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.

In the context of Vehicle-to-Everything (V2X) networks, semantic metrics such as the Age of Information (AoI) have emerged as alternatives to conventional performance metrics. This has prompted a re-evaluation of well-established channel access protocols such as Semi-Persistent Scheduling (SPS) and Carrier-Sense Multiple Access with Collision Avoidance (CSMA/CA) from the perspective of these newly-introduced metrics. In parallel to this trend, new protocols such as Irregular Repetition Slotted ALOHA (IRSA) are proposed for application in V2X communication. In this paper, we investigate the AoI performance of IRSA in the context of V2X communication, by using SPS as a benchmark. The provided comparison indicates that, while SPS can achieve higher throughput than IRSA, IRSA results in a notably lower mean AoI and reduced age violation probability in certain operation regimes. These observations are of particular relevance to safety-related applications for autonomous driving, where the channel access protocols are required to support the broadcast of Cooperative Awareness Messages (CAMs) carrying time-sensitive status updates.

In this paper, we formalize an optimization frame-work for analog beamforming in the context of monostatic integrated sensing and communication (ISAC), where we also address the problem of self-interference in the analog domain. As a result, we derive semidefinite programs to approach detection-optimal transmit and receive beamformers, and we devise a superiorized iterative projection algorithm to approximate them. Our simulations show that this approach outperforms the detection performance of well-known design techniques for ISAC beamforming, while it achieves satisfactory self-interference sup-pression.

In this paper, we focus on Semi-persistent Scheduling (SPS) in the context of New Radio for Vehicle-to-Everything (NR-V2X) communications. SPS is a reservation-based channel access protocol without feedback, in which nodes broadcast time-sensitive information (e.g., status updates) to their neighbors in a wireless network setting. In such scenarios, semantic-aware metrics such as the Age of Information (AoI) often capture fundamental performance trade-offs better than traditional metrics such as the Packet Delivery Ratio (PDR) or Packet Inter-Reception Time (PIR). Focusing on NR-V2X, we relate the AoI to the concepts of service availability and reliability, and investigate the impact of blind retransmissions on the AoI performance. Going beyond the assumption of exogenous data arrivals, we investigate the joint impact of the traffic generation process (sampling rate) and the channel access mechanism. The analysis reveals that in the presence of time-correlated errors, caused by consecutive packet loss due to simultaneous reservations, it is beneficial to use retransmissions instead of a higher sampling rate. For a realistic evaluation, we use an NR-V2X simulator in OMNET++ and simulate a SUMO scenario.

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.

In this paper, we propose deep-unfolded versions of the recently proposed superiorized adaptive projected subgradient method for MIMO detection. The proposed methods require a single matrix inverse for initialization, and they have a quadratic per-iteration complexity. Extensive simulations with realistic channel models show that the proposed deep-unfolded detectors outperform not only their untrained counterpart, but also existing methods with similar complexity.

This paper considers a general framework for massive random access based on sparse superposition coding. We provide guidelines for the code design and propose the use of constant-weight codes in combination with a dictionary design based on Gabor frames. The decoder applies an extension of approximate message passing (AMP) by iteratively exchanging soft information between an AMP module that accounts for the dictionary structure, and a second inference module that utilizes the structure of the involved constant-weight code. We apply the encoding structure to (i) the unsourced random access setting, where all users employ a common dictionary, and (ii) to the “sourced” random access setting with user-specific dictionaries. When applied to a fading scenario, the communication scheme essentially operates non-coherently, as channel state information is required neither at the transmitter nor at the receiver. We observe that in regimes of practical interest, the proposed scheme compares favorably with state-of-the art schemes, in terms of the (per-user) energy-per-bit requirement, as well as the number of active users that can be simultaneously accommodated in the system. Importantly, this is achieved with a considerably smaller size of the transmitted codewords, potentially yielding lower latency and bandwidth occupancy, as well as lower implementation complexity.

In this paper, we propose a set-theoretic framework for MIMO detection. Various low-complexity MIMO detection algorithms achieve excellent performance on i.i.d. Gaussian channels, but they typically incur high performance loss if realistic channel models are considered. Compared to existing low-complexity iterative detectors such as approximate message passing (AMP), the proposed algorithms do not impose any structure the channel matrix. Simulations with a realistic channel model show that the proposed methods are competitive with detectors based on orthogonal AMP (OAMP), which compute matrix inverses in each iteration. At the same time, the proposed methods do not require matrix inverses, and their complexity is similar to AMP.

Channel charting (CC) is an emerging machine learning method for learning a lower-dimensional representation of channel state information (CSI) in multi-antenna systems while simultaneously preserving spatial relations between CSI samples. The driving objective of CC is to learn these representations or channel charts in a fully unsupervised manner, i.e., without the need for having access to explicit geographical information. Based on recent findings in deep manifold learning, this paper addresses the problem of CC via the "not-too-deep" (N2D) approach for deep manifold learning. According to the proposed approach, an embedding of the global channel chart is first learned using a deep neural network (DNN)-based autoencoder (AE), and this embedding is subsequently searched for the underlying manifold using shallow clustering methods. In this way we are able to counter the problem of collapsing extremities - a well known deficiency of channel charting methods, which in previous research efforts could only be mitigated by introducing side-information in form of distance constraints. To further exploit the ever-increasing spatio-temporal CSI resolution in modern multi-antenna systems, we propose to augment the employed AE with convolutional neural network (CNN) input layers. The resulting convolutional autoencoder (CAE) architecture is able to automatically extract sparsely distributed spatio-temporal features from beamspace domain CSI, yielding a reduced computational complexity of the resulting model.

To enable safety-related applications for autonomous driving, Vehicle-to-Everything (V2X) networks are required to support the dissemination of real-time status updates among neighboring vehicles. In this scenario, the ‘freshness’ of information with respect to an application-specific threshold is crucial for the availability of the application. In this paper, we rely on the Age of Information (AoI) as a metric that quantifies the availability of safety-related applications in V2X. Under the assumption of a distributed reservation-based channel access, we derive a probabilistic model that allows us to characterize the statistical properties of AoI and, consequently, evaluate the availability of the application in question. The analytical derivations are validated via numerical simulations.

The cellular Vehicle-to-Everything (C-V2X) standard from 3GPP specifies semi-persistent scheduling (SPS) for medium access control (MAC). While SPS was originally designed with regular status updates in mind, experimental studies have shown some variability in the generation of V2X messages. As a result, packets might arrive that cannot be transferred via SPS reservations within a predefined delay budget. This triggers new reservations, which has a negative effect on MAC operation and performance. With this in mind, we propose a modification to the SPS protocol that uses single-shot transfers while maintaining the current SPS reservations. We evaluate the performance of the modified SPS scheme using both analytical and numerical simulations for Poisson traffic. Specifically, we derive a closed solution for throughput as a function of the delay budget and the resource reservation interval. We provide a performance comparison with SPS for periodic traffic and quantify the performance loss due to the aperiodic traffic.

A fog-radio access network (F-RAN) architecture is studied for an Internet-of-Things (IoT) system in which wireless sensors monitor a number of multi-valued events and transmit in the uplink using grant-free random access to multiple edge nodes (ENs). Each EN is connected to a central processor (CP) via a finite-capacity fronthaul link. In contrast to conventional information-agnostic protocols based on separate source-channel (SSC) coding, where each device uses a separate codebook, this paper considers an information-centric approach based on joint source-channel (JSC) coding via a non-orthogonal generalization of type-based multiple access (TBMA). By leveraging the semantics of the observed signals, all sensors measuring the same event share the same codebook (with non-orthogonal codewords), and all such sensors making the same local estimate of the event transmit the same codeword. The F-RAN architecture directly detects the events’ values without first performing individual decoding for each device. Cloud and edge detection schemes based on Bayesian message passing are designed and trade-offs between cloud and edge processing are assessed.

Channel charting (CC) is an emerging machine learning framework that aims at learning lower-dimensional representations of the radio geometry from collected channel state information (CSI) in an area of interest, such that spatial relations of the representations in the different domains are preserved. Extracting features capable of correctly representing spatial properties between positions is crucial for learning reliable channel charts. Most approaches to CC in the literature rely on range distance estimates, which have the drawback that they only provide accurate distance information for colinear positions. Distances between positions with large azimuth separation are constantly underestimated using these approaches, and thus incorrectly mapped to close neighborhoods. In this paper, we introduce a correlation matrix distance (CMD) based dissimilarity measure for CC that allows us to group CSI measurements according to their co-linearity. This provides us with the capability to discard points for which large distance errors are made, and to build a neighborhood graph between approximately collinear positions. The neighborhood graph allows us to state the problem of CC as an instance of an Euclidean distance matrix completion (EDMC) problem where side-information can be naturally introduced via convex box-constraints.

Consider an Internet-of-Things (IoT) system that monitors a number of multi-valued events through multiple sensors sharing the same bandwidth. Each sensor measures data correlated to one or more events, and communicates to the fusion center at a base station using grant-free random access whenever the corresponding event is active. The base station aims at detecting the active events, and, for each active event, to determine a scalar value describing each active event's state. A conventional solution based on Separate Source-Channel (SSC) coding would use a separate codebook for each sensor and decode the sensors' transmitted packets at the base station in order to subsequently carry out events' detection. In contrast, this paper considers a potentially more efficient solution based on Joint Source-Channel (JSC) coding via a non-orthogonal generalization of Type-Based Multiple Access (TBMA). Accordingly, all sensors measuring the same event share the same codebook (with non-orthogonal codewords), and the base station directly detects the events' values without first performing individual decoding for each sensor. A novel Bayesian message-passing detection scheme is developed for the proposed TBMA-based protocol, and its performance is compared to conventional solutions.

This paper presents a Bayesian nonparametric latent feature model specially suitable for exploratory analysis of high-dimensional count data. We perform a non-negative doubly sparse matrix factorization that has two main advantages: not only we are able to better approximate the row input distributions, but the inferred topics are also easier to interpret. By combining the three-parameter and restricted Indian buffet processes into a single prior, we increase the model flexibility, allowing for a full spectrum of sparse solutions in the latent space. We demonstrate the usefulness of our approach in the analysis of countries' economic structure. Compared to other approaches, empirical results show our model's ability to give easy-to-interpret information and better capture the underlying sparsity structure of data.

Cloud-radio access network (C-RAN) is characterized by a hierarchical structure, in which the baseband-processing functionalities of remote radio heads (RRHs) are implemented by means of cloud computing at a central unit (CU). A key limitation of C-RANs is given by the capacity constraints of the fronthaul links connecting RRHs to the CU. In this letter, the impact of this architectural constraint is investigated for the fundamental functions of random access and active user equipment (UE) identification in the presence of a potentially massive number of UEs. In particular, the standard C-RAN approach based on quantize-and-forward and centralized detection is compared to a scheme based on an alternative CU-RRH functional split that enables local detection. Both techniques leverage Bayesian sparse detection. Numerical results illustrate the relative merits of the two schemes as a function of the system parameters.

We provide nonasymptotic upper and lower bounds on the sum-rate capacity of Rayleigh block-fading multiple-access channels for the set up where a priori channel state information is not available. The upper bound relies on a dual formula for channel capacity and on the assumption that the users can cooperate perfectly. The lower bound is derived assuming a noncooperative scenario where each user employs unitary space-time modulation (independently from the other users). Numerical results show that the gap between the upper and the lower bound is small already at moderate SNR values. This suggests that the sum-rate capacity gains obtainable through user cooperation are minimal for the scenarios considered in the paper.

Energy efficiency is gaining importance in wireless communication networks which have nodes with limited energy supply and signal processing capabilities. We present a numerical study of cooperative communication scenarios based on simple local rules. This is in contrast to most of the approaches in the literature which enforce cooperation by using complex algorithms and require strategic complexity of the network nodes. The approach is motivated by recent results in evolutionary biology which suggest that, if certain mechanism is at work, cooperation can be favored by natural selection, i. e. even selfish actions of the individual nodes can lead to emergence of cooperative behavior in the network. The results of the simulations in the context of wireless communication networks verify these observations and indicate that uncomplicated local rules, followed by simple fitness evaluation, can generate network behavior which yields global energy efficiency.

This paper introduces a communication model inspired by two practical scenarios. The first scenario is related to the concept of protocol coding, where information is encoded in the actions taken by an existing communication protocol. We investigate strategies for protocol coding via combinatorial reordering of the labelled user resources (packets, channels) in an existing, primary system. However, the degrees of freedom of the reordering are constrained by the operation of the primary system. The second scenario is related to communication systems with energy harvesting, where the transmitted signals are constrained by the energy that is available through the harvesting process. We have introduced a communication model that covers both scenarios and elicits their key feature, namely the constraints of the primary system or the harvesting process. We have shown how to compute the capacity of the channels pertaining to the communication model when the resources that can be reordered have binary values. The capacity result is valid under arbitrary error model in which errors in each resource (packet) occur independently. Inspired by the information-theoretic analysis, we have shown how to design practical error-correcting codes suited for the communication model. It turns out that the information-theoretic insights are instrumental for devising superior design of error-control codes.

Motivated from results for the point-to-point block MIMO fading channels without channel knowledge, we propose an AF scheme for the non-coherent two-way relaying channel with the half-duplex constraint. The communication takes place in two phases, multicast and broadcast. In the multicast phase the terminals send the signals simultaneously. In the broadcast phase, the relays resend the signal received in the broadcast phase. The code construction is based on subspace packings in the Grassmann manifolds. We show, that although no channel knowledge is assumed at either the terminals or at the relays, the terminals are able to subtract the self-interference part of the received signal. The performance analysis shows that the noncoherent two-way relaying outperforms non-coherent one-way relaying and delivers the promise of increased spectral efficiency.

Rate bounds for the non-coherent two-way relaying channel under the high SNR assumption are derived. No channel knowledge is assumed at neither the terminals nor at the relays. An upper and lower bound on the achievable rates are derived and shown that they differ only by a constant in the high SNR regime. As corollary from the analysis, the degrees of freedom of the non-coherent two-way channel are derived. Additionally, it is shown that the degrees of freedom can be achieved by an AF scheme.

In this paper we consider secondary communication channels, defined via utilization of the protocol overhead information in legacy communication systems, here referred to as primary systems. The secondary communication is carried out by rearranging the order of the packets which are put forward for transmission by the primary system. To motivate the models, we provide several scenarios with wireless primary communication systems. The secondary communication can be seen as a special case of cognitive radio, since additional information is sent by using the same spectrum as the primary system and without affecting the performance of the primary system. The primary system is frame-based and it determines a set of packets that need to be delivered within a frame. The arrangements of the packets within the frame is irrelevant for the primary system and that is creating the opportunity for secondary communication. We define two generic communication models, the permutation model and the combinatorial model, respectively. For both models, we define suitable discrete communication channels, where the error models are obtained by suitably transforming the packet erasures in the primary system. The capacity of these channels is analyzed. It turns out that the capacity results for the combinatorial communication model are related to the fundamental result by Shannon regarding channels with causal side information at the transmitter. The ideas presented in this paper set several interesting directions for future work.

We propose a distributed differential space-time coding scheme for two-way relaying over unknown block Rayleigh fading channels. The accent is on a two-time slots scheme, where in the first stage both terminals transmit their signals to the relays and in the second stage the relays broadcast to the terminals. Although we do not assume channel knowledge neither at the relays nor at the terminals, we show that it is possible for the terminals to subtract their contribution from the broadcasted signal and decide on the desired signal from the other terminal. The performance analysis reveals that the two-time slots differential scheme offers advantage over the traditional four-time slots differential scheme.

A non-coherent distributed space-time coding scheme for wireless relay networks is presented. The scheme is the counterpart of non-coherent coding based on Grassmann codes, for cooperative wireless networks with relays. It is shown that special classes of Grassmann codes obtained from unitary codes such as Alamouti or Sp(n) codes, can be used as noncoherent distributed space-time codes. The scheme is extended to construct non-coherent distributed space-time codes of large block lengths in a recursive procedure.

This paper presents a method for construction of high-dimensional Grassmann space-time codes for non-coherent block fading MIMO channels, aiming at codes with large block lengths compared to the number of transmit antennas. The construction method is recursive, starting from a low-dimensional coherent or non-coherent code. The performance analysis of the constructed codes supports the theoretical prediction of performance improvement when increasing the code block length. Additionally, the code structure could potentially offer a possibility for complexity reduction of the decoding procedure.