet reception capabilities.

In this paper, we analyze the impact of caching on the performance of a cache enabled system with heterogeneous traffic where one of the users need to be served with confidential data. In this setup, a wireless helper system always serves a dedicated user and it can also serve a user requesting cached content. A cellular network access point is also available to serve the latter user if it cannot retrieve the requested data from the helper’s cache. The impact of caching and secrecy on throughput and delay performance for each user is then examined when the access point can deploy superposition coding to serve both users simultaneously. Two decoding schemes are considered in this work. The first decoding scheme treats interference from parallel transmissions as noise while the second one utilizes the parallel transmission to apply successive decoding for the intended data. Furthermore, network and cache related factors are identified and their impact on the overall performance of the system are analyzed. In order to find the optimal transmission power allocations, two distinct optimization problems are set in this context comparing the two decoding schemes. This will assist to identify the benefits of the considered decoding schemes for each user satisfying the secrecy requirements of the dedicated user and reducing its impact on the overall performance of the system.

Future mobile networks supporting Internet of Things are expected to provide both high throughput and low latency to user-specific services. One way to overcome this challenge is to adopt Network Function Virtualization (NFV) and Multi-access Edge Computing (MEC). Besides latency constraints, these services may have strict function chaining requirements. The distribution of network functions over different hosts and more flexible routing caused by service function chaining raise new challenges for end-to-end performance analysis. In this paper, as a first step, we analyze an end-to-end communication system that consists of both MEC servers and a server at the core network hosting different types of virtual network functions. We develop a queueing model for the performance analysis of the system consisting of both processing and transmission flows. We propose a method in order to derive analytical expressions of the performance metrics of interest, i.e., end-to-end delay, system throughput, task drop rate. Then, we show how to apply the similar method to a larger system and derive a stochastic model for such systems. We observe that the simulation and analytical results are very close. By evaluating the system under different scenarios, we provide insights for the decision making on traffic flow control and its impact on critical performance metrics.

We investigate the effect of bursty traffic in a long term evolution (LTE) and Wi-Fi aggregation (LWA)-enabled network. The LTE base station routes packets of the same IP flow through the LTE and Wi-Fi links independently. We motivate the use of superposition coding at the LWA-mode Wi-Fi access point (AP) so that it can serve LWA users and Wi-Fi users simultaneously. A random access protocol is applied in such system, which allows the native-mode AP to access the channel with probabilities that depend on the queue size of the LWA-mode AP to avoid impeding the performance of the LWA-enabled network. We analyze the throughput of the native Wi-Fi network and the delay experienced by the LWA users, accounting for the native-mode AP access probability, the traffic flow splitting between LTE and Wi-Fi, and the operating mode of the LWA user with both LTE and Wi-Fi interfaces. Our results show some fundamental tradeoffs in the throughput and delay behavior of LWA-enabled networks, which provide meaningful insight into the operation of such aggregated systems.

In this paper, we investigate the effect of bursty traffic and random availability of caching helpers in a wireless caching system. More explicitly, we consider a general system consisting of a caching helper with its dedicated user in proximity and another non-dedicated user requesting for content. Both the non-dedicated user and the helper have limited storage capabilities. When the user is not able to locate the requested content in its own cache, then its request shall be served either by the caching helper or by a large data center. Assuming bursty request arrivals at the caching helper from its dedicated destination, its availability to serve other users is affected by the request rate, which will further affect the system throughput and the delay experienced by the non-dedicated user. We characterize the maximum weighted throughput and the average delay per packet of the considered system, taking into account the request arrival rate of the caching helper, the request probability of the user, and the availability of the data center. Our results provide fundamental insights in the throughput and delay behavior of such systems, which are essential for further investigation in larger topologies.

In this paper, we analyze the probabilistic cooperation of a full-duplex relay in a multiuser random-access network. The relay is equipped with on/off modes for the receiver and the transmitter independently. These modes are modeled as probabilities by which the receiver and the transmitter are activated. We provide analytical expressions for the performance of the relay queue, such as arrival and service rates, stability conditions, and the average queue size. We optimize the relay's operation setup to maximize the network-wide throughput while, simultaneously, we keep the relay's queue stable and lower the relay's receptions and transmissions. Furthermore, we study the effect of the SINR threshold and the self-interference coefficient on the per-user and network-wide throughput. For low SINR threshold, we show under which circumstances it is beneficial to switch off the relay completely, or switch off the relay's receiver only.