This paper studies an integrated sensing and communication (ISAC) system within a centralized cell-free massive MIMO (multiple-input multiple-output) network for target detection. ISAC transmit access points serve the user equipments in the downlink and optionally steer a beam toward the target in a multi-static sensing framework. A maximum a posteriori ratio test detector is developed for target detection in the presence of clutter, so-called target-free signals. Additionally, sensing spectral efficiency (SE) is introduced as a key metric, capturing the impact of resource utilization in ISAC. A power allocation algorithm is proposed to maximize the sensing signal-to-interference-plus-noise ratio while ensuring minimum communication requirements. Two ISAC configurations are studied: utilizing existing communication beams for sensing and using additional sensing beams. The proposed algorithm’s efficiency is investigated in realistic and idealistic scenarios, corresponding to the presence and absence of the target-free channels, respectively. Despite performance degradation in the presence of target-free channels, the proposed algorithm outperforms the interference-unaware benchmark, leveraging clutter statistics. Comparisons with a fully communication-centric algorithm reveal superior performance in both cluttered and clutter-free environments. The incorporation of an extra sensing beam enhances detection performance for lower radar cross-section variances. Moreover, the results demonstrate the effectiveness of the integrated operation of sensing and communication compared to an orthogonal resource-sharing approach.

For the energy-efficient deployment of cell-free massive MIMO functionality in a practical wireless network, the end-to-end (from radio site to the cloud) energy-aware operation is essential. In line with the cloudification and virtualization in the open radio access networks (O-RAN), it is indisputable to envision prospective cell-free infrastructure on top of the O-RAN architecture. In this paper, we explore the performance and power consumption of cell-free massive MIMO technology in comparison with traditional small-cell systems, in the virtualized O-RAN architecture. We compare two different functional split options and different resource orchestration mechanisms. In the end-to-end orchestration scheme, we aim to minimize the end-to-end power consumption by jointly allocating the radio, optical fronthaul, and virtualized cloud processing resources. We compare end-to-end orchestration with two other schemes: 1) “radio-only” where radio resources are optimized independently from the cloud; and 2) “local cloud coordination” where orchestration is only allowed among a local cluster of radio units. We develop several algorithms to solve the end-to-end power minimization and sum spectral efficiency maximization problems. The numerical results demonstrate that end-to-end resource allocation with fully virtualized fronthaul and cloud resources provides a substantial additional power saving than the other resource orchestration schemes.

Migration from distributed to centralized radio access networks (C-RANs) can be expensive in terms of capital expenditures due to the initial investment while it has lower operational expenditures due to pooling baseband processing into the cloud and reduced power consumption. Partial centralization can be also an option by employing network function splitting and keeping lower physical layer functions colocated with the RUs. This increases the power consumption but relaxes the high capacity requirement in the fronthaul. It is not intuitive which migration strategy is more cost effective. In this article, we formulate a pool placement optimization problem as an integer linear programming (ILP), which minimizes the total cost of ownership (TCO), and evaluate the migration cost to C-RAN with both full centralization of network functions, and partial centralization by using function splitting. We define a network upgrade optimization problem, by adding new cells to the network, as a revisited version of the original optimization problem to evaluate the upgradability of the architectures. We solve the problem with both ILP for optimality, and genetic algorithm for scalability. Simulation results show that partial centralization results in optimal TCO with lower crossover time compared to C-RAN with full centralization.

Telecommunications play a pivotal role in shaping today’s interconnected world by fostering global development, supporting seamless information exchange across vast distances, and revolutionizing the interactions between individuals, businesses, and governments. Accessible and reliable communication networks transcend geographical barriers, promoting economic growth, the dissemination of knowledge, and societal connectivity. The integration of artificial intelligence into telecommunications has been transformative, revolutionizing the entire industry by improving system efficiency, allowing new services, and reducing complexity. By leveraging machine learning algorithms, telecommunication operators analyze vast data sets to gain insights into customer behavior, network performance, and market trends. This data-driven approach enhances service efficiency, leading to optimized network deployment, improved customer experience, and targeted marketing strategies. Machine learning’s impact extends to resource allocation optimization. Intelligent management of network resources reduces latency, congestion, and downtime, ensuring enhanced user experiences and increased overall network capacity. This optimization is vital for integrating emerging technologies like the Internet of Things and future generations of mobile systems and promoting sustainability by reducing energy consumption, contributing to a greener future. As technology evolves, the synergy between telecommunications and artificial intelligence will pave the way for a more connected, intelligent, and prosperous future. Given the relevance of this research topic, this paper presents a comprehensive survey of machine learning techniques applied to resource allocation in wireless communication systems. The objective is to guide the scientific and industrial community in the optimized selection of machine learning techniques according to network demands and network resource allocation to be refined. Additionally, it aims to encourage an in-depth discussion regarding the limitations presented in the current literature and future challenges for researchers.

Aerial vehicles (AVs) such as electric vertical take-off and landing (eVTOL) aircraft make aerial passenger transportation a reality in urban environments. However, their communication connectivity is still under research to realize their safe and full-scale operation. This paper envisages a multi-connectivity (MC) enabled aerial network to provide ubiquitous and reliable service to AVs. Vertical heterogeneous networks with direct air-to-ground (DA2G) and air-to-air (A2A) communication, high altitude platforms (HAPs), and low Earth orbit (LEO) satellites are considered. We evaluate the end-to-end (E2E) multi-hop reliability and network availability of the downlink of AVs for remote piloting scenarios, and control/telemetry traffic. Command and control (C2) connectivity service requires ultra-reliable and low-latency communication (URLLC), therefore we analyse E2E reliability and latency under the finite blocklength (FBL) regime. We explore how different MC options satisfy the demanding E2E connectivity requirements taking into account antenna radiation patterns and unreliable backhaul links. Since providing seamless connectivity to AVs is very challenging due to the line-of-sight (LoS) interference and reduced gains of downtilt ground base station (BS) antennas, we use coordinated multi-point (CoMP) among ground BSs to alleviate the inter-cell interference. Furthermore, we solve an optimization problem to select the best MC path under the quality of service (QoS) constraints. We maximize spectral efficiency (SE) to specify the optimum MC path with the minimum number of required links. Based on the simulation results, we find out that even with very efficient interference mitigation, MC is the key enabler for safe remote piloting operations.

Mobility management for terrestrial users is mostly concerned with avoiding radio link failure for the edge users where the cell boundaries are defined. The problem becomes interesting for an aerial user experiencing fragmented coverage in the sky and line-of-sight conditions with multiple ground base stations (BSs). For aerial users, mobility management is not only concerned with avoiding link failures but also avoiding unnecessary handovers while maintaining extended service availability, especially in up-link communication. The line of sight conditions from an Unmanned Aerial Vehicle (UAV) to multiple neighboring BSs make it more prone to frequent handovers, leading to control packet overheads and delays in the communication service. Depending on the use cases, UAVs require a certain level of service availability, which makes their mobility management a critical task. The current mobility robustness optimization (MRO) procedure that adaptively manages handover parameters to avoid unnecessary handovers is optimized only for terrestrial users. It needs to be updated to capture the unique mobility challenges of aerial users. In this work, we propose two approaches to accomplish this: 1) A model based service availability-aware MRO where handover control parameters, such as handover margin and time to trigger are tuned to maintain high service availability with a minimum number of handovers, and, 2) A deep Q-network based model free approach for decreasing unnecessary handovers while maintaining high service availability. Simulation results demonstrate that both the proposed algorithms converge promptly and increase the service availability by more than 40% while the number of handovers is reduced by more than 50% as compared to traditional approaches.