Satellite communication (SatCom) is regarded as a key enabler for bridging connectivity and capacity gaps in sixth-generation (6G) networks. However, the proliferation of Low Earth Orbit (LEO) satellites raises significant intersystem interference risks with Geostationary Earth Orbit (GEO) systems. This paper introduces a cooperative multi-satellite multi-reconfigurable intelligent surface (RIS) transmission framework to mitigate such interference while enhancing LEO SatCom performance. Specifically, cooperative beamforming is designed under a non-coherent cell-free paradigm, considering both adaptive and max ratio (MR) precoding, as well as statistical and two-timescale channel state information (CSI), aiming to synthesize the advantages of cell-free and RIS into SatCom in a practical way. Firstly, an alternating optimization (AO)-based design leveraging statistical CSI with adaptive precoding is proposed. Then, we propose a power allocation algorithm under MR precoding with given RIS phase shifts obtained from the former, along with a direct two-stage design bypassing prior results. Additionally, we extend derived closed-form expressions and proposed algorithms to exploit two-timescale CSI. Numerical results demonstrate the impact of intersystem interference mitigation constraints, compare the performance of proposed algorithms, draw insights into the effects of transmit power, interference threshold, and Rician factors, validate SatCom performance enhancements achieved by RISs, and discuss the advantages of multi-satellite cooperation.

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

In this article, we introduce a method to optimize 5G massive multiple-input multiple-output (mMIMO) connectivity for unmanned aerial vehicles (UAVs) on aerial highways through strategic cell association. UAVs operating in 3D space encounter distinct channel conditions compared to traditional ground user equipment (gUE); under the typical line of sight (LoS) condition, UAVs perceive strong reference signal received power (RSRP) from multiple cells within the network, resulting in a large set of suitable serving cell candidates and in low signal-to-interference-plus-noise ratio (SINR) due to high interference levels. Additionally, a downside of aerial highways is to pack possibly many UAVs along a small portion of space which, when taking into account typical LoS propagation conditions, results in high channel correlation and severely limits spatial multiplexing capabilities. In this paper, we propose a solution to both problems based on the suitable selection of serving cells based on a new metric which differs from the classical terrestrial approaches based on maximum RSRP. We then introduce an algorithm for optimal planning of synchronization signal block (SSB) beams for this set of cells, ensuring maximum coverage and effective management of UAVs cell associations. Simulation results demonstrate that our approach significantly improves the rates of UAVs on aerial highways, up to four times in achievable data rates, without impacting ground user performance.

The problem of resource-efficient beam alignment is a long standing one within massive MIMO (mMIMO) enabled wireless communication networks. In D2D networks, the beam alignment problem is repeated for every new pair that appears and wishes to communicate, leading to a seemingly unbounded resource expenditure as the network grows dense. In this paper, we develop a new approach that uses the implicit geometric structure of such networks to break the spell. Instead of combatting it, our method exploits densification to facilitate alignment with minimal resources. As far as we know, we are the first to address the beam alignment problem in mMIMO D2D networks, as most previous research has concentrated on single point-to-point or single base station to multiple users scenarios. Assuming a static (or slow varying) network, the intuition behind our approach is to utilize the beam alignment solutions at prior device pairs to predict optimal alignment in future pairs at independent new locations. We show the equivalence between this problem and a non-linear matrix completion (MC) problem under some sparsity condition. To solve it, we design an MC technique based on attention-based graph neural network (GNN) which proves effective to predict optimal beam pairs with little side-information.

This paper investigates the performance of cellular networks assisted by unmanned aerial vehicles (UAVs) acting as flying base stations (FlyBSs). We focus on a scenario with multi-hop relaying via FlyBSs to deliver data from a ground base station (GBS) to users in a challenging case with the channels reused at all hops to exploit radio resources efficiently. Our objective is to maximize the sum capacity of the users via an optimization of FlyBSs’ position in 3D, association of users to either GBS or to one of the FlyBSs, allocation of channels for communication at individual hops, and allocation of transmission power for all channels. Moreover, practical constraints on the FlyBSs’ movement, transmission and propulsion power, and backhaul capacity are taken into account. Due to a non-convexity and discreetness of the objective and some constraints, there is no optimal solution to the formulated problem. Thus, we propose an analytical solution based on an alternating optimization of an energy-efficient placement of the FlyBSs, channel allocation, user association, and transmission power. Each subproblem in the alternating optimization is substituted either by a linear programming (LP) problem through a change of variables, or by a convex problem via a conversion of the objective and constraints. The results show an increase in sum capacity by 35%–60% compared to related works while the FlyBSs’ propulsion power consumption is not increased.

The development of vehicular intelligence and networking has led to the emergence of vehicular crowdsensing as a new perception paradigm. By integrating edge computing, vehicular intelligence, and Internet of Vehicles technologies, vehicular crowdsensing is poised to have far-reaching implications in the domains of intelligent transportation, industrial sensing, and smart cities. In urban sensing scenarios, recruiting a large number of users can provide a lot of useful data, yet is costly due to expected financial incentive plans. As a solution, sparse mobile crowdsensing techniques have been proposed to collect data at a subset of sensing grids for data inference. However, the majority of these methods rely solely on explicit connections between sensing grids and do not consider implicit relations, which are crucial for accurate data inference. To achieve both high-quality data inference and cost reduction, we propose a cost-efficient vehicular crowdsensing scheme based on implicit relation-aware graph attention networks (CVC-IRGAT), which combines missing data inference with active grid selection. First, we design the IRGAT model to capture implicit and explicit relations between grids through a dual-channel mechanism of relation-aware and graph attention. Then, we design a method to assess the inferred data based on the Gaussian mixture model. Given the assessment values, a deviation information score function is proposed to measure the importance of the inferred values and model errors. Finally, we introduce active learning iterations to select the grids in accordance with this function. Extensive experiments have been conducted on real-world datasets, which demonstrate the superiority of the proposed CVC-IRGAT.

Standard Bayesian learning is known to have suboptimal generalization capabilities under misspecification and in the presence of outliers. Probably approximately correct (PAC)-Bayes theory demonstrates that the free energy criterion minimized by Bayesian learning is a bound on the generalization error for Gibbs predictors (i.e., for single models drawn at random from the posterior) under the assumption of sampling distributions uncontaminated by outliers. This viewpoint provides a justification for the limitations of Bayesian learning when the model is misspecified, requiring ensembling, and when data are affected by outliers. In recent work, PAC-Bayes bounds-referred to as PACm-were derived to introduce free energy metrics that account for the performance of ensemble predictors, obtaining enhanced performance under misspecification. This work presents a novel robust free energy criterion that combines the generalized logarithm score function with PACm ensemble bounds. The proposed free energy training criterion produces predictive distributions that are able to concurrently counteract the detrimental effects of misspecification-with respect to both likelihood and prior distribution-and outliers.

We focus on prediction of channel quality between any two devices using Deep Neural Network (DNN) from information already known to mobile networks. The DNN-based prediction reduces a cost of a common pilot-based channel quality measurement in scenarios with many ad-hoc communicating devices. However, collecting a sufficient number of high-quality and well-distributed training samples in real-world is not feasible. Hence, in this paper, we develop and validate a concept of DNN-based channel quality prediction between any two devices based on a low-complexity and easy-to-create digital twin. The digital twin serves for a generation of a large synthetic training dataset for channel quality prediction. As the low-complexity digital twin cannot capture all real-world aspects of the channels, we enhance the digital twin with real-world measured and artificially augmented inputs via transfer learning. The proposed concept is implemented and validated in software defined mobile network. We demonstrate that the proposed concept predicts the channel quality with a very high accuracy (mean average error of only 0.66 dB) in a real-world complex indoor scenario. Such error is sufficient for practical applications of the developed channel quality prediction concept and the error is few times lower than the error achievable by state-of-the-art solutions.

In mobile networks, unmanned aerial vehicles (UAVs) acting as flying base stations (FlyBSs) can effectively improve performance. Nevertheless, such potential improvement requires an efficient positioning of the FlyBS. In this paper, we study the problem of sum downlink capacity maximization in FlyBS-assisted networks with mobile users and with a consideration of wireless backhaul with channel reuse while a minimum required capacity to every user is guaranteed. The problem is formulated under constraints on the FlyBS's flying speed, propulsion power consumption, and transmission power for both of flying and ground base stations. None of the existing solutions maximizing the sum capacity can be applied due to the combination of these practical constraints. This paper pioneers in an inclusion of all these constraints together with backhaul to derive the optimal 3D positions of the FlyBS and to optimize the transmission power allocation for the channels at both backhaul and access links as the users move over time. The proposed solution is geometrical based, and it shows via simulations a significant increase in the sum capacity (up by 19%-47%) compared with baseline schemes where one or more of the aspects of backhaul communication, transmission power allocation, and FlyBS's positioning are not taken into account.

Cellular connected unmanned aerial vehicles (CCUAVs) are expected to enable new disruptive verticals with a significant impact on different business sectors. In particular, to enable connected and safe operations, the concept of drone corridors has recently received attention. In general, CCUAVs suffer from poor received signal strength, and they perceive large interference due to the high line-of-sight probability with the interfering sectors. In this paper, we propose an ADAM-based algorithm to optimize the electronic tilt of base stations deployed in an LTE network to improve the quality of service in predefined aerial corridors. Importantly, the numerical analysis results indicate that it is feasible to re-tune antenna sector directions to distribute, in an optimized manner, more power in the targeted corridors while minimizing interference, with a minimum impact for the ground, allowing usage of an already deployed LTE network for beyond line of sight (BLoS) communication.

In this work, we investigate the problem of an online trajectory design for an Unmanned Aerial Vehicle (UAV) in a Federated Learning (FL) setting where several communities exist, each defined by a unique task to be learned. In this setting, spatially distributed devices belonging to each community collaboratively contribute towards training their community model via wireless links provided by the UAV. Accordingly, the UAV acts as a mobile orchestrator coordinating the transmissions and the learning schedule among the devices in each community, intending to accelerate the learning process of all tasks. We propose a heuristic metric as a proxy for the training performance of the different tasks. Capitalizing on this metric, a surrogate objective is defined which enables us to jointly optimize the UAV trajectory and the scheduling of the devices by employing convex optimization techniques and graph theory. The simulations illustrate the out-performance of our solution when compared to other handpicked static and mobile UAV deployment baselines.

Deployment of multi-hop network of unmanned aerial vehicles (UAVs) acting as flying base stations (FlyBSs) presents a remarkable potential to effectively enhance the performance of wireless networks. Such potential enhancement, however, relies on an efficient positioning of the FlyBSs as well as a management of resources. In this paper, we study the problem of sum capacity maximization in an extended model for mobile networks where multiple FlyBSs are deployed between the ground base station and the users. Due to an inclusion of multiple hops, the existing solutions for two-hop networks cannot be applied due to the incurred backhaul constraints for each hop. To this end, we propose an analytical approach based on an alternating optimization of the FlyBSs' 3D positions as well as the association of the users to the FlyBSs over time. The proposed optimization is provided under practical constraints on the FlyBS's flying speed and altitude as well as the constraints on the achievable capacity at the backhaul link. The proposed solution is of a low complexity and extends the sum capacity by 23%-38% comparing to state-of-the-art solutions.

The estimation of Channel State Information (CSI) is one of the most difficult tasks for massive multiple-input multiple-output (MIMO) in frequency division duplex (FDD) mode. It is even more challenging in high-mobility scenarios. In this paper, we consider an FDD massive MIMO system with high-mobility and CSI delay and aim to predict the downlink (DL) channel under a realistic multipath channel model. The key novelty lies in the fact that for the first time we devise a joint angle-delay-Doppler (JADD) channel estimation framework. The main idea of our framework is to reconstruct the DL channel with the DL channel parameters estimated from the uplink (UL) channel samples and scalar feedback coefficients. To alleviate the feedback overhead, we design a wideband beamformer for the base station (BS) based on the DL angle-delay-Doppler parameters. The user equipment (UE) then estimates the DL channel parameters and feeds back some Doppler-related scalar coefficients back to the BS. We show that the feedback and DL pilot training overhead are independent of the number of BS antennas. The lower bound performance of our framework is also derived. Numerical results under the industrial channel model in rich scattering environments demonstrate that our framework works well from medium mobility scenario of 30 km/h to high mobility settings of 350 km/h.

Reliable pedestrian crash avoidance mitigation (PCAM) systems are crucial components of safe autonomous vehicles (AVs). The nature of the vehicle-pedestrian interaction where decisions of one agent directly affect the other agent’s optimal behavior, and vice versa, is a challenging yet often neglected aspect of such systems. We address this issue by modeling a Markov decision process (MDP) for a simulated AV-pedestrian interaction at an unmarked crosswalk. The AV’s PCAM decision policy is learned through deep reinforcement learning (DRL). Since modeling pedestrians realistically is challenging, we compare two levels of intelligent pedestrian behavior. While the baseline model follows a predefined strategy, our advanced pedestrian model is defined as a second DRL agent. This model captures continuous learning and the uncertainty inherent in human behavior, making the AV-pedestrian interaction a deep multi-agent reinforcement learning (DMARL) problem. We benchmark the developed PCAM systems according to the collision rate and the resulting traffic flow efficiency with a focus on the influence of observation uncertainty on the decision-making of the agents. The results show that the AV is able to completely mitigate collisions under the majority of the investigated conditions and that the DRL pedestrian model learns an intelligent crossing behavior.

The use of unmanned aerial vehicles (UAVs) acting as flying base stations (FlyBSs) is considered as an effective tool to improve performance of the mobile networks. Nevertheless, such potential improvement requires an efficient positioning of the FlyBS. In this paper, we maximize the sum downlink capacity of the mobile Internet of Things devices (IoTD) served by the FlyBSs while a minimum required capacity to every device is guaranteed. To this end, we propose a geometrical approach allowing to derive the 3D positions of the FlyBS over time as the IoTDs move and we determine the transmission power allocation for the IoTDs. The problem is formulated and solved under practical constraints on the FlyBS's transmission and propulsion power consumption as well as on flying speed. The proposed solution is of a low complexity and increases the sum capacity by 15% -46% comparing to state-of-the-art works.

In massive multiple-input multiple-output orthogonal frequency division multiplexing (MIMO-OFDM) systems, a challenging problem is how to predict channel state information (CSI) (i.e., channel prediction) accurately in mobility scenarios. However, a practical obstacle is caused by CSI non-stationary and nonlinear dynamics in temporal domain. In this paper, we propose a spatio-temporal neural network (STNN) to achieve better performance by carefully taking into account the spatio-temporal characteristics of CSI. Specifically, STNN uses its encoder and decoder modules to capture the spatial correlation and temporal dependence of CSI. Further, the differencing-attention module is designed to deal with the non-stationary and nonlinear temporal dynamics and realize adaptive feature refinement for more accurate multi-step prediction. Additionally, an advanced training scheme is adopted to reduce the discrepancy between STNN training and testing. Evaluated on a realistic channel model with enhanced mobility and spherical waves, experimental results show that STNN can effectively improve the accuracy of prediction and perform well with respect to different signal to noise ratios (SNRs). Visualization and testing for unit root illustrate STNN is able to learn CSI time-varying patterns by alleviating series non-stationarity.

In this paper, we analyze the high-SNR regime of the$M\times K$Network MISO channel in which each transmitter has access to a different channel estimate, possibly with different precision. It has been recently shown that, for some regimes, this setting attains the same Degrees-of-Freedom as the ideal centralized setting with perfect Channel State Information (CSI) sharing, in which all the transmitters are endowed with the best estimate available at any transmitter. This result is restricted by the limitations of the Degrees-of-Freedom metric, as it only provides information about the slope of growth of the capacity as a function of the SNR, without any insight about the possible performance at a given SNR. In order to overcome this limitation, we analyze the affine approximation of the rate on the high-SNR regime for this decentralized Network MISO setting for the antenna configurations in which it achieves the Degrees-of-Freedom of the centralized setting. We show that, for a regime of antenna configurations, it is possible to asymptotically attain the same achievable rate as in the ideal centralized scenario. Consequently, it is possible to achieve the beamforming gain of the ideal perfect-CSI-sharing setting even if only a subset of transmitters is endowed with precise CSI, which can be exploited in scenarios such as distributed massive MIMO where the number of transmit antennas is much bigger than the number of served users. This outcome is a consequence of the synergistic compromise between CSI precision at the transmitters and consistency between the locally-computed precoders, which is an inherent trade-off of decentralized settings that does not exist in the centralized CSI configuration. We propose a precoding scheme achieving the previous result, which is built on an uneven structure in which some transmitters reduce the precision of their own precoding vector for the sake of using transmission parameters that can be more easily predicted by the other transmitters.

This paper addresses high-resolution vehicle positioning and tracking. In recent work, it was shown that a fleet of independent but neighboring vehicles can cooperate for the task of localization by capitalizing on the existence of common surrounding reflectors, using the concept of Team Channel-SLAM. This approach exploits an initial (e.g. GPS-based) vehicle position information and allows subsequent tracking of vehicles by exploiting the shared nature of virtual transmitters associated to the reflecting surfaces. In this paper, we show that the localization can be greatly enhanced by joint sensing and mapping of reflecting surfaces. To this end, we propose a combined approach coined Team Channel-SLAM Evolution (TCSE) which exploits the intertwined relation between (i) the position of virtual transmitters, (ii) the shape of reflecting surfaces, and (iii) the paths described by the radio propagation rays, in order to achieve high-resolution vehicle localization. Overall, TCSE yields a complete picture of the trajectories followed by dominant paths together with a mapping of reflecting surfaces. While joint localization and mapping is a well researched topic within robotics using inputs such as radar and vision, this paper is first to demonstrate such an approach within mobile networking framework based on radio data.

This article studies a novel distributed precoding design, coinedteam minimum mean-square error(TMMSE) precoding, which rigorously generalizes classical centralized MMSE precoding to distributed operations based on transmitter-specific channel state information (CSIT). Building on the so-calledtheory of teams, we derive a set of necessary and sufficient conditions for optimal TMMSE precoding, in the form of an infinite dimensional linear system of equations. These optimality conditions are further specialized to cell-free massive MIMO networks, and explicitly solved for two important examples, i.e., the classical case of local CSIT and the case of unidirectional CSIT sharing along a serial fronthaul. The latter case is relevant, e.g., for the recently proposedradio stripeconcept and the related advances on sequential processing exploiting serial connections. In both cases, our optimal design outperforms the heuristic methods that are known from the previous literature. Duality arguments and numerical simulations validate the effectiveness of the proposed team theoretical approach in terms of ergodic achievable rates under a sum-power constraint.

This paper tackles the problem of downlink data transmission in massive multiple-input multiple-output (MIMO) systems where user equipments (UEs) exhibit high spatial correlation and channel estimation is limited by strong pilot contamination. Signal subspace separation among UEs is, in fact, rarely realized in practice and is generally beyond the control of the network designer (as it is dictated by the physical scattering environment). In this context, we propose a novel statistical beamforming technique, referred to asMIMO covariance shaping, that exploits multiple antennas at the UEs and leverages the realistic non-Kronecker structure of massive MIMO channels to target a suitable shaping of the channel statistics performed at the UE-side. To optimize the covariance shaping strategies, we propose a low-complexity block coordinate descent algorithm that is proved to converge to a limit point of the original nonconvex problem. For the two-UE case, this is shown to converge to a stationary point of the original problem. Numerical results illustrate the sum-rate performance gains of the proposed method with respect to spatial multiplexing in scenarios where the spatial selectivity of the base station is not sufficient to separate closely spaced UEs.

Massive multiple-input multiple-output (MIMO) is believed to deliver unrepresented spectral efficiency gains for 5G and beyond. However, a practical challenge arises during its commercial deployment, which is known as the “curse of mobility”. The performance of massive MIMO drops alarmingly when the velocity level of user increases. In this paper, we tackle the problem in frequency division duplex (FDD) massive MIMO with a novel Channel State Information (CSI) acquisition framework. A joint angle-delay-Doppler (JADD) wideband precoder is proposed for channel training. Our idea consists in the exploitation of the partial channel reciprocity of FDD and the angle-delay-Doppler channel structure. More precisely, the base station (BS) estimates the angle-delay-Doppler information of the UL channel based on UL pilots using Matrix Pencil (MP) method. It then computes the wideband JADD precoders according to the extracted parameters. Afterwards, the user estimates and feeds back some scalar coefficients for the BS to reconstruct the predicted DL channel. Asymptotic analysis shows that the CSI prediction error converges to zero when the number of BS antennas and the bandwidth increases. Numerical results with industrial channel model demonstrate that our framework can well adapt to high speed (350 km/h), large CSI delay (10 ms) and channel sample noise.

The acquisition of channel state information (CSI) in Frequency Division Duplex (FDD) massive MIMO has been a formidable challenge. In this paper, we address this problem with a novel CSI feedback framework enabled by the partial reciprocity of uplink and downlink channels in the wideband regime. We first derive the closed-form expression of the rank of the wideband massive MIMO channel covariance matrix for a given angle-delay distribution. A low-rankness property is identified, which generalizes the well-known result of the narrow-band uniform linear array setting. Then we propose a partial channel reciprocity (PCR) codebook, inspired by the low-rankness behavior and the fact that the uplink and downlink channels have similar angle-delay distributions. Compared to the latest codebook in 5G, the proposed PCR codebook scheme achieves higher performance, lower complexity at the user side, and requires less feedback. We derive the feedback overhead necessary to achieve asymptotically error-free CSI feedback. Two low-complexity alternatives are also proposed to further reduce the complexity at the base station side. Simulations with the practical 3GPP channel model show the significant gains over the latest 5G codebook, which prove that our proposed methods are practical solutions for 5G and beyond.

Deep Reinforcement Learning (DRL) is gaining attention as a potential approach to design trajectories for autonomous unmanned aerial vehicles (UAV) used as flying access points in the context of cellular or Internet of Things (IoT) connectivity. DRL solutions offer the advantage of on-the-go learning hence relying on very little prior contextual information. A corresponding drawback however lies in the need for many learning episodes which severely restricts the applicability of such approach in real-world time- and energy-constrained missions. Here, we propose a model-aided deep Q-learning approach that, in contrast to previous work, considerably reduces the need for extensive training data samples, while still achieving the overarching goal of DRL, i.e to guide a battery-limited UAV on an efficient data harvesting trajectory, without prior knowledge of wireless channel characteristics and limited knowledge of wireless node locations. The key idea consists in using a small subset of nodes as anchors (i.e. with known location) and learning a model of the propagation environment while implicitly estimating the positions of regular nodes. Interaction with the model allows us to train a deep Q-network (DQN) to approximate the optimal UAV control policy. We show that in comparison with standard DRL approaches, the proposed model-aided approach requires at least one order of magnitude less training data samples to reach identical data collection performance, hence offering a first step towards making DRL a viable solution to the problem.

In this work, we develop an algorithm to construct radio maps that can predict the received signal strength between a UAV-mounted base station and arbitrary ground users. The novelty of the work lies in the fact that these maps are constructed by fusing UAV-user radio signal strength measurements, and depth information of the surrounding environment which is obtained by an on-board laser range finder sensor. The proposed approach exploits both line-of-sight (LoS) and non-line-of-sight (NLoS) nature of UAV-user channels and depth information to first obtain the 3D map of the city and then later use it to estimate the radio map. Numerical results demonstrate the significant gain brought by the fusion of radio and depth measurements as opposed to a system which only relies on radio measurements.

This article considers the problem of localizing outdoor ground radio users with the help of an unmanned aerial vehicle (UAV) on the basis of received signal strength (RSS) measurements in an urban environment. We assume that the propagation model parameters are not known a priori, and depending on the UAV location, the UAV-user link can experience either Line-of-Sight (LoS) or Non-Line-of-Sight (NLoS) propagation condition. We assume that a 3-D map of the environment is available which the UAV can exploit in the localization process. Based on the proposed map-aided estimator, we devise an optimal UAV trajectory to accelerate the learning process under a limited mission time. To do so, we borrow tools, such as Fisher information from the theory of optimal experiment design. Our map-aided estimator achieves superior localization accuracy compared to the map-unaware methods, and our simulations show that optimized UAV trajectory achieves superior learning performance compared to random trajectories.

Massive access for Internet-of-Things (IoT) in beyond 5G networks represents a daunting challenge for conventional bandwidth-limited technologies. Millimeter-wave technologies (mmWave)-which provide large chunks of bandwidth at the cost of more complex wireless processors in harsher radio environments-is a promising alternative to accommodate massive IoT but its cost and power requirements are an obstacle for wide adoption in practice. In this context, meta-materials arise as a key innovation enabler to address this challenge by Re-configurable Intelligent Surfaces (RISs). In this article we take on the challenge and study a beyond 5G scenario consisting of a multi-antenna base station (BS) serving a large set of single-antenna user equipments (UEs) with the aid of RISs to cope with non-line-of-sight paths. Specifically, we build a mathematical framework to jointly optimize the precoding strategy of the BS and the RIS parameters in order to minimize the system sum mean squared error (SMSE). This novel approach reveals convenient properties used to design two algorithms, RISMA and Lo- RISMA, which are able to either find simple and efficient solutions to our problem (the former) or accommodate practical constraints with low-resolution RISs (the latter). Numerical results show that our algorithms outperform conventional benchmarks that do not employ RIS (even with low-resolution meta-surfaces) with gains that span from 20% to 120% in sum rate performance.

This paper studies a memoryless state-dependent multiple access channel (MAC) where two transmitters wish to convey a message to a receiver under the assumption of causal and imperfect channel state information at transmitters (CSIT) and imperfect channel state information at receiver (CSIR). In order to emphasize the limitation of transmitter cooperation between physically distributed nodes, we focus on the so-called distributed CSIT assumption, i.e., where each transmitter has its individual channel knowledge, while the message can be assumed to be partially or entirely shared a priori between transmitters by exploiting some on-board memory. Under this setup, the first part of the paper characterizes the common message capacity of the channel at hand for arbitrary CSIT and CSIR structure. The optimal scheme builds on Shannon strategies, i.e., optimal codes are constructed by letting the channel inputs be a function of current CSIT only. For a special case when CSIT is a deterministic function of CSIR, the considered scheme also achieves the capacity region of a common message and two private messages. The second part addresses an important instance of the previous general result in a context of a cooperative multi-antenna Gaussian channel under i.i.d. fading operating in frequency-division duplex mode, such that CSIT is acquired via an explicit feedback of perfect CSIR. The capacity of the channel at hand is achieved by distributed linear precoding applied to Gaussian codes. Surprisingly, we demonstrate that it is suboptimal to send a number of data streams bounded by the number of transmit antennas as typically considered in a centralized CSIT setup. Finally, numerical examples are provided to evaluate the sum capacity of the binary MAC with binary states as well as the Gaussian MAC with i.i.d. fading.

Optimal unmanned aerial vehicle (UAV) placement in a 3-dimensional (3D) space to build a connection between a base station (BS) and a ground user is studied herein. A key challenge is to avoid signal propagation blockage due to obstacles. Much prior work uses probabilistic terrain models with model parameters learned from the statistics over a large area, and therefore, the optimization for a specific user in a small local area is poor. In contrast, this paper seeks the optimal UAV position over actual and fine-grained terrain, and develops efficient UAV positioning strategy adaptive to the degree of location-dependent line-of-sight (LOS) condition measured on the fly. It is proven that the globally optimal UAV position in 3D can be determined from the proposed search trajectory which has merely linear length in the diameter of the target area. Therefore, the proposed strategy can be practically implemented. Numerical experiments are performed over a real-world urban topology and demonstrate superior performance gain over existing strategies based on probabilistic models.

Autonomous deployment of unmanned aerial vehicles (UAVs) supporting next-generation communication networks requires efficient trajectory planning methods. We propose a new end-to-end reinforcement learning (RL) approach to UAV-enabled data collection from Internet of Things (IoT) devices in an urban environment. An autonomous drone is tasked with gathering data from distributed sensor nodes subject to limited flying time and obstacle avoidance. While previous approaches, learning and non-learning based, must perform expensive recomputations or relearn a behavior when important scenario parameters such as the number of sensors, sensor positions, or maximum flying time, change, we train a double deep Q-network (DDQN) with combined experience replay to learn a UAV control policy that generalizes over changing scenario parameters. By exploiting a multi-layer map of the environment fed through convolutional network layers to the agent, we show that our proposed network architecture enables the agent to make movement decisions for a variety of scenario parameters that balance the data collection goal with flight time efficiency and safety constraints. Considerable advantages in learning efficiency from using a map centered on the UAV's position over a non-centered map are also illustrated.

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

Cellular connected unmanned aerial vehicles (UAVs) that can operate safely in beyond visual line of sight conditions are expected to open important future opportunities in the areas of transportation, goods delivery, and system monitoring. A key challenge in this area lies in the design of trajectories which, while allowing the completion of the UAV mission, can guarantee reliable cellular connectivity all along the path. Previous approaches in this domain have considered either simplistic propagation model assumptions (e.g. Line of Sight based) or more advanced models but with computationally demanding optimization solutions. In this paper, we propose a novel approach for trajectory design using a coverage map that can be obtained with a combination of a 3D map of the environment and radio propagation models. Leveraging on the convexity of subregions within the coverage map, we propose a low-complexity graph based algorithm which is shown to achieve quasi-optimal performance at a fraction of the computational cost of known optimal methods.

The problem of optimally precoding over cooperative MIMO channels when the transmitters are endowed with different noisy channel state information is a long standing and challenging open problem. Recently an information theoretic result was obtained which characterized the common message capacity of a channel with two transmitters and a single receiver with such distributed channel state information (DCSIT) generated from different feedback links. While classical MIMO precoding with centralized CSIT implies the transmission of a number of spatial streams bounded by the number of transmit and receiver antennas, the above result suggests that, surprisingly, the transmission of additional streams may be beneficial. In this work, we explore the operational implications of the above intuition to optimally tackle the problem of ergodic rate optimization under distributed feedback. In particular, we propose a method for joint distributed precoding and feedback design under asymmetric feedback rate constraints. In doing so, we also optimize the number of spatial data streams under practical complexity constraints. Finally, we provide numerical simulations and illustrate the performance gains compared to conventional precoder design.

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

Massive MIMO is a key technology for 5th generation (5G) mobile communications. The large excess of base station (BS) antennas brings unprecedented spectral efficiency. However, during the initial phase of industrial testing, a practical challenge arises which undermines the actual deployment of massive MIMO and is related to mobility. In fact, testing teams reported that in moderate-mobility scenarios, e.g., 30 km/h of UE speed, the performance may drop 50% compared to the low-mobility scenario, a problem not foreseen by theoretical papers on the subject. In order to deal with this challenge, we propose a Prony-based angular-delay domain (PAD) prediction method, which is built on exploiting the angle-delay-Doppler structure of the multipath. Our theoretical analysis shows that when the number of base station antennas and the bandwidth are large, the prediction error of our PAD algorithm converges to zero for any UE velocity level, provided that only two accurate enough previous channel samples are available. Simulation results show that under the realistic channel model of 3GPP in rich scattering environment, our proposed method even approaches the performance of stationary scenarios where the channels do not vary at all.

Coverage path planning (CPP) is the task of designing a trajectory that enables a mobile agent to travel over every point of an area of interest. We propose a new method to control an unmanned aerial vehicle (UAV) carrying a camera on a CPP mission with random start positions and multiple options for landing positions in an environment containing no-fly zones. While numerous approaches have been proposed to solve similar CPP problems, we leverage end-to-end reinforcement learning (RL) to learn a control policy that generalizes over varying power constraints for the UAV. Despite recent improvements in battery technology, the maximum flying range of small UAVs is still a severe constraint, which is exacerbated by variations in the UAV's power consumption that are hard to predict. By using map-like input channels to feed spatial information through convolutional network layers to the agent, we are able to train a double deep Q-network (DDQN) to make control decisions for the UAV, balancing limited power budget and coverage goal. The proposed method can be applied to a wide variety of environments and harmonizes complex goal structures with system constraints.

In this work, we study the impact of imperfect sharing of the Channel State Information (CSI) available at the transmitters on a Network MIMO setting in which a set of M transmit antennas, possibly not co-located, jointly serve two multi-antenna users endowed with N1and N2antennas, respectively. We consider the case where only a subset of k transmit antennas have access to perfect CSI, whereas the other M - k transmit antennas have only access to finite precision CSI. The analysis of this configuration aims to answer the question of how much an extra informed antenna can help. We model this scenario as a Decentralized MIMO Broadcast Channel (BC) and characterize the Degrees-of-Freedom (DoF) region, showing that only k = max(N1, N2) antennas with perfect CSI are needed to achieve the DoF of the conventional BC with ubiquitous perfect CSI.

Since flying base stations (FlyBSs) are energy constrained, it is convenient for them to act as transparent relays with minimal communication control and management functionalities. The challenge when using the transparent relays is the inability to measure the relaying channel quality between the relay and user equipment (UE). This channel quality information is required for communication-related functions, such as the UE association, however, this information is not available to the network. In this letter, we show that it is possible to determine the UEs' association based only on the information commonly available to the network, i.e., the quality of the cellular channels between conventional static base stations (SBSs) and the UEs. Our proposed association scheme is implemented through deep neural networks, which capitalize on the mutual relation between the unknown relaying channel from any UE to the FlyBS and the known cellular channels from this UE to multiple surrounding SBSs. We demonstrate that our proposed framework yields a sum capacity that is close to the capacity reached by solving the association via exhaustive search.

Massive MIMO is widely touted as an enabling technology for 5th generation (5G) mobile communications and beyond. On paper, the large excess of base station (BS) antennas promises unprecedented spectral efficiency gains. Unfortunately, during the initial phase of industrial testing, a practical challenge arose which threatens to undermine the actual deployment of massive MIMO: user mobility-induced channel Doppler. In fact, testing teams reported that in moderate-mobility scenarios, e.g., 30 km/h of user equipment (UE) speed, the performance drops up to 50% compared to the low-mobility scenario, a problem rooted in the acute sensitivity of massive MIMO to this channel Doppler, and not foreseen by many theoretical papers on the subject. In order to deal with this “curse of mobility”, we propose a novel form of channel prediction method, named Prony-based angular-delay domain (PAD) prediction, which is built on exploiting the specific angle-delay-Doppler structure of the multipath. In particular, our method relies on the high angular-delay resolution which arises in the context of 5G. Our theoretical analysis shows that when the number of base station antennas and the bandwidth are large, the prediction error of our PAD algorithm converges to zero for any UE velocity level, provided that only two accurate enough previous channel samples are available. Moreover, when the channel samples are inaccurate, we propose to combine the PAD algorithm with a denoising method for channel estimation phase based on the subspace structure and the long-term statistics of the channel observations. Simulation results show that under a realistic channel model of 3GPP in rich scattering environment, our proposed method is able to overcome this challenge and even approaches the performance of stationary scenarios where the channels do not vary at all.

This work identifies the optimal Degrees-ofFreedom (DoF) of the K-User MISO Broadcast Channel (BC) with delayed Channel-State Information at the Transmitter (CSIT) and with additional current noisy CSIT where the current channel estimation error scales in P-αfor α ∈ [0, 1]. These two settings had in the past been studied separately; the setting of imperfect current CSIT has attracted considerable interest over the last decade, while the setting of delayed CSIT was studied in the seminal work of Maddah-Ali and Tse in 2010 1 where an optimal DoF of K/ Σk=1K1/k was established. Since k=1 then there have been several efforts to combine the two settings of delayed and imperfect-current CSIT. Our work establishes for the first time the optimal DoF in this joint setting, capitalizing on a novel transmission scheme that is presented here, which combines a structurally new approach of handling past and current interference, to achieve the optimal performance. We establish the once elusive optimal DoF to be of the form 1 αK + (1 - α)K/(K/ Σk=1K1/k). This further shows that the two k=1 types of DoF gains, from current and delayed CSIT, can be combined additively.

In this work, we consider the sum rate performance of joint processing coordinated multi-point transmission network (JP-CoMP, a.k.a Network MIMO) in a so-called distributed channel state information (D-CSI) setting. In the D-CSI setting, the various transmitters (TXs) acquire a local, TX-dependent, estimate of the global multi-user channel state matrix obtained via terminal feedback and limited backhauling. The CSI noise across TXs can be independent or correlated, so as to reflect the degree to which TXs can exchange information over the backhaul, hence allowing to model a range of situations bridging fully distributed and fully centralized CSI settings. In this context we aim to study the price of CSI distributiveness in terms of sum rate at finite SNR when compared with conventional centralized scenarios. We consider the family of JP-CoMP precoders known as regularized zero-forcing (RZF). We conduct our study in the large scale antenna regime, as it is currently envisioned to be used in real 5G deployments. It is then possible to obtain accurate approximations on so-called deterministic equivalents of the signal to interference and noise ratios. Guided by the obtained deterministic equivalents, we propose an approach to derive a RZF scheme that is robust to the distributed aspect of the CSI, whereby the key idea lies in the optimization of a TX-dependent power level and regularization factor. Our analysis confirms the improved robustness of the proposed scheme with respect to CSI inconsistency at different TXs, even with moderate number of antennas and receivers (RXs).

We study the Degrees-of-Freedom (DoF) in a wireless setting in which K Transmitters (TXs) aim at jointly serving K users. The performance is studied when the TXs are faced with a distributed Channel State Information (CSI) configuration in which each TX has access to its own multi-user imperfect channel estimate based on which it designs its transmit coefficients. The channel estimates are not only imperfectly acquired but they are also imperfectly shared between the TXs. Our first contribution consists of computing a genie-aided upper bound for the DoF of that setting. Our main contribution is then to develop a new robust transmission scheme that leverages the different qualities of CSI available at the TXs to improve the achieved DoF. We show the surprising result that there is a CSI regime, coined the Weak-CSIT regime, in which the genie-aided upper bound is achieved by the proposed transmission scheme. Interestingly, the optimal DoF in the Weak-CSIT regime only depends on the CSI quality at the best informed TX and not on the CSI quality at all other TXs.

This paper studies the optimal unmanned aerial vehicle (UAV) placement problem for wireless networking. The UAV operates as a flying wireless relay to provide coverage extension for a base station (BS) and deliver capacity boost to a user shadowed by obstacles. While existing methods rely on statistical models for potential blockage of a direct propagation link, we propose an approach capable of leveraging local terrain information to offer performance guarantees. The proposed method allows to strike the best trade-off between minimizing propagation distances to ground terminals and discovering good propagation conditions. The algorithm only requires several propagation parameters, but it is capable to avoid deep propagation shadows and is proven to find the globally optimal UAV position. Only a local exploration over the target area is required, and the maximum length of search trajectory is linear to the geographical scale. Hence, it lends itself to online search. Significant throughput gains are found when compared to other positioning approaches based on statistical propagation models.

Device-to-device (D2D) communication, which enables a direct connection between users while bypassing the cellular channels to base stations (BSs), is a promising way to offload the traffic from conventional cellular networks. In D2D communication, optimizing the resource allocation requires the knowledge of D2D channel gains. However, such knowledge is hard to obtain at reasonable signaling costs. In this paper, we show this problem can be circumvented by tapping into the information provided by the estimated cellular channels between the users and surrounding BSs as these channels are estimated anyway for a normal operation of the network. While the cellular and D2D channel gains exhibit independent fast fading behavior, we show that average gains of the cellular and D2D channels share a non-explicit relation, which is rooted into the network topology, terrain, and buildings setup. We propose a deep learning approach to predict the D2D channel gains from seemingly independent cellular channels. Our results show a high degree of convergence between the true and predicted D2D channel gains. Moreover, we demonstrate the robustness of the proposed scheme against environment changes and inaccuracies during the offline training. The predicted gains allow to reach a near-optimal capacity in many radio resource management algorithms.

Using out-of-band (OOB) side-information has recently been shown to accelerate beam selection in single-user millimeter wave (mmWave) massive MIMO (m-MIMO) communications. In this paper, we propose a novel OOB-aided beam selection framework for a mmWave uplink multi-user system. In particular, we exploit spatial information extracted from lower (sub-6 GHz) bands in order to assist with an inter-user coordination scheme at mmWave bands. Our strategies consider the existence of a low-rate direct device-to-device (D2D) link between suitable pairs of users (UEs), enabling some information exchange. The decentralized coordination mechanism allows the suppression of the so-called co-beam interference which would otherwise lead to irreducible interference at the base station (BS) side, thereby triggering substantial spectral efficiency (SE) gains.

The synergetic gains of spectrum sharing and millimeter wave (mmWave) communication networks have recently attracted attention, owing to the interference canceling benefits of highly-directional beamforming in such systems. In principle, fine-tuned coordinated scheduling and beamforming can drastically reduce cross-operator interference. Unfortunately, this goes at the expense of the exchange of channel state information which is not realistic in particular when considering inter-operator coordination. Indeed, such an exchange of information is expensive in terms of backhaul infrastructure and it raises sensitive privacy issues between otherwise competing operators. In this paper, we expose the existence of a trade-off between coordination and privacy. We propose an algorithm capable of balancing spectrum sharing performance with privacy preservation based on the sharing of a low-rate beam index information where the information is subject to a data obfuscation mechanism borrowed from the digital security literature so as to control the privacy.

Multicast services, whereby a common valuable message needs to reach a whole population of user equipments (UEs), are gaining attention on account of new applications such as vehicular networks. As it proves challenging to guarantee decodability by every UE in a large population, service reliability is indeed the Achilles' heel of multicast transmissions. To circumvent this problem, a two-phase protocol capitalizing on device-to-device (D2D) links between UEs has been proposed, which overcomes the vanishing behavior of the multicast rate. In this paper, we revisit such a D2D-aided protocol in the new light of precoding capabilities at the base station (BS). We obtain an enhanced scheme that aims at selecting a subset of UEs who cooperate to spread the common message across the rest of the network via D2D retransmissions. With the objective of maximizing the multicast rate under some outage constraint, we propose an algorithm with provable convergence that jointly identifies the most pertinent relaying UEs and optimizes the precoding strategy at the BS.

In this paper1, we analyze a Network MIMO channel with 2 Transmitters (TXs) jointly serving 2 users, where each TX has a different multi-user Channel State Information (CSI), potentially with a different accuracy. Recently it was shown the surprising result that this decentralized setting can attain the same Degrees-of-Freedom (DoF) as its genie-aided centralized counterpart in which both TXs share the best-quality CSI. However, the DoF derivation alone does not characterize the actual rate and the question was left open as to how big the rate gap between the centralized and the decentralized settings was going to be. In this paper, we considerably strengthen the previous intriguing DoF result by showing that it is possible to achieve asymptotically the same sum rate as that attained by Zero-Forcing (ZF) precoding in a centralized setting endowed with the best-quality CSI. This result involves a novel precoding scheme which is tailored to the decentralized case. The key intuition behind this scheme lies in the striking of an asymptotically optimal compromise between i) realizing high enough precision ZF precoding while ii) maintaining consistent-enough precoding decisions across the non-communicating cooperating TXs.

The availability of accurate and, most importantly, shared channel state information at the transmitter (CSIT) is one of the key factors that enable transmitters cooperation in decentralized wireless systems. However, in some cases, channel information may not be easily or perfectly shared among the transmitters, thus limiting their coordination capabilities. In this paper we shed some light on the fundamental limits of networks with cooperating transmitters impaired by a general distributed CSIT assumption. To this end, we consider a state-dependent memory-less multiple-access channel with common message, and with noisy causal CSIT and noisy channel state information at the receiver (CSIR). Perhaps surprisingly, and in contrast to the same setting in absence of common message, we show that distributed precoding based on current CSIT only (namely, a Shannon strategy) achieves the sum-rate capacity of this channel, for every degree of CSIT and CSIR. By focusing on the transmission of a common message only, we then illustrate this result in a practically relevant Gaussian setting.

We consider a scenario where an unmanned aerial vehicle (UAV)-mounted flying base station is providing data communication services to a number of radio nodes spread over the ground. We focus on the problem of resource-constrained UAV trajectory design with: 1) optimal channel parameters learning and 2) optimal data throughput as key objectives, respectively. While the problem of throughput optimized trajectories has been addressed in prior works, the formulation of an optimized trajectory to efficiently discover the propagation parameters has not yet been addressed. When it comes to the communication phase, the advantage of this paper comes from the exploitation of a 3-D city map. Unfortunately, the communication trajectory design based on the raw map data leads to an intractable optimization problem. To solve this issue, we introduce a map compression method that allows us to tackle the problem with standard optimization tools. The trajectory optimization is then combined with a node scheduling algorithm. The advantages of the learning-optimized trajectory and of the map compression method are illustrated in the context of intelligent Internet of Things data harvesting.

Machine learning and data driven approaches have recently received much attention as a key enabler for future 5G and beyond wireless networks. Yet, the evolution towards learning-based data driven networks is still in its infancy, and much of the realization of the promised benefits requires thorough research and development. Fundamental questions remain as to where and how ML can really complement the well-established, well-tested communication systems designed over the last four decades. Moreover, adaptation of machine learning methods is likely needed to realize their full potential in the wireless context. This is particularly challenging for the lower layers of the protocol stack, where the constraints, problem formulation, and even the objectives may fundamentally differ from the typical scenarios to which machine learning has been successfully applied in recent years. In addition, a thorough understanding of the fundamental performance limits is also essential in order to establish quality-of-service guarantees that are common in communication system design. Such challenges, which lie at the core of the special issue, can be categorized into a number of research topics ranging from the optization of neural networks architectures that are suited to wireless communication links (inclusing autoencoders, generative adversarial networks, reinforcement based networks etc) to performance analysis, to the acceleration of data-driven training, and possibly in distributed settings. The application domains within the wireless realm are also quite diverse in nature with promising preliminary results in the area of physical layer design and resource allocation as well as for network service orchestrations. Testbeds and experimental evaluations are also begining to be reported.

Machine learning and data driven approaches have recently received much attention as a key enabler for future 5G and beyond wireless networks. Yet, the evolution towards learning-based data driven networks is still in its infancy, and much of the realization of the promised benefits requires thorough research and development. Fundamental questions remain as to where and how ML can really complement the well-established, well-tested communication systems designed over the last four decades. Moreover, adaptation of machine learning methods is likely needed to realize their full potential in the wireless context. This is particularly challenging for the lower layers of the protocol stack, where the constraints, problem formulation, and even the objectives may fundamentally differ from the typical scenarios to which machine learning has been successfully applied in recent years. In addition, a thorough understanding of the fundamental performance limits is also essential in order to establish quality-of-service guarantees that are common in communication system design. Such challenges, which lie at the core of the special issue, can be categorized into a number of research topics ranging from the optization of neural networks architectures that are suited to wireless communication links (inclusing autoencoders, generative adversarial networks, reinforcement based networks etc) to performance analysis, to the acceleration of data-driven training, and possibly in distributed settings. The application domains within the wireless realm are also quite diverse in nature with promising preliminary results in the area of physical layer design and resource allocation as well as for network service orchestrations. Testbeds and experimental evaluations are also begining to be reported.

Thanks to the recent advances in processing speed, data acquisition and storage, machine learning (ML) is penetrating every facet of our lives, and transforming research in many areas in a fundamental manner. Wireless communications is another success story - ubiquitous in our lives, from handheld devices to wearables, smart homes, and automobiles. While recent years have seen a flurry of research activity in exploiting ML tools for various wireless communication problems, the impact of these techniques in practical communication systems and standards is yet to be seen. In this paper, we review some of the major promises and challenges of ML in wireless communication systems, focusing mainly on the physical layer. We present some of the most striking recent accomplishments that ML techniques have achieved with respect to classical approaches, and point to promising research directions where ML is likely to make the biggest impact in the near future. We also highlight the complementary problem of designing physical layer techniques to enable distributed ML at the wireless network edge, which further emphasizes the need to understand and connect ML with fundamental concepts in wireless communications.

In this paper, a novel covariance-based channel feedback mechanism is investigated for frequency division duplexing (FDD) massive multi-input multi-output (MIMO) systems. The concept capitalizes on the notion of user statistical separability which was hinted in several prior works in the massive antenna regime but has not fully exploited so far. We propose a hybrid statistical-instantaneous feedback mechanism where the users are separated into two classes of feedback design based on their channel covariance. Under the hybrid framework, each user either operates on a statistical feedback mode or quantized instantaneous channel feedback mode. The key challenge lies in the design of a covariance-aware classification algorithm which can handle the complex mutual interactions among all users. The classification is derived from rate bound principles and a precoding method is also devised under the mixed statistical and instantaneous feedback model. Simulations are performed to validate our analytical results and illustrate the sum rate advantages of the proposed feedback scheme under a global feedback overhead constraint.

The low-rank behavior of massive multiple-input multiple-output (MIMO) channel covariance matrices and its exploitation for pilot decontamination and statistical beamforming are well documented. Existing algorithms, however, rely on signal subspace separation among user equipments (UEs) and, as such, they tend to fail when the distance between UEs becomes small. This paper proposes a solution to this problem via covariance shaping at the UE-side in the case where the UEs are equipped with (a small number of) multiple antennas. The key resides in: i) exploiting general non-Kronecker MIMO channel structures that allow the transmitter to suitably alter the channel statistics perceived by the base station, and ii) sacrificing some spatial degrees of freedom at each UE so as to improve the statistical orthogonality between closely spaced UEs. Numerical results illustrate the sum-rate performance gains of the proposed covariance shaping method with respect to existing ones.

Imposing fairness in resource allocation incurs a loss of system throughput, known as the Price of Fairness (PoF). In wireless scheduling, PoF increases when serving users with very poor channel quality because the scheduler wastes resources trying to be fair. This paper proposes a novel resource allocation framework to rigorously address this issue. We introduce selective fairness: being fair only to selected users, and improving PoF by momentarily blocking the rest. We study the associated admission control problem of finding the user selection that minimizes PoF subject to selective fairness, and show that this combinatorial problem can be solved efficiently if the feasibility set satisfies a condition; in our model it suffices that the wireless channels are stochastically dominated. Using selective fairness, we formulate the PoF minimization subject to an SLA, which ensures that an ergodic subscriber is served frequently enough. In this context, we propose an online policy that combines the DriftPlus-Penalty technique with Gradient-Based Scheduling experts, and we prove it achieves the optimal PoF. Simulations show that our intelligent blocking outperforms by 40% in throughput the baseline approach which satisfies the SLA by blocking low-SNR users without considering the overall PoF minimization.

The main result of this paper is that an orthogonal access scheme, such as time division multiple access achieves the all-unicast degrees of freedom (DoF) region of the topological interference management problem if and only if the network topology graph is chordal bipartite, i.e., every cycle that can contain a chord, does contain a chord. The all-unicast DoF region includes the DoF region for any arbitrary choice of a unicast message set, so e.g., the results of Maleki and Jafar on the optimality of orthogonal access for the sum-DoF of one-dimensional convex networks are recovered as a special case. The result is also established for the corresponding topological representation of the index coding problem.

This paper considers the problem of 3D city map reconstruction. The key novelty here lies in the sole exploitation of UAV-bound radio measurements as a way to recover the map data, i.e. no image of the city is taken or processed. The proposed approach relies on the unique ability for a UAV- to-ground communication system to detect and classify line-of-sight (LoS) vs. non line-of-sight (NLoS) channels towards ground users using machine learning tools. Once classification is carried out, the LoS vs. NLoS data is fed as input to a building position and height reconstruction algorithm. The map reconstruction quality is analyzed as a function of user density and UAV altitude, revealing the notion of an optimal height for the UAV which is predicted using an analytical model.

Location-aided beam alignment has been proposed recently as a potential approach for fast link establishment in millimeter wave (mmWave) massive MIMO (mMIMO) communications. However, due to mobility and other imperfections in the estimation process, the spatial information obtained at the base station (BS) and the user (UE) is likely to be noisy, degrading beam alignment performance. In this paper, we introduce a robust beam alignment framework in order to exhibit resilience with respect to this problem. We first recast beam alignment as a decentralized coordination problem where BS and UE seek coordination on the basis of correlated yet individual position information. We formulate the optimum beam alignment solution as the solution of a Bayesian team decision problem. We then propose a suite of algorithms to approach optimality with reduced complexity. The effectiveness of the robust beam alignment procedure, compared with classical designs, is then verified on simulation settings with varying location information accuracies.

In this paper, we propose a novel user cooperation framework aimed at optimizing power efficiency in MIMO-OFDM uplink, where cooperation is directed towards minimizing the transmit signal PAPR at all cooperating users. We consider different degrees of cooperation: 1) joint data transmission with PAPR reduction, 2) only joint data transmission and 3) the reference non-cooperative case based on a zero forcing receiver at the base station. In the cooperative case, we also account for the power required to exchange data between users. The main idea of our scheme is to exploit the unused space-frequency resources induced by the user cooperation to assign dummy symbols on the empty subcarriers and optimize these symbols to affect the outbound waveform. This is achieved as a result for a convex optimization problem which aims to simultaneously minimize the transmit PAPR and dummy symbol power allocation. We further introduce a rate maximization scheme, which maximizes the data power allocation for a given transmit peak power constraint by executing a one-dimensional search. The results show that our proposed PAPR minimizing bit and power allocation scheme can achieve significantly higher throughput at short and medium UE-UE distances.

This paper considers the exploitation of unmanned aerial vehicles (UAVs) in wireless networking, with which communication-enabled robots operate as flying wireless relays to help fill coverage or capacity gaps in the network. We focus on the particular problem of (automatic) UAV positioning, which is known to crucially affect performance. Existing methods typically rely on statistical models of the air-to-ground channel, and thus, they fail to exploit the fine-grained information of line-of-sight (LOS) conditions at some locations. This paper develops an efficient algorithm to find the best position of the UAV based on the fine-grained LOS information. In spite of the complex terrain topology, the algorithm is able to converge to the optimal UAV position to maximize the end-to-end throughput without a global exploration of a signal strength radio map. Numerical results demonstrate that in a dense urban area, the UAV-aided wireless system with the optimal UAV position can almost double the end-to-end capacity from the base station (BS) to the user as compared to that of a direct BS to user link.

This paper targets the promising area of unmanned aerial vehicle (UAV)-assisted wireless networking, by which communication-enabled robots operate as flying wireless relays to help fill coverage or capacity gaps in the networks. In order to feed the UAV's autonomous path planning and positioning algorithm, a radio map is exploited, which must be, in practice, reconstructed from UAV-based measurements from a limited subset of locations. Unlike existing methods that ignore the segmented propagation structure of the radio map, this paper proposes a machine learning approach to reconstruct a finely structured map by exploiting both segmentation and signal strength models. A data clustering and parameter estimation problem is formulated using a maximum likelihood approach, and solved by an iterative clustering and regression algorithm. Numerical results demonstrate significant performance advantage in radio map reconstruction as compared to the baseline.

This work1analyzes the Generalized Degrees-of-Freedom (GDoF) of the 2-User Multiple-Input Single-Output (MISO) Broadcast Channel (BC) in the so-called Distributed CSIT regime, with application to decentralized wireless networks. This regime differs from the classical limited CSIT one in that the CSIT is not just noisy but also imperfectly shared across the transmitters (TXs). Hence, each TX precodes data on the basis of local CSIT and statistical quality information at other TXs. We derive the GDoF result and obtain the surprising outcome that by specific accounting of the pathloss information, it becomes possible for the decentralized precoded network to reach the same performance as a genie-aided centralized network where the central node has obtained the estimates of both TXs. The key idea allowing this surprising robustness is to let the TXs have asymmetrical roles such that the most informed TX is able to balance the lower CSIT quality at the other TX.

Channel state information (CSI) feedback is a challenging issue in frequency division duplexing (FDD) massive MIMO systems. This paper studies a cooperative feedback scheme, where the users first exchange their CSI with each other through device-to-device (D2D) communications, then compute the precoder by themselves, and feedback the precoder to the base station (BS). Analytical results are derived to show that the cooperative precoder feedback is more efficient than the CSI feedback in terms of interference mitigation. To reduce the delays for CSI exchange, we develop an adaptive CSI exchange strategy based on signal subspace projection and optimal bit partition. Numerical results demonstrate that the proposed cooperative precoder feedback scheme with adaptive CSI exchange significantly outperforms the CSI feedback scheme, even under moderate delays for CSI exchange via D2D.

This paper considers the problem of efficient feedback design for massive multiple-input multiple-output (MIMO) downlink transmissions in frequency division duplexing (FDD) bands, where some partial channel state information (CSI) can be directly exchanged between users via device-to-device (D2D) communications. Drawing inspiration from classical point-to-point MIMO, where efficient mechanisms are obtained by feeding back directly the precoder, this paper proposes a new approach to bridge the channel feedback and the precoder feedback by the joint design of the feedback and precoding strategy following a team decision framework. Specifically, the users and the base station (BS) minimize a common mean squared error (MSE) metric based on their individual observations on the imperfect global CSI. The solutions are found to take similar forms as the regularized zero-forcing (RZF) precoder, with additional regularizations that capture any level of uncertainty in the exchanged CSI, in case the D2D links are absent or unreliable. Numerical results demonstrate superior performance of the proposed scheme for an arbitrary D2D link quality setup.

Obtaining accurate global channel state information (CSI) at multiple transmitter devices is critical to the performance of many coordinated transmission schemes. Practical CSI local feedback often leads to noisy and partial CSI estimates at each transmitter. With rate-limited bi-directional backhaul, transmitters have the opportunity to exchange few CSI-related bits to establish global channel state information at transmitter (CSIT). This paper investigates possible strategies toward this goal. We propose a novel decentralized algorithm that produces minimum mean square error-optimal global channel estimates at each device from combining local feedback and information exchanged through backhauls. The method adapts to arbitrary initial information topologies and feedback noise statistics and can do that with a combination of closed-form and convex approaches. Simulations for coordinated multi-point transmission systems with two or three transmitters exhibit the advantage of the proposed algorithm over conventional CSI exchange mechanisms when the coordination backhauls are limited.

In this paper we propose a novel robust channel estimation algorithm exploiting path diversity in both angle and power domains, relying on a suitable combination of the spatial filtering and amplitude based projection. The proposed approach is able to cope with a wide range of system and topology scenarios, including those where interference channel may overlap with desired channels in terms of multipath angles of arrival (AoA) or exceed them in terms of received power. We establish the analytical conditions under which the proposed channel estimator is fully decontaminated.

In this paper1we consider the Network MIMO channel under the so-called Distributed Channel State Information at the Transmitters (D-CSIT) configuration. In this setting, the precoder is designed in a distributed manner at each Transmitter (TX) on the basis of local versions of Channel State information (CSI) of various quality. Although the use of simple Zero-Forcing (ZF) was recently shown to reach the optimal DoF for a Broadcast Channel (BC) under noisy, yet centralized, CSI at the TX (CSIT), it can turn very inefficient when faced with D-CSIT: The number of Degrees-of-Freedom (DoF) achieved is then limited by the worst CSI accuracy across TXs. To circumvent this effect, we develop a new robust transmission scheme improving the DoF. A surprising result is uncovered by which, in the regime of so-called weak CSIT, the proposed scheme is shown to be DoF-optimal and to achieve a centralized outerbound consisting in the DoF of a genie-aided centralized setting in which the CSIT versions of all TXs are available everywhere. Building upon the insight obtained in the weak CSIT regime, we develop a general D-CSIT robust scheme for the 3-user case which improves over the DoF obtained by conventional robust approaches for any arbitrary CSIT configuration.

This work1derives the optimal Degrees-of-Freedom (DoF) of the K-User MISO Broadcast Channel (BC) with delayed Channel-State Information at the Transmitter (CSIT) and with additional current noisy CSIT where the channel estimation error scales in P-αfor α ∈ [0, 1]. The optimal sum DoF takes the simple form (1 - α)K/HK+ αK where HK =△ Σk=1K1/k. This optimal performance is the result of a novel scheme which deviates from existing efforts as it digitally combines interference, decodes symbols of any order in the MAT alignment [1], and utilizes a hierarchical quantizer whose output is distributed across rounds in a way that minimizes unwanted interference. These jointly deliver, for the first time, the elusive DoF-optimal combining of MAT and ZF.

In a multiuser MIMO broadcast channel, the rate performance is affected by multiuser interference when the channel state information at the transmitter (CSIT) is imperfect. To tackle the detrimental effects of the multiuser interference, a rate-splitting (RS) approach has been proposed recently, which splits one selected user's message into a common and a private part, and superimposes the common message on top of the private messages. The common message is drawn from a public codebook and decoded by all users. In this paper, we generalize the idea of RS into the large-scale array regime with imperfect CSIT. By further exploiting the channel second-order statistics, we propose a novel and general framework hierarchical-rate-splitting (HRS) that is particularly suited to massive MIMO systems. HRS simultaneously transmits private messages intended to each user and two kinds of common messages that are decoded by all users and by a subset of users, respectively. We analyze the asymptotic sum rate of RS and HRS and optimize the precoders of the common messages. A closed-form power allocation is derived which provides insights into the effects of various system parameters. Finally, numerical results validate the significant sum rate gain of RS and HRS over various baselines.

In this paper, the operation of a licensed shared access system is investigated, considering downlink communication. The system comprises a multiple-input-single-output (MISO) incumbent transmitter (TX)-receiver (RX) pair, which offers a spectrum sharing opportunity to a MISO licensee TX-RX pair. Our main contribution is the design of a coordinated transmission scheme, inspired by the underlay cognitive radio (CR) approach, with the aim of maximizing the average rate of the licensee, subject to an average rate constraint for the incumbent. In contrast to most prior works on the underlay CR, the coordination of the two TXs takes place under a realistic channel state information (CSI) scenario, where each TX has solely access to the instantaneous direct channel of its served terminal. Such a CSI knowledge setting brings about a formulation based on the theory of Team Decisions, whereby the TXs aim at optimizing a common objective given the same constraint set, on the basis of individual channel information. Consequently, a novel set of applicable precoding schemes consisting in letting the two TXs cooperate on the basis of the statistical information is proposed. We verify by simulations that this novel, practically relevant, coordinated precoding scheme outperforms the standard underlay CR approach.

In this paper, the problem of joint design of spectrum sensing (SS) and receive beamforming, with reference to a cognitive radio (CR) system, is considered. The aim of the proposed design is the maximization of the achievable average uplink rate of a secondary user, subject to an outage-based quality-of-service constraint for primary communication. A hybrid CR system approach is studied, according to which the system either operates as an interweave (i.e., opportunistic) or as an underlay (i.e., spectrum sharing) CR system, based on SS results. A realistic channel state information framework is assumed, according to which the direct channel links are known by the multiple antenna receivers, while merely statistical (covariance) information is available for the interference links. A new closed-form approximation is derived for the outage probability of primary communication, and the problem of rate-optimal selection of SS parameters and receive beamformers is addressed for hybrid, interweave, and underlay CR systems. It is proved that our proposed system design outperforms both underlay and interweave CR systems for a range of system scenarios.

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We determine the achievable distortion region when the correlated source samples are transmitted by two energy harvesting (EH) sensor nodes to the destination over orthogonal fading channels. A time slotted system is considered in which the energy and the source samples arrive at the beginning of each time slot (TS), and both the correlation between source samples at the two nodes and fading coefficients change over time but remain constant in each TS. Assuming non-causal knowledge of these time-varying source statistics, energy arrivals and the channel gains, i.e., under the offline optimization framework, we obtain the optimal transmission and coding schemes that achieve the points on the Pareto boundary of the total distortion region. An iterative directional 2D waterfilling algorithm is proposed to obtain two specific points on this boundary.

Obtaining accurate Channel State Information (CSI) at multiple Transmitters (TXs) is critical to the performance of many cooperative transmission schemes, including joint precoding in the context of network MIMO. Practical CSI feedback and limited backhaul-based sharing creates degradations of CSI which are specific to each TX, giving rise to a Distributed (D-) CSI configuration. In the D-CSI broadcast channel setting, each TX implements separate elements of the joint multi-user precoder based on its own individual CSI estimate. In this work, we presents a first finite-SNR regime rate analysis for a network-MIMO (broadcast channel) under a distributed CSI setting. Of particular importance is the notion of a “price of distributedness” which penalizes the D-CSI setting over the conventional centralized one with the same overall feedback quality. To tackle this problem, we apply tools from the field of Random Matrix Theory (RMT) to derive deterministic equivalents of the Signal to Interference plus Noise Ratio (SINR) for a popular class of precoders. Our key finding lies in the notion that the price of distributedness converges to a predictable value, bounded away from zero, as the number of antennas grows.1

Optimization of a point-to-point (p2p) multiple-input single-output (MISO) communication system is considered when both the transmitter (TX) and the receiver (RX) have energy harvesting (EH) capabilities. The RX is interested in feeding back the channel state information (CSI) to the TX to help improve the transmission rate. The objective is to maximize the throughput by a deadline, subject to the EH constraints at the TX and the RX. The throughput metric considered is an upper bound on the ergodic rate of the MISO channel with beamforming and limited feedback. Feedback bit allocation and transmission policies that maximize the upper bound on the ergodic rate are obtained. Tools from majorization theory are used to simplify the formulated optimization problems. Optimal policies obtained for the modified problem outperform the naive scheme in which no intelligent management of energy is performed.

Studies of the MISO Broadcast Channel (BC) with delayed Channel State Information at the Transmitter (CSIT) have so far focused on the sum-rate and Degrees-of-Freedom (DoF) region analysis. In this paper, we investigate for the first time, the error rate performance at finite SNR and the diversity-multiplexing tradeoff (DMT) at infinite SNR of a space-time encoded transmission over a two-user MISO BC with delayed CSIT. We consider the so-called MAT protocol obtained by Maddah-Ali and Tse, which was shown to provide 33% DoF enhancement over TDMA. While the asymptotic DMT analysis shows that MAT is always preferable to TDMA, the Pairwise Error Probability analysis at finite SNR shows that MAT is in fact not always a better alternative to TDMA. Benefits can be obtained over TDMA only at a very high rate or once concatenated with a full-rate full-diversity space-time code. The analysis is also extended to spatially correlated channels, and the influence of transmit correlation matrices and user pairing strategies on the performance are discussed. Relying on statistical CSIT, signal constellations are further optimized to improve the error rate performance of MAT and make it insensitive to user orthogonality. Finally, other transmission strategies relying on delayed CSIT are discussed.

Interference networks with no channel state information at the transmitter except for the knowledge of the connectivity graph have been recently studied under the topological interference management framework. In this paper, we consider a similar problem with topological knowledge but in a distributed broadcast channel setting, i.e., a network where transmitter cooperation is enabled. We show that the topological information can also be exploited in this case to strictly improve the degrees of freedom (DoF) as long as the network is not fully connected, which is a reasonable assumption in practice. Achievability schemes from graph theoretic and interference alignment perspectives are proposed. Together with outer bounds built upon generator sequence, the concept of compound channel settings, and the relation to index coding, we characterize the symmetric DoF for the so-called regular networks with constant number of interfering links, and identify the sufficient and/or necessary conditions for the arbitrary network topologies to achieve a certain amount of symmetric DoF.

In this paper, an analytical performance study for multi-antenna Cognitive Radio (CR) systems is presented. The two most popular CR approaches, namely, the interweave and underlay system designs, are considered and based on the derived analytical framework, a throughput-based comparison of these two system designs is presented. The system parameters are selected such that a quality of service (QoS) constraint on primary communication, is satisfied. Closed form expressions for the outage probability at the Primary User (PU), as well as expressions for the ergodic rate of the Secondary User (SU) are derived, for both system designs. The derived expressions are functions of key design parameters, such as the sensing time and the detection threshold in the case of interweave CR, and the maximum allowable interference power received by the PU, in the case of underlay CR. Based on the derived expressions, for interweave CR, the sensing parameters, i.e., sensing time and energy detection threshold, are optimized such as to maximize the secondary system rate. By comparing the throughput performance for both system designs, the existence of specific regimes (in terms of primary activity, number of transmit and receive antennas as well as the outage probability of the PU), where one CR approach outperforms the other, is highlighted.

A subspace method for channel estimation has been recently proposed for tackling the pilot contamination effect, which is regarded by some researchers as a bottleneck in massive MIMO systems. It was shown in the literature that if the power ratio between the desired signal and interference is kept above a certain value, the received signal spectrum splits into signal and interference eigenvalues, namely, the “pilot contamination” effect can be completely eliminated. However, in the literature an independently distributed (i.d.) channel is assumed, which is actually not much the case in practice. Considering this, a more sensible finite-dimensional physical channel model (i.e., a finite scattering environment, where signals impinge on the base station (BS) from a finite number of angles of arrival (AoA)) is employed in this paper. Via asymptotic spectral analysis, it is demonstrated that, compared with the i.d. channel, the physical channel imposes a penalty in the form of an increased power ratio between the useful signal and the interference. Furthermore, we demonstrate an interesting “antenna saturation” effect, i.e., when the number of the BS antennas approaches infinity, the performance under the physical channel withPAoAs is limited by and nearly the same as the performance under the i.d. channel withPreceive antennas.

This paper considers the problem of transmitter (TX) cooperation with distributed channel state information (CSI), where two or more transmitters seek to jointly precode messages while communicating over a rate-limited coordination link. Specifically we address a so-called master-slave scenario where one master (M-) TX is endowed with perfect CSI while K slave (S-) TXs have zero prior CSI information. We are interested in possible strategies for how the M-TX may efficiently guide the S-TXs over the coordination links so as to maximize the network's figure of merit. Strategies related to communicating quantized CSI or quantized precoding decisions are described and compared. Optimal and sub-optimal low complexity approaches are shown, exhibiting gains over conventional methods.

This paper considers the problem of interference control in networks where base stations signals are coherently combined (aka network MIMO). Building on an analogy with so-called massive MIMO, we show how second-order statistical properties of channels can be exploited when the massive MIMO array corresponds in fact to many antennas randomly spread over a two-dimensional network. Based on the classical one-ring model, we characterize the low-rankness of channel covariance matrices and show the rank is related to the scattering radius. The application of the low-rankness property to channel estimation's denoising and low complexity interference filtering is highlighted.

Interference networks with no channel state information at the transmitter (CSIT) except for the knowledge of the connectivity graph have been recently studied under the topological interference management (TIM) framework. In this paper, we consider a similar topological knowledge but in a distributed broadcast channel setting, i.e. a network where transmitter cooperation is enabled. We show that the interference topology can also be exploited in this case to strictly improve the degrees of freedom (DoF) as long as the network is not fully connected, which is a reasonable assumption in practice. A fractional graph coloring based interference avoidance and a subspace interference alignment approaches are proposed to characterize the symmetric DoF for so-called regular networks with constant interfering degree, and to identify achievable DoF for arbitrary network topologies.

In this paper, we study the problem of the optimal dissemination of channel state information (CSI) among K spatially distributed transmitters (TXs) jointly cooperating to serve K receivers. One of the particularities of this paper lies in the fact that the CSI is distributed in the sense that each TX obtains its own estimate of the global multiuser MIMO channel with no further exchange of information being allowed between the TXs. Although this is well suited to model the cooperation between noncolocated TXs, e.g., in cellular coordinated multipoint schemes, this type of setting has received little attention so far in the information theoretic society. We study in this paper what are the CSI requirements at every TX, as a function of the network geometry, to ensure that the maximal number of degrees-of-freedom (DoF) is achieved, i.e., the same DoF as obtained under perfect CSI at all TXs. We advocate the use of the generalized DoF to take into account the geometry of the network in the analysis. Consistent with the intuition, the derived generalized DoF maximizing CSI allocation policy suggests that TX cooperation should be limited to a specific finite neighborhood around each TX. This is in sharp contrast with the conventional (uniform) CSI dissemination policy, which induces CSI requirements that grow unbounded with the network size. The proposed CSI allocation policy suggests an alternative to clustering, which overcomes fundamental limitations, such as: 1) edge interference and 2) unbounded increase of the CSIT requirements with the cluster size. Finally, we show how finite neighborhood CSIT exchange translates into finite neighborhood message exchange so that finally global interference management is possible at finite SNR with only local cooperation.

We consider the temporally correlated multiple-input multiple-output (MIMO) broadcast channels (BC) and interference channels (IC) where the transmitter(s) has/have 1) delayed channel state information (CSI) obtained from a latency-prone feedback channel as well as 2) imperfect current CSIT, obtained, e.g., from prediction on the basis of these past channel samples based on the temporal correlation. The degrees of freedom (DoF) regions for the two-user broadcast and interference MIMO networks with general antenna configuration under such conditions are fully characterized, as a function of the prediction quality indicator. Specifically, a simple unified framework is proposed, allowing us to attain optimal DoF region for the general antenna configurations and current CSIT qualities. Such a framework builds upon block-Markov encoding with interference quantization, optimally combining the use of both outdated and instantaneous CSIT. A striking feature of our work is that, by varying the power allocation, every point in the DoF region can be achieved with one single scheme. As a result, instead of checking the achievability of every corner point of the outer bound region, as typically done in the literature, we propose a new systematic way to prove the achievability.

In this work we study the impact of having only incomplete channel state information at the transmitters (CSIT) over the feasibility of interference alignment (IA) in a K-user MIMO interference channel (IC). Incompleteness of CSIT refers to the perfect knowledge at each transmitter (TX) of only a sub-matrix of the global channel matrix, where the sub-matrix is specific to each TX. This paper investigates the notion of IA feasibility for CSIT configurations being as incomplete as possible, as this leads to feedback overhead reductions in practice. We distinguish between antenna configurations where (i) removing a single antenna makes IA unfeasible, referred to as tightly-feasible settings, and (ii) cases where extra antennas are available, referred to as super-feasible settings. We show conditions for which IA is feasible in strictly incomplete CSIT scenarios, even in tightly-feasible settings. For such cases, we provide a CSIT allocation policy preserving IA feasibility while reducing significantly the amount of CSIT required. For super-feasible settings, we develop a heuristic CSIT allocation algorithm which exploits the additional antennas to further reduce the size of the CSIT allocation. As a byproduct of our approach, a simple and intuitive algorithm for testing feasibility of single stream IA is provided.

We consider the problem of downlink precoding for Network (multi-cell) MIMO networks where Transmitters (TXs) are provided with imperfect Channel State Information (CSI). Specifically, each TX receives a delayed channel estimate with the delay being specific to each channel component. This model is particularly adapted to the scenarios where a user feeds back its CSI to its serving base only as it is envisioned in future LTE networks. We analyze the impact of the delay during the backhaul-based CSI exchange on the rate performance achieved by Network MIMO. We highlight how delay can dramatically degrade system performance if existing precoding methods are to be used. We propose an alternative robust beamforming strategy which achieves the maximal performance, in DoF sense. We verify by simulations that the theoretical DoF improvement translates into a performance increase at finite Signal-to-Noise Ratio (SNR) as well1.

Pilot contamination is known to severely limit the performance of large-scale antenna (“massive MIMO”) systems due to degraded channel estimation. This paper proposes a twofold approach to this problem. First we show analytically that pilot contamination can be made to vanish asymptotically in the number of antennas for a certain class of channel fading statistics. The key lies in setting a suitable condition on the second order statistics for desired and interference signals. Second we show how a coordinated user-to-pilot assignment method can be devised to help fulfill this condition in practical networks. Large gains are illustrated in our simulations for even small antenna array sizes.

The degrees of freedom (DoF) of a K-User MISO broadcast channel (BC) is studied when the transmitter (TX) has access to a delayed channel estimate in addition to an imperfect estimate of the current channel. The current estimate could be for example obtained from prediction applied on past estimates, in the case where feedback delay is within the coherence time. Prior results in this setting are promising, yet remain limited to the two-user case. In contrast, we consider here an arbitrary number of users. We develop a new transmission scheme, called the Kα-MAT scheme, which builds upon both the principle of the MAT alignment from Maddah-Ali and Tse and zero-forcing (ZF) to achieve a larger DoF in the channel state information (CSI) configuration previously described. We also develop a new upper bound for the DoF to compare with the DoF achieved by Kα-MAT. Although not optimal, the Kα-MAT scheme performs well when the CSIT quality is not too delayed or K is small. The Kα-MAT scheme can be seen as a robust version of ZF with respect to the delay in the CSI feedback.

The two-user Multiple-Input Multiple-Output (MIMO) broadcast channel (BC) with arbitrary antenna configuration is considered, in which the transmitter obtains (i) delayed channel state information (CSI) from a latency-prone feedback channel as well as (ii) imperfect current CSI, e.g., from prediction based on these past channel samples. The degrees of freedom (DoF) region under such a setting is fully characterized as a function of a prediction quality exponent. This work extends prior work, previously limited to MISO, to fully general antenna settings. An intriguing by-product of our results is to reveal the benefits of dealing with an asymmetric multi-user MIMO configuration (i.e., one in which terminals do not have the same number of antennas) in the case of non-perfect CSIT (e.g., caused by feedback delays or limited preciseness).

In this paper, the problem of the coexistence of two multiple-antenna wireless links is addressed in a cognitive radio scenario. The novelty brought by our setup is three-fold: First we consider a more realistic rate target constraint at the primary receiver instead of the less meaningful maximum interference temperature, second we propose a limited channel state information (CSI) structure whereby transmitters only have access to partly instantaneous feedback (i.e., about the direct channels) and partly statistical feedback (i.e., about the interference channels). Third, we formulate a distributed decision making scenario, by which channel information is not shared among primary and secondary transmitters. Instead, a transmitter must make a precoding decision based on local CSI only. The problem is recast as a team decision theoretic problem and the optimal precoders are obtained by solving semidefinite programs (SDPs). A distributed algorithm is derived and compared with classical precoding solutions and gains are illustrated over a range of scenarios.

Coordinated beamforming is an important technique for dealing with interference channels arising in multicell systems. However, the performance of coordinated beamforming suffers significantly from Channel State Information (CSI) feedback delay and back-haul delay caused by exchanging CSI through the back-haul link. Hence, designing a beamforming scheme that takes into consideration the different delays incurred by the local and interfering channel links is an important problem. In this paper, average Virtual SINR based criterion is used to design an optimal combination of Maximum Ratio Transmission (MRT) and Intercell Interference Cancellation (IIC) beamformers as a function of the feedback and back-haul delays. Although it is well known that MRT is close to optimality at low SNR and IIC better at high SNR, we demonstrate how the optimal solution is modified when taking feedback delays into account. In particular, our results suggest the egoistic strategy is much more robust with respect to delays, thus affecting the shape of the optimal beamformer and giving extra performance for a wide range of scenarios.

In this paper, two new power policies (PPs), namely the Rate Maximization Policy (RMP) and the Rate-Bound Maximization Policy (RBMP), for Cognitive Radio (CR) systems are developed based on an expected rate maximization criterion. Both policies are designed such as to satisfy a constraint related to the average interference caused by the CR network to primary communication. The key novelty here is that the proposed PPs take into account the existence of limited (hybrid) channel feedback, where the direct secondary channel is known instantaneously while the interference caused by the primary transmission to the secondary receiver is only known statistically. The optimal policy (RMP) is characterized, and a low complexity algorithm is presented that allows for its efficient and accurate implementation. The two policies are compared and RMP is shown to lead to substantial energy consumption savings at equal rate performance.

The uplink of the two-user multiple-antenna interference channel is considered and the optimal (in the ergodic rate sense) beamforming (BF) problem is posed and solved. Specifically, a feedback scenario is studied whereby a base station (BS) is allowed to estimate the instantaneous channel vector information from the users it serves, but not from out-of-cell interference users. That is to say, only statistical information can be obtained regarding the interference. In contrast with most previous works, the motivation behind the presumed feedback scenario is the compliance with current cellular network standards. For this scenario, we derive new expressions for the ergodic user rates. Exploiting the derived expressions, the optimal, with respect to ergodic rate maximization, receive BF vectors are found in closed-form. Finally, new user scheduling schemes are proposed, which exploit the derived BF solution and allow an efficient use of combined instantaneous and statistical information.

This work considers the benefits of allowing spectrum sharing among co-located wireless service providers operating in the same multicell network. Although spectrum sharing was shown to be valuable in some scenarios where the created interference can be eliminated, the benefits have not clearly shown for multicell networks with aggressive reuse. We explore this question and show that spectrum sharing is preferred for just a certain subset of the users defined by their distance from the serving bases, while beyond this distance, an orthogonal division of resources between operators gives better results. The claims are backed with theoretical analysis matching our simulations.

This paper addresses the problem of channel estimation in multi-cell interference-limited cellular networks. We consider systems employing multiple antennas and are interested in both the finite and large-scale antenna number regimes (so-called "massive MIMO"). Such systems deal with the multi-cell interference by way of per-cell beamforming applied at each base station. Channel estimation in such networks, which is known to be hampered by the pilot contamination effect, constitutes a major bottleneck for overall performance. We present a novel approach which tackles this problem by enabling a low-rate coordination between cells during the channel estimation phase itself. The coordination makes use of the additional second-order statistical information about the user channels, which are shown to offer a powerful way of discriminating across interfering users with even strongly correlated pilot sequences. Importantly, we demonstrate analytically that in the large-number-of-antennas regime, the pilot contamination effect is made to vanish completely under certain conditions on the channel covariance. Gains over the conventional channel estimation framework are confirmed by our simulations for even small antenna array sizes.

We consider the time correlated multiple-input single-output (MISO) broadcast channel where the transmitter has imperfect knowledge of the current channel state, in addition to delayed channel state information. By representing the quality of the current channel state information asP-αfor the signal-to-noise ratioPand some constant α ≥ 0, we characterize the optimal degrees of freedom region for this more general two-user MISO broadcast correlated channel. The essential ingredients of the proposed scheme lie in the quantization and multicast of the overheard interferences, while broadcasting new private messages. Our proposed scheme smoothly bridges between the scheme recently proposed by Maddah-Ali and Tse with no current state information and a simple zero-forcing beamforming with perfect current state information.

Recent information theoretic results suggest that precoding on the multi-user downlink MIMO channel with delayed channel state information at the transmitter (CSIT) could lead to data rates much beyond the ones obtained without any CSIT, even in extreme situations when the delayed channel feedback is made totally obsolete by a feedback delay exceeding the channel coherence time. This surprising result is based on the ideas of interference repetition and alignment which allow the receivers to reconstruct information symbols which canceling out the interference completely, making it an optimal scheme in the infinite SNR regime. In this paper, we formulate a similar problem, yet at finite SNR. We propose a first construction for the precoder which matches the previous results at infinite SNR yet reaches a useful trade-off between interference alignment and signal enhancement at finite SNR, allowing for significant performance improvement in practical settings. We present two general precoding methods with arbitrary number of users by means of virtual MMSE and mutual information optimization, achieving good compromise between signal enhancement and interference alignment. Simulation results show substantial improvement due to the compromise between those two aspects.

Recent information theoretic results suggest that precoding on the multi-user downlink MIMO channel with delayed channel state information at the transmitter (CSIT) could lead to data rates much beyond the ones obtained without any CSIT, even in extreme situations when the delayed channel feedback is made totally obsolete by a feedback delay exceeding the channel coherence time. This surprising result is based on the ideas of interference forwarding and alignment which allow the receivers to reconstruct an information allowing them to cancel out the interference completely, making it an optimal scheme in the infinite SNR regime. In this paper, we formulate a similar problem, yet at finite SNR. We propose a new construction for the precoder which matches the previous results at infinite SNR yet reaches a useful tradeoff between interference alignment and signal enhancement at finite SNR, allowing for significant performance improvements in practical settings.

In this work1, we consider the joint precoding across K transmitters (TXs), sharing the knowledge of the user's data symbols being transmitted to K single-antenna receivers (RXs). We consider a distributed channel state information (DCSI) configuration where each TX has its own local estimate of the overall multiuser MIMO channel. Our focus is on the optimization of the allocation of the CSI feedback subject to a constraint on the total amount of feedback. As a starting point, we consider the Wyner model where we derive a distance-based CSI allocation achieving close to the optimal performance using only a small percentage of the total feedback. The approach relies on the exploitation of the attenuation to restrict the cooperation at a local scale. Indeed, the CSI and the user's data symbols are then shared to only a finite number of neighbors such that our approach appears as an improved alternative to clustering.

We consider the time correlated MISO broadcast channel where the transmitter has partial knowledge on the current channel state, in addition to delayed channel state information (CSI). Rather than exploiting only the current CSI, as the zero-forcing precoding, or only the delayed CSI, as the Maddah-Ali-Tse (MAT) scheme, we propose a seamless strategy that takes advantage of both. The achievable degrees of freedom of the proposed scheme is characterized in terms of the quality of the current channel knowledge.

We consider the two-user MISO broadcast channel where the transmitter has imperfect knowledge on the current channel state, in addition to delayed channel state information. The degree of freedom region is completely characterized. The optimal scheme smoothly bridges between the scheme recently proposed by Maddah-Ali and Tse with no current state information and a simple zero-forcing beamforming with perfect current state information. The essential ingredients of this scheme lie in the quantization and multicasting of the overheard interferences, while broadcasting new private messages.

In this work, we consider the joint precoding across K distant transmitters (TXs) towards K single-antenna receivers (RXs) and we let the TXs have access to perfect Channel State Information (CSI). Instead of considering the conventional method of clustering to allocate the user's data symbols, we focus on determining the most efficient symbol sharing patterns. Consequently, we optimize directly the user's data symbol allocation subject to a constraint on the total number of user's data bits transmitted through the core network. We develop a novel approach whereby sparse precoders approximating the true precoders are computed. These precoders require only a fraction of the overall symbol sharing overhead while introducing only limited losses. Thereby, allocating the symbols only to their nonzero coefficients leads to very efficient symbol sharing (or routing) algorithms. Furthermore, these algorithms have a much lower complexity that conventional approaches. By simulations, we show that our approach outperforms clustering-based multicell MIMO methods from the literature and that the routing obtained is mainly dependent on the pathloss structure and can be applied using only long term CSI with reduced losses.

In the next generation cellular systems, such as LTE-A (Release 10 and beyond), relay node (RN) deployment has been adopted due to its potentials in enlarging coverage and increasing system throughput, even with primitive relaying functionalities. For example, in LTE-A Release 10 only Type-I (non-transparent) RNs are considered wherein no cooperative transmission to the Donor evolved-NodeBs (DeNBs) is allowed. In this paper, we would like to add more functionalities to the RNs and see the advantages of using cooperative relaying, i.e., Type-II RNs. In particular, we study an interference relay channel (IRC) consisting of two single-antenna transmitter-receiver pairs and a shared multiple-antenna RN, which is exploited in a way that interferer's signal components at each receiver node are eliminated. Specifically, at the RN a transmit filtering is performed such that the compound received signal at each user equipment (UE) has a structure similar to the receiver structure for Alamouti's space-time coding [1]. We also show that it is not always required to have more complex receiver structure at the RN in order to achieve better spectral efficiencies.

In this paper, we discuss the joint precoding with finite rate feedback in the so-called network multiple-input multiple-output (MIMO) where the TXs share the knowledge of the data symbols to be transmitted. We introduce a distributed channel state information (DCSI) model where each TX has its own local estimate of the overall multiuser MIMO channel and must make a precoding decision solely based on the available local CSI. We refer to this channel as the DCSI-MIMO channel and the precoding problem as distributed precoding. We extend to the DCSI setting the work from Jindal in 2006 for the conventional MIMO broadcast channel (BC) in which the number of degrees of freedom (DoFs) achieved by zero forcing (ZF) was derived as a function of the scaling in the logarithm of the signal-to-noise ratio of the number of quantizing bits. Particularly, we show the seemingly pessimistic result that the number of DoFs at each user is limited by the worst CSI across all users and across all TXs. This is in contrast to the conventional MIMO BC where the number of DoFs at one user is solely dependent on the quality of the estimation of his own feedback. Consequently, we provide precoding schemes improving on the achieved number of DoFs. For the two-user case, the derived novel precoder achieves a number of DoFs limited by the best CSI accuracy across the TXs instead of the worst with conventional ZF. We also advocate the use of hierarchical quantization of the CSI, for which we show that considerable gains are possible. Finally, we use the previous analysis to derive the DoFs optimal allocation of the feedback bits to the various TXs under a constraint on the size of the aggregate feedback in the network, in the case where conventional ZF is used.

We consider interference alignment (IA) in the L-interfering multiple access channels (MACs) network, a particular case of the partially connected interfering MACs network whereby the number of interference links per MAC is bounded regardless of the total number of MACs in the network. Conversely to the fully-connected case, we show that interference alignment can be achievable in a network of arbitrary size, while the per-user signaling dimension remains bounded. We provide necessary conditions for the feasibility of IA, discuss their sufficiency, and introduce an algorithm capable of providing practical solutions. These results also apply to the dual case of partially connected interfering broadcast channels (IBCs), and have practical applications to both the uplink and downlink of large cellular networks.

In this paper, we assess the random coding error exponents (EEs) corresponding to decode-and-forward (DF), compress-and-forward (CF) and quantize-and-forward (QF) relaying strategies for a parallel relay network (PRN), consisting of two sources, two relay stations (RSs) and single destination where the RSs access to the destination via orthogonal, error-free, limited-capacity backhaul links. Among these relaying strategies, the DF and QF studied in this paper differ from their well-known conventional versions in certain aspects. In the DF relaying, each RS applies maximum-likelihood (ML) detection and sends the message corresponding to the detected signal along with a reliability information to the destination which finalize the decision on the transmitted message. In QF relaying, as opposed to the Gaussian codebook and vector quantization (VQ) theoretical model used for deriving bounds, we consider a simple and practical relaying strategy consisting of finite-alphabet constellations (i.e., M-QAM) at the sources and symbol-by-symbol uniform scalar quantizers (uSQs) at the RSs. We also show, through numerical analysis, that the proposed QF relaying can provide better EEs than the others when the modulation constellation sizes selected by the sources match to the network conditions, i.e., operating signal-to-noise ratio (SNR), and the backhaul capacity is sufficient. This behavior is due to the structure inherent in the considered modulation alphabets, which Gaussian signaling lacks.

Motivated by the femtocell networks where cross-tier interference management is crucial to achieve higher system throughput, we consider a basic interference model consisting of a MIMO two-way relay communication network in presence of a one-way point-to-point MIMO communication link. We propose an upper-bound on the maximum degrees of freedom of this channel. Precoders using signal-space alignment strategies at the transmitter and zero-forcing (ZF) receivers are shown to achieve the maximum degrees-of-freedom. The proposed solution is particularly interesting since it allows the macro transmitter to be oblivious to the interfering two-way link, which is an important feature for obtaining distributed solutions.

In this work1, the joint precoding across two distant transmitters (TXs), sharing the knowledge of the data symbols to be transmitted, to two receivers (RXs), each equipped with one antenna, is discussed. We consider a distributed channel state information (CSI) configuration where each TX has its own local estimate of the channel and no communication is possible between the TXs. Based on the distributed CSI configuration, we introduce a concept of distributed MIMO precoding. We focus on the high signal-to-noise ratio (SNR) regime such that the two TXs aim at designing a precoding matrix to cancel the interference. Building on the study of the multiple antenna broadcast channel, we obtain the following key results: We derive the multiplexing gain (MG) as a function of the scaling in the SNR of the number of bits quantizing at each TX the channel to a given RX. Particularly, we show that the conventional Zero Forcing precoder is not MG maximizing, and we provide a precoding scheme optimal in terms of MG. Beyond the established MG optimality, simulations show that the proposed precoding schemes achieve better performances at intermediate SNR than known linear precoders.

A multihop relaying system is analyzed where data sent by a multi-antenna source is relayed by successive multi-antenna relays until it reaches a multi-antenna destination. Assuming correlated fading at each hop, each relay receives a faded version of the signal from the previous level, performs linear precoding and retransmits it to the next level. Using free probability theory and assuming that the noise power at relays— but not at destination— is negligible, the closed-form expression of the asymptotic instantaneous end-to-end mutual information is derived as the number of antennas at all levels grows large. The so-obtained deterministic expression is independent from the channel realizations while depending only on channel statistics. This expression is also shown to be equal to the asymptotic average end-to-end mutual information. The singular vectors of the optimal precoding matrices, maximizing the average mutual information with finite number of antennas at all levels, are also obtained. It turns out that these vectors are aligned to the eigenvectors of the channel correlation matrices. Thus, they can be determined using only the channel statistics. As the structure of the singular vectors of the optimal precoders is independent from the system size, it is also optimal in the asymptotic regime.

We analyze the sum rate performance in multicell single-hop networks where access points are allowed to cooperate in terms of a joint resource allocation. The resource allocation policies considered here combine power control and user scheduling. Although promising from a conceptual point of view, the optimization of the sum of per-link rates hinges on tough issues such as computational complexity and the requirement for heavy receiver-to-transmitter and cell-to-cell channel information feedback. In this paper, however, we show that simple distributed algorithms can scale optimally in terms of rates, when the number of users per cell U is allowed to grow large. We use extreme value theory to provide scaling laws for upper and lower bounds for the network sum-rate (sum of single user rates over all cells), corresponding to zero-interference and worst-case interference scenarios. We show that the scaling is either dominated by path loss statistics or by small-scale fading, depending on the regime and user location scenario. A surprising result is that the well known log log U rate behavior exhibited in i.i.d. fading channels with maximum rate schedulers is transformed into a log U behavior when path loss is accounted for. Additionally, by showing that upper and lower rate bounds behave in fact identically, asymptotically, our results suggest, remarkably, that the impact of multicell interference on the rate (in terms of scaling) actually vanishes asymptotically, when appropriate resource allocation policies are used.

This paper considers the so-called Multiple-Input Multiple-Output interference channel (MIMO-IC) which has relevance in applications such as multi-cell coordination in cellular networks as well as spectrum sharing in cognitive radio networks among others. We address the design of preceding (i.e. beamforming) vectors at each sender with the aim of striking a compromise between beamforming gain at the intended receiver (Egoism) and the mitigation of interference created towards other receivers (Altruism). Combining egoistic and altruistic beamforming has been shown previously to be instrumental to optimizing the rates in a Multiple-Input-SingleOutput (MISO) interference channel (i.e. where receivers have no interference canceling capability). Here we explore these game-theoretic concepts in the more general context of MIMO channels and use the framework of Bayesian games which allows us to derive (semi-)distributed precoding techniques. We draw parallels with important existing work on the MIMO-IC, including rate-optimizing and interference-alignment precoding techniques, and show how such techniques may be re-interpreted through a common prism based on balancing egoistic and altruistic beamforming. Our analysis and simulations attest the improvements in terms of complexity and performance.

We consider a multi-pair two-way relay channel (TWRC) where the single-antenna mobile terminals (MT) on each pair seek to communicate, and can do so, via a common multiple antenna relay station (RS). In the multi-pair TWRC, the main bottleneck on system performance is the interference seen by each MT due to the other communicating MT pairs. In this paper, we try to tackle this problem in the spatial domain by using multiple antennas at the RS. Considering Amplify-and-Forward (AF) and Quantize-and-Forward (QF) relaying strategies, different transmit/receive beamforming schemes at the RS are proposed. We compare our proposed schemes to each other and to the Decode-and-Forward (DF) relaying strategy with achievable sum rate taken as a performance metric and show that in a wide range of signal-to-noise ratio (SNR) our schemes outperform the DF relaying strategy.

We consider a multiple access MAC fading channel with two users communicating with a common destination, where each user mutually acts as a relay for the other one as well as wishes to transmit his own information as opposed to having dedicated relays. We wish to evaluate the usefulness of relaying from the point of view of the system's throughput (sum rate) rather than from the sole point of view of the user benefiting from the cooperation as is typically done. We do this by allowing a trade-off between relaying and fresh data transmission through a resource allocation framework. We develop capacity expressions for our scheme and derive the rate-optimum power allocation in closed form for distributed frameworks. In the distributed scenario, partially statistical and partially instantaneous channel information is exploited. Our results indicate that the sum rate is maximized when both mobiles act selfishly.

In this paper, we assess the random coding error exponents (EEs) corresponding to decode-and-forward (DF), compress-and-forward (CF) and quantize-and-forward (QF) relaying strategies for a parallel relay network (PRN), consisting of a single source and two relays. Moreover, through numerical analysis we show that the EEs achieved by using QF relaying along with non-Gaussian signaling (coded modulation, M-QAM) at the source and symbol-by-symbol uniform scalar quantizers (uSQs) at the relays is better than that achieved by DF and CF relaying strategies when the system is in the low signal-to-noise ratio (SNR) regime and the backhaul capacity is sufficient. This behavior is due to the structure of coded modulation, as opposed to Gaussian signaling, which leads to better EEs for simple relaying strategies compared to its more complex counterparts.

This paper presents an overview of the theory and currently known techniques for multi-cell MIMO (multiple input multiple output) cooperation in wireless networks. In dense networks where interference emerges as the key capacity-limiting factor, multi-cell cooperation can dramatically improve the system performance. Remarkably, such techniques literally exploit inter-cell interference by allowing the user data to be jointly processed by several interfering base stations, thus mimicking the benefits of a large virtual MIMO array. Multi-cell MIMO cooperation concepts are examined from different perspectives, including an examination of the fundamental information-theoretic limits, a review of the coding and signal processing algorithmic developments, and, going beyond that, consideration of very practical issues related to scalability and system-level integration. A few promising and quite fundamental research avenues are also suggested.

The one tutorial paper and eight contributed papers in this special issue focus on cooperative communications in MIMO cellular networks.

In this letter, we address the problem of distributed multi-antenna cooperative transmission in a cellular system. Most research in this area has so far assumed that base stations not only have the data dedicated to all the users but also share the full channel state information (CSI). In what follows, we assume that each base station (BS) only has local CSI knowledge. We propose a suboptimal, yet efficient, way in which the multicell MISO precoders may be designed at each BS in a distributed manner, as a superposition of so-called virtual SINR maximizations: a virtual SINR maximizing transmission scheme yields Pareto optimal rates for the MISO Interference Channel (IC); its extension to the multicell MISO channel is shown to provide a distributed precoding scheme achieving a certain fairness optimality for the two link case. We illustrate the performance of our algorithm through Monte Carlo simulations.

This paper considers downlink multiantenna communication with base stations that perform cooperative precoding in a distributed fashion. Most previous work in the area has assumed that transmitters have common knowledge of both data symbols of all users and full or partial channel state information (CSI). Herein, we assume that each base station only has local CSI, either instantaneous or statistical. For the case of instantaneous CSI, a parametrization of the beamforming vectors used to achieve the outer boundary of the achievable rate region is obtained for two multi-antenna transmitters and two single-antenna receivers. Distributed generalizations of classical beamforming approaches that satisfy this parametrization are provided, and it is shown how the distributed precoding design can be improved using the so-called virtual SINR framework. Conceptually analog results for both the parametrization and the beamforming design are derived in the case of local statistical CSI. Heuristics on the distributed power allocation are provided in both cases, and the performance is illustrated numerically.

The communication between a multiple-antenna transmitter and multiple receivers (users) with either a single or multiple-antenna each can be significantly enhanced by providing the channel state information at the transmitter (CSIT) of the users, as this allows for scheduling, beamforming and multiuser multiplexing gains. The traditional view on how to enable CSIT has been as follows so far: In time-division duplexed (TDD) systems, uplink (UL) and downlink (DL) channel reciprocity allows for the use of a training sequence in any given uplink slot, which is exploited to obtain an uplink channel estimate. This estimate is in turn recycled in the next downlink slot. In frequency-division duplexed (FDD) systems, which lack the UL and DL reciprocity, the CSIT is provided via the use of a dedicated feedback link of limited capacity between the receivers and the transmitter. In this paper, we focus on TDD systems and show that the traditional TDD CSIT acquisition fails to fully exploit the channel reciprocity in its true sense. In fact, we show that the system can benefit from a combined CSIT acquisition strategy mixing the use of limited feedback and that of a training sequence. We demonstrate the potential of our approach in terms of improved CSIT quality under a global training and feedback resource constraint.

Coordination in a multi-cell/link environment has been attracting a lot of attention in the research community recently. In this paper, we consider the problem of coordinated beamforming where base stations (BS) equipped with multiple antennas attempt to serve a separate user each despite the interference generated by the other bases. We propose a framework for a distributed optimization of the beamformers at each base, where distributed is defined as using "local CSIT" only. We present and compare two distributed approaches (one iterative and another direct approach) which have in common the optimization of the beamformers as a combination of so-called egoistic and altruistic solutions for this problem. We provide the intuitions behind these approaches and some theoretical grounds for optimality in certain cases. Performance is finally illustrated through numerical simulations.

This paper introduces a new method for distributed interference mitigation in full spectral-reuse OFDMA cellular networks. This considers the use of pre-defined frequency-domain power profiles helping make the interference more predictable across the subcarriers. We propose a method for computing the power profiles so as to maximize the capacity of the system in case of maximum throughput scheduling, and a simple linear model implemented also in presence of a fairness-oriented scheduler. We prove that our idea of power planning gives substantial improvements in terms of outage capacity in case of fairness-oriented scheduling. The advantage of our method over previously proposed approaches for interference mitigation based on power control is that our algorithm is fully distributed and does not require any exchange of signaling between the different cells.

In this work we study the capacity of multi-user multiple-input multiple-output (MU-MIMO) downlink channels with codebook-based limited feedback using real measurement data. Several aspects of MU-MIMO channels are evaluated. Firstly, we compare the sum rate of different MU-MIMO precoding schemes in various channel conditions. Secondly, we study the effect of different codebooks on the performance of limited feedback MU-MIMO. Thirdly, we relate the required feedback rate with the achievable rate on the downlink channel. Real multi-user channel measurement data acquired with the Eurecom MIMO OpenAir Sounder (EMOS) is used. To the best of our knowledge, these are the first measurement results giving evidence of how MU-MIMO precoding schemes depend on the precoding scheme, channel characteristics, user separation, and codebook. For example, we show that having a large user separation as well as codebooks adapted to the second order statistics of the channel gives a sum rate close to the theoretical limit. A small user separation due to bad scheduling or a poorly adapted codebook on the other hand can impair the gain brought by MU-MIMO. The tools and the analysis presented in this paper allow the system designer to trade-off downlink rate with feedback rate by carefully choosing the codebook.

Network-wide optimization of transmit power with the goal of maximizing the total throughput, promises significant system capacity gains in interference-limited data networks. Finding distributed solutions to this global optimization problem however, remains a challenging task. In this work, we first focus on the maximization of the weighted sum-rate capacity, as this allows the incorporation of QoS criteria in the objective function. For the case of two links, we are able to analytically characterize the optimal solution to the weighted sum-rate maximization problem. However, computing the optimal solution requires centralized knowledge of network information. We thus formulate a framework for distributed power optimization valid for N mutually interfering links, based on the concept of channel state partitioning. By assuming instantaneous knowledge of local information and statistical knowledge of non-local information, we derive a distributed power allocation algorithm, which we first analyze for the case of N = 2. Although a gain is observed over equal power allocation, the distributed algorithm shows a performance gap as compared to a centralized solution, as expected. We show however, that minimal information message passing (in this case one bit) between interfering links can help reduce this gap substantially. Finally, we also propose a method to incorporate user scheduling into the distributed power allocation algorithm.

In multi-user multiple-input multiple-output (MU-MIMO) systems, channel state information at the transmitter (CSIT) allows for multi-user spatial multiplexing and thus increases the system throughput. We assume that CSIT is obtained by means of a finite-rate feedback channel through channel vector quantization (CVQ) at the receiver. In this paper we use real channel measurements to study the effect of CVQ on the sum rate of a MU-MIMO system employing linear precoding. The measurement data has been acquired using Eurecom's MIMO Openair Sounder (EMOS). The EMOS can perform realtime MIMO channel measurements synchronously over multiple users. We consider CVQ using a Fourier codebook, a random codebook and a random codebook exploiting the second order statistics of the channel. For comparison, we also show the capacity of a single-user system using time division multiple access (TDMA) with no CSIT at all. The results show that the Fourier codebook shows very poor performance in the measured channels. Random codebooks - although suboptimal - provide a much better performance in the measured channels.

In this paper, we consider Gaussian parallel relay networks with phase fading where a source node wants to communicate with a destination node with the assistance of two intermediate relay nodes. For this scenario, outer bounds are derived and three achievable schemes are considered. As well as amplify-and-forward (AF) and decode-and-forward (DF) schemes, we also consider a scheme where the relays exploit block quantization and random binning, which we call BQRB relaying. We show that in the broadcast channel limited regime, where received powers at the relay nodes are very small, BQRB outperforms the other schemes with increasing multiple access channel quality. Moreover, it is seen that BQRB achievable rate performance tends to the rate achievable by a point-to-point single-input multiple-output system.

In this paper, we consider single-cell cellular networks assisted with fixed relay station (RS), used by mobile stations (MS) to access the base station (BTS) via a relaying strategy. The RSs are positioned around the BTS, in such a way that wireless channels on the relay link (from RSs to the BTS) are line-of-sight, we analyze the achievable sum-of-rates for up-link communications. We compare two relaying strategies at the RSs, namely amplify-and-forward (AF) and compress-and-forward (CF). It is assumed that mobile signals and relay signals are emitted on orthogonal bands (FDD), with the possibility of having a larger bandwidth (BW) on the relay-to-base links. We predict the system gains bought by relays, in comparison with two other reference systems. One reference is an ideal relay-based system where the relays enjoy noiseless communications to the BTSs, i.e. a so-called distributed antenna system (DAS). The second reference is offered by a conventional cellular systems without relays, but same number of overall infrastructure antennas. In this paper, it is demonstrated the surprising result that with a relay bandwidth just twice that of the mobile's bandwidth, the system capacity approaches that of an ideal distributed antenna system, (while probably being much superior in practice in terms of ease of deployment and cost). The capacity gains of the relay-assisted network over a conventional network are also analyzed.

Multi-cell cooperative processing (MCP) has recently attracted a lot of attention because of its potential for co-channel interference (CCI) mitigation and spectral efficiency increase. MCP inevitably requires increased signaling overhead and inter-base communication. Therefore in practice, only a limited number of base stations (BSs) can cooperate in order for the overhead to be affordable. The intrinsic problem of which BSs shall cooperate in a realistic scenario has been only partially investigated. In this contribution linear beamforming has been considered for the sum-rate maximisation of the uplink. A novel dynamic greedy algorithm for the formation of the clusters of cooperating BSs is presented for a cellular network incorporating MCP. This approach is chosen to be evaluated under a fair MS scheduling scenario (round-robin). The objective of the clustering algorithm is sum-rate maximisation of the already selected MSs. The proposed cooperation scheme is compared with some fixed cooperation clustering schemes. It is shown that a dynamic clustering approach with a cluster consisting of 2 cells outperforms static coordination schemes with much larger cluster sizes.

We analyze a multi-level MIMO relaying system where a multiple-antenna transmitter sends data to a multiple-antenna receiver through several relay levels, also equipped with multiple antennas. Assuming correlated fading in each hop, each relay receives a faded version of the signal transmitted by the previous level, performs precoding on the received signal and retransmits it to the next level. Using free probability theory and assuming that the noise power at the relay levels - but not at the receiver - is negligible, a closed-form expression of the end-to-end asymptotic instantaneous mutual information is derived as the number of antennas in all levels grow large with the same rate. This asymptotic expression is shown to be independent from the channel realizations, to only depend on the channel statistics and to also serve as the asymptotic value of the end-to-end average mutual information. We also provide the optimal singular vectors of the precoding matrices that maximize the asymptotic mutual information : the optimal transmit directions represented by the singular vectors of the precoding matrices are aligned on the eigenvectors of the channel correlation matrices, therefore they can be determined only using the known statistics of the channel matrices and do not depend on a particular channel realization.

We consider a MIMO broadcast channel where the channel state information at the transmitter (CSIT), to be used for user scheduling and beamforming, is gained through a limited-rate feedback channel. In view of optimizing the overall spectral efficiency of the system, we propose an adaptive scheme in which the feedback rate is no longer constant but rather optimized as a function of the time-dependent channel quality seen at the user side. One key idea is that, under an average feedback rate constraint, a user ought to provide more feedback at moments when it is more likely to be scheduled.We provide the theoretical grounds for our approach then derive quasi-optimal feedback resource allocation schemes, the performance of which is illustrated through Monte Carlo simulations.

We consider a cognitive radio scenario in which two (or more) operators providing services in the same area wish to share the same licensed band of spectrum. This scenario differs from the classical cognitive setup with a primary and a secondary operators, as both operators here are instead on an equal footing. The operators face the choice of competition or cooperation in the way they choose their transmission parameters (here beamforming vectors) to communicate with their respective users. We build on interesting recently published work [5] [6] analyzing the gains of cooperation in this context and propose novel techniques for beamforming within this interference channel. The proposed techniques outperform classical non-cooperative game solutions and mimic known cooperative game solutions while introducing a distributed aspect for the algorithm.

In multi-user multiple-input multiple-output (MU-MIMO) systems, spatial multiplexing can be employed to increase the throughput without the need for multiple antennas and expensive signal processing at the user equipments. In theory, MU-MIMO is also more immune to most of propagation limitations plaguing single-user MIMO (SU-MIMO) systems, such as channel rank loss or antenna correlation. However, in this paper we show that this is not always true. We compare the capacity and the correlation of measured MU-MIMO channels for both outdoor and indoor scenarios. The measurement data has been acquired using Eurecompsilas MIMO openair sounder (EMOS). The EMOS can perform real-time MIMO channel measurements synchronously over multiple users. The results show that in most scenarios MU-MIMO provides a higher throughput than SU-MIMO also in the measured channels. However, in outdoor scenarios with a line of sight, the capacity drops significantly when the users are close together, due to high correlation at the transmitter side of the channel. In such a case, the performance of SU-MIMO and MU-MIMO is comparable.

This paper considers the performance of the MAC protocols ALOHA and CSMA in wireless ad hoc networks, where the total system bandwidth may be divided into smaller subbands. In the network model used, the arrival of users/packets follows a Poisson point process, communication between nodes is continuous in time, selection of a subband to transmit across is made randomly at each transmitter, and the outage assessments made in the network are based on SINR measurements. Accurate bounds on the probability of outage for the MAC protocols are derived, and evaluated with respect to the number of subbands. It is observed that there exists an optimal number of subbands for each protocol, for which the probability of outage is minimized. For ALOHA, we obtain an analytical expression for this optimal value, while in CSMA, the optimal value is observed through simulations. Furthermore, we improve the performance of CSMA by introducing channel sensing across all subbands, in order to decrease the probability that a packet is in outage upon arrival. The obtained results are used to compare the performance of the two MAC protocols. Finally, we also evaluate the performance of our network in terms of sum capacity.

Multicell cooperative processing (MCP) has been recognised as an efficient technique for increasing spectral efficiency of future cellular systems. However the provided benefits come at the cost of increased overhead and computational complexity; Mobile Stations (MSs) need to feed back to their assigned Base Station (BS) their local channel state information (CSI) which in turn needs to be transmitted to the Control Unit that coordinates the cooperating BSs. Furthermore user data needs to be routed to and from all cooperating BSs on the downlink and uplink respectively. Therefore in order for the overhead to be affordable, it is admitted that cooperating BSs shall be organised in clusters of a limited size. Nevertheless, it is still crucial that CSI feedback and inter-base information exchange be reduced. In this paper linear precoding is considered with the target of overhead minimisation of the downlink. A novel technique is proposed which allows MSs not to feed back the channel coefficients related to the cluster BSs that provide weak channel quality. This is shown to provide a good trade-off between performance and overhead.

We propose a dynamic blind beamforming scheme which allows to benefit from antenna directivity in large mobile ad hoc networks while avoiding heavy feedback to track mobile nodes localization. By orienting its directional antenna successively in all directions, a source surely but blindly hits its destination without knowing its exact position. Performance is analyzed in terms of total network throughput and connectivity and the optimal number of rotations allowing to maximize performance is shown to result from a trade-off between delay and improvements in terms of interference. In large ad hoc networks, known to be interference limited, we show that dynamic blind beamforming can outperform omnidirectional transmissions both in terms of capacity and connectivity.

Recent research has demonstrated that by utilizing channel state information at the transmitter, the physical layer can be optimized to provide higher link capacity and throughput, more efficiently share the channel with multiple users, increase range by exploiting diversity due to spatial and frequency selectivity, and simplify multi-user receivers through known interference cancellation. Unfortunately, acquiring channel state information at the transmitter is difficult. In most systems, the only opportunity for the transmitter to learn about the channel is through a feedback control channel. Because feedback information is control overhead, the rate of the feedback channel is limited. This motivates the study of limited feedback techniques where only partial or quantized information from the receiver is conveyed back to the transmitter.

We consider the downlink of a wireless system with an M-antenna base station and K single-antenna users. A limited feedback-based scheduling and precoding scenario is considered that builds on the multiuser random beamforming (RBF). Such a scheme was shown to yield the same capacity scaling, in terms of multiplexing and multiuser diversity gain, as the optimal full CSIT-based (channel state information at transmitter) precoding scheme, in the large number of users K regime. Unfortunately, for more practically relevant (low to moderate) K values, RBF yields degraded performance. In this work, we investigate solutions to this problem. We introduce a two-stage framework that decouples the scheduling and beamforming problems. In our scenario, RBF is exploited to identify good, spatially separable, users in a first stage. In the second stage, the initial random beams are refined based on the available feedback to offer improved performance toward the selected users. Specifically, we propose beam power control techniques that do not change the direction of the second-stage beams, offering feedback reduction and performance tradeoffs. The common feature of these schemes is to restore robustness of RBF with respect to sparse network settings (low K), at the cost of moderate complexity increase.

It is now well known that employing channel adaptive signaling in wireless communication systems can yield large improvements in almost any performance metric. Unfortunately, many kinds of channel adaptive techniques have been deemed impractical in the past because of the problem of obtaining channel knowledge at the transmitter. The transmitter in many systems (such as those using frequency division duplexing) can not leverage techniques such as training to obtain channel state information. Over the last few years, research has repeatedly shown that allowing the receiver to send a small number of information bits about the channel conditions to the transmitter can allow near optimal channel adaptation. These practical systems, which are commonly referred to as limited or finite-rate feedback systems, supply benefits nearly identical to unrealizable perfect transmitter channel knowledge systems when they are judiciously designed. In this tutorial, we provide a broad look at the field of limited feedback wireless communications. We review work in systems using various combinations of single antenna, multiple antenna, narrowband, broadband, single-user, and multiuser technology. We also provide a synopsis of the role of limited feedback in the standardization of next generation wireless systems.

Recent work on channel modeling and prediction has shown the feasibility of predicting the mobile radio channel, quite accurately, several milliseconds ahead in time for realistic Doppler spreads. Motivated by these results we consider opportunistic scheduling algorithms that exploit both current and future channel estimates. We demonstrate how this extra channel information can be used to improve the scheduling. Simulations show that the proposed algorithm can improve the inherent tradeoff between throughput, fairness and delay. The current approach builds on proportional fair scheduling but can also be generalized to other criteria.

We develop and analyze new cooperative strategies for ad hoc networks that are more spectrally efficient than classical decode & forward (DF) protocols. Using analog network coding, our strategies preserve the practical half-duplex assumption but relax the orthogonality constraint. The introduction of interference due to non-orthogonality is mitigated thanks to precoding, in particular dirty paper coding. Combined with smart power allocation, our cooperation strategies allow to save time and lead to a more efficient use of the bandwidth and to improved network throughput with respect to classical repetition-DF and parallel-DF.

We consider allocating the transmit powers for a wireless multi-link (N-link) system, in order to maximize the total system throughput under interference and noise impairments, and short term power constraints. Employing dynamic spectral reuse, we allow for centralized control. In the two-link case, the optimal power allocation then has a remarkably simple nature termed binary power control: depending on the noise and channel gains, assign full power to one link and minimum to the other, or full power on both. Binary power control (BPC) has the advantage of leading towards simpler or even distributed power control algorithms. For N>2 we propose a strategy based on checking the corners of the domain resulting from the power constraints to perform BPC. We identify scenarios in which binary power allocation can be proven optimal also for arbitrary N. Furthermore, in the general setting for N>2, simulations demonstrate that a throughput performance with negligible loss, compared to the best non-binary scheme found by geometric programming, can be obtained by BPC. Finally, to reduce the complexity of optimal binary power allocation for large networks, we provide simple algorithms achieving 99% of the capacity promised by exhaustive binary search.

We address the problem of multicell co-channel scheduling in view of mitigating interference in a wireless data network with full spectrum reuse. The centralized joint multicell scheduling optimization problem, based on the complete co-channel gain information, has so far been justly considered impractical due to complexity and real-time cell-to-cell signaling overhead. However, we expose here the following remarkable result for a large network with a standard power control policy. The capacity maximizing joint multicell scheduling problem admits a simple and fully distributed solution. This result is proved analytically for an idealized network. From the constructive proof, we propose a practical algorithm that is shown to achieve near maximum capacity for realistic cases of simulated networks of even small sizes.

Recent results show that sum-rate maximizing multicell power allocation promises significant gains in interference- limited data networks. Finding practical, i.e. distributed, versions of this global optimization problem however remains a challenging task. In this work, we establish a general framework for the distributed power allocation problem for N mutually interfering links enabling us to derive a fully distributed power allocation algorithm. Although a gain for N = 2 is observed, a performance gap is still observed compared to a centralized algorithm. As a way to fill that gap, we propose minimal information (in this case 1 bit) message passing between interfering links to improve performance. Numerical results show these algorithms to exploit a substantial amount of the capacity gain offered by centralized optimization.

A systematic theory is introduced for finding the derivatives of complex-valued matrix functions with respect to a complex-valued matrix variable and the complex conjugate of this variable. In the framework introduced, the differential of the complex-valued matrix function is used to identify the derivatives of this function. Matrix differentiation results are developed for use in signal processing and communications applications. Several other examples are given.

We consider a downlink channel where a base station equipped with M transmit antennas communicates with K ≥ M single-antenna receivers and has partial channel knowledge obtained via a limited rate feedback channel. We propose scalar feedback metrics that provide an estimate of the received signal-to-noise plus interference ratio (SINR), which are combined with efficient user selection algorithms and zero-forcing beamforming. The asymptotic system sum rate for large K is analyzed and numerical results are provided, showing the performance of each metric in different scenarios.

We investigate the design of a wireless sensor network (WSN), where distributed source coding (DSC) for pairs of nodes is used. More precisely, we minimize the compression sum rate for noiseless channels and the sum power for noisy orthogonal channels in a context of pairwise DSC. In both cases, the minimization can be separated into a matching problem and a pairwise rate-power control problem (that admits a simple closed-form solution). Using this separation, we obtain an optimization procedure of polynomial (in the number of nodes in the network) complexity. Finally, we show that the overall optimization can be readily interpreted. For noiseless channels, the optimization matches close nodes whereas, for noisy channels, there is a tradeoff between matching close nodes and matching nodes with different distances to the sink. We provide examples of the proposed optimization method based on empirical measures. We show that the matching technique provides substantial gains in either storage capacity or power consumption for the WSN.

We consider a multiuser MAC fading channel with two users communicating with a common destination, where each user mutually acts as a relay for the other one as well as transmits his own information. We propose a power control-enhanced cooperative transmission scheme allowing each user to allocate a certain amount of power for his own transmitted data while the rest is devoted to relaying. The underlying protocol is based on a modification of the so-called non-orthogonal amplify and forward (NAF) protocol in Azarian, K. et al, (2005). We develop capacity expressions for our scheme and derive the rate-optimum power allocation, in closed form. Our results indicate that even in a mutual cooperation setting like ours, on any given realization of the channel, one of the users will always allocate zero power to relaying the data of the other one, and thus act selfishly.

We consider the downlink of a multiuser MIMO channel, corresponding to a single cell with an Nt-antenna base station and K single-antenna mobile terminals (MTs). It is known that when full channel state information (CSI) is available at the transmitter (full CSIT) the capacity of the system scales as Ntlog(P/Nt- log K), under a total power constraint P [1], While, when the transmitter has no CSI, scaling reduces to that of a TDMA system. This paper examines the more realistic case of having an intermediate state of CSI. The key idea is based on a split of the allotted feedback between two stages: A first stage devoted to scheduling followed by a second stage for precoder design for the selected users. Based on an approximation of the achievable sum rate, we introduce a method for determining the splitting of the feedback rate so as to maximize performance and provide intuitions. We illustrate the gains of the 2-stage approach via Monte Carlo simulations.

The problem of joint linear beamforming and scheduling in a MIMO broadcast channel is considered. We show how orthogonal linear beamforming (OLBF) can be efficiently combined with a low-complexity user selection algorithm to achieve a large portion of the multiuser capacity. The use of orthogonal transmission enables the transmitter to calculate exact signal-to-interference plus noise ratio (SINR) values during the user selection process. The knowledge of multiuser interference proves to be of particular importance for user scheduling as both the number of users in the cell and the average signal-to-noise ratio (SNR) decrease. The sum capacity of our scheme is characterized in the low-SNR regime, providing analytical results on the performance gain over zero-forcing beamforming (ZFBF). Numerical results show gains over both suboptimal and optimal ZFBF techniques in different scenarios.

Joint optimization of transmit power and scheduling in wireless data networks promises significant system-wide capacity gains. However, this problem is known to be NP-hard and thus difficult to tackle in practice. We analyze this problem for the downlink of a multicell full reuse network with the goal of maximizing the overall network capacity. We propose a distributed power allocation and scheduling algorithm which provides significant capacity gain for any finite number of users. This distributed cell coordination scheme, in effect, achieves a form of dynamic spectral reuse, whereby the amount of reuse varies as a function of the underlying channel conditions and only limited inter-cell signaling is required.

A sensible design of wireless networks involves striking a good balance between an aggressive reuse of the spectral resource throughout the network and managing the resulting co-channel interference. Traditionally, this problem has been tackled using a ldquodivide and conquerrdquo approach. The latter consists in deploying the network with a static or semidynamic pattern of resource reutilization. The chosen reuse factor, while sacrificing a substantial amount of efficiency, brings the interference to a tolerable level. The resource can then be managed in each cell so as to optimize the per cell capacity using an advanced air interface design. In this paper, we focus our attention on the overall network capacity as a measure of system performance. We consider the problem of resource allocation and adaptive transmission in multicell scenarios. As a key instance, the problem of joint scheduling and power control simultaneously in multiple transmit-receive links, which employ capacity-achieving adaptive codes, is studied. In principle, the solution of such an optimization hinges on tough issues such as the computational complexity and the requirement for heavy receiver-to-transmitter feedback and, for cellular networks, cell-to-cell channel state information (CSI) signaling. We give asymptotic properties pertaining to rate-maximizing power control and scheduling in multicell networks. We then present some promising leads for substantial complexity and signaling reduction via the use of newly developed distributed and game theoretic techniques.

We compare space-time coding (transmit diversity) and random "opportunistic" beamforming in a space-division multiple access/time-division multiple access single-cell downlink system with random packet arrivals, correlated block-fading channels, and non-perfect channel state information at the transmitter due to a feedback delay. Our comparison is based on system stability. The ability of accurately predicting the channel signal-to-noise ratio dominates the performance of opportunistic beamforming, even under the optimistic assumption that the sequence of beamforming matrices is perfectly known a priori by the receivers. Our results show that the relative merit of opportunistic beamforming versus space-time coding strongly depends on the channel Doppler bandwidth. Therefore, previous naive conclusions on the fact that transmit diversity always hurts the system performance under multiuser-diversity scheduling should be taken with great care

In this paper we propose a channel state feedback algorithm that uses multiple feedback thresholds to reduce the number of users transmitting feedback to a minimum. The users are polled with lower and lower threshold values and only the users that are above a threshold value transmit feedback to the base station. We show how this feedback algorithm can be used for any scheduling algorithm and show how closed-form expressions for the optimal threshold values can be obtained for two well-known scheduling algorithms. Finally, we propose a two-step optimization procedure for optimizing the feedback algorithm for real-life cellular standards.

In this letter we develop an expression for the approximate throughput guarantee violation probability (TGVP) for users in time-slotted networks for any scheduling algorithm with a given mean and variance of the bit-rate in a time-slot, and a given distribution for the number of time-slots allocated within a time-window. Based on this general result, we evaluate closed-form expressions for the TGVPs for four well-known scheduling algorithms. Through simulations we also show that our TGVP approximation is tight for a realistic network with moving users with correlated channels and realistic throughput guarantees.

A memoryless precoder is designed for orthogonal space-time block codes (OSTBCs) for multiple-input multiple-output (MIMO) channels exhibiting joint transmit-receive correlation. Unlike most previous similar works which concentrate on transmit correlation only and pair-wise error probability (PEP) metrics. 1) The precoder is designed to minimize the exact symbol error rate (SER) as function of the channel correlation coefficients, which are fed back to the transmitter. 2) The correlation is arbitrary as it may or may not follow the so-called Kronecker structure. 3) The proposed method can handle general propagation settings including those arising from a cooperative macro-diversity (multi-base) scenario. We present two algorithms. The first is suboptimal, but provides a simple closed-form precoder that handles the case of uncorrelated transmitters, correlated receivers. The second is a fast-converging numerical optimization of the exact SEE which covers the general case. Finally, a number of novel properties of the minimum SER precoder are derived

Cooperative diversity is a rapidly emerging topic for wireless communications, with ad hoc and hybrid/relay networks as two main applications so far. In this paper, we investigate the cooperative diversity concept for MIMO multicell networks, where the processing must be optimized to account for the variability of the channel conditions across the cooperative devices. This can be done via distributed preceding and is realistically based on channel statistics (average gains, correlations, etc.). We give a new approach to the previously coined equal diversity spread principle, through minimization of an approximated SER expression. Next, we focus on a low-complexity approach to minimizing a PEP-based performance measure. Gains are evaluated in a multicell scenario with collaborating base stations.

In this paper we analyze achievable throughput guarantees in wireless time-division multiplexing (TDM) networks. Approximations of the throughput guarantee violation probability (TGVP) for users communicating in time-slotted systems are obtained for any scheduling algorithm with a given mean and variance of the number of bits transmitted in a time- slot and a distribution for the number of time-slots allocated to a user within a time-window. We investigate the corresponding TGVPs for three scheduling algorithms, namely (i) Round Robin Scheduling, (ii) Maximum Carrier-to-Noise Ratio Scheduling, and (iii) Opportunistic Round Robin Scheduling, when the users' channels are independently and identically distributed.

The problem of joint scheduling and beamforming for a multiuser multiple-input multiple-output (MIMO) network with partial channel state information at the transmitter (CSIT) is addressed here. Unlike most previous work that rely on full instantaneous CSIT and require unacceptable overhead feedback to the transmitter, we point out here that useful information relevant to the scheduler lies untapped in the long term statistical information of the user's channels. We show how statistical CSIT can be efficiently combined with partial instantaneous CSIT to derive a scheduling rule for the downlink of multiuser MIMO systems

This paper explores the idea of cooperative spatial multiplexing for use in MIMO multicell networks. We imagine applying this cooperation for several multiple antenna access-points to jointly transmit streams towards multiple single-antenna user terminals to neighbouring cells. We make the setting more realistic by introducing a constraint on the hybrid channel state information (HCSI), assuming that each transmitter has full CSI for its own channel, but only statistical information about other transmitters' channels. Each cooperating transmitter then makes guesses about the behaviour of the other transmitters, using the statistical CSI. We show two of several possible transmission strategies under this setting, and include simple optimization at the receiver to improve performance. Comparisons are made with fully cooperative (full CSI) and non-cooperative schemes. Simulation results show a substantial cooperation gain despite the lack of instantaneous information

In this paper, we investigate the cooperative diversity concept for use in MIMO multi-cell networks. We show that, in such networks, cooperative diversity processing must be optimized to account for the variability of channel conditions across the cooperative devices. This can be done via distributed precoding and, in mobile networks, it is based realistically on channel statistics. The cooperative MIMO correlation matrix admits a special structure which is used to optimize the precoder. We investigate algorithms for exact error-rate and low-complexity approximated optimization. Gains are evaluated in multi-cell scenarios with collaborating base stations.

We analyze the sum capacity of multicell wireless networks with full resource reuse and channel-driven opportunistic scheduling in each cell. We address the problem of finding the co-channel (throughout the network) user assignment that results in the optimal joint multicell capacity, under a resource-fair constraint and a standard power control strategy. This problem in principle requires processing the complete co-channel gain information, and thus, has so far been justly considered unpractical due to complexity and channel gain signaling overhead. However, we expose here the following key result: the multicell optimal user scheduling problem admits a remarkably simple and fully distributed solution for large networks. This result is proved analytically for an idealized network. From this constructive proof, we propose a practical algorithm that is shown to achieve near maximum capacity for realistic cases of simulated networks of even small sizes

A memoryless linear precoder is designed for orthogonal space-time block codes (OSTBC) for improved performance over block-fading flat correlated Rayleigh fading multiple-input multiple-output (MIMO) channels. Original features of the proposed technique include 1) the precoder can handle both transmit and receive correlation, and 2) the precoder handles any arbitrary joint correlation structure, including the so-called Kronecker (non-Kronecker) correlation models. The precoder is designed to minimize a symbol error-based metric as function of the joint slowly-varying channel correlation coefficients, which are supposed to be known to the transmitter. Several useful properties of the optimal precoder are given, evidencing the impact of receive correlation on transmitter optimization in certain situations. An iterative fast-converging numerical optimization algorithm is proposed. Monte Carlo simulations over fading channels are used to validate our claims.

A scheme exploiting memory in opportunistic multiuser beamforming is proposed. The scheme builds on recent advances realized in M. Sharif and B. Hassibi, 2005, in the area of multi-user downlink preceding and scheduling based on partial transmitter channel state information (CSIT). Although the preceding and scheduling done in M. Sharif and B. Hassibi, 2005 is optimal within the set of unitary precoders, it is only so asymptotically for large number of users. Secondly, this scheme is unable to exploit potential time correlation of the channel. In this paper, we show (1) that exploiting memory in the transmitter allows to fill the gap to optimality for fixed (even low) number of users for time correlated channels, (2) how such schemes can be extended to take fairness into account in the proportional fair sense

This paper addresses the problem of multiple-input-multiple-output (MIMO) spatial multiplexing (SM) systems in the presence of antenna fading correlation. Existing SM schemes (e.g., V-BLAST) rely on the linear independence of transmit antenna channel responses for stream separation and suffer considerably from high levels of fading correlation. As a result, such algorithms simply fail to extract the nonzero capacity that is present even in highly correlated spatial channels. The authors make the simple but key point that only one transmit antenna is needed to send several independent streams if those streams are appropriately superposed to form a high-order modulation (e.g., two 4-quadratic amplitude modulation (4-QAM) signals form a 16-QAM). The concept builds upon constellation multiplexing (CM) (D. Gesbert and J. Akhtar, Smart Antennas in Europe-State-of-the-Art, 2005) whereby distinct QAM streams are superposed to form a higher order constellation with rate equivalent to the sum of rates of all original streams. In contrast to SM transmission, the substreams in CM schemes are differentiated through power scaling rather than through spatial signatures. The authors build on this idea to present a new transmission scheme based on a precoder, adjusting the phase and power of the input constellations in closed form as a function of the antenna correlation. This yields a rate-preserving MIMO multiplexing scheme that can operate smoothly at any degree of correlation. At the extreme correlation case (identical channels), the scheme behaves equivalent to sending a single higher order modulation whose independent components are mapped to the different antennas.

This paper presents a closed-form linear precoder for a MIMO spatial multiplexing (SM) system in the presence of transmit correlation and a Ricean component. Existing SM (V-BLAST and similar schemes), based upon channel matrix inversion, rely on the linear independence of antenna channel responses for stream separation and suffer considerably from high levels of fading correlation and/or dominating ill-conditioned line-of-sight channel components. We propose a simple algorithm that adjusts the transmitted constellation through power weighting and phase shifts that can be interpreted in some extreme cases as a higher order constellation design scheme. We obtain a rate-preserving MIMO multiplexing scheme that can operate smoothly at any degree of transmit correlation and any type of LOS channel component.

A memoryless precoder is designed for orthogonal space-time block codes for multiple-input multiple-output channels exhibiting joint transmit-receive correlation. Unlike most previous similar work which concentrated on transmit correlation only and pair-wise error probability (PEP) metrics, the precoder is designed to minimize the exact symbol error rate (SER) as a function of the channel correlation coefficients, which are fed back to the transmitter, and the correlation may or may not follow the so-called Kronecker structure. The proposed method can handle general propagation settings including those arising from a cooperative macro-diversity (multi-base) scenario. We present two algorithms. The first is suboptimal, but provides a simple closed-form precoder that handles the case of uncorrelated transmitters, correlated receivers. The second is a fast-converging numerical optimization which covers the general case. The results show the SER based precoder has small gains over the PEP based precoder for moderate signal-to-noise ratios (SNR). Several properties of the minimum SER precoder are given.

Wireless scheduling algorithms can extract multi-user diversity (MUDiv) via prioritizing the users with best current channel conditions. One drawback of MUDiv is the required feedback carrying the instantaneous channel rates from from all active subscribers to the access point/base station. This paper shows that this feedback load is, for the most part, unjustified. To alleviate this problem, we propose a technique allowing to dramatically reduce the feedback (by up to 90%) needs while preserving the essential of the scheme performance. We provide a theoretical analysis of the feedback load as function of the system's ergodic and outage capacity for both the traditional MUDiv scheme and the new scheme.

In this paper we study the performance of a recently proposed scheduling technique, known as selective multiuser diversity scheme, when the users in the system have unequal average signal-to-noise ratios (SNRs). Numerical examples confirm that selective multiuser diversity reduces dramatically the feedback load but maintains a good portion of multiuser diversity gain.

During the last few years a number of space-time block codes have been proposed for use in multiple transmit antennas systems. We propose a method to extend any space-time code constructed for m transmit antennas to m p transmit antennas through group-coherent codes (GCCs). GCCs make use of very limited feedback from the receiver (as low as 1 bit). In particular the scheme can be used to extend any orthogonal code (e.g., Alamouti code) to more than two antennas while preserving low decoding complexity, full diversity benefits, and full data rate.

The paper addresses the problem of MIMO spatial-multiplexing (SM) systems in the presence of antenna fading correlation. Existing SM (V-BLAST and related) schemes rely on the linear independence of transmit antenna channel responses for stream separation and suffer considerably from high levels of fading correlation. As a result, such algorithms simply fail to extract the non-zero capacity present in highly correlated spatial channels. We make the simple, but key, point that just one transmit antenna is needed to send several independent streams if those streams are appropriately superposed to form a high-order modulation (such as QAM). We build on this idea to present a new transmission scheme based on a precoder adjusting the phase and power of the input constellations in closed-form as a function of the antenna correlation. This yields a rate-preserving MIMO multiplexing scheme that can operate smoothly at any degree of correlation.

This paper presents an overview of progress in the area of multiple input multiple output (MIMO) space-time coded wireless systems. After some background on the research leading to the discovery of the enormous potential of MIMO wireless links, we highlight the different classes of techniques and algorithms proposed which attempt to realize the various benefits of MIMO including spatial multiplexing and space-time coding schemes. These algorithms are often derived and analyzed under ideal independent fading conditions. We present the state of the art in channel modeling and measurements, leading to a better understanding of actual MIMO gains. Finally, the paper addresses current questions regarding the integration of MIMO links in practical wireless systems and standards.

We address the capacity limits of size-constrained multi-input multi-output (MIMO) arrays. While most work on MIMO focuses on arrays with of a small number of sufficiently spaced, low correlated antenna elements, we look at the case where a fixed small space is being filled up with antenna elements. We obtain a dense MIMO system whose rate performance is analyzed. We establish capacity limits and an equivalence with the capacity of MIMO arrays with unlimited aperture.

Identification of the channel matrix is of main concern in wireless MIMO (Multiple Input Multiple Output) systems. Here, we present an SVO-based approach for blind identification of the main independent parallel channels. The right and left singular vectors are estimated directly (no channel matrix estimation is necessary) and continuously updated during normal transmission. The approach is related to the iterative Power Method (8), as well as the time reversal approach ([4]).

This paper deals with the capacity behavior of wireless orthogonal frequency-division multiplexing (OFDM)-based spatial multiplexing systems in broad-band fading environments for the case where the channel is unknown at the transmitter and perfectly known at the receiver. Introducing a physically motivated multiple-input multiple-output (MIMO) broad-band fading channel model, we study the influence of physical parameters such as the amount of delay spread, cluster angle spread, and total angle spread, and system parameters such as the number of antennas and antenna spacing on ergodic capacity and outage capacity. We find that, in the MIMO case, unlike the single-input single-output (SISO) case, delay spread channels may provide advantages over flat fading channels not only in terms of outage capacity but also in terms of ergodic capacity. Therefore, MIMO delay spread channels will in general provide both higher diversity gain and higher multiplexing gain than MIMO flat fading channels

We present a new model for multiple-input-multiple-output (MIMO) outdoor wireless fading channels and their capacity performance. The proposed model is more general and realistic than the usual independent and identically distributed (i.i.d.) model, and allows us to investigate the behavior of channel capacity as a function of the scattering radii at transmitter and receiver, distance between the transmit and receive arrays, and antenna beamwidths and spacing. We show how the MIMO capacity is governed by spatial fading correlation and the condition number of the channel matrix through specific sets of propagation parameters. The proposed model explains the existence of "pinhole" channels which exhibit low spatial fading correlation at both ends of the link but still have poor rank properties, and hence, low ergodic capacity. In fact, the model suggests the existence of a more general family of channels spanning continuously from full rank i.i.d. to low-rank pinhole cases. We suggest guidelines for predicting high rank (and hence, high ergodic capacity) in MIMO channels, and show that even at long ranges, high channel rank can easily be sustained under mild scattering conditions. Finally, we validate our results by simulations using ray tracing techniques. Connections with basic antenna theory are made.

In practice large antenna spacings are needed to achieve high capacity gains in multiple-input multiple-output (MIMO) wireless systems. The use of dual-polarized antennas is a promising cost effective alternative where two spatially separated antennas can be replaced by a single antenna element employing orthogonal polarizations. This paper investigates the performance of spatial multiplexing in MIMO wireless systems with dual-polarized antennas. We compute estimates of the symbol error rate as a function of cross-polarization discrimination (XPD) and spatial fading correlations. Using these estimates, we show that dual-polarized antennas can significantly improve the performance of spatial multiplexing systems. It is demonstrated that improvements in terms of symbol error rate of up to an order of magnitude are possible. We furthermore find that in general for a given SNR there is an optimum XPD for which the symbol error rate is minimum. Finally, we present simulation results and we show that our estimates closely match the numerical results.

We present a new model for multiple-input multiple-output (MIMO) outdoor wireless fading channels which is more general and realistic than the usual i.i.d. model. We investigate the channel capacity as a function of parameters such as the local scattering radius at the transmitter and the receiver, the distance between the transmit (TX) and receive (RX) arrays, and the antenna beamwidths and spacing. We point out the existence of "pin-hole" channels which exhibit low fading correlation between antennas but still have poor rank properties and hence low capacity. Finally we show that even at long ranges high channel rank can easily be obtained under mild scattering conditions.

We compute the capacity of wireless orthogonal frequency division multiplexing (OFDM)-based spatial multiplexing systems in delay spread environments. Introducing an abstract model to characterize the statistical properties of the space-time channel, we provide a Monte-Carlo method for estimating the capacity cumulative distribution function, expected capacity, and outage capacity for the case where the channel is unknown at the transmitter and perfectly known at the receiver. We study the influence of the propagation environment and system parameters on capacity, and we apply our method to spatial versions of standard channels taken from the GSM recommendations. This allows us to make statements about achievable data rates of OFDM-based spatial multiplexing systems operating in practical broadband propagation environments.

We consider the problem of linear polyphase blind equalization (BE), i.e. we are interested in equalizing the output of a single-input-multiple-output (SIMO) channel, without observing its input. A previous result by Liu and Dong (see IEEE Trans. on Circuits and Systems, vol.44, no.5, 1997) showed that if the sub-channel polynomials are co-prime in the z-domain, then the equalizer output whiteness is necessary and sufficient for the equalization of a white input. Based on this observation, we propose a simple decorrelation criterion for second-order based BE. Due to its second-order nature, this criterion is insensitive to the distance of the input from Gaussianity, hence it achieves BE even for Gaussian or non-Gaussian inputs. Moreover, unlike other second-order techniques, our approach bypasses channel estimation and computes directly the equalizer. By doing so, it avoids the problem of ill-conditioning due to channel order mismatch which is crucial to other techniques. Combined to its good convergence properties, these characteristics make the proposed technique an attractive option for robust polyphase BE, as evidenced by both our analysis and computer simulation results.

The recovery of code-division multiple access (CDMA) information signals in a frequency selective fading channel is a problem of great theoretical and practical interest. This paper addresses the estimation of an optimal (within the class of linear detectors) multi-user CDMA receiver. A novel approach is introduced that enables the estimation of the minimum mean-square error (MMSE) detector in a blind setting. The MMSE detector is obtained through a double subspace projection that exploits the subspace structure associated with both the code of the desired user and the estimated signal subspace of the covariance matrix for the observed signals. The technique allows for interference rejection without requiring the knowledge of the codes for the interferers.

This paper describes a direct equalization approach for channels with some underlying structure. A semi-blind approach is taken here where a small amount of training symbols is available. A family of MMSE equalizers is obtained that includes some prior information about the channel structure. The channel structure assumed in this paper is that the channel vector lies approximately in the subspace of a matrix associated with the samples of the transmit pulse shape. Blind identifiability issues of the structured equalizer are also addressed. Numerical results using experimental indoor channel data indicate that these structured equalisers can achieve bit error rates that are significantly lower than traditional non-blind MMSE equalizers.

This paper addresses the problem of multi-user detection in the context of cellular CDMA and frequency selective channels. Structures and algorithms are proposed for the receiver design, that combat both strong multiple access interference (MAI) and inter-chip interference (ICI) in possibly asynchronous networks. Two novel receiver structures are proposed. The first one allows for the estimation of the channel characteristics for one or multiple users before signal detection. The second approach is simpler and directly acquires the coefficients of several multi-user detectors with multiple delays. These methods are blind in the sense that no pilot sequences are used to estimate the coefficients of the receiver. Only the knowledge of the spreading code for the user(s) of interest is exploited here. The proposed technique is shown to outperform existing comparable receiver estimation procedures.

This contribution deals with the problem of blind channel identification and equalization based on the (temporally or spatially) oversampled channel output. A novel algorithm is presented which builds on a multistep prediction (MSP) approach and can be viewed as a certain generalization of the initial linear prediction algorithm (LPA) proposed in 1994. Our algorithm improves on related works in that it is theoretically and practically unsensitive to the critical and expected problem of channel length mismatch. Moreover, the MSP scheme improves on the conventional LPA by increasing the robustness of this earlier algorithm. In contrast with the LPA, the proposed prediction scheme exploits the full channel structure, thus providing more statistical efficiency in channel identification. A direct symbol recovery algorithm (requiring no channel estimate) is also straightforwardly drawn from our approach.

Blind channel identification methods based on the oversampled channel output is a problem of theoretical and practical interest. It is first demonstrated that the subspace methods developed in Moulines are not robust to errors in the determination of the model order. An alternative solution is then proposed, based on a linear prediction approach. The effect of overestimating the channel order is investigated by simulations: it is demonstrated that the prediction error method is "robust" to over-determination.