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.

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.

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.

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 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.

Deep equilibrium (DEQ) models are widely recognized as a memory efficient alternative to standard neural networks, achieving state-of-the-art performance in language modeling and computer vision tasks. These models solve a fixed point equation instead of explicitly computing the output, which sets them apart from standard neural networks. However, existing DEQ models often lack formal guarantees of the existence and uniqueness of the fixed point, and the convergence of the numerical scheme used for computing the fixed point is not formally established. As a result, DEQ models are potentially unstable in practice. To address these drawbacks, we introduce a novel class of DEQ models called positive concave deep equilibrium (pcDEQ) models. Our approach, which is based on nonlinear Perron-Frobenius theory, enforces nonnegative weights and activation functions that are concave on the positive orthant. By imposing these constraints, we can easily ensure the existence and uniqueness of the fixed point without relying on additional complex assumptions commonly found in the DEQ literature, such as those based on monotone operator theory in convex analysis. Furthermore, the fixed point can be computed with the standard fixed point algorithm, and we provide theoretical guarantees of its geometric convergence, which, in particular, simplifies the training process. Experiments demonstrate the competitiveness of our pcDEQ models against other implicit models.

We use fixed point theory to analyze nonnegative neural networks, which we define as neural networks that map nonnegative vectors to nonnegative vectors. We first show that nonnegative neural networks with nonnegative weights and biases can be recognized as monotonic and (weakly) scalable mappings within the framework of nonlinear Perron-Frobenius theory. This fact enables us to provide conditions for the existence of fixed points of nonnegative neural networks having inputs and outputs of the same dimension, and these conditions are weaker than those recently obtained using arguments in convex analysis. Furthermore, we prove that the shape of the fixed point set of nonnegative neural networks with nonnegative weights and biases is an interval, which under mild conditions degenerates to a point. These results are then used to obtain the existence of fixed points of more general nonnegative neural networks. From a practical perspective, our results contribute to the understanding of the behavior of autoencoders, and we also offer valuable mathematical machinery for future developments in deep equilibrium models.

We introduce the concept of inverse feasibility for linear forward models as a tool to enhance Over-the-Air (OTA) federated learning (FL) algorithms. Inverse feasibility is defined as an upper bound on the condition number of the forward operator as a function of its parameters. We analyze an existing OTA FL model using this definition, identify areas for improvement, and propose a new OTA FL model. Numerical experiments illustrate the main implications of the theoretical results. The proposed framework, which is based on inverse problem theory, can potentially complement existing notions of security and privacy by providing additional desirable characteristics to networks.

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.

We derive a novel uplink-downlink duality principle for optimal joint precoding design under per-transmitter power and information constraints. The main application is to cell-free networks, where each access point (AP) must typically satisfy an individual power constraint, and form its transmit signal on the basis of possibly partial data and channel state information sharing. By measuring performance using the popular hardening inner bound on the ergodic capacity, we show that optimal joint precoders can be interpreted as optimal joint combiners on a dual uplink channel with properly designed transmit and noise powers, and that they can be obtained using a variation of the recently developed team minimum mean-square error method. We finally apply our results to the numerical evaluation of optimal centralized and local precoding in a typical user-centric cell-free network subject to per-AP power constraints.

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.

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.

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.

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.

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.

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.

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.

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.

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.

We propose an effective interference management and beamforming mechanism for uplink communication systems that yields fair allocation of rates. In particular, we consider a hotspot area of a millimeter-wave (mmWave) access network consisting of multiple user equipment (UE) in the uplink and multiple access points (APs) with directional antennas and adjustable beam widths and directions (beam configurations). This network suffers tremendously from multi-beam multi-user interference, and, to improve the uplink transmission performance, we propose a centralized scheme that optimizes the power, the beam width, the beam direction of the APs, and the UE - AP assignments. This problem involves both continuous and discrete variables, and it has the following structure. If we fix all discrete variables, except for those related to the UE-AP assignment, the resulting optimization problem can be solved optimally. This property enables us to propose a heuristic based on simulated annealing (SA) to address the intractable joint optimization problem with all discrete variables. In more detail, for a fixed configuration of beams, we formulate a weighted rate allocation problem where each user gets the same portion of its maximum achievable rate that it would have under non-interfered conditions. We solve this problem with an iterative fixed point algorithm that optimizes the power of UEs and the UE - AP assignment in the uplink. This fixed point algorithm is combined with SA to improve the beam configurations. Theoretical and numerical results show that the proposed method improves both the UE rates in the lower percentiles and the overall fairness in the network.

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.

We propose a hybrid dictionary approach for distributed kernel-based adaptive learning of a nonlinear function by a network of nodes. The hybrid dictionary incorporates a local part to improve learning of high frequency components in the function within the local domain of each node and a global part to provide a consensus estimate of the function over the whole region of interest. We apply our scheme to the reconstruction of a spatial distribution by a network of mobile nodes. Performance evaluations show that high frequency components are reconstructed accurately by our hybrid dictionary approach while common schemes are not able to recover them completely.

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 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 novel resource allocation algorithms that have the objective of finding a good tradeoff between resource reuse and interference avoidance in wireless networks. To this end, we first study properties of functions that relate the resource budget available to network elements to the optimal utility and to the optimal resource efficiency obtained by solving max-min utility optimization problems. From the asymptotic behavior of these functions, we obtain a transition point that indicates whether a network is operating in an efficient noise-limited regime or in an inefficient interference-limited regime for a given resource budget. For networks operating in the inefficient regime, we propose a novel partial resource muting scheme to improve the efficiency of the resource utilization. The framework is very general. It can be applied not only to the downlink of 4G networks, but also to 5G networks equipped with flexible duplex mechanisms. Numerical results show significant performance gains of the proposed scheme compared to the solution to the max-min utility optimization problem with full frequency reuse.

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.

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.

We present an algorithm that minimizes asymptotically a sequence of non-negative convex functions over diffusion networks. To account for possible node failures, position changes, and/or reachability problems (because of moving obstacles, jammers, etc), the algorithm can cope with dynamic networks and cost functions, a desirable feature for online algorithms where information arrives sequentially. Many projection-based algorithms can be straightforwardly extended to diffusion networks with the proposed scheme. We use the acoustic source localization problem in sensor networks as an example of a possible application.

One of the main drawbacks of the OFDM modulation is the high peak-to-average power ratio (PAPR) of the transmitted signal. In this study, we devise a low-complexity transmitter that mitigates the PAPR problem and satisfies multiple requirements of a given system, such as error vector magnitude (EVM) constraints, minimum transmitted power of the data symbols, among others. To reduce the PAPR, we use the adaptive projected subgradient method to suppress a sequence of convex cost functions over closed convex sets describing desired properties of the transmitted signal. Numerical examples show that the proposed scheme reduces the PAPR to reasonable levels with few iterations.

We introduce adaptive linear filters based on the adaptive projected subgradient method that are suitable for online implementation of multiple access interference (MAI) suppression in OSTBC-MIMO systems. The proposed adaptive filters track the optimal solution of a new cost function that is robust against channel state information (CSI) mismatch. The adaptive update algorithm is based on projections onto closed convex sets that contain the optimal solution with high probability. The main features of the adaptive filters are that no matrix inversion of a sample covariance matrix is required and that a low-complexity recursive implementation is possible. Convergence analysis and simulation results show the effectiveness of the proposed schemes.

Constrained normalized adaptive filters are used as a computationally efficient class of receivers to decrease multiple access interference (MAI). Some receivers estimate the amplitude and/or use different normalization parameters in order to improve the convergence speed. In this paper, we derive the steady-state performance of this class of receivers, from which it is revealed that the normalization parameters that aim at increasing the convergence speed deteriorate the steady-state performance if the step-size is not changed. Additionally, we prove that an estimate of the desired user's amplitude can greatly improve the steady-state performance. Computer simulations show remarkably good agreement with our analysis

The adaptive projected subgradient method offers a unified mathematical perspective for the adaptive (set-membership/set-theoretic) filtering schemes. In this paper, we introduce an overview of its recent theoretical advances and successful applications to robust signal processing problems including the stereo acoustic echo canceling, the MAI suppression in DS/CDMA receivers, and the robust adaptive beamforming with array antenna systems

This paper presents a family of multiple access interference (MAI) suppression receivers based on the adaptive projected subgradient method. The proposed scheme can be applied to many different channel models and modulations in a unified manner. Moreover, it is suitable for both blind and nonblind receivers. The adaptive projected subgradient method realizes excellent convergence to a set including an optimal solution with high probability by asymptotically minimizing a sequence of nonnegative convex functions. Simulation results show much better (variable) tradeoff between speed and performance at steady-state as compared to existing techniques

The paper presents two novel blind set-theoretic adaptive filtering algorithms for multiple access interference (MAI) suppression in DS/CDMA systems. We naturally formulate the problem of MAI suppression as minimizing asymptotically a sequence of cost functions under some linear constraint defined by the desired user's signature. The proposed algorithms embed the constraint in the direction of adaptation, and thus the adaptive filter moves toward the optimal filter without stepping away from the constraint set. In addition, using parallel processors, the proposed algorithms attain good performance behavior with low computational complexity. Geometric interpretation clarifies an advantage of the proposed methods over some conventional methods. Simulation results demonstrate that the proposed algorithms achieve much faster convergence than conventional methods with a moderate number of concurrent processors.