The rapid proliferation of devices brought about by the Internet of Things (IoT) has underpinned the inclusion of sensing capabilities in wireless systems. Therefore, integrated sensing and communication (ISAC) is emerging as a key use case for next-generation systems. The dual functionality of ISAC systems increases interference, which multiple-input multipleoutput (MIMO) arrays can mitigate through spatial diversity. However, achieving substantial diversity gains with MIMO requires large antenna arrays, which could amplify the system complexity. Fluid antennas (FAs) provide an efficient alternative, delivering comparable spatial gains without the need for massive arrays, making them a promising candidate for ISAC systems. In this paper, we propose a quality-of-service (QoS) aware powerefficient transceiver design for an FA-ISAC system. We jointly design the antenna position vector, transmit beamformers, radar signal and receive combiners to meet the sensing and communication performance constraints. Our results demonstrate a 2 dB improvement compared to conventional MIMO systems.

With the mass inclusion of machines in network architectures, integrated sensing and communication (ISAC) is emerging as a key use-case of 6 G communication systems. The dual functionality of ISAC introduces prohibitive energy and spectrum costs, which are further exacerbated by the increasing scale of modern networks. This escalating demand highlights the urgent need for green network architectures that minimize power consumption and improve energy efficiency. Backscatter technology offers a promising avenue for alleviating resource constraints, as it utilizes existing radio-frequency signals, enhancing both spectrum and energy efficiency. However, the interference between the sensing and communication signals can further increase resource consumption. Hence, green communication, with a directed focus on power minimisation and energy efficiency, is essential for next-generation systems. In this paper, we present a green, quality-of-service (QoS) -aware transceiver design for integrated sensing and backscatter communication (ISABC) systems. The sensing and communication QoS constraints ensure the functional operation of target sensing and backscatter communication. We jointly optimize the radar signal, precoding vector, and receive combiners to minimize power consumption at the dual-function base station while meeting both sensing and communication requirements. The proposed green ISABC design demonstrates a 12 dB performance improvement compared to equivalent radar sensing and uplink communication systems.

The rapid surge in network sizes, driven by the proliferation of wireless applications, has placed unprecedented demands on wireless systems leading to high interference, spectrum bottlenecks, and increased power utilization. Reconfigurable intelligent surfaces (RISs) have emerged as a promising candidate for these challenges owing to their passive beamforming action, but have limited gains due to independently functioning elements. Consequently, beyond-diagonal RISs (BDRISs) are proposed as the key-enabler of next-generation systems. In this paper, we propose a fairness-aware design for BDRIS-assisted fluid antenna systems. We jointly optimize the antenna position vector, beamforming vectors, and BDRIS configuration matrix to address service fairness while meeting the power budget in a multi-user downlink system. To tackle this non-convex problem, we utilize proximal policy optimization and obtain a fast-converging solution. Our results demonstrate a 2.8 bps/Hz mean rate improvement as compared to conventional RIS systems.

The applications of networking have undergone drastic evolutions since the commercialization of the first generation (1 G) wireless systems. These evolutions have gone from human-to-human (H2H) voice and text communications to the seamless network of people, devices, and objects, which exchange information to assist, accelerate, monitor, and actuate different social, personal, and interpersonal processes. The next-generation systems envision a unified networking architecture which meets the diverse technical specifications of industry 5.0 systems. Currently deployed systems do not provide an eco-friendly architecture for the mass-scale integration of wireless networking. Additionally, the high capital costs of deployment hinder the realization of seamless connectivity for the next industrial revolution. Intelligent reflecting surfaces (IRS) have emerged as a potentially revolutionary and environmentally sustainable technology to enable the concentrated real-world networks required to support advanced cyber-physical systems in the Internet-of-everything (IoE). In this work, we propose a machine learning-enabled resource allocation architecture for IRS-aided unmanned aerial vehicle (UAV) networks. Simulations show that the proposed scheme outperforms traditional resource allocation systems, and is feasible for real-time wireless network optimizations.

While conventional communication systems sufficiently meet the demands of human-to-human (H2H) information exchange, they cannot support the seamless mass-scale inclusion of non-human communication entities for next generation technological applications. In this context, intelligent reflecting surfaces (IRS) appear as a promising eco-friendly disruptive technology for the extremely dense practical realizations of wireless infrastructures required for futuristic cyber-physical systems. To better exploit IRS to enable ultra-massive connectivity, this work employs element-sharing between multiple users in a practical reflection model enabled IRS-aided wireless system. The element-sharing paradigm of allotment provides spectral efficiency gains while keeping the scale of IRS panels in feasible physical deployment and cost constraints. We investigate the dedicated and shared element-sharing schemes for IRSs under different operating conditions. Simulation results show that element-sharing outperforms dedicated element-allotment in systems with a higher number of served users and a limited number of reflecting elements while also being more robust to channel estimation and phase optimization errors.

Localization has gained great attention in recent years, where different technologies have been utilized to achieve high positioning accuracy. Fingerprinting is a common technique for indoor positioning using short-range radio frequency (RF) technologies such as Bluetooth Low Energy (BLE). In this paper, we investigate the suitability of LoRa (Long Range) technology to implement a positioning system using received signal strength indicator (RSSI) fingerprinting. We test in real line-of-sight (LOS) and non-LOS (NLOS) environments to determine appropriate LoRa packet specifications for an accurate RSSI-to-distance mapping function. To further improve the positioning accuracy, we consider the environmental context. Extensive experiments are conducted to examine the performance of LoRa at different spreading factors. We analyze the path loss exponent and the standard deviation of shadowing in each environment.