This paper explores the integration of neuromorphic computing with wireless sensing, focusing on radar processing within the framework of Integrated Sensing and Communication (ISAC). In this context, we propose a neuromorphic signal processing module that employs an extension of the Spiking Locally Competitive Algorithm (S-LCA) to perform delay-Doppler estimation in an OFDM-based sensing setup. As sensing reference signals, we utilize sequences from a finite-dimensional Gabor frame constructed from time-frequency translates of a seed vector. We refer to a Gabor frame construction based on the Alltop seed vector due to its low mutual coherence and hardware-friendly implementation. The proposed system is deployed on the SpiNNaker neuromorphic platform, demonstrating notable power savings compared to traditional hardware.

Integrated sensing and communication (ISAC) is a cornerstone of sixth-generation (6G) wireless networks, enabling the seamless integration of high-speed communication with precise sensing and localization. The design of ISAC systems typically involves trade-offs between communication and sensing performance. This paper explores different aspects of orthogonal frequency-division multiplexing (OFDM) waveforms for monostatic radar, aiming to improve sensing performance while maintaining communication capabilities. Our derivation shows that range resolution is influenced by the shape of the baseband transmission pulse. However, as more bandwidth is allocated for sensing, the pulse's impact on resolution becomes negligible. Additionally, we investigate how resource allocation strategies affect resolution and ambiguity in both range and Doppler. Several adjacent and non-adjacent schemes are evaluated through simulations using a realistic ISAC framework developed at Fraunhofer HHI. The results highlight key trade-offs and provide recommendations for ISAC waveform design, laying a solid foundation for future research in this area.

Open radio access network (O-RAN) is a paradigm shift in telecommunications, facilitating interoperability and innovation through the disaggregation of traditional monolithic architecture, empowering operators to select equipment from diverse vendors. However, within the multi-vendor O-RAN ecosystem, individual xApps may pursue conflicting objectives. While fine-tuned coordination can alleviate conflicts, it often requires extensive information exchange, raising privacy concerns among competing vendors. This paper delves into these challenges, particularly focusing on the interplay between different xApps, such as energy efficiency (EE) and load balancing (LB), and highlights the tradeoff between performance and level of coordination. To address this, we propose novel algorithms to optimize performance across varying levels of coordination. Initial findings underscore the diminishing returns of coordination, with significant performance gains from zero to partial coordination, yet a more modest increase with full coordination.

We study the problem of selecting a subset of vectors from a large set to obtain the best signal representation over a family of functions. Although greedy methods have been widely used to tackle this problem and many of those have been analyzed under the lens of (weak) submodularity, none of these algorithms are explicitly devised using such a functional property. Here, we revisit the vector-selection problem and introduce a function that is shown to be submodular in expectation. This function not only guarantees near-optimality through a greedy algorithm in expectation but also alleviates the existing deficiencies in commonly used matching pursuit (MP) algorithms. We further show the relation between the single-point-estimate version of the proposed greedy algorithm and MP variants. Moreover, we discuss extending the signal representation problem to instances with knapsack and matroid constraints. Our theoretical findings are supported by numerical experiments on the angle of arrival estimation problem, a typical signal representation task, demonstrating the benefits of our method compared to traditional MP algorithms.

The “near-field” propagation modeling of wireless channels is necessary to support sixth-generation (6G) technologies, such as intelligent reflecting surface (IRS), that are enabled by large aperture antennas and higher frequency carriers. As the conventional far-field model proves inadequate in this context, there is a pressing need to explore and bridge the gap between near and far-field propagation models. Although far-field models are simple and provide computationally efficient solutions for many practical applications, near-field models provide the most accurate representation of wireless channels. This paper builds upon the foundations of electromagnetic wave propagation theory to derive near and far-field models as approximations of the Green's function (Maxwell's equations). We characterize the near and far-field models both theoretically and with the help of simulations in a line-of-sight (LOS)-only scenario. In particular, for two key applications in multiantenna systems, namely, beamforming and multiple-access, we showcase the advantages of using the near-field model over the far-field, and present a novel scheduling scheme for multiple-access in the near-field regime. Our findings offer insights into the challenge of incorporating near-field models in practical wireless systems, fostering enhanced performance in future communication technologies.

Thanks to its slow-varying characteristic and relatively low requirement for estimation overhead, the covariance matrix has been extensively researched in sixth-generation (6G) wireless systems. Nevertheless, user mobility in practice will cause a gradual change in the covariance matrix, thereby deteriorating the system's performance if no update of the covariance matrix is applied. In this paper, we study the problem of efficient detection of gradual changes in the covariance matrix. We first introduce four change-point detectors that directly map the observations to change in our target KPI. Then, we propose a low-overhead detection algorithm that omits unnecessary channel estimations by adapting an AoA-based estimation trigger. Simulation results show that our proposed scheme can provide near-optimal performance while drastically reducing the estimation and computation overhead.

Future sixth-generation (6G) wireless networks will have to support ubiquitous communication, together with highly accurate sensing and localization services. This paper considers the problem of user-centric monostatic sensing aided by a reconfigurable intelligent surface (RIS). A major challenge in user-centric sensing is typically the limited hardware capability of user equipments (UEs), which makes it difficult in practice to fulfill the stringent requirements of some sensing applications. In this context, this paper proposes using RIS to provide a virtual bistatic perspective that complements UE-based sensing. The main motivation is that the high angular resolution of RIS, thanks to its typically large surface, can be combined with the (relatively high) ranging accuracy of the UE to achieve more accurate and reliable sensing. Simulation results demonstrate that the combination, i.e. the complementary use of RIS and UE, can significantly improve the sensing performance, especially in challenging radio propagation environments. Effectively, this would enable UEs with reduced capabilities to perform sensing with the required precision, potentially impacting various applications, such as target detection and tracking in robotic sensing, as well as simultaneous localization and (environmental) mapping.

This paper addresses load balancing in open radio access networks (O-RAN), which aims to enhance network avail-ability without overloading the network when accommodating new user equipment (UEs) while ensuring an efficient allocation of resources to meet the data rate requirement of existing UEs. More precisely, we propose a resource allocation framework that balances the utilization of resource blocks (PRBs) at the radio units (RU s) as well as the computational resources at the distributed units (DUs) while maintaining the quality of service (QoS) demands of UEs. Given the combinatorial nature of the optimization problems, we propose, 1) a supermodular algorithm to find UE-RU assignments and 2) a job scheduling-inspired method to assign RUs to respective DUs. Through comprehensive simulations, we validate the effectiveness of our approach by showcasing substantial enhancements in the network load con-ditions and highlighting the superiority of the provided resource allocation scheme in terms of key performance indicators such as the call block ratio (CBR).

Due to their passive nature and thus low energy consumption, intelligent reflecting surfaces (IRSs) have shown promise as means of extending coverage as a proxy for connection reliability. The relative locations of the base station (BS), IRS, and user equipment (UE) determine the extent of the coverage that IRS provides which demonstrates the importance of IRS placement problem. More specifically, locations, which determine whether BS-IRS and IRS-UE line of sight (LOS) links exist, and surface orientation, which determines whether the BS and UE are within the field of view (FoV) of the surface, play crucial roles in the quality of provided coverage. Moreover, another challenge is high computational complexity, since IRS placement problem is a combinatorial optimization, and is NP-hard. Identifying the orientation of the surface and LOS channel as two crucial factors, we propose an efficient IRS placement algorithm that takes these two characteristics into account in order to maximize the network coverage. We prove the submodularity of the objective function which establishes near-optimal performance bounds for the algorithm. Simulation results demonstrate the performance of the proposed algorithm in a real environment.

Software defined networking (SDN) is a promising technology in fifth generation wireless networks (5G) where due to the adoption of a centralized SDN-controller, resources such as processing and storage, can be utilized in an optimal manner. Although SDN was first considered with a logically centralized controller, due to delay, reliability, and scalability challenges, moving towards multiple distributed controllers is inevitable. In distributed control schemes, an assignment that associates a controller with each switch leads to three challenges of (1) Computational complexity, since the assignment is an NP-hard problem, (2) Resource and energy efficiency, to obtain an assignment with the lowest number of controllers in order to reduce resource and energy consumption, and (3) Dynamicity, where a dynamic approach of assignment is required to adapt to the network’s traffic changes. In this paper, we investigate the controller-switch assignment problem given the aforementioned challenges, and propose efficient algorithms for static and dynamic scenarios, that even achieve quantitative optimality guarantees in special cases. As shown through simulations, the proposed lower complexity algorithms not only outperform earlier works but also approach the performance of exhaustive search schemes, in some scenarios.

The ability for a wireless network to precisely localize the radio nodes composing it is a great tool towards system optimization and is increasingly seen as a basic service requirement. In the past, model-free algorithms such as weighted centroid localization (WCL) have proved popular, especially in the context of sensor networks, due to their simplicity and robustness to temporal changes in wireless propagation properties. However, WCL algorithms are biased since they implicitly require a uniform sensor distribution around the source in all directions. In this paper, we demonstrate that instead of employing all the sensors that result in a possibly unbalanced sensing pattern, it is better to reduce the number of sensors such that the subset of selected sensors symmetrically distributes around the source, which in principle would need to know the source location in advance. Here, we develop a sensor selection algorithm which manages that goal while blindly. Using less than half of the sensors, a 30% reduction in localization error is demonstrated from our numerical experiments.

We address the problem of resource allocation (RA) for spectrum underlay in a cognitive radio (CR) communication system with multiple secondary operators sharing resource with an incumbent primary operator. The multiple secondary operator RA problem is well known to be especially challenging because of the inter-operator coupling constraints arising in the optimization problem, which render impractical inter-operator information exchange necessary. In this paper, we consider a satellite setting for multi-operator CR. In the CR maturation regime, i.e., the period in which the secondary subscriber density is growing yet remains much below that of incumbent users, we show that in fact the inter-operator mutual constraints can be neglected, thus making distributed (across secondary operators) optimization possible. Furthermore, we establish analytically that the mutual constraints asymptotically vanish with the primary user density.

It is shown that the detection performance can be significantly improved using the recent technology of multiple‐input multiple‐output (MIMO) radar systems. This is a result of the spatial diversity in such systems due to the viewing of the target from different angles. On the other hand, the moving target indication (MTI) processing has long been known and applied in the traditional pulse radars to detect weak moving targets in the presence of strong clutter signals. The authors propose a procedure based on the compressive sensing idea, in order to apply the MTI processing in a MIMO radar with widely separated antennas. Although a clutter is included in the signal model and a different radar cross‐section value for each transmitter–receiver pair is considered which makes the problem more complex, the complexity dimension is preserved as low as possible by converting the block sparse problem into a regular sparse problem.