We study the impact of imperfect line-of-sight (LoS) phase tracking on the uplink performance of cell-free massive MIMO networks. Unlike prior works that assume perfectly known or completely unknown phases, we consider a realistic regime where LoS phases are estimated with residual uncertainty due to hardware impairments, mobility, and synchronization errors. To this end, we propose a Rician fading model where LoS components are rotated by imperfect phase estimates and attenuated by a deterministic phase-error penalty factor.We derive a linear MMSE channel estimator that accounts for statistical phase errors and unifies prior results, reducing to the Bayesian MMSE estimator when phase is perfectly known and to a zero-mean model when no phase information is available. To address the non-Gaussian setting, we introduce a virtual uplink model that preserves second-order statistics of channel estimation, enabling the derivation of tractable virtual centralized and distributed MMSE beamformers. To ensure fair assessment of network performance, we apply these virtual beamformers to the operational uplink model that reflects the actual physical channel and compute the spectral efficiency bounds available in the literature.Numerical results show that our framework bridges idealized assumptions and practical tracking limitations, providing rigorous performance benchmarks and design insights for 6G cell-free networks.

This paper addresses uplink long-term joint power control, beamformer design, and time-sharing in overloaded cell-free massive MIMO networks, aiming to achieve max-min fairness among users. Resource allocation leverages slowly-varying channel statistics rather than instantaneous channel state information, reducing information-sharing overhead and enabling optimization over longer timescales. We propose a block coordinate ascent algorithm in which each subproblem is optimally solved using a combination of bisection search, fixed point iterations, and linear programming, guaranteeing convergence to a local optimum. Numerical simulations corroborate the gains of the proposed joint approach in terms of spectral efficiency and power savings over more conventional disjoint time-sharing schemes adopting baseline scheduling policies.

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.

This paper proposes a novel two-stage joint wave-form design framework for multi-antenna Integrated Sensing and Communication (ISAC) systems that simultaneously enable Multiple-Input Multiple-Output (MIMO) radar sensing and Multi-User MIMO communication. First, a transmit waveform covariance matrix is designed by solving a convex matrix nearness problem for beampattern synthesis that simultaneously maximizes transmit power in desired directions and minimizes cross-directional correlations, while accommodating independent antenna power constraints and supporting interference suppression through radiation null steering. Second, a waveform conforming to the designed covariance is synthesized while additionally enforcing inter-user interference suppression via the zero-forcing approach and imposing practical implementation constraints, notably limiting the peak-to-average-power-ratio on the individual antenna elements. Simulation results demonstrate that the approach significantly enhances ISAC waveform design flexibility, performance, and computational efficiency compared to the alternative methods in the literature.

20929

We propose a beam codebook design for integrated sensing and communication (ISAC) that reduces self-interference (SI) to alleviate analog distortion. Our optimization framework, which considers either tapered beamforming or phased arrays for both analog and hybrid schemes, modifies given reference codebooks such that a certain SI power level is achieved. In contrast to other low-SI codebooks, which often rely on hardly interpretable optimization parameters, we provide design guidelines to obtain sensing performance guarantees by deriving analytical bounds on saturation and analog-to-digital quantization in relation to the multipath SI level. By selecting standard reference codebooks in our simulations, we show how our method substantially improves the signal-to-noise ratio for sensing with little impact on 5G-NR communication.

Traditional rule-based or static management approaches struggle to cope with the dynamic, multi-layered nature of 5G/6G networks, creating a strong motivation for AI-native solutions – management systems built from the ground up with artificial intelligence – to enable autonomous, real-time network control. In this work, we introduce GNN-ATIVE, an AI-native orchestration framework that leverages Graph Neural Networks (GNNs) and knowledge graphs (KGs) in a unified graph-based paradigm for network management. GNN-ATIVE uses a semantic knowledge graph to represent the network’s state and context, employing standard ontologies to ensure consistency and interoperability. Building on this foundation, we design Knowledge Graph enabled Generative Pretrained Transformer (KG-GPT), a novel graph-to-graph Transformer model that performs knowledge-driven reasoning on the KG. KG ingests the structured network state (nodes, links, and attributes) and infers optimal configurations or management actions, serving as a high-level decision engine for the orchestrator. We implement and evaluate GNN-ATIVE on an Optical Transport Network (OTN) testbed using real network components. The results demonstrate that GNN-ATIVE can effectively manage OTN resources and adapt to network changes while achieving low-latency inference for decision making.

In this paper, we perform a comparative study of common wireless communication waveforms, namely the single carrier (SC), orthogonal frequency-division multiplexing (OFDM), and orthogonal time-frequency-space (OTFS) modulation in a millimeter wave (mmWave) downlink multi-connectivity scenario, where multiple access points (APs) jointly serve a given user under imperfect time and frequency synchronization errors. For a fair comparison, all the three waveforms are evaluated using variants of common frequency domain equalization (FDE). To this end, a novel cross domain iterative detection for OTFS is proposed. The performance of the different waveforms is evaluated numerically in terms of pragmatic capacity. The numerical results show that OTFS significantly outperforms SC and OFDM at cost of reasonably increased complexity, because of the low cyclic-prefix (CP) overhead and the effectiveness of the proposed detection.

Network management systems for beyond 5G (B5G) and 6G networks today require efficient approaches for handling increased heterogeneity, network-function dis-aggregation, performance requirements, and optimizing networks to support highly diverse use cases. While the Open-Radio Access Network (O-RAN) Service Management and Orchestration (SMO) frameworks efficiently manage RAN and cloud infrastructure, the fragmentation of management platforms across RAN, Core Network (CN), and Transport Network (TN) introduces operational inefficiencies, particularly in Non-Public Networks (NPNs) deployments. This paper proposes a novel extension to the O-RAN SMO architecture, integrating CN and TN management functionalities into a unified control framework. By exploiting AI/ML-driven$\mathrm{x} / \text{rApps}$and a converged data analytics pipeline, the proposed architecture enhances SMO's fault management, resource optimization, and service continuity capabilities. Our implementation validates the feasibility of the proposed SMO extension, demonstrating subscriber-specific QoS assurance through O-RAN-based mobility management mechanisms. The proposed approach successfully shows how RAN-/Core-converged SMOs enable significant enhancements to O-RAN's x/rApps, allowing for subscriber-specific as well as application-specific differentiated QoS assurance.

This study investigates the application of adaptive beamforming techniques within D-band sub-terahertz (sub-THz) communication systems tailored for 6G applications. We present a hardware-in-the-loop transmission system utilizing newly developed prototypes of transmit and receive front ends operating in the frequency range from 150 to 170 GHz. The front ends’ analog beamforming architecture enables the generation of eight distinct beams with 6° separation in azimuth, thus creating a field of view with 48° one dimensional angular scanning range on either side of the link. The system implements dynamic beamforming based on the 5G New Radio (NR) waveform’s design principles for the initial access/beam acquisition procedure. Experimental performance assessments conducted in an indoor environment prove the system’s potential to support mobility and mitigate the effects of signal blockage. Specifically, when encountering a lineof-sight link blockage event, the system successfully transitioned to a reflected propagation path, incurring approximately 4dB of loss. Additionally, a mobile receiver could be tracked while maintaining a 1dB variation in the wireless link budget. These results demonstrate the feasibility of D-band adaptive beamforming.

In this study, we revisit the performance analysis of distributed beamforming architectures in dense user-centric cell-free massive multiple-input multiple-output (mMIMO) systems in line-of-sight (LoS) scenarios. By incorporating a recently developed optimal distributed beamforming technique, called the team minimum mean square error (TMMSE) technique, we depart from previous studies that rely on suboptimal distributed beam-forming approaches for LoS scenarios. Supported by extensive numerical simulations that follow 3GPP guidelines, we show that such suboptimal approaches may often lead to significant underestimation of the capabilities of distributed architectures, particularly in the presence of strong LoS paths. Considering the anticipated ultra-dense nature of cell-free mMIMO networks and the consequential high likelihood of strong LoS paths, our findings reveal that the team MMSE technique may significantly contribute in narrowing the performance gap between centralized and distributed architectures.

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 propose a new proof method for direct coding theorems for wiretap channels where the eavesdropper has access to a quantum version of the transmitted signal on an infinite-dimensional Hilbert space and the legitimate parties communicate through a classical channel or a classical input, quantum output (cq) channel. The transmitter input can be subject to an additive cost constraint, which specializes to the case of an average energy constraint. This method yields errors that decay exponentially with increasing block lengths. Moreover, it provides a guarantee of a quantum version of semantic security, which is an established concept in classical cryptography and physical layer security. Therefore, it complements existing works which either do not prove the exponential error decay or use weaker notions of security. The main part of this proof method is a direct coding result on channel resolvability which states that there is only a doubly exponentially small probability that a standard random codebook does not solve the channel resolvability problem for the cq channel. Semantic security has strong operational implications meaning essentially that the eavesdropper cannot use its quantum observation to gather any meaningful information about the transmitted signal. We also discuss the connections between semantic security and various other established notions of secrecy.

This study investigates simpler alternatives to coherent joint transmission for supporting robust connectivity against signal blockage in mmWave/sub-THz access networks. By taking an information-theoretic viewpoint, we demonstrate analytically that with a careful design, full macrodiversity gains and significant SNR gains can be achieved through canonical receivers and minimal coordination and synchronization requirements at the infrastructure side. Our proposed scheme extends non-coherent joint transmission by employing a special form of diversity to counteract artificially induced deep fades that would otherwise make this technique often compare unfavorably against standard transmitter selection schemes. Additionally, the inclusion of an Alamouti-like space-time coding layer is shown to recover a significant fraction of the optimal performance. Our conclusions are based on a statistical single-user multi-point intermittent block fading channel model that, although simplified, enables rigorous ergodic and outage rate analysis, while also considering timing offsets due to imperfect delay compensation. In addition, we validate our theoretical approach by means of deterministic ray-tracing simulations that capture the essential features of next generation mmWave/sub-THz communications.

In this study we derive novel optimal algorithms for joint power control and beamforming design in modern large-scale MIMO systems, such as those based on the cell-free massive MIMO and XL-MIMO concepts. In particular, motivated by the need for scalable system architectures, we formulate and solve nontrivial two-timescale extensions of the classical uplink power minimization and max-min fair resource allocation problems. In our formulations, we let the beamformers befunctionsmapping partial instantaneous channel state information (CSI) to beamforming weights, and we jointly optimize these functions and the power control coefficients based on long-term statistical CSI. This long-term approach mitigates the severe scalability issues of competing short-term iterative algorithms in the literature, where a central controller endowed with global instantaneous CSI must solve a complex optimization problem for every channel realization, hence imposing very demanding requirements in terms of computational complexity and signaling overhead. Moreover, our approach outperforms the available long-term approaches, which do not jointly optimize powers and beamformers. The obtained optimal long-term algorithms are then illustrated and compared against existing short-term and long-term algorithms via numerical simulations in a cell-free massive MIMO setup with different levels of cooperation.

This paper quantifies the weight of the different physical mechanisms (reflection, diffraction, and scattering) in a typical indoor THz wireless communication environment and provides an intercomparison of raytracing tools. Two state-of-the-art raytracing tools – Wireless InSite and Sionna – are utilized to analyze the capabilities of currently available open-source and commercial raytracing engines for THz simulations. A channel sounder measurement campaign at 300 GHz was conducted in a conference room at Fraunhofer HHI, which is used to validate the raytracing simulations. Additionally, the measurements are compared to a proprietary raytracer, optimized for THz simulations. This paper presents a guideline to increase the capabilities of state-of-the-art raytracing tools, to obtain good results for high frequency simulations. The comparisons show, that currently used raytracing tools are not sufficiently accurate for THz simulations. However, these inaccuracies can be mitigated by the implementation of new features, such as the inclusion of different scattering mechanisms and the incorporation of atmospheric attenuation, while utilizing precise geometry and accurate material parameter models.

We propose a method for predicting the location of user equipment (UE) using wireless fingerprints in dynamic indoor non-line-of-sight (NLoS) environments. In particular, our method copes with the challenges posed by the drift, birth, and death of scattering clusters resulting from dynamic changes in the wireless environment. Prominent examples of such dynamic wireless environments include factory floors or offices, where the geometry of the environment undergoes changes over time. These changes affect the distribution of wireless fingerprints, demonstrating some similarity between the distributions before and after the change. Consequently, the performance of a location estimator initially designed for a specific environment may degrade significantly when applied after changes have occurred in that environment. To address this limitation, we propose a domain adaptation framework that utilizes neural networks to align the distributions of wireless fingerprints collected both before and after environmental changes. By aligning these distributions, we design an estimator capable of predicting UE locations from their wireless fingerprints in the new environment. Experiments validate the effectiveness of the proposed methods in localizing UEs in dynamic wireless environments.

In recent years, sleep-mode or access point (AP) on-off switch techniques have attracted significant attention for reducing the energy consumption of cell-free massive MIMO systems. In this context, this work considers the problem of finding the smallest subset of active access points (APs) needed to satisfy minimum quality-of-service requirements while considering the optimal configuration of uplink transmit powers and (potentially distributed) beamformers. To address this challenging problem, we judiciously combine novel fixed-point methods for jointly optimal power control and distributed beamforming design with a global optimization framework based on surrogate machine-learning models. Numerical results show that our proposed on-off switch technique can achieve a significantly higher reduction in total APs power consumption than a baseline that does not jointly configure the uplink transmit powers and the beamformers.

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.

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.

It is generally believed that downlink cell-free net-works perform best under centralized implementations where the local channel state information (CSI) acquired by the access-points (AP) is forwarded to one or more central processing units (CPU) for the computation of the joint precoders based on global CSI. However, mostly due to limited fronthaul capabilities, this procedure incurs some delay that may lead to partially outdated precoding decisions and hence performance degradation. In some scenarios, this may even lead to worse performance than distributed implementations where the precoders are locally computed by the APs based on partial yet timely local CSI. To address this issue, this study considers the problem of robust precoding design merging the benefits of timely local CSI and delayed global CSI. As main result, we provide a novel distributed precoding design based on the recently proposed team minimum mean-square error method. As a byproduct, we also obtain novel insights related to the AP-CPU functional split problem. Our main conclusion, corroborated by simulations, is that the oppor-tunity of performing some local precoding computations at the APs should not be neglected, even in centralized implementations.

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.

This paper presents a dual-functional waveform design approach for integrated sensing and communication systems with a base station simultaneously communicating with multiple downlink users and illuminating directions of interest for radar sensing. The joint design goals include matching a desired beam-pattern for sensing and minimization of interference between users for communication, where the trade-off between the two goals is controlled by a tuning parameter. The proposed approach also efficiently integrates practical constraints on per-antenna powers and peak-to-average-power-ratio into the design. Through simulations, we show that the proposed approach achieves lower symbol error rate and better beampattern matching performance compared to the popular baseline state-of-the-art method.

This paper reports the development outcomes of the world's first integrated analog beamforming - transmit and receive - wireless front-ends operating in the D-band (a sub-THz band), particularly designed to operate within 150 GHz to 170 GHz frequency range. Front-end hardware development includes monolithic microwave integrated circuit (MMIC) chips of the multichannel power amplifier, multichannel low-noise amplifier, and RF switches. Moreover, the development includes an analog beamforming RF network and a low-profile microstrip 1-by-8 uniform linear antenna array, which were both developed on a resistive silicon substrate. The paper also features a successful long-range (320-meter) line-of-sight unidirectional point-to-point wireless transmission experiment, utilizing a 5G-NR orthogonal frequency division multiplexing (OFDM) waveform over a 160-GHz carrier frequency. The experiment demonstrated a successful transmission of OFDM waveforms using up to a 16-QAM (quadrature amplitude modulation) scheme in the D-band.

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).

In this work, we propose novel HARQ prediction schemes for Cloud RANs (C-RANs) that use feedback over a rate-limited feedback channel (2 - 6 bits) from the Remote Radio Heads (RRHs) to predict at the User Equipment (UE) the decoding outcome at the BaseBand Unit (BBU) ahead of actual decoding. In particular, we propose a Dual Autoencoding 2-Stage Gaussian Mixture Model (DA2SGMM) that is trained in an end-to-end fashion over the whole C-RAN setup. Using realistic link-level simulations in the sub-THz band at 100 GHz, we show that the novel DA2SGMM HARQ prediction scheme clearly outperforms all other adapted and state-of-the-art schemes. The DA2SGMM shows a superior performance in terms of blockage detection as well as HARQ prediction in the no-blockage and single-blockage cases. In particular, the DA2SGMM with 4 bit feedback achieves a more than 200 % higher throughput in average compared to its best alternative. Compared to regular HARQ, the DA2SGMM reduces the maximum transmission latency by more than 72.4 %, while maintaining more than 75 % of the throughput in the no-blockage scenario. In the single-blockage scenario, DA2SGMM significantly increases the throughput for most of the evaluated Signal-to-Noise-Ratios (SNRs) compared to regular HARQ.

In this paper, we propose a novel channel estimation scheme for pulse-shaped multicarrier systems using smoothness regularization for ultra-reliable low-latency communication (URLLC). It can be applied to any multicarrier system with or without linear precoding to estimate challenging doubly-dispersive channels. A recently proposed modulation scheme using orthogonal precoding is orthogonal time-frequency and space modulation (OTFS). In OTFS, pilot and data symbols are placed in delay-Doppler (DD) domain and are jointly precoded to the time-frequency (TF) domain. On the one hand, such orthogonal precoding increases the achievable channel estimation accuracy and enables high TF diversity at the receiver. On the other hand, it introduces leakage effects which requires extensive leakage suppression when the piloting is jointly precoded with the data. To avoid this, we propose to precode the data symbols only, place pilot symbols without precoding into the TF domain, and estimate the channel coefficients by interpolating smooth functions from the pilot samples. Furthermore, we present a piloting scheme enabling a smooth control of the number and position of the pilot symbols. Our numerical results suggest that the proposed scheme provides accurate channel estimation with reduced signaling overhead compared to standard estimators using Wiener filtering in the discrete DD domain.

Federated learning (FL) involves several devices that collaboratively train a shared model without transferring their local data. FL reduces the communication overhead, making it a promising learning method in UAV-enhanced wireless networks with scarce energy resources. Despite the potential, implementing FL in UAV-enhanced networks is challenging, as conventional UAV placement methods that maximize coverage increase the FL delay significantly. Moreover, the uncertainty and lack of a priori information about crucial variables, such as channel quality, exacerbate the problem. In this paper, we first analyze the statistical characteristics of a UAV-enhanced wireless sensor network (WSN) with energy harvesting. We then develop a model and solution based on the multi-objective multi-armed bandit theory to maximize the network coverage while minimizing the FL delay. Besides, we propose another solution that is particularly useful with large action sets and strict energy constraints at the UAVs. Our proposal uses a scalarized best-arm identification algorithm to find the optimal arms that maximize the ratio of the expected reward to the expected energy cost by sequentially eliminating one or more arms in each round. Then, we derive the upper bound on the error probability of our multi-objective and cost-aware algorithm. Numerical results show the effectiveness of our approach.

The Open RAN paradigm offers data-driven, intelligent optimization of the radio access network (RAN). The disaggregated nature of the Open RAN combined with virtualization on general-purpose CPUs with limited computation capacity creates different load types at multiple levels, making it more challenging to balance the load within the network. This paper proposes a learning framework that learns the assignment of users (UEs) to network nodes to balance the communication and computation load in the network. The framework incorporates communication resources consumed by the users in the radio unit (RU), and computation resources needed for baseband processing in the virtualized distributed unit (DU). The goal is thus to balance the communication load between RUs and the computation load between DUs to avoid overloading network elements or to handle higher peak data rate demands when new users arrive in the network. We apply a novel utility-based approach to jointly optimize the UE-RU and RU-DU assignments taking into account the users' QoS (quality of service) requirements. Simulations demonstrate that the proposed method generates the assignments that significantly improve the network load conditions compared to baseline schemes, thereby enabling more available communication and computation resources for incoming peak data rate users in the network.

We propose a truly decentralized algorithm for solving distributed convex optimization problems with possibly time-varying objectives and dynamic networks. It is especially suitable for solving convex feasibility problems with possibly infinitely many sets, and its main novelty is that it covers practical wireless communication systems that have not been considered in previous distributed solvers. In particular, it can be used with a novel proposed protocol based on the "over-the-air" function computation (OTA-C) technology, which is tailored to achieve fast consensus in large and dense wireless networks. In contrast to most OTA-C protocols, the proposed protocol does not require estimates of channel statistics of individual links. Our main theoretical results establish sufficient conditions for the agents to agree on a time-invariant solution asymptotically, assuming that such a solution exists. These results are verified via simulations, where we tackle a real-world problem of distributed random field estimation in an online supervised learning framework.

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.

We establish necessary and sufficient conditions for a network configuration to provide utilities that are both fair and efficient in a well-defined sense. To cover as many applications as possible with a unified framework, we consider utilities defined in an axiomatic way, and the constraints imposed on the feasible network configurations are expressed with a single inequality involving a monotone norm. In this setting, we prove that a necessary and sufficient condition to obtain network configurations that are efficient in the weak Pareto sense is to select configurations attaining equality in the monotone norm constraint. Furthermore, for a given configuration satisfying this equality, we characterize a criterion for which the configuration can be considered fair for the active links. We illustrate potential implications of the theoretical findings by presenting, for the first time, a simple parametrization based on power vectors of achievable rate regions in modern cell-less systems subject to practical impairments.

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.

We provide a mathematical framework to analyze the limits of Hybrid Automatic Repeat reQuest (HARQ) and derive analytical expressions for the most powerful test for estimating the decodability under maximum-likelihood decoding and t-error decoding. Furthermore, we numerically approximate the most powerful test for sum-product decoding. We compare the performance of previously studied HARQ prediction schemes and show that none of the state-of-the-art HARQ prediction is most powerful to estimate the decodability of a partially received signal vector under maximum-likelihood decoding and sum-product decoding. Furthermore, we demonstrate that decoding in general is suboptimal for predicting the decodability.

The evolution of wireless communications into 6G and beyond is expected to rely on new machine learning (ML)-based capabilities. These can enable proactive decisions and actions from wireless-network components to sustain quality-of-service (QoS) and user experience. Moreover, new use cases in the area of vehicular and industrial communications will emerge. Specifically in the area of vehicle communication, vehicle-to-everything (V2X) schemes will benefit strongly from such advances. With this in mind, we have conducted a detailed measurement campaign that paves the way to a plethora of diverse ML-based studies. The resulting datasets offer GPS-located wireless measurements across diverse urban environments for both cellular (with two different operators) and sidelink radio access technologies, thus enabling a variety of different studies towards V2X. The datasets are labeled and sampled with a high time resolution. Furthermore, we make the data publicly available with all the necessary information to support the on-boarding of new researchers. We provide an initial analysis of the data showing some of the challenges that ML needs to overcome and the features that ML can leverage, as well as some hints at potential research studies.

Due to the lack of a feedback channel in the C-V2X sidelink, finding a suitable modulation and coding scheme (MCS) is a difficult task. However, recent use cases for vehicle-to-everything (V2X) communication with higher demands on data rate necessitate choosing the MCS adaptively. In this paper, we propose a machine learning approach to predict suitable MCS levels. Additionally, we propose the use of quantile prediction and evaluate it in combination with different algorithms for the task of predicting the MCS level with the highest achievable data rate. Thereby, we show significant improvements over conventional methods of choosing the MCS level. Using a machine learning approach, however, requires larger real-world data sets than are currently publicly available for research. For this reason, this paper presents a data set that was acquired in extensive drive tests, and that we make publicly available.

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 work, we extend two autoencoder-based HARQ prediction schemes to exploit subcode-based features and SNR-based features jointly. We apply the proposed HARQ prediction schemes to Cloud-RAN (C-RAN) and Single-RAN (S-RAN) architectures. Furthermore, we conduct realistic link-level simulations to test the performance and compare to state-of-the-art prediction schemes that rely solely on either subcode-based features or SNR-based features. Compared to the state-of-the-art, we show that the proposed schemes reduce the transmitted redundancy at a target error rate of$5\cdot 10^{-5}$and$2\cdot 10^{-5}$by 12.3% - 27.3% in C-RAN architectures and 10.5% - 11.0% in S- RAN architectures, respectively.

We derive a fast and optimal algorithm for solving practical weighted max-min SINR problems in cell-free massive MIMO networks. For the first time, the optimization problem jointly covers long-term power control and distributed beam-forming design under imperfect cooperation. In particular, we consider user-centric clusters of access points cooperating on the basis of possibly limited channel state information sharing. Our optimal algorithm merges powerful power control tools based on interference calculus with the recently developed team theoretic framework for distributed beamforming design. In addition, we propose a variation that shows faster convergence in practice.

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.

Advanced vehicular applications are foreseen to rely on wireless connectivity. By predicting wireless network performance, application adaptations can be done a priori to ensure robust and efficient operations. However, determining accurate predictions of network performance or factors affecting it is challenging in highly dynamic vehicular environments. In this paper, we address the problem of determining prediction intervals for future observations. Specifically, prediction intervals are determined for wireless link quality between two vehicles that communicate directly with each other. Evaluations based on real-world vehicle-to-vehicle dataset show that the proposed mechanism is able to determine prediction intervals with the desired confidence level in dynamic scenarios. Furthermore, the mechanism is capable of identifying the intervals that are unreliable with respect to coverage requirements.

Lagrange coded computation (LCC) is essential to solving problems about matrix polynomials in a coded distributed fashion; nevertheless, it can only solve the problems that are representable as matrix polynomials. In this paper, we propose AICC, an AI-aided learning approach that is inspired by LCC but also uses deep neural networks (DNNs). It is appropriate for coded computation of more general functions. Numerical simulations demonstrate the suitability of the proposed approach for the coded computation of different matrix functions that are often utilized in digital signal processing.

We propose a new proof method for direct coding theorems for wiretap channels where the eavesdropper has access to a quantum version of the transmitted signal on an infinite dimensional Hilbert space. This method yields errors that decay exponentially with increasing block lengths. Moreover, it provides a guarantee of a quantum version of semantic security, which is an established concept in classical cryptography and physical layer security. Semantic security has strong operational implications meaning essentially that the eavesdropper cannot use its quantum observation to gather any meaningful information about the transmitted signal. Therefore, it complements existing works which either do not prove the exponential error decay or use weaker notions of security. The main part of this proof method is a direct coding result on channel resolvability which states that there is only a doubly exponentially small probability that a standard random codebook does not solve the channel resolvability problem for the classical-quantum channel.

Many common instances of power control problems for cellular and cell-free massive MIMO networks can be interpreted as max-min utility optimization problems involving affine interference mappings and polyhedral constraints. We show that these problems admit a closed-form solution which depends on the spectral radius of known matrices. In contrast, previous solutions in the literature have been indirectly obtained using iterative algorithms based on the bisection method, or on fixed-point iterations. Furthermore, we also show an asymptotically tight bound for the optimal utility, which in turn provides a simple rule of thumb for evaluating whether the network is operating in the noise or interference limited regime. We finally illustrate our results by focusing on classical max-min fair power control for cell-free massive MIMO networks.

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.

With the advent of 5G, network lifecycle operations such as service initial deployment, configuration changes, upgrades, optimization, and self-healing to name a few, should be fully automated processes to reduce capital expenditure (CAPEX) and operational expenditure (OPEX), and also to allow new players such as industry owners, to come into the scene as nontraditional network operators. To this end, self-organized networks functions (SF) have been proposed as a first attempt to provide self-adaptation capabilities to mobile networks on different fronts and to reduce the error-prone human intervention. Nevertheless, deploying multiple optimization functions in a network brings demanding challenges in terms of conflicting objectives in coordination. Automatically coordinating all those functions is paramount for industry owners in campus networks (CN) since they often do not have a deep expertise to carry out network optimization in an agile manner. Typically, each SF aim at individual goals modifying coupled network parameters, generally in dissonant directions with respect to other SF, jeopardizing the global stability of the system. This work presents an explicit formulation of the joint optimization problem when load balancing optimization (LBO) and coverage and capacity optimization (CCO) are instantiated in a CN.

In this work, we introduce an iterative algorithm for the Euclidean distance matrix completion (EDMC) problem with noisy and incomplete distance measurements. The proposed method is based on semidefinite programming, utilizes a Pareto iterative approach, and performs a projection-free convex optimization over the spectrahedron to solve a level-set problem relevant to EDMC problems. The optimality trade-off between the trace of a positive semidefinite matrix and a loss function is pursued over Pareto optimal points with simple, derivative-free, costly efficient nonlinear equation root finding iterations called Illinois-type methods. We evaluate our approach numerically in a scenario where distance measurements are affected by multiplicative noise.

We propose a deep learning-based method that uses spatial and temporal information extracted from the sub-6GHz band to predict/track beams in the millimeter-wave (mmWave) band. In more detail, we consider a dual-band communication system operating in both the sub-6GHz and mmWave bands. The objective is to maximize the achievable mutual information in the mmWave band with a hybrid analog/digital architecture where analog precoders (RF precoders) are taken from a finite codebook. Finding a RF precoder using conventional search methods incurs large signalling overhead, and the signalling scales with the number of RF chains and the resolution of the phase shifters. To overcome the issue of large signalling overhead in the mmWave band, the proposed method exploits the spatiotemporal correlation between sub-6GHz and mmWave bands, and it predicts/tracks the RF precoders in the mmWave band from sub-6GHz channel measurements. The proposed method provides a smaller candidate set so that performing a search over that set significantly reduces the signalling overhead compared with conventional search heuristics. Simulations show that the proposed method can provide reasonable achievable rates while significantly reducing the signalling overhead.

In autonomous driving, several applications like teleoperated driving, back-end status verification, or online gaming for customer infotainment rely on low-latency communication. Ideally, we can select a route that best supports the applications’ requirements before the journey. Therefore, route selection for autonomous vehicles might require in-advance latency predictions. End-to-end (E2E) latency prediction is a difficult task, especially when considering that it needs to be achieved with limited active probing due to cost constraints. We study continuous latency prediction and application feasibility assessment (in terms of meeting the applications’ E2E latency requirements), using a custom-designed deep learning model that leverages feature engineering for prediction error reduction. We provide insights into the model behavior utilizing recent advances in explainable artificial intelligence. Moreover, we present a novel model-agnostic approach based on active learning to leverage passive probing data. A pre-trained model performs certainty sampling, predicts artificial labels to enlarge the training dataset, and trains iteratively on the augmented set. The results show a 5 % reduction in mean average error for continuous latency prediction and an increase of up to 2.8 % in macro F1 score due to the use of passive probing data.

We propose a new method to protect Over-The-Air (OTA) computation schemes against passive eavesdropping. Our method uses a friendly jammer whose signal is – contrary to common intuition – stronger at the legitimate receiver than it is at the eavesdropper. It works for a large class of analog OTA computation schemes and we give two examples for such schemes that are contained in this class. The key ingredients in proving the security guarantees are a known result on channel resolvability and a generalization of existing results on coding for compound channels.

In a realistic fifth generation (5G) massive multiple-input multiple-output (MIMO) system, hardware constraints often pose challenges towards network design that are not sufficiently considered in the literature. In this work, we consider a time division duplex (TDD) network where user equipments (UEs) are equipped with N> 1 antennas for receiving in the downlink (DL) but only with a single antenna for transmitting in the uplink (UL). Thus it is not possible to learn the complete downlink channel in a single timeslot from the uplink utilizing channel reciprocity. In this paper, we propose a novel solution based on deep learning with auxiliary input of the estimated single antenna channel in the uplink to accomplish the downlink channel completion for full rank transmission from the base station (BS). We use synthetic data for deep learning training and testing provided by the stochastic quasi-deterministic radio channel generator (QuaDRiGa). Evaluation results show that our work outperforms existing deep learning based algorithms and can provide highly effective recovered channels even with complex channel data and low compression ratio.

The number of always-online vehicles continuously increases, and these vehicles will form an immense mobile sensor network. For example, cars can upload live temperature and precipitation information to enhance weather forecasting, and also transmit live cellular network measurements to the cloud. We leverage this vast amount of data, particularly the reference signal received power, to estimate the channel distribution information (CDI) for the vehicular environment. In particular, the proposed CDI maps depict the small-scale fading statistics for spatially separated regions, in contrast to the large-scale fading averages of classical radio maps (path loss and shadow fading). Our map generation framework includes a heuristic for clustering and predicts the fast-fading density per cluster via the Dirichlet process mixture model. This Bayesian nonparametric approach allows for modeling any fast-fading distribution (e.g., Rayleigh, Rice) without prior knowledge. We justify the choice of this approach by benchmarking it against other density estimation methods. Moreover, we support our assumption of local medium-term channel stationarity by measurements and show the framework’s effectiveness for anticipatory networking.

Advanced wireless communication use cases relying on sidelinks require guaranteed network performance. However, fulfilling the desired Quality-of-Service (QoS) requirements can-not be always ensured due to factors such as varying interference and dynamicity in the network. To reduce the impact of varying network performance on sidelink applications, we address the problem of long-term proactive adaptation of sidelink applications based on network performance. Specifically, we propose a modular algorithmic framework comprising of data-driven prediction models and model-based optimization to predict the feasibility of given application data rate requirements. Further-more, the framework is capable of predicting the simultaneously achievable data rate demands (in the max-min sense). A vehicular communication scenario is considered as an example to show the applicability of the framework. Evaluations show that variations in feasibility and achievable data rate demands can be well captured to enable robust application layer adaptations.

In the future, mobility use cases will depend on precise predictions, with Quality of Service (QoS) prediction being a prominent example. This paper presents realistic measurements from today’s vehicles to support robust QoS prediction in the future. Based on a dedicated and controlled measurement campaign, we highlight aspects of the wireless environment and the device characteristics, like the sampling rates, that influence the collected datasets. If not properly handled, such characteristics might hinder the performance of Artificial Intelligence-based algorithms for QoS prediction. Therefore, we also provide insights on dataset characteristics that should be further used to enable easier adoption of AI-based algorithms. New AI-based algorithms should be able to operate in very diverse radio environments with data captured from different devices. We provide several examples that highlight the importance of thoroughly understanding the datasets and their dynamics.

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.

The integration of functions into future communication systems that predict crucial Quality of Service (QoS) parameters is expected to enable many new or enhanced use cases, for example, in vehicular networks and Industry 4.0. Especially with high user mobility, QoS prediction is required in an End-to-End (E2E) fashion to guarantee uninterrupted connectivity and provisioning of real-time applications. In this paper, we present a concise list of mobility use cases, both from automotive and industrial production domains, that benefit from Artificial Intelligence-based QoS prediction. These applications are investigated in the publicly-funded research project AI4Mobile by a representative consortium of industry and academia. Based on a literature review, we identify the main challenges in realizing predictive QoS at high mobility, and we propose research directions to enable the envisioned E2E solutions.

The possibility of predicting Quality of Service, and particularly data rates, in mobile networks will enable new applications for future automated and connected mobility, such as teleoperated driving. Since network data is difficult to acquire and usually of low granularity, robust prediction approaches are required that need to be trained with data sets and measurements generated by end devices. In this paper, we present uplink and downlink data sets, measured in extensive drive tests, that are made available for the evaluation of machine learning methods. Based on this data, we compare the data rate prediction performance in uplink and downlink for neural network, random forest and gradient boosting approaches. Our results show a significantly higher achievable accuracy in uplink than in downlink, and that, even with reduced feature sets, gradient boosting is particularly suited for the prediction task. Moreover, we investigate the use of quantile estimation methods for predicting bounds on the achievable data rate. We show that conformalized approaches, both based on neural networks and random forests, can predict quantiles with very high accuracy.

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.

Sparse level-set formulations allow practitioners to find the minimum 1-norm solution subject to likelihood constraints. Prior art requires this constraint to be convex. Extending these approaches to nonconvex likelihood constraints enables outlier robust methods. In this letter, we develop an efficient approach for nonconvex likelihoods, using Regula Falsi root-finding techniques to solve the level-set formulation. Regula Falsi methods are simple, derivative-free and efficient. The approach provably extends level-set methods to the broader class of nonconvex inverse problems. Practical performance is illustrated using$\ell_1$-regularized Student's t inversion, which is a nonconvex problem used to develop outlier-robust approaches.

We propose two algorithms that use both models and datasets to estimate angular power spectra from channel covariance matrices in massive MIMO systems. The first algorithm is an iterative fixed-point method that solves a hierarchical problem. It uses model knowledge to narrow down candidate angular power spectra to a set that is consistent with a measured covariance matrix. Then, from this set, the algorithm selects the angular power spectrum with minimum distance to its expected value with respect to a Hilbertian metric learned from data. The second algorithm solves an alternative optimization problem with a single application of a solver for nonnegative least squares programs. By fusing information obtained from datasets and models, both algorithms can outperform existing approaches based on models, and they are also robust against environmental changes and small datasets.

In autonomous driving, several safety-related connected applications will coexist with infotainment services for passenger entertainment. Serving the resulting set of diverse quality of service (QoS) requirements poses a tremendous challenge for future cellular networks. For example, safety-related applications require low latency, while infotainment services are associated with high throughput demands. To address the coexistence challenge, we propose a multi-cell anticipatory networking framework with interference coordination based on channel distribution information. The iterative approach first optimizes packet transmission times by so-called statistical look-ahead scheduling leveraging service properties. Interference calculus is applied for estimating the network's load in each step. Finally, packets are forwarded to an online scheduler based on the found transmission schedule. Simulations show that inter-cell interference management is crucial in provisioning the desired QoS. The iterative optimization framework offers superior transmission reliability and spectral efficiency.

Orthogonal time frequency and space (OTFS) modulation is a pulse-shaped Gabor signaling scheme with additional time-frequency (TF) spreading using the symplectic finite Fourier transform (SFFT). With a sufficient amount of accurate channel information and sophisticated equalizers, it promises performance gains in terms of robustness for high mobility users. To fully exploit diversity in OTFS, the 2D-deconvolution implemented by a linear equalizer should approximately invert the doubly dispersive channel operation, which however is a twisted convolution. In theory, this is achieved in a first step by matching the TF grid and the Gabor synthesis and analysis pulses to the delay and Doppler spread of the channel. However, in practice, one always has to balance between supporting high granularity in delay-Doppler (DD) spread, and multi-user and network aspects. In this paper, we propose mobility modes with distinct grid and pulse matching for different doubly dispersive channels. To account for remaining self-interference, we tune the minimum mean square error (MMSE) linear equalizer without the need of estimating channel cross-talk coefficients. We evaluate our approach with the QuaDRiGa channel simulator and with OTFS transceiver architecture based on a polyphase implementation for orthogonalized Gaussian pulses. In addition, we compare OTFS to a IEEE 802.11p compliant design of cyclic prefix (CP) based orthogonal frequency-division multiplexing (OFDM). Our results indicate that with an appropriate mobility mode, the potential OTFS gains can be indeed achieved with linear equalizers to significantly outperform OFDM.

Full-Duplex (FD) Amplify-and-Forward (AF) Multiple-Input Multiple-Output (MIMO) relaying has been the focus of several recent studies, due to the potential for achieving a higher spectral efficiency and lower latency, together with the inherent processing simplicity. However, when the impact of hardware distortions is considered, such relays suffer from a distortion-amplification loop, due to the inter-dependent nature of the relay transmit signal covariance and the residual self-interference covariance. The aforementioned behavior leads to a significant performance degradation for a system with a low or medium hardware accuracy. In this work, we analyse the relay transfer function as well as the Mean Squared- Error (MSE) performance of an FD-AF MIMO relay-assisted communication, under the consideration of collective sources of additive and multiplicative transmit and receive impairments. An optimization problem is then devised over the linear transmit and receive strategies to minimize the communication MSE and solved by employing the recently proposed Penalty Dual Decomposition (PDD) method. The proposed solution converges to a stationary point of the original problem via a sequence of quadratic convex programs. Numerical simulations verify the significance of the proposed distortion-aware design compared to the common simplified approaches, as the hardware accuracy degrades.

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.

We propose a novel algorithm to estimate the channel covariance matrix of a desired user in multiuser massive MIMO systems. The algorithm uses only knowledge of the array response and rough knowledge of the angular support of the incoming signals, which are assumed to be separated in a well-defined sense. To derive the algorithm, we study interference patterns with realistic models that treat signals as continuous functions in infinite dimensional Hilbert spaces. By doing so, we can avoid common and unnatural simplifications such as the presence of discrete signals, ideal isotropic antennas, and infinitely large antenna arrays. An additional advantage of the proposed algorithm is its computational simplicity: it only requires a single matrix-vector multiplication. In some scenarios, simulations show that the estimates obtained with the proposed algorithm are close to those obtained with standard estimation techniques operating in interference-free and noiseless systems.

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.

Hybrid analog-/digital beamforming architectures are a promising means of reducing power consumption and hardware costs in large multi-antenna transceivers. However, channel estimation becomes more complicated compared with conventional (fully-digital) architectures because multiple measurements (pilots in subsequent time slots) are required to reconstruct the channel matrix. In this paper, we use variants of the adaptive projected subgradient method to devise online estimation algorithms that exploit temporal correlations of channel samples. Simulations show that these methods are competitive with conventional batch methods in terms of estimation error at a significantly reduced computational cost. We further improve the performance of the proposed methods by exploiting their potential to adapt the analog combiner at runtime.

Conventional multiuser detection techniques either require a large number of antennas at the receiver for the desired performance, or they are too complex for practical implementation. Moreover, many of these techniques, such as successive interference cancellation (SIC), suffer from errors in parameter estimation (user channels, covariance matrix, noise variance, etc.) that are performed before the detection of user data symbols. As an alternative to conventional methods, this paper proposes and demonstrates a low-complexity practical machine learning-based receiver that achieves similar (and at times better) performance to the SIC receiver. The proposed receiver does not require parameter estimation. Instead, it uses supervised learning to detect user modulation symbols directly. We perform comparisons with minimum mean square error (MMSE) and SIC receivers in terms of symbol error rate (SER) and complexity.

This paper addresses the problem of Over-The-Air (OTA) computation in wireless networks which has the potential to realize huge efficiency gains for instance in training of distributed ML models. We provide non-asymptotic, theoretical guarantees for OTA computation in fast-fading wireless channels where the fading and noise may be correlated. The distributions of fading and noise are not restricted to Gaussian distributions, but instead are assumed to follow a distribution in the more general sub-gaussian class. Furthermore, our result does not make any assumptions on the distribution of the sources and therefore, it can, e.g., be applied to arbitrarily correlated sources. We illustrate our analysis with numerical evaluations for OTA computation of two example functions in large wireless networks: the arithmetic mean and the Euclidean norm.

Autonomous driving will rely on a multitude of connected applications with stringent quality of service (QoS) requirements in terms of low latency and high reliability. At the same time, passengers relieved of steering duty have the opportunity to enjoy infotainment services that are often associated with high data rates, e.g. video streaming. The simultaneous usage of such safety-related and infotainment services leads to diverse QoS requirements which are difficult to satisfy in current wireless networks. In an effort to address this issue, we propose a two-layer predictive resource allocation framework that leverages the services’ properties and incomplete channel information. First, we optimize packet transmission times by a so-called statistical look-ahead scheduling to enhance the network’s QoS and spectral efficiency based upon channel distribution information. Second, packets are forwarded to an online scheduler according to the outcome of this first optimization. Physical resources are assigned considering the services’ QoS requirements and current channel state. We present a novel heuristic that performs real-time resource assignment. Simulations show that our approach has a potential for improving transmission reliability and spectral efficiency.

To meet the Quality-of-Service (QoS) requirements of vehicular applications, some knowledge of future wireless channel statistics is essential. We address the problem of predicting channel quality between vehicles in terms of path loss which, exhibits strong fluctuations over time due to highly dynamic vehicular environment. We propose a framework for data-driven path loss prediction models that are obtained from datasets comprising information related to message transmissions and the communication scenario. By combining changepoint detection method and online learning, the proposed framework adapts the current prediction model based on its performance, thus accounting for the dynamics in the environment and the cost of re-training. Evaluations using real world Vehicle-to-Vehicle communications datasets show that adapting the prediction function using the proposed framework can achieve prediction accuracy comparable to that of online learning case, while significantly reducing the number of data samples required for re-training.

We propose novel approaches to identify the existence of fixed points of the so-called weakly standard interference mappings, which include the well-known standard and general interference mappings as particular cases. The approaches are based on the concept of spectral radius of asymptotic mappings, a mathematical tool recently introduced to study the behavior of wireless networks. We show that, for arbitrary weakly standard interference mappings, knowledge of the spectral radius of an associated asymptotic mapping gives a sufficient condition to determine the existence of fixed points or their absence in the positive orthant. If the mapping has a fixed point, we further prove that the set of fixed points has a minimal element that can be easily computed with a simple fixed point algorithm. The theory developed here is applied to the problem of power control for load planning in LTE networks. Unlike previous approaches in the literature, the proposed solution takes into account the limited number of modulation and coding schemes of practical transceivers.

Semidefinite relaxation followed by randomization is a well-known approach for approximating a solution to the NP-hard max-min fair multicast beamforming problem. While providing a good approximation to the optimal solution, this approach commonly involves the use of computationally demanding interior point methods. In this study, we propose a solution based on superiorization of bounded perturbation resilient iterative operators that scales to systems with a large number of antennas. We show that this method outperforms the randomization techniques in many cases, while using only computationally simple operations.

Predicting the performance of vehicular communication networks is challenging due to the interplay of multiple factors. One prominently influencing factor is the wireless channel between the transmitter and the receiver. We address the problem of predicting the path loss between two communicating vehicles by using a non-parameterized, data-driven approach. Specifically, we apply Random Forest, a non-parametric learning method, to real world vehicle-to-vehicle communications dataset and evaluate it with respect to its prediction accuracy and generalization capability. We show that availability of additional information to the non-parametric model results in better performance than the well known parameterized log distance path loss model. We further discuss the relative contribution of different features for the model accuracy and conclude that careful selection of features can achieve results nearly as accurate as using all available features. Finally, we discuss several aspects that need to be considered while using such data-driven prediction models along with applications of V2V path loss prediction.

We derive novel bounds for the performance of algorithms that estimate the downlink covariance matrix from the uplink covariance matrix in frequency division duplex (FDD) massive multiple-input multiple-output (MIMO) systems. The focus is on algorithms that use estimates of the angular power spectrum as an intermediate step. Unlike previous results, the proposed bounds follow from simple arguments in possibly infinite dimensional Hilbert spaces, and they do not require strong assumptions on the array geometry or on the propagation model. Furthermore, they are suitable for the analysis of set-theoretic methods that can efficiently incorporate side information about the angular power spectrum. This last feature enables us to derive simple techniques to enhance set-theoretic methods without any heuristic arguments. In particular, we show that the performance of a simple algorithm that requires only a simple matrix-vector multiplication cannot be improved significantly in some practical scenarios, especially if coarse information about the support of the angular power spectrum is available.

We propose a learning algorithm for cell-load approximation in wireless networks. The proposed algorithm is robust in the sense that it is designed to cope with the uncertainty arising from a small number of training samples. This scenario is highly relevant in wireless networks where training has to be performed on short time scales because of a fast time-varying communication environment. The first part of this work studies the set of feasible rates and shows that this set is compact. We then prove that the mapping relating a feasible rate vector to the unique fixed point of the non-linear cell-load mapping is monotone and uniformly continuous. Utilizing these properties, we apply an approximation framework that achieves the best worst-case performance. Furthermore, the approximation preserves the monotonicity and continuity properties. Simulations show that the proposed method exhibits better robustness and accuracy for small training sets in comparison with standard approximation techniques for multivariate data.

Important problems in wireless networks can often be solved by computing fixed points of standard or contractive interference mappings, and the conventional fixed point algorithm is widely used for this purpose. Knowing that the mapping used in the algorithm is not only standard but also contractive (or only contractive) is valuable information because we obtain a guarantee of geometric convergence rate, and the rate is related to a property of the mapping called modulus of contraction. To date, contractive mappings and their moduli of contraction have been identified with case-by-case approaches that can be difficult to generalize. To address this limitation of existing approaches, we show in this study that the spectral radii of asymptotic mappings can be used to identify an important subclass of contractive mappings and also to estimate their moduli of contraction. In addition, if the fixed point algorithm is applied to compute fixed points of positive concave mappings, we show that the spectral radii of asymptotic mappings provide us with simple lower bounds for the estimation error of the iterates. An immediate application of this result proves that a known algorithm for load estimation in wireless networks becomes slower with increasing traffic.

Knowledge of second-order statistics of channels (e.g. in the form of covariance matrices) is crucial for the acquisition of downlink channel state information (CSI) in massive MIMO systems operating in the frequency division duplexing (FDD) mode. Current MIMO systems usually obtain downlink covariance information via feedback of the estimated covariance matrix from the user equipment (UE), but in the massive MIMO regime this approach is infeasible because of the unacceptably high training overhead. This paper considers instead the problem of estimating the downlink channel covariance from uplink measurements. We propose two variants of an algorithm based on projection methods in an infinite-dimensional Hilbert space that exploit channel reciprocity properties in the angular domain. The proposed schemes are evaluated via Monte Carlo simulations, and they are shown to outperform current state-of-the art solutions in terms of accuracy and complexity, for typical array geometries and duplex gaps.

Non-orthogonal multiple access (NOMA) has emerged as a promising radio access technique for enabling the performance enhancements promised by the fifth-generation (5G) networks in terms of connectivity, latency, and spectrum efficiency. In the NOMA uplink, detection based on successive interference cancellation (SIC) with device clustering has been suggested. If the receivers are equipped with multiple antennas, SIC can be combined with minimum mean-squared error (MMSE) beamforming. However, there exists a tradeoff between the NOMA cluster size and the incurred SIC error. Larger clusters lead to larger errors but they are desirable from the spectrum efficiency and connectivity point of view. To enable the deployment of large clusters, we propose a novel online learning detection method for the NOMA uplink. We design an online adaptive filter in the sum space of linear and Gaussian reproducing kernel Hilbert spaces (RKHSs). Such a sum space design is robust against variations of a dynamic wireless network that can deteriorate the performance of a purely nonlinear adaptive filter. We demonstrate by simulations that the proposed method outperforms (symbol level) MMSE-SIC based detection for large cluster sizes.

We characterize the resolvability region for a large class of point-to-point channels with continuous alphabets. In our direct result, we prove not only the existence of good resolvability codebooks, but adapt an approach based on the Chernoff-Hoeffding bound to the continuous case showing that the probability of drawing an unsuitable codebook is doubly exponentially small. For the converse part, we show that our previous elementary result carries over to the continuous case easily under some mild continuity assumption.

We consider the pairing problem in network-assisted device-to-device communications. The pairing problem is stated as a rate estimation problem. To this end, we develop a framework that we call compressive rate estimation. We assume that the composite channel gain matrix (i.e., the matrix of all channel gains between all network nodes) is compressible and develop a novel sensing and reconstruction protocol for the estimation of achievable rates. The proposed sensing protocol exploits the superposition principle of the wireless channel and enables the receiving nodes to obtain non-adaptive random measurements of columns of the composite channel matrix. The random measurements are fed back to a central controller who decodes the composite channel gain matrix (or parts of it) and estimates individual user rates. We analyze the rate loss gap for a linear and a non-linear decoder and find the scaling laws according to the number of non-adaptive measurements.

We propose a novel power control algorithm for the minimization of the peak load in the widely used load coupled network model, which is an abstract model able to capture the behavior of current and possibly future wireless networks. We first prove that the solution to the optimization problem we pose here requires all base stations with the same load. This necessary condition for optimality gives rise to a solver based on a bisection algorithm that requires an oracle able to answer whether a probed load is greater than the optimal value. By exploiting known properties of concave mappings, we devise an iterative oracle that, with a very mild assumption, provably gives the correct answer with a finite number of iterations. Simulations in an ultra-dense network show that the proposed algorithm can decrease the peak load by around 40% when compared to the peak load induced by the common approach of fixing the power of every base station to the maximum value.

We propose a novel utility optimization algorithm for wireless networks modeled by systems of nonlinear equations based on the load at the base stations. Unlike previous studies, the algorithm solves a max-min utility optimization problem over the joint space of network load, transmit power, and rates. In more detail, our first main contribution is to show that, in the optimum, users operate at the same rate, base stations are fully loaded, and at least one base station transmits at the maximum power. This characterization of the optimal solution enables a reformulation of the optimization task as a conditional eigenvalue problem associated with a concave mapping that relates the transmit power to the network load. With this reformulation, an efficient iterative solver becomes readily available. Our second main contribution is the derivation of a simple lower bound for conditional eigenvalues of general positive concave mappings. These bounds are of particular interest to network designers, because conditional eigenvalues can often be related to the optimal rates (or the optimal signal-to-interference-noise ratio) of a large class of utility optimization problems, and, in this paper, these bounds are used to derive performance limits of load coupled networks.

In this paper, we utilize the framework of compressed sensing (CS) for device detection and distributed resource allocation in large-scale machine-to-machine (M2M) communication networks. The devices are partitioned into clusters according to some pre-defined criteria, e.g., proximity or service type. Moreover, by the sparse nature of the event occurrence in M2M communications, the activation pattern of the M2M devices can be formulated as a particular block sparse signal with additional in-block structure in CS based applications. This paper introduces a novel scheme for distributed resource allocation to the M2M devices based on block-CS related techniques, which mainly consists of three phases: (1) In a full-duplex acquisition phase, the network activation pattern is collected in a distributed manner. (2) The base station detects the active clusters and the number of active devices in each cluster, and then assigns a certain amount of resources accordingly. (3) Each active device detects the order of its index among all the active devices in the cluster and accesses the corresponding resource for transmission. The proposed scheme can efficiently reduce the acquisition time with much less computation complexity compared with standard CS algorithms. Finally, extensive simulations confirm the robustness of the proposed scheme under noisy conditions.

Anomaly detection has various applications including condition monitoring and fault diagnosis. The objective is to sense the environment, learn the normal system state, and then periodically classify whether the instantaneous state deviates from the normal one or not. Wireless sensor networks provide a flexible and cost-effective way of monitoring a system state. In traditional wireless sensor networks, sensors encode their observations and transmit them to a fusion center using some interference avoiding channel access method. The fusion center decodes all the data and then classifies the corresponding system state. As this approach is in general highly inefficient, this paper proposes a transmission scheme that, instead of avoiding the interference, exploits it for carrying out the anomaly detection directly in the air. In other words, the wireless channel helps the fusion center to retrieve the sought classification outcome immediately from the channel output. To achieve this, the chosen learning model is a linear support vector machine. After proving the reliability of the proposed scheme, numerical examples are presented that demonstrate its ability to reduce the energy consumption for anomaly detection by up to 53 % compared to a strategy that uses time division multiple-access.

This paper extends the weak secrecy results of Liu et al. for broadcast channels with two confidential messages to strong secrecy. Our results are based on an extension of the techniques developed by Hou and Kramer on bounding Kullback-Leibler divergence in the context of resolvability and effective secrecy.

3GPP/LTE provisions for a Multimedia Broadcast Multicast Service (MBMS), where a common data stream is sent to many users (a multicast group) simultaneously from multiple base stations, transmitting on the same frequency channel, i.e., forming a Single-Frequency Network (SFN). This setting has been extensively treated as a max-min fair beamforming problem, where the beamforming vector is optimized as a function of the instantaneous channel state information in order to maximize the instantaneous (per-slot) common rate over all users. Unfortunately, such common rate vanishes as the number of users grows for fixed number of base station antennas. In this paper we consider the ergodic regime, where coding across multiple slots affected by independent fading is allowed. We formulate the problem as an ergodic compound channel, subject to a per-slot and per-group of antennas power constraint, and we provide an efficient algorithm that approximates the compound capacity to any desired degree of accuracy. Then, in line with the current implementation of MBMS-FSN in 3GPP, we consider also the multicast throughput achievable by a concatenated coding scheme, where inner physical layer coding is applied on a per-slot basis, and outer packet erasure coding is used at the application layer. The optimal strategy in this case is NP-Hard, and we propose a convex relaxation approach with good performance and low complexity.

This paper provides a performance comparison of location-based and Channel State Information (CSI)-based resource allocation for device-to-device (D2D) communication. Our focus is on a system where the available cellular uplink resources are reused for the exchange of messages between vehicles in D2D underlay manner. In this context, we define a heuristic resource allocation algorithm with a spectral radius feasibility check that aims to satisfy the requirements of automotive applications. Simulations show that the developed CSI-based approach achieves higher spectral efficiency as compared to a location-based scheme. However, the gains come at a price of increased feedback overhead due to the CSI acquisition.

In this work, a recently proposed analog transmission scheme is considered, which harnesses interference for reliably and efficiently computing real-valued functions over a wireless channel. To better understand the corresponding trade-off between the conflicting demands of reliability and efficiency, in this paper we choose an information theoretic perspective by analyzing the scheme within the framework of computation coding. Towards this end, we first adapt the standard notions of a computation code and an achievable computation rate to our specific needs and then provide rate expressions for some simple but insightful examples. It turns out that the achievable computation rates not only depend on the function to be computed but also on the desired accuracy and the number of concurrently active transmitters.

We study a joint channel allocation and power control problem for device-to-device (D2D) transmission underlaying a conventional single-cell cellular network. In such networks, direct transmissions are allowed among device pairs with local needs, provided that the adverse effects of D2D communications on cellular users is negligible and cellular users are given the priority in using limited wireless resources. Moreover, as D2D users are not in contact with the base station (BS), providing them with channel and/or network knowledge imposes excessive overhead. As a result, it becomes imperative to seek for new resource management mechanisms that fit the limitations of this concept. In this paper we consider a realistic model with respect to the information availability, and propose a joint channel allocation and power control scheme by using game- and graph theory. In particular, we first decompose the resource management problem into two cascaded channel allocation and power control problems, by proving a lower bound on the aggregate utility of cellular users. Afterwards we propose a centralized graph-theoretical channel allocation approach jointly for D2D and cellular users. Given the channel allocation, the subsequent power control problem is modeled as a game with incomplete information. We analyze the characteristics of this game and solve it in a distributed manner, by using a multi-agent Q-learning strategy. We evaluate the proposed resource allocation scheme both analytically and numerically.

Distributed power control schemes have been intensively studied in the literature for uplink transmissions in cellular networks as well as in ad hoc networks. In the schemes with active link protection, the signal to interference plus noise (SINR) requirements of the new users are gradually approached without violating the existing links. In this paper, we consider a downlink cellular scenario, in which new nomadic cells seek admission to the network. A distributed power control algorithm with active cell protection is presented, where a cell is said to be active if it has sufficient resources to support the connected users and, otherwise, it is said to be inactive. With the proposed algorithm, inactive cells lower their loads by gradually performing power ramping, while active cells scale their transmission power accordingly, to avoid being overloaded. We prove the active cell protection property and compare the convergence of the algorithm under different interference assumptions. Further, we present an algorithm for adapting the power ramping factor in power limited scenarios for further performance enhancements.

With an increasing demand in user experience and service quality, there is a strong need for a massively enhanced throughput of current wireless networks. It has been shown that the capacity of a random ad hoc network does not scale well with the growing number of nodes [1]. This paper considers a random hybrid wireless network, in which nodes access the wireless channel in an uncoordinated manner to transfer their messages to a network of base stations interconnected by high-rate wired communication links. Moreover, physical-layer network coding (PLNC) is used to harness the interference, which presents the main performance bottleneck in wireless systems. Assuming a random hybrid network, we analyze the impact of PLNC on the throughput scaling. We show that each node can achieve a higher throughput that scales sublinearly or linearly with the number of base stations. This is in fact a significant improvement when compared to previous studies in which the impact of adding base stations on the network throughput is insignificant whenever the number of base stations grows slower than some threshold.

In this paper, a clustered wireless sensor network is considered that is modeled as a set of coupled Gaussian multiple-access channels. The objective of the network is not to reconstruct individual sensor readings at designated fusion centers but rather to reliably compute some functions thereof. Our particular attention is on real-valued functions that can be represented as a post-processed sum of pre-processed sensor readings. Such functions are called nomographic functions and their special structure permits the utilization of the interference property of the Gaussian multiple-access channel to reliably compute many linear and nonlinear functions at significantly higher rates than those achievable with standard schemes that combat interference. Motivated by this observation, a computation scheme is proposed that combines a suitable data pre- and post-processing strategy with a nested lattice code designed to protect the sum of pre-processed sensor readings against the channel noise. After analyzing its computation rate performance, it is shown that at the cost of a reduced rate, the scheme can be extended to compute every continuous function of the sensor readings in a finite succession of steps, where in each step a different nomographic function is computed. This demonstrates the fundamental role of nomographic representations.

We consider the distributed channel selection problem in the context of device-to-device (D2D) communication as an underlay to a cellular network. Underlaid D2D users communicate directly by utilizing the cellular spectrum, but their decisions are not governed by any centralized controller. Selfish D2D users that compete for access to the resources form a distributed system where the transmission performance depends on channel availability and quality. This information, however, is difficult to acquire. Moreover, the adverse effects of D2D users on cellular transmissions should be minimized. In order to overcome these limitations, we propose a network-assisted distributed channel selection approach in which D2D users are only allowed to use vacant cellular channels. This scenario is modeled as a multi-player multi-armed bandit game with side information, for which a distributed algorithmic solution is proposed. The solution is a combination of no-regret learning and calibrated forecasting, and can be applied to a broad class of multi-player stochastic learning problems, in addition to the formulated channel selection problem. Theoretical analysis shows that the proposed approach not only yields vanishing regret in comparison to the global optimal solution but also guarantees that the empirical joint frequencies of the game converge to the set of correlated equilibria.

This paper presents an optimization framework for energy savings in nomadic relay networks, where interference and load are changing over time due to varying assignments. We prove the existence of an explicit load function taking assignments as arguments and we show the function is continuously differentiable. Based on the properties of the load function, we design iterative relay and user association algorithm for energy savings, where the non-convex load constraints are linearly approximated such that each sub-problem is a linear program and hence can be solved efficiently. Simulation results confirm that our proposed algorithmic approach leads to a significant reduction of the energy consumption when compared with algorithms considering the worst-case interference.

This paper studies device-to-device (D2D) communication underlaying cellular infrastructure, where each device pair is provided with two transmission modes: indirect and direct. Indirect transmission is a two-hop interference-free transmission via a base station. Despite being interference-free, this transmission type might be inefficient in communications scenarios where short-distance connections can be established. Moreover, the need for centralized resource allocation and utilizing extra hardware may lead to excessive complexity and unacceptable costs. In such scenarios, direct transmissions can utilize the proximity- and hop gains to achieve higher rates and lower end-to-end latencies. While having a potential for huge performance gains, direct D2D communications poses some fundamental challenges resulting from the absence of a devoted controller such as uncoordinated interference and unavailability of permanent direct channels. Roughly speaking, in an average sense, while indirect transmission pays safe and steady reward, direct transmission is risky, yielding a stochastic reward which might be lower than the guaranteed reward of indirect transmission, despite the proximity-and hop gains. Transmitters should therefore choose the most efficient transmission mode in the presence of limited information. This paper characterizes the reward process for each transmission mode to model the mode selection problem as a two-armed Levy-bandit game. Accordingly, the reward of the risky arm (direct mode) is considered to be a pure-jump Levy process, following compound Poisson distribution. Mathematical results from bandit and learning theories are used to solve the selection problem. Numerical results complete the paper.

We present a statistical approach for estimating the interference coupling coefficients in an LTE network based on a set of various measurements available at the network and terminal level. The proposed approach combines the measurements with prior information (spatial correlation among interference links) and takes into account measurement uncertainty. The result is a simple closed-form estimator that allows for fast realtime interference estimation.

This paper presents an optimization framework for energy-aware relay selection and user association in cellular networks aided by nomadic relays. The framework of sparse optimization is used to minimize network energy consumption for desired service provisioning of the terminals. We show that some constraints in the underlying optimization are of quadratic form due to the assumption of relays with wireless backhaul links. Hence, previously proposed algorithms for activation of network elements with wired backhaul links are not applicable. In this paper, therefore, novel algorithms based on different relaxations of the quadratic constraints are proposed and evaluated for energy savings. Simulation results confirm that the proposed algorithms may significantly reduce the overall energy consumption of cellular networks compared with conventional cell selection schemes.

This paper addresses some aspects of the general problem of information transfer and distributed function computation in wireless networks. Many applications of wireless technology foresee networks of autonomous devices executing tasks that can be posed as distributed function computation. In today's wireless networks, the tasks of communication and (distributed) computation are performed separately, although an efficient network operation calls for approaches in which the information transfer is dynamically adapted to time-varying computation objectives. Thus, wireless communications and function computation must be tightly coupled and it is shown in this paper that information theory may play a crucial role in the design of efficient computation-aware wireless communication and networking strategies. This is explained in more detail by considering the problem of computing ℓp-norms over multiple access channels.

Next generation mobile and wireless communication systems beyond 2020, aka Fifth Generation (5G) systems, aim at providing ubiquitous user experience with the utmost in quality. One of the promising technologies targeted for 5G systems is the flexible network deployment based on nomadic nodes (NNs). An NN is a low-power movable access node that provides coverage extension and capacity improvement on demand. Yet, NNs require flexible backhaul. One possible cost-efficient realization for flexible backhaul is in-band relaying. In this context, the capacity of the wireless backhaul link between an NN and its serving base station (BS) has a crucial role in the achievable end-to-end performance. The flexible backhaul can be exploited by dynamic NN selection to overcome the limitations of the backhaul link and, thus, to enhance the system performance. To this end, dynamic NN selection is carried out via selecting the serving NN from a set of available candidates considering the signal-to-interference-plus-noise ratio (SINR) on the backhaul link. In this regard, coarse NN selection takes into account only shadowing. Nevertheless, as NNs are stationary or slowly moving during operation, the wireless channels pertaining to NNs are usually subject to simultaneous impairments by both shadowing and multi-path fading, i.e., composite fading/shadowing. In this paper, we present the performance of coarse NN selection in composite fading/shadowing environments with co-channel interference. Further, we evaluate the performance in terms of backhaul link SINR, link rates, and end-to-end rate. Results show that coarse NN selection can yield high performance improvements.

In this paper, we study a relaying network employing a rateless coding scheme proposed in the literature. We consider the problem of an energy-efficient operation of the scheme and derive algorithms that maximize the achievable rate for specified energy-per-bit bounds at the relay nodes. For this, we identify the rate function as a standard interference function, which allows to design efficient algorithmic solutions for solving the problem.

In this paper, a wireless sensor network is considered in which the objective is not to communicate individual sensor readings over a Gaussian multiple-access channel to a fusion center but rather to reliably compute some nomographic function thereof. Nomographic functions are exactly those multivariate functions that can be represented as a post-processed sum of pre-processed sensor readings. This special structure permits the utilization of the interference property of the Gaussian multiple-access channel for computing some nomographic functions at significantly higher rates than those achievable with traditional schemes. In this paper, a corresponding coding scheme is presented that protects the sum of pre-processed sensor readings against the channel noise by letting each node use the same nested lattice code.

This paper deals with the challenge of providing information-theoretic secrecy in a two-hop wireless channel with multiple untrusted relay nodes. Instead of perfect secrecy (in the information-theoretic sense), of interest here is a cross-layer approach where only parts of relayed information are protected at the physical layer against eavesdropping at relay nodes. For both decode-and-forward and amplify-and-forward relaying strategies, we present a novel framework for designing secrecy schemes for two-hop relay channels that ensure some predefined level of partial secrecy. The paper is concluded by some numerical simulations to show the interplay between different system parameters such as the the rate, secrecy factor, the number of relays and antennas.

Multi-armed bandit (MAB) problems form a class of sequential optimization problems, in which a player sequentially pulls an arm, selected from a known and finite set of arms, in order to achieve an initially unknown reward. The player aims at maximizing the accumulated reward over a predefined game horizon. Clearly, in bandit setting, a dilemma appears between pushing the currently most promising arm, i.e. the arm with the highest empirical mean reward, on the one hand (exploitation) and on the other sampling arms in order to improve the estimation of the reward generating processes of arms (exploration). In this paper we study a specific subset of MAB problems, namely stochastic covariate bandits, where it is assumed that the series of instantaneous rewards generated by each arm can be attributed to a specific distribution, and that some side information (covariate) is revealed to the player at the beginning of each game trial. In this setting, we address the exploitation-exploration dilemma by proposing two strategies for arm selection (allocation rule). Provided that the underlying regression process is trust-worthy, the proposed strategies are strongly consistent, in the sense that the accumulated reward is equivalent to that based on the best arm, asymptotically almost surely. Further, it is illustrated that the covariate bandit model and our allocation strategies are applicable to wireless networking scenarios by considering the relay selection problem as case study.

This paper proposes a novel feedback protocol for relay-based two-hop networks, in which the channel state information matrix of the second hop is compressible due to the presence of spatial correlation and distance dependent path loss among the communication channels from the relay nodes to the users. The proposed protocol makes use of recent developments in the fields of low rank matrix recovery and compressed sensing to approximate the channel matrix by a low rank and sparse matrix. As a result, accurate channel state information can be provided to the base station for optimal relay selection, while significantly reducing pilot contamination and feedback overhead. Simulations demonstrate that approximately 50% of the training and feedback overhead can be saved if the compressibility of the channel matrix is taken into account.

Modern vehicles will have strong requirements with regard to seamless mobility support in future cellular systems, in order to enable advanced cooperative driver assistance and infotainment systems that guarantee traffic safety and efficiency. In this work, we introduce street-specific handover parameters for vehicular terminals. In particular, we propose an adaptive optimization algorithm that exploits vehicle context information in order to tune the handover parameters. Simulation results confirm that the proposed concept has the potential to improve handover performance significantly.

The mobility robustness optimization (MRO) problem in LTE self-organizing networks (SON) is a multi-objective optimization problem; it involves a set of non-convex contradicting objective functions that depend on multiple variables such as handover (HO) parameters and user mobility classes. This paper exploits the framework of stochastic processes to develop a novel method of successively choosing a sequence of multi-variate training points for multi-objective optimization. Combined with the collected statistics and a priori knowledge, the proposed method is used in the design of an efficient MRO algorithm. The performance of the algorithm is evaluated by simulations to illustrate significant improvements with respect to both HO-related ratio link failures (RLFs) and unnecessary HOs.

Multi-armed bandit games form a class of sequential optimization problems, in which a player sequentially pulls an arm, selected from a known and finite set of arms, in order to receive an a priori unknown reward. Since the player does not know the arm with the highest reward in advance, it utilizes a well-designed selection strategy to minimize the so-called regret, which, roughly speaking, results from the lack of this information. This paper studies cooperative transmission in a dense mobile network, where users compete for utilizing a number of relays to improve the quality of transmissions. Under the assumption of no side information available to the users, the relay selection and assignment problem is formulated as an adversarial multi-player multi-armed bandit game. Based on this formulation, a selection strategy is proposed that is shown to guarantee the convergence of the empirical frequencies of the game to a correlated equilibrium. Moreover, applying the experimental regret testing protocol shows that the empirical frequencies of the relay selection game converges to Nash equilibrium. Finally, experimental evaluations are carried out to compare the performance of various selection strategies and with it to demonstrate the effectiveness of the proposed approach. The proposed game model and selection strategies can be used in a wide range of wireless networking scenarios, such as spectrum pulling in cognitive radio networks and base station assignment in cellular networks.

Wireless sensor network applications often involve the computation of pre-defined functions of the measurements such as for example the arithmetic mean or maximum value. Standard approaches to this problem separate communication from computation: digitized sensor readings are transmitted interference-free to a fusion center that reconstructs each sensor reading and subsequently computes the sought function value. Such separation-based computation schemes are generally highly inefficient as a complete reconstruction of individual sensor readings at the fusion center is not necessary to compute a function of them. In particular, if the mathematical structure of the channel is suitably matched (in some sense) to the function of interest, then channel collisions induced by concurrent transmissions of different nodes can be beneficially exploited for computation purposes. This paper proposes an analog computation scheme that allows for an efficient estimate of linear and nonlinear functions over the wireless multiple-access channel. A match between the channel and the function being evaluated is thereby achieved via some pre-processing on the sensor readings and post-processing on the superimposed signals observed by the fusion center. After analyzing the estimation error for two function examples, simulations are presented to show the potential for huge performance gains over time- and code-division multiple-access based computation schemes.

In the downlink of Long Term Evolution (LTE) systems, feedback and processing delays cause a mismatch between the current channel state and the Channel Quality Information (CQI) at the base station. This CQI aging leads to inaccurate channel adaptation and can, thus, highly degrade the cell capacity. To compensate for this performance loss, we study several CQI predictors under realistic delay and channel assumptions. Our results on cell throughput show that linear prediction with Stochastic Approximation provides at least the performance gains of the computationally more complex covariance-based linear predictors and Kalman filters. This surprising result points to Stochastic Approximation as a powerful and practical technique to increase downlink performance with limited channel knowledge.

Multi-sensor signal detection based on the the largest eigenvalue of the received sample covariance matrix is known to be optimal (asymptotically in the sample size and under Gaussian assumption) in the Neyman-Pearson sense. In this paper we propose two decentralized algorithms to implement this type of signal detector in distributed wireless networks without fusion center. The proposed solutions are based on iterative numerical algorithms (power method and Lanczos algorithm), implemented in a decentralized manner with matrix and vector products computed via average consensus. Numerical results show that such methods, in particular the decentralized Lanczos method, outperform the recently proposed decentralized energy detector after a very small number of iterations.

We propose novel set-theoretic distributed adaptive filters for cooperative signal detection in diffusion networks, a problem that has been gaining attention owing to its application to cooperative cognitive radio networks. In the proposed method, nodes in a network detect the presence of a signal of interest by means of an inner product between the current term of a series and a known reference vector. Each term of the series is computed from information fusion among neighboring nodes and projections onto closed convex sets, which are constructed with a priori knowledge of the signal of interest and measurements obtained by nodes. In particular, we show that sets based on a priori knowledge are useful to decrease the communication overhead and to provide good detection performance. Our results are rigorous in the sense that no approximations are used to prove convergence properties. In particular, we show conditions to guarantee that the series converge to a point that can reliably identify the signal of interest. Furthermore, we also show that recent results in distributed optimization for dynamic systems can be used to derive algorithms where nodes exchange not only the current vectors of their sequences (as in previous distributed set-theoretic filters), but also side information that influences the above-mentioned sets.

Recently, it has been shown that the superposition property of wireless multiple-access channels can be exploited to compute functions in sensor networks much more efficiently. By using appropriate pre- and post-processing functions operating on real sensor readings and the superimposed signal received by a fusion center, every function of the measurements is in principle computable by means of the wireless channel in which the pre-processing functions, and therefore the transmitting nodes, do not depend on the function of interest. In this paper we extend these general considerations by examining how robust this kind of universality is against variations in network topology due to nodes that drop out of the network or due to new nodes that connect to the network.

This paper presents a novel cell outage detection algorithm based on statistics and performance metrics, which enables a base station (BS) to detect a failure/outage of a neighbor cell. The algorithm is a weighted combination of three hypothesis tests based on: 1) the distribution of the channel quality indicator (CQI), 2) the time correlation of the CQI differential, and 3) the registration request (RRQ) frequency. The weights of the combined test are functions of the predicted traffic load in neighboring cells, which is motivated by the fact that the reliability of a individual test depends on the load state. To detect the change-point in the CQI distribution, we use an efficient discriminant function related to the “universal code” proposed by [1], which can be shown to be asymptotically optimal in the sense of the modified Neyman-Pearson criterion. The simulation results indicate that the proposed algorithm can detect the outage problem in a real-time and reliable manner.

In a multicast scenario, the performance is usually determined, and therefore limited, by the weakest link present in the system. With multiple co-channel multicast groups, the problem is further exacerbated due to interference from other transmissions. In this work we investigate an alternative communication scheme, in which additional coding at the application layer is used, spanning over a number of channel realizations. Aiming at maximization of the weighted sum of rates achieved in each group, we show that the optimal transmission strategy depends only on the current channel realization, which, assuming multiple antennas at the base station, allows for formulation of an interesting transmit beamforming problem. In order to find the solution of the problem, we show that the utility-based power control framework, developed for a network consisting of a number of point-to-point wireless links, can be generalized to the case of multigroup multicast. Building upon this framework, we propose iterative beamforming algorithms which can be applied in scenarios both with and without additional coding at the application layer. Numerical experiments are included in the paper to demonstrate the performance of the proposed algorithms.

We develop an efficient relay selection protocol for two-hop transmission in cognitive radio networks where secondary users interfere with a primary user. Our protocol provides joint benefits of two well-known relaying strategies, namely relay sub-set selection and incremental relaying. It aims at minimizing the outage probability for delay-constrained applications by utilizing rateless codes. The advantages of the proposed scheme compared to conventional relay selection protocols can be summarized as follows: (1) feedback overhead is significantly reduced, (2) resources are exploited efficiently, (3) outage probability is minimized. In order to evaluate the proposed strategy, analytical and numerical results are presented.

In this paper we propose a gossip algorithm for average consensus in clustered wireless sensor networks called superposition gossiping, where the nodes in each cluster exploit the natural superposition property of wireless multiple-access channels to significantly decrease local averaging times. More precisely, the considered network is organized into single-hop clusters and in each cluster average values are computed at a designated cluster head via the wireless channel and subsequently broadcasted to update the entire cluster. Since the clusters are activated randomly in a time division multiple-access fashion, we can apply well-established techniques for analyzing gossip algorithms to prove the convergence of the algorithm to the average consensus in the second moment and almost surely, provided that some connectivity condition between clusters is fulfilled. Finally, we follow a semidefinite programming approach to optimize wake up probabilities of cluster heads that further accelerates convergence.

In this paper we formulate a novel utility-cost optimization problem for routing and power control in multi-hop wireless networks. As the problem is non-convex and non-separable (no assumption on high or low SINR regime), we approach it by solving a sequence of convex approximation problems. If the initial convex approximate is feasible, it is shown that the solution sequence converges to a KKT point to the original utility-cost optimization problem. The convex approximation problems are solved recursively by means of primal-dual methods that are shown to be amenable to distributed implementation. The seamless interaction between the successive convex approximation and the primal-dual algorithm constitutes the proposed successive primal-dual convex approximation (SPDCA) algorithm.

We study the impact of physical layer (PHY) transmit rate control on energy efficient estimation in wireless sensor networks. A sensor network collects measurements about an unknown evolving process. Each sensor controls its sampling rate and its PHY transmit rate to the next hop or to the Fusion Center (FC). The FC performs estimation of the unknown process based on sensor measurements and needs to adhere to an estimation accuracy constraint. The objective is to maximize sensor network lifetime. The tradeoff is that, high PHY transmit rates consume more energy per transmitted bit, but they increase the amount of transmitted sensor measurement data per unit time, and thus they aid in improving estimation quality and in satisfying the estimation error constraint. First, we study a single-hop network where sensors transmit directly to the FC. In this case, sensor sampling rates are directly mapped onto PHY transmit rates. We identify fundamental structural properties of the optimal solution, and we propose a distributed, iterative sensor PHY rate adaptation algorithm for reaching a solution, based on light-weight feedback from the FC. Next, we consider the multi-hop version of the problem, where the sensor measurement (sampling) rates, PHY transmit rates and data flows to the FC are controlled. We extend the distributed optimization framework above to include all controllable parameters, and we devise an iterative algorithm for maximizing network lifetime.

We consider a power-controlled wireless network with an established network topology in which the communication links (transmitter-receiver pairs) are subject to general constraints on transmit powers and corrupted by the co-channel interference and background noise. In this paper, we characterize the max-min SIR power allocation and provide a saddle point characterization of this power allocation under weaker conditions. This characterization is a basis for novel algorithms for computing a max-min SIR power allocation.

A communication scheme for the multiple antenna multicast fading channel is proposed, in which the transmission is coded at the application layer over a number of channel realizations. The scheme gives rise to a novel multicast transmit beamforming problem. The properties of the proposed scheme are presented, in particular the scaling of the achievable rate for the increasing number of users is investigated and the rate is shown not to decrease to zero, which is an improvement over multicast schemes without coding, e.g. so-called max-min transmit beamforming. Algorithms for solving the resulting beamforming problem are proposed and evaluated in simulation.

In the current work the effects of hop-by-hop packet loss and retransmissions via ARQ protocols are investigated within a Mobile Ad-hoc NET-work (MANET). A success probability function is related to each link, which can be controlled by power and rate allocation. The expression for the network's stability region is initially derived where the success function plays a critical role. The investigation considers functions with specific properties which are shown to be satisfied for various expressions of the success probability related to different modulation and coding schemes as well as outage events. A Network Utility Maximization problem (NUM) with stability constraints is further formulated which decomposes into the input rate control and the scheduling problem. Under certain assumptions the latter is relaxed to a simpler form. This allows application of supermodular game theory and the algorithmic approach in is adapted to include the family of success functions of interest. It is shown finally that interference measurements per node drastically reduce the amount of information exchange required for solving the scheduling problem.

We study the impact of physical layer (PHY) transmit rate control on energy efficient estimation in wireless sensor networks. A sensor network collects measurements and transmits them to a Fusion Center (FC) with controllable PHY transmission rates. The FC performs estimation of an unknown parameter process based on sensor measurements, and it needs to adhere to an estimation error constraint. The objective is to maximize network lifetime. High transmission rates consume more energy per transmitted bit, however they convey larger amount of data per unit time and thus can aid in satisfying the estimation error constraint. We identify basic structural properties of the optimal solution, and we propose an iterative algorithm for reaching a solution based on light-weight feedback from the FC.

We view a wireless sensor network as a collection of sensor nodes that observe sources of information, process the picked up data and send it to a sink node, with the goal of computing a desired function of the measurements. To this end, we consider a previously proposed coding scheme that exploits the underlying fading multiple-access channel (MAC) to efficiently estimate the function values. The main problem addressed in this paper is how much channel state information (CSI) is needed at the sensor nodes to obtain sufficiently good estimates? First we show that there is no performance loss, independent of fading distributions, if, instead of perfect CSI, each sensor node has only access to the modulus of its channel coefficient. In the case of multiple antenna elements at the sink node and specific independent distributed fading environments, it is shown that CSI at sensor nodes is not necessary and a very simple correction of fading effects can be performed at the sink based on some statistical channel knowledge. In many cases, fading improves the estimation accuracy due to the multiple-access nature of the channel.

An admission control algorithm for power-controlled wireless networks, proposed previously for the case of linear interference functions, is considered in this paper. We analyze the properties of the algorithm using the framework of standard interference functions, which makes it applicable to many system designs. Furthermore, we introduce individual power constraints into the system. The key property of the algorithm is the protection of active users, which guarantees that as new users attempt to join the network, the quality of the established links is sustained. We present conditions under which this key property is preserved under power constraints and analyze the convergence properties of the scheme.

In this paper, we address the problem of the feasible SNR region for a multicast MIMO downlink channel. We characterize the feasible SNR region for a multicast group consisting of two mobile users. Based on this characterization, we propose solving the max-min SNR problem over the smallest convex and downward comprehensive superset of the feasible SNR region. We show, that the max-min SNR transmit beamformer is achievable via maximization of a weighted sum of SNRs. Both transmitter and the receivers are equipped with multiple antenna elements. The receive beamformers are fixed but arbitrary and each transmission is a single-mode transmission (no multiplexing and parallel transmissions of symbols).

We consider a power-controlled wireless network with an established network topology in which the communication links (transmitter-receiver pairs) are subject to some constraints on transmit powers and corrupted by the cochannel interference and background noise. The interference is completely determined by a so-called gain matrix. Assuming irreducibility of the gain matrix, we provide an elegant characterization of the max-min SIR-balanced power allocation under general power constraints. This characterization gives rise to two types of algorithms for computing the max-min SIR-balanced power allocation. It also allows for an interesting saddle point characterization of the Perron root of extended gain matrices.

In wireless sensor networks, the identity of a particular sensor node and a complete reconstruction of sensed data at a designated sink node may be not needed. Indeed, the objective is often to compute a certain function of the sensed data. Such desired functions can be for example the arithmetic mean, the geometric mean, polynomials and other functions that adequately match the mathematical characteristic of the underlying multiple-access channel. In this paper, we propose a simple practical approach to compute desired functions of sensor network data, exploiting explicitly the mathematical characteristic of the wireless sensor multiple-access channel (WS-MAC). In contrast to traditional schemes that are designed to combat interference caused by other connections, we exploit this interference with the goal of computing the desired functions, which is in a sense a paradigm shift. This leads directly to a higher data rate in terms of function computation or a higher SNR in comparison to other schemes like time division multiple access (TDMA). Our approach needs no extensive symbol or phase synchronization, since the measured values are converted into the transmit power of a specific random transmit sequence with unit norm. Only a coarse block synchronization is necessary so that the proposed scheme is easy to implement.

In this paper we underline the importance of utilizing unequal powers in wireless ad hoc networks. Recent results from percolation theory indicate that a threshold exists after which a very large randomly positioned ad hoc network becomes disconnected almost surely for a given communication configuration. In this paper we prove that it is possible to extend the region of connectivity by allocating the transmit power of each node in an intelligent manner.

This paper addresses the problem of joint transmit beamforming and power control with receive beamforming for a multigroup multicast MIMO downlink channel. As receive beamformers, we consider the matched-filter beamformers and the optimal MMSE beamformers. We propose two low complexity transmit strategies and compare them with some known approximations of the solutions to certain max-min problems that are NP-hard. The data rate of each multicast group is determined by the worst-case SINR. To guarantee fairness between different multicast groups, the total power budget is allocated according to some max-min power control scheme. Additionally, we consider SINR - proportional and equal power distribution schemes. Numerical results of the proposed algorithms are presented.

In the current work the effects of hop-by-hop packet loss and retransmissions via ARQ protocols are investigated within a Mobile Ad-hoc NET-work (MANET). Errors occur due to outages and a success probability function is related to each link, which can be controlled by power and rate allocation. We first derive the expression for the network's capacity region. A Network Utility Maximization problem (NUM) with stability constraints is further formulated which decomposes into (a) the input rate control problem and (b) the scheduling problem. The NUM problem can be solved in a fully decentralized manner if (b) is solved distributedly. Use of supermodular game theory suggests a price based algorithm that requires minimum information exchange between interfering nodes and converges to a power allocation which satisfies the necessary optimality conditions of (b). Simulations illustrate that the suggested algorithm brings near optimal results.

In this letter we propose a power allocation iteration which optimizes the weighted aggregate performance of a single-hop network. We show that the proposed iteration is a competitive alternative to conventional gradient iterations in terms of convergence and computational effort.

In this paper, we prove a new achievable rate region for the two-phase two-way relay channel under half-duplex constraints. The points in the region are achieved by a combination of a compress-and-forward strategy at the relay node (potentially including partial decoding) with a kind of joint decoding at the receivers. For some channels, the achievable rate region presented in this paper is shown to be a proper superset of a rate region proven in some recent papers where a separate decoding was assumed.

This paper addresses the problem of incorporating QoS support into the traditional utility-based power control problem. We present a novel problem formulation, prove relevant properties of an optimal power allocation and propose a decentralized recursive algorithm with global convergence.

In this paper, we state a new achievable rate region for the two-phase two-way relay channel with a half-duplex relay node. The new region is obtained using a partial-decode-and-forward protocol, which is a superposition of both, decode-and-forward and compress-and-forward. It contains the achievable rate regions of [1] and [2] as special cases.

The feasible log-SIR region is defined as a set of all signal-to-interference ratios (SIR) expressed in logarithmic scale that can be supported in a wireless network by means of power control and with all users being active concurrently. Recently, the feasible log-SIR region was shown to be a convex set, which is a key ingredient in the development of some power control strategies for wireless systems. In this paper, under the assumption of a noiseless channel, we strengthen these results by proving a necessary and sufficient condition for the feasible log-SIR region to be a strictly convex set. The strict convexity property is of interest since it is closely related to the problem of the existence and uniqueness of a so-called log-SIR fair power vector.

Efficient design of online power allocation policies relies strongly on convex-analytic and optimization-theoretic properties of the optimization problem on hand. In this context we study the optimization of power allocation in cellular networks with so-called best-effort traffic. Our results exhibit a specific role of link QoS parameters, for which the dependence on the corresponding link SINR is log-convex. In such case the region of achievable QoS vectors is shown to be convex, the considered problem is globally solvable and can be easily transformed into a favorable convex form.

We propose a unifying framework for max-min fairness in orthogonal networks and networks with interference. First, a universal formulation of the max-min fairness problem for orthogonal networks and networks with interference is presented. This shows that orthogonal networks and networks with interference can be universally described by a graph, induced by time sharing of resources and interference coupling, respectively. As a consequence, a unifying graph-related characterization of performance achieved under max-min fairness is obtained.

In this work, we provide asymptotically almost sure lower and upper bounds on the max-min fair performance in large single-hop networks with arbitrary channel fading. Our results are asymptotic in nature and we consider two cases of the limiting regime where the number of links (users) tends to infinity. The provided bounds apply to orthogonal networks as well as to a special class of nonorthogonal networks.

This paper deals with the problem of joint hop-by-hop congestion control and power control in wireless networks. We couple the back-pressure policy and window-based congestion control at source nodes with power control to provide end-to-end fairness. Our definition of fairness includes arbitrarily close approximation of max-min fairness. Numerical experiments indicate good transient response to channel variations.

The feasible quality-of-service (QoS) region is the set of all QoS vectors that can be provided to the users by means of power control, with interference treated as noise. In an interference-limited scenario, this set is determined by the Perron root of some QoS-dependent nonnegative matrix. In a previous work, we showed that if the signal-to-interference ratio (SIR) is a log-convex function of the QoS, then the Perron root is a log-convex function. This implies convexity of the feasible QoS region. In this correspondence, we prove that the log-convexity property is also necessary for the Perron root to be convex for any choice of the (path) gain matrix. Interestingly, a significantly less restrictive property is sufficient when the gain matrix is confined to be symmetric positive semidefinite.

This work addresses the problem of designing efficient resource allocation algorithms for wireless ad-hoc networks with best-effort traffic. Relying on the framework of generalized Lagrangeans and duality we propose an optimization concept that combines the decentralization with quadratic quotient convergence and unconstrained iteration character

In a previous work, it was shown that the l1-norm of the minimum power vector is log-convex if the signal-to-interference ratio (SIR) is establish log-convex function of the quality-of-service (QoS) value of interest. Assuming irreducibility of the gain matrix, the l1-norm was further shown to be strictly log-convex in two special cases of SIR-QoS functions. In this paper, we extend this result in two directions: First, we drop the requirement on irreducibility of the gain matrix and, secondly, we consider any log-convex SIR-QoS relationship. Finally, we point out a connection of these results to strong convexity of a utility-based power control problem.

The infeasible signal-to-interference ratio (SIR) region is a set of SIRs that are not supportable in a power-controlled wireless network. It was conjectured that this set is convex in networks constrained on total power. We disprove the conjecture and discuss how this result may impact optimal medium-access control policies.

This correspondence establishes an interesting connection between the Kullback-Leibler divergence and the Perron root of nonnegative irreducible matrices. In the second part of the correspondence, we apply these results to the power control problem in wireless communications networks to show a fundamental tradeoff between fairness and efficiency. A power vector is said to be efficient if it maximizes the overall network efficiency expressed in terms of an aggregate network utility function parameterized by some weight vector. For two widely used examples of utility functions, the correspondence identifies the unique weight vector for which a power vector is both efficient and max-min fair in the sense that each communication link has the same quality-of-service. These results also give rise to new saddle point characterizations of the Perron root

The paper deals with the problem of power control in distributed wireless networks. The objective is to allocate transmit powers to the links so as to optimize the sum of weighted link-layer QoS levels. Instead of focusing on a specific QoS measure such as data rate, delay or bit error rate, we consider a generic framework that includes some interesting QoS metrics as special cases. The paper presents an iterative gradient based algorithm that is shown to converge to a global optimum. Moreover, we propose a simple procedure based on the use of an adjoint network that allows an efficient implementation of the algorithm in distributed networks.

We propose a general framework for joint resource allocation and interference control for classes of interference functions based on an axiomatic model. This model holds for a wide range of multiuser channels and even allows the incorporation of interference suppression techniques. The quality of service (QoS) of each user is modeled as a function of the interference. It is shown that for certain mappings between interference and QoS, the resulting achievable region is a convex set. Furthermore, we show that the problem of optimizing the sum of weighted QoS over the transmit powers is convex for certain classes of QoS functions. The choice of the weights determines the trade-off between fairness and overall efficiency. The problem can be solved efficiently by standard convex optimization techniques.

This paper deals with the geometry of a feasible SIR region, which is defined as a set of all signal-to-interference ratios that can be supported in a power-controlled wireless network when no link scheduling is involved. It was conjectured that the complement of the feasible SIR region, a so-called infeasible SIR region, is in general a convex set under a total power constraint. The conjecture was supported by some partial results and the fact that the infeasible SIR region is a convex set in the 2 dimensional case. This paper disproves this conjecture, thereby showing that the geometry of the infeasible SIR region is more complicated than it was supposed to be. The paper discusses some possible implications of these results on optimal link scheduling policies

We consider a wireless ad-hoc network for data applications using an orthogonal quasi-synchronous CDMA air interface with a linear receiver structure. The throughput performance of such networks is highly affected by a limited number of signature sequences and by individual power constraints on each node. This paper deals with strategies for allocating powers and sequences to nodes. These strategies exploit the relative delay tolerance of data applications and the random packet arrivals to improve the throughput performance. We provide an upper bound of the optimal centralized strategy, and then propose a centralized heuristic algorithm and two distributed approximations of this strategy based on clustering.

In this work, we are concerned with quality of service (QoS) control in wireless cellular networks utilizing linear receiver structures. We investigate the issues of fairness and operator utility efficiency. We disprove the common conjecture on contradiction between min-max fairness and utility optimality by characterizing the case in which both goals can be accomplished. Our analysis shows that such fair and utility efficient allocation of powers and prices is a saddle point of the utility optimization objective. This means that the min-max fair utility optimum, when existent, is the worst case one and indicates a trade-off between social goals and operator goals.

The paper investigates a single-cell multiuser system with a linear antenna array at the base station. We consider both the downlink and the uplink. The quality of service (QoS) parameter of interest depends monotonically on the signal-to-interference+noise ratio (SINR) through a given QoS-SINR mapping. In the case of no power constraints, the set of all QoS parameter values that can be supported by the system is determined by the spectral radius of a certain nonnegative irreducible matrix. The paper's main result shows that if the spectral radius is required to be a convex function of the QoS parameter values for an arbitrary link gain matrix, then the inverse of the QoS-SINR mapping must be log-convex, which is, in general, stronger than the convexity property. We show that the log-convexity requirement can be dropped in two special cases of link gain matrices. These results provide a theoretical framework for the design of medium access control strategies.

In this correspondence, we consider a code-division multiple-access (CDMA) channel with a linear receiver structure whose inputs are subject to a total power constraint. Each user is required to satisfy a certain quality-of-service (QoS) requirement expressed, for instance, in terms of data rate or delay. The set of all feasible QoS requirements is called the feasibility region. It is shown that if the signal-to-interference ratio (SIR) at the output of each linear receiver is a bijective and log-convex function of the QoS parameter of interest, the minimum total power needed to satisfy the QoS requirements is a jointly log-convex function of the QoS parameters. Furthermore, in two special cases of practical interest, we show that the minimum total power is strictly log-convex. These results imply that the corresponding feasibility regions are convex sets. The convexity property is a key ingredient in the development of access control strategies for wireless communications systems.

Dynamic power control and scheduling strategies provide efficient mechanisms for improving performance of wireless communications networks. A common objective is to maximize throughput performance of a network or to minimize the total transmission power while satisfying quality-of-service (QoS) requirements of the users. The achievement of these objectives requires the development of medium access control (MAC) strategies that optimally utilize scarce resources in wireless networks. When developing such strategies, a good understanding of the structure of the feasibility region is essential. The feasibility region is defined as a set of all QoS requirements that can he supported by a network with all users active concurrently. Thus, the structure of this set shows when (if at all) scheduling strategies can improve network performance. In particular, if the feasibility region is a convex set, then concurrent transmission strategies are optimal and the optimal power allocation can be obtained efficiently via a convex optimization. Other important problems are how the total transmission power depends on QoS requirements and what the optimal QoS tradeoff is. In this paper, we address all these problems and solve them completely in some important cases. The purpose of this paper is to explore the interrelationship between QoS requirements and physical quantities such as transmission power. Although the results are obtained in the context of a power-controlled CDMA system, they also apply to some other communications systems. A key assumption is that there is a monotonous relationship between a QoS parameter of interest (such as data rate) and the signal-to-interference ratio at the output of a linear receiver.

This paper characterizes the Perron root of nonnegative irreducible matrices in terms of the Kullback Leibler distance (generalized to positive discrete measures). By Perron-Frobenius theory, the Perron root of any nonnegative irreducible matrix is equal to its spectral radius. Thus, the paper establishes a connection between two fundamental concepts of information theory and linear algebra. Moreover, these results are shown to have interesting applications to the classical power control problem in wireless communications networks. Finally, we prove new saddle point characterizations of the Perron root and present possible extensions of the results to more general functions.

Link scheduling and power control are efficient mechanisms to provide quality-of-service (QoS) to individual users in wireless networks. When developing optimal access-control strategies, a good understanding of the geometry of the feasible QoS region is essential. In particular, if the feasible QoS region is a convex set, the effect on scheduling is to prefer simultaneous transmission of users. Moreover, the convexity property plays a key role in the development of optimal power-control strategies. This paper provides sufficient conditions for the convexity of the feasible QoS region in systems with and without power constraints. Furthermore, we prove necessary conditions for the feasibility of QoS requirements to better understand the optimal QoS tradeoff. Finally, the paper provides insight into the interrelationship between QoS requirements and the minimum transmission power necessary to meet them. Although the results are obtained in the context of a power-controlled code-division multiple-access system, they also apply to some other communications systems. A key assumption is that there is a one-to-one relationship between a QoS parameter of interest (data rate, service delay, and etc.) and the signal-to-interference ratio at the output of a linear receiver.

Power control is an important mechanism for interference management in CDMA channels. When developing power control strategies, CDMA system designers need to ensure that each user meets its signal-to-interference ratio (SIR) requirement. The minimal total power at which each user meets its SIR requirement is called the minimal feasible total power (MFTP). This paper shows that MFTP is a log-convex function on the set of feasible inverse SIR requirements. This implies that the set of feasible inverse SIR requirements is a convex set.

Large area synchronized (LAS)-CDMA has been proposed to enhance third generation and fourth generation wireless systems. LAS-CDMA is based on multiple access codes that result from a combination of LA codes and pulse compressing LS codes. To reduce multiple access interference and intersymbol interference in time dispersive channels, LS codes have perfect auto-correlation and cross-correlation functions in a certain vicinity of the zero shift. In this paper, we provide systematic methods and the underlying theory for the construction of such codes that go far beyond the examples revealed by LinkAir (2000).

For the mean-square errors of the maximum-likelihood channel estimate, the noise enhancement factor of an aperiodic invertible sequence is an optimality criterion. Massey posed a question as to whether there exist binary sequences that achieve the absolute optimum for the sequence length N/spl rarr//spl infin/ since then they would be preeminently eligible for channel estimation. However, the noise enhancement factor is extremely difficult to examine both analytically and numerically often making extensive investigations of sequences impossible. For this reason, it is convenient to consider the l/sup 1/-norm of out-of-phase peaks of the aperiodic auto-correlation magnitude. In this paper, it is shown that the l/sup 1/-norm can never have the required behaviour for solving Massey's problem in the case of binary sequences and that the best possible binary sequences in this connection are skew-symmetric Barker sequences. The paper is of interest in mobile radio channels' study.