This paper presents a fully automated, data-driven framework for the large-scale deployment of reconfigurable intelligent surfaces (RISs) in cellular networks. Leveraging physically consistent ray tracing and empirical data from a commercial deployment in the UK, the proposed method jointly optimizes RIS placement, orientation, configuration, and base station beamforming in dense urban environments across frequency bands (corresponding to 4G, 5G, and a hypothetical 6G system). Candidate RIS locations are identified via reflection- and scattering-based heuristics using calibrated electromagnetic models within the Sionna Ray Tracing (RT) engine. Outage users are clustered to reduce deployment complexity, and the tradeoff between coverage gains and infrastructure cost is systematically evaluated. It is shown that achieving meaningful coverage improvement in urban areas requires a dense deployment of large-aperture RIS units, raising questions about cost-effectiveness. To facilitate reproducibility and future research, the complete simulation framework and RIS deployment algorithms are provided as opensource software.

This letter casts the problem of optimum discrete beamforming as the computation of the Minkowski sum of convex polygons, which is itself a convex polygon. The number of vertices of the latter is at most the sum of the number of vertices of the original polygons, enabling its efficient computation. This original and intuitive formulation confirms that the optimum beamforming solution can be found efficiently in broad generality.

Suitably equipped with cameras and sensors, uncrewed aerial vehicles (UAVs) can be instrumental for wildfire prediction, tracking, and monitoring, provided that uninterrupted connectivity can be guaranteed even if some of the ground access points (APs) are damaged by the fire itself. A cell-free network structure, with UAVs connecting to a multiplicity of APs, is therefore ideal in terms of resilience. This work proposes a trajectory optimization framework for a UAV swarm tracking a wildfire while maintaining cell-free connectivity with ground APs. Such optimization entails a constant repositioning of the multiplicity of UAVs as the fire evolves to ensure that the best possible view is acquired and transmitted reliably, while respecting altitude limits, avoiding collisions, and proceeding to recharge batteries as needed. Given the complexity and time-varying nature of this multi-UAV trajectory optimization, reinforcement learning is leveraged, specifically the twin-delayed deep deterministic policy gradient algorithm. The approach is shown to be highly effective for wildfire tracking and coverage and could be likewise applicable to survey other natural and man-made phenomena, including weather events, earthquakes, or chemical spills.

The use of uncrewed aerial vehicles (UAVs) in cell-free networks is poised to unleash a number of new opportunities to further improve wireless networks. However, cell-free UAV networks present major challenges related to the wireless nature of access and fronthaul links. This manuscript studies the uplink of cell-free systems where users connect to UAVs, the latter devices forwarding the information to a processing point through imperfect wireless fronthaul links. Three multiple access alternatives are considered for the fronthaul, namely frequency division multiples access, spatial division multiple access, and combinations thereof. Deterministic equivalent expressions for the spectral efficiency under these fronthaul schemes and minimum mean-square error reception are derived. Then, the optimization subproblems of (a) the 3D deployment of the UAVs, (b) the user transmit powers, and (c) the UAV transmit powers, are investigated. The joint optimization of these subproblems yields superior performance, with the 3D deployment being the main source of improvement.

With increasing frequencies, bandwidths, and array apertures, the phenomenon of beam squint arises as a serious impairment to beamforming. Fully digital arrays with true time delay per antenna element are a potential solution, but they require downconversion at each element. This paper shows that hybrid arrays can perform essentially as well as digital arrays once the number of radio-frequency chains exceeds a certain threshold that is far below the number of elements. This threshold is determined by only a few physical parameters—bandwidth, array size, and beamforming direction—and can be expressed in a remarkably simple closed form. The result is robust, holding also for suboptimum yet highly appealing beamspace architectures.

Recently, cell-free (CF) architectures, in which every user can potentially communicate with every base station, have received a lot of attention. This paper considers the uplink of fully and partially centralized CF networks where unmanned aerial vehicles serve as flying base stations (FBSs). A subset of FBSs participates in the reception of each user and a subset of users is received by each FBS. Deterministic equivalent expressions, exact asymptotically in the subset sizes and approximate for finite dimensions thereof, are derived for the spectral efficiency under Rician fading. Capitalizing on these expressions, the FBS deployment problem is investigated for different receiver architectures. The nonconvex deployment problem, tackled through a combination of gradient-based and Gibbs sampling algorithms, results in a superior performance with respect to a square grid deployment; this superiority extends to the minimum and aggregate spectral efficiency for both fully and partially centralized cell-free networks.

This paper deals with line-of-sight (LOS) MIMO communication via an intelligent reflecting surface (IRS). It is shown that the number of spatial degrees of freedom (DOF) afforded by this setting grows in proportion with the IRS aperture, as opposed to being dictated by the transmit and receive apertures; this buttresses the interest in IRS deployments at mmWave and terahertz frequencies, with wavelengths and transmission ranges small enough to enable LOS MIMO. Explicit and simple-to-implement IRS phase shifts are put forth that achieve, not only the maximum number of DOF, but the capacity, under a certain geometrical condition. The insights leading to the proposed phase shifts, and to the optimality condition itself, are asymptotic in nature, yet extensive simulations confirm that the performance is excellent for a broad range of settings and even if the optimality condition is not strictly met.

Motivated by the widespread adoption of the parabolic wavefront model for line-of-sight (LOS) multiple-input multiple-output (MIMO) communication, this paper presents a comprehensive analysis of this model’s validity and simple conditions that ensure its applicability. Then, with the model’s scope clearly delineated, the paper expounds a number of properties of the channel that results from applying it. Connections are drawn among these properties under the umbrella of a Fourier interpretation, and their significance to LOS MIMO communication is substantiated.

This paper provides a deterministic channel model for a scenario where wireless connectivity is established through a reflection off a smooth planar surface of an infinite extent. The developed model is rigorously built upon the physics of wave propagation and is as precise as tight are the unboundedness and smoothness assumptions on the surface. This model allows establishing how line-of-sight multiantenna communication is altered by a reflection off an electrically large surface, a situation of high interest for mmWave and terahertz frequencies.

A solution is presented to the spatial-wideband effect in line-of-sight multiple-input multiple-output transmission. This effect, which arises once the bandwidth is large enough that the propagation delay differences for distinct transmit-receive antenna pairs become comparable to the symbol period, causes frequency selectivity and intersymbol interference. The proposed solution, which only entails channel-independent DFT precoding and reception, plus delay lines, performs close to capacity for most array orientations and is compatible with both multicarrier and single-carrier signals.

This paper addresses the spatial-wideband effect in line-of-sight multiple-input multiple-output channels. This effect arises once the bandwidth is large enough that the differences in propagation delays for distinct transmit-receive antenna pairs cease to be negligible relative to the symbol period; this, in turn, gives rise to intersymbol interference. The impact of this effect is quantified as a function of the relevant geometric parameters (range, array orientations, antenna spacings) and a family of scalable solutions is proposed to counter it. In particular, a solution based on per-antenna delay lines at transmitter and receiver is shown to be highly effective, and a criterion is derived to set those delay lines as a function of the channel.

We provide a deterministic channel model for a scenario where wireless connectivity is established through a reflection from a planar smooth surface of an infinite extent. The developed model is rigorously built upon the physics of wave propagation, and is as precise as tight are the unboundedness and smoothness assumptions on the surface. This model allows establishing that line-of-sight spatial multiplexing can take place via reflection off an electrically large surface, a situation of high interest for mmWave and terahertz frequencies.

The millimeter wave bands are being increasingly considered for wireless communication to unmanned aerial vehicles (UAVs). Critical to this undertaking are statistical channel models that describe the distribution of constituent parameters in scenarios of interest. This paper presents a general modeling methodology based on data-training a generative neural network. The proposed generative model has a two-stage structure that first predicts the link state (line-of-sight, non-line-of-sight, or outage), and subsequently feeds this state into a conditional variational autoencoder (VAE) that generates the path losses, delays, and angles of arrival and departure for all the propagation paths. The methodology is demonstrated for$\mathrm {28~GHz}$air-to-ground channels between UAVs and a cellular system in representative urban environments, with training datasets produced through ray tracing. The demonstration extends to both standard base stations (installed at street level and downtilted) as well as dedicated base stations (mounted on rooftops and uptilted). The proposed approach is able to capture complex statistical relations in the data and it significantly outperforms standard 3GPP models, even after refitting the parameters of those models to the data.

Cell-free (CF) structures are expected to be a game changer for beyond-5G wireless networks. With every user potentially communicating with every base station, cooperation at a central processing point is poised to provide much higher spectral efficiencies. At the same time, the growing interest in unmanned aerial vehicles (UAVs) makes CF-UAV networks an appealing scenario. This paper investigates the uplink of a CF network where UAVs serve as flying base stations. The optimization of the UAV locations is shown to markedly increase the minimum local-average signal-to-interference-plus-noise ratio, which in turn increases the spectral efficiency. The improvements are associated to pilot contamination and to geometry.

This paper considers line-of-sight multiple-input multiple-output communication, of interest at millimeter-wave and sub-terahertz frequencies. Building on how, with an angular rotation dependent on the signal-to-noise ratio (SNR), uniform linear arrays (ULAs) can tightly approach the capacity of such channels, we assess the performance of rotatable uniform rectangular arrays (URAs). The changeover from ULAs to URAs is motivated by the interest in reducing the array footprints. For both isotropic and directive antennas, various types of rotatable URAs are devised that-except at very low SNRs-perform remarkably close to their ULA counterparts with considerably smaller footprints.

With growing interest in mmWave connectivity for unmanned aerial vehicles (UAVs), a basic question is whether networks intended for terrestrial service can provide sufficient aerial coverage as well. To assess this possibility in the context of urban environments, extensive system-level simulations are conducted using a generative channel model recently proposed by the authors. It is found that standard downtilted base stations at street level, deployed with typical microcellular densities, can indeed provide satisfactory UAV coverage. Interestingly, this coverage is made possible by a conjunction of antenna sidelobes and strong reflections. As the deployments become sparser, the coverage is only guaranteed at progressively higher UAV altitudes. The incorporation of base stations dedicated to UAV communication, rooftop-mounted and uptilted, would strengthen the coverage provided their density is comparable to that of the standard deployment, and would be instrumental for sparse deployments of the latter.

Wireless communication at millimeter wave frequencies is an attractive option for high-bit-rate connectivity to unmanned aerial vehicles (UAVs). However, conducting the channel measurements necessary to assess the communication performance at these frequencies has been challenging due to the severe payload and power restrictions in commercial UAVs. This work presents a novel lightweight (approximately 1.3kg) channel measurement system at 28GHz installed on a commercially available UAV. A ground transmitter equipped with a horn antenna conveys sounding signals to a UAV equipped with a lightweight spectrum analyzer. We demonstrate that the measurements can be highly influenced by the onboard antenna pattern as shaped by the UAV’s frame. A calibration procedure is presented to correct for the resulting angular variations in antenna gain. The measurement setup is then validated on real flights from an airstrip at distances in excess of 300m.

This paper establishes an upper bound on the capacity of line-of-sight multiantenna channels over all possible antenna arrangements and shows that uniform linear arrays (ULAs) with an SNR-dependent rotation of transmitter and/or receiver can closely approach such capacity-and in fact achieve it at low and high SNR, and asymptotically in the numbers of antennas. Then, as an alternative to mechanically rotating ULAs, we propose to electronically select among multiple ULAs having a radial disposition at either transmitter or receiver, and we bound the shortfall from capacity as a function of the number of such ULAs. With only three ULAs, properly angled, 96% of the capacity can be achieved. Finally, we further introduce reduced-complexity precoders and linear receivers that capitalize on the structure of the channels spawned by these configurable ULA architectures.

Drawing on the notion of parallel interference cancellation, this paper formulates a one-shot linear receiver for the uplink of centralized, possibly cloud-based, radio access networks (C-RANs) operating in a cell-free fashion. This receiver exhibits substantial interference rejection abilities, yet it does not involve any matrix inversions; rather, its structure hinges on the pairwise projections of the users' channel vectors. Its performance is markedly superior to that of matched-filter beamforming, while the computational cost is decidedly inferior to that of an MMSE filter, altogether constituting an attractive alternative in terms of performance vs cost. Furthermore, with a proper sparsification of the channel matrix that it estimates and processes, the proposed receiver can be rendered scalable in the sense of its computational cost per access point not growing with size of the network. Uplink power control is also readily accommodated.

This paper tackles the problem of zero-forcing (ZF) precoding for the downlink of centralized radio-access networks operating in a cell-free fashion. While the customary workhorse of ZF precoding is the channel pseudo-inverse, because of the separate power constraint at each participating access point, the pseudo-inverse is not optimum. Rather, it can be improved upon by other inverses that allow conveying stronger signals (or, equivalently, consuming less power) while respecting the same ZF conditions. Motivated by the enormous disparity in computational cost between simple pseudo-inversion and general inversion, we ascertain the performance disadvantage of the former in a wide range of conditions. The welcome conclusion is that pseudo-inversion is close-to-optimum for all operational regimes of interest.

We establish an upper bound on the information-theoretic capacity of line-of-sight (LOS) multiantenna channels with arbitrary antenna arrangements and identify array structures that, properly configured, can attain at least 96.6% of such capacity at every signal-to-noise ratio (SNR). In the process, we determine how to configure the arrays as a function of the SNR. At low- and high-SNR specifically, the configured arrays revert to simpler structures and become capacity-achieving.

This paper formulates linear MMSE receivers that are both network- and user-centric for the uplink of cell-free wireless networks with centralized processing. Precisely, every user's reception involves a distinct subset of access points (APs) while every AP participates in the reception of a distinct subset of users, hence the moniker subset MMSE receivers. These subsets, defined on the basis of the large-scale channel gains between users and APs, capture the most relevant signal and interference contributions while disregarding those whose processing is cost-ineffective and whose associated channel estimations would incur unnecessary overheads. With that, subset reception approaches the performance of network-wide MMSE reception, offering a multiple-fold improvement over cellular and matched-filtering counterparts, while being scalable in terms of cost and channel estimation. Moreover, because the subsets overlap considerably, they can sometimes be advantageously combined and the computation of the corresponding receivers can share a hefty amount of processing.

We consider the problem of mobile computation offloading to both an edge cloud and to peer devices, and propose bandwidth incentives for those peer devices. Based on this idea, we formulate a joint optimization of the share of computations that are offloaded to the cloud and to peers, the identity of those assisting peers, and the allocation of computational resources at the cloud. The solutions derived from this optimization allow reducing by roughly 40% the completion time of computation tasks at the mobile devices, thereby facilitating the operation of latency-demanding applications.

This paper proposes a power control policy for the uplink of cell-free wireless networks. Such policy, which generalizes the fractional power control used extensively in cellular networks, relies only on large-scale quantities, is fully distributed, and features a single control parameter. By adjusting this parameter, the SIR distribution experienced by the users can be compressed or expanded, effecting a tradeoff between average performance and fairness.

This paper studies the viability of feedforward neural networks (NNs) for centralized power control in the uplink of cell-free wireless systems with matched-filter reception. The formulation relies only on large-scale channel behaviors as inputs, without the need for user location information, and on unsupervised learning, to avoid the onerous precomputation of training data that supervised learning would necessitate for every system or environment modification. Two different power control objectives are entertained, and for both of them the NN closely approximates the optimum solutions produced by convex solvers while vastly reducing the complexity, thereby opening the door to power control implementations for very large systems.

This paper presents analytical expressions for the signal-to-interference ratio and the spectral efficiency in macrocellular networks with massive MIMO conjugate beamforming, both with a uniform and a channel-dependent power allocation. These expressions, which apply to very general network geometries, are asymptotic in the strength of the shadowing. Through Monte-Carlo simulation, we verify their accuracy for relevant network topologies and shadowing strengths. Also, since the analysis does not include pilot contamination, we further gauge through Monte-Carlo simulation, the deviation that this phenomenon causes with respect to our results, and hence the scope of the analysis.

We present a measurement-driven algorithm to map the large-scale channel losses observed between a cellular base station and any point in its coverage area. The algorithm is on-line, meaning that it operates on continuously arriving measurements. Its distinguishing features are the use of two kernel functions, suitably chosen for the problem at hand, and a simple technique to sparsify the dictionary of measurements retained in memory. Evaluations in campus and urban settings indicate that the proposed algorithm reduces, roughly in half, the prediction error of existing single- kernel and multikernel algorithms.

The application of stochastic geometry to the analysis of wireless networks is shown to be propelled by (i) a clean separation of time scales, (ii) abstraction of small-scale effects via ergodicity, and (iii) an interference model reflecting the receiver's lack of knowledge of how individual interference terms are faded. These steps render the analysis simpler and more precise, and more amenable to incorporating subsequent features. Specifically, the paper presents easy-to-evaluate expressions for the ergodic spectral efficiency of cellular networks with single-user multiple-input multiple-output (MIMO).

This paper shows how the application of stochastic geometry to the analysis of wireless networks is greatly facilitated by: (i) a clear separation of time scales; (ii) the abstraction of small-scale effects via ergodicity; and (iii) an interference model that reflects the receiver's lack of knowledge of how each individual interference term is faded. These procedures render the analysis both more manageable and more precise, as well as more amenable to the incorporation of subsequent features. In particular, the paper presents analytical characterizations of the ergodic spectral efficiency of cellular networks with single-user multiple-input multiple-output and sectorization. These characterizations, in the form of easy-to-evaluate expressions, encompass the coverage, the distribution of spectral efficiency over the network locations, and the average thereof.

This paper investigates the feasibility of mmWave frequencies for personal networks of wireless wearable devices in enclosed settings (e.g., commuter trains, subways, airplanes, airports, or offices). At these frequencies, specular reflections off surfaces are expected to contribute to the capture of intended signal power and, simultaneously, to aggravate the interference at the receivers. Meanwhile, blockages by obstacles and people-including the individuals wearing the devices-are expected to shield receivers from interference. With the aid of stochastic geometry and random shape theory, we assess the interplay of surface reflections and blockages for dense deployments of wearable networks equipped with directional antenna arrays in relevant indoor settings.

This paper characterizes, through a stochastic geometry analysis, the increase in spectral efficiency that full-duplex transmission brings about in wireless networks. While, on isolated links, full-duplex promises a doubling of the spectral efficiency, in the context of a network this is weighted down by the corresponding rise in interference, and our characterization captures the balance of these effects. The analysis encompasses both the forward link (FL) and the reverse link (RL) with single-user and multiuser transmissions. And, as a complement to the analysis, Monte-Carlo simulations on a Vodafone LTE field test network are also presented. In the FL, the rise in interference is found to have minor impact and a doubling in spectral efficiency can indeed be approached, especially in microcellular networks. In the RL, however, a major difficulty arises in the form of exceedingly strong interference among base stations. This renders full-duplex transmission all but unfeasible in macrocellular networks (unless major countermeasures could be implemented) and undesirable in dense microcellular networks. Only in microcells with sufficient spacing among base stations does RL full-duplex pay off. Thus, full-duplex is seen not to blend easily with densification.

This paper investigates the design of precoders for single-user multiple-input multiple-output (MIMO) channels, and, in particular, for finite-alphabet signals. Based on an asymptotic expression for the mutual information of channels exhibiting line-of-sight components and rather general antenna correlations, precoding structures that decompose the general channel into a set of parallel subchannel pairs are proposed. Then, a low-complexity iterative algorithm is devised to maximize the sum mutual information of all pairs. The proposed algorithm significantly reduces the computational load of existing approaches with only minimal loss in performance. The complexity savings increase with the number of transmit antennas and with the cardinality of the signal alphabet, making it possible to support values thereof that were unmanageable with existing solutions. Most importantly, the proposed solution does not require instantaneous channel state information (CSI) at the transmitter, but only statistical CSI.

In this paper, we investigate the design of multiple-input multiple-output single-user precoders for finite-alphabet signals under the premise of statistical channel-state information at the transmitter. Based on an asymptotic expression for the mutual information of channels exhibiting antenna correlations, we propose a low-complexity iterative algorithm that radically reduces the computational load of existing approaches by orders of magnitude with only minimal losses in performance. The complexity savings increase with the number of transmit antennas and with the cardinality of the signal alphabet, making it possible to support values thereof that were unwieldy in existing solutions.

Device-to-device (D2D) communication allows serving local wireless traffic bypassing the system's infrastructure. One way to control the interference in D2D networks is to carefully channelize transmissions. This paper presents an analytical characterization of ITLinQ, one of the principal D2D channelization schemes proposed to date. Recognizing that it captures well the spatial characteristics of D2D networks, a stochastic geometry setting is utilized for this analysis. The derived expressions enable gleaning insights on how ITLinQ avoids situations of excessive interference, and they facilitate optimizing the controllable parameters of ITLinQ so as to maximize the system spectral efficiency (bits/s/Hz per unit area). With the parameters thus optimized, the ultimate performance of ITLinQ can be evaluated with respect to other D2D channel allocation schemes. In particular, performance evaluation comparisons with the FlashLinQ scheme are provided, and the gains with respect to an unchannelized network are quantified.

This paper addresses the benefits of introducing exclusion regions around both transmitters and receivers in D2D wireless networks. Such exclusion regions offer protection from interference at the expense of a sparser spatial reuse of spectrum, bringing about a tradeoff whose resolution entails optimizing the size of the exclusion regions as function of relevant system parameters. Our figure of merit for this optimization is the spectral efficiency. We first characterize this quantity for a given size of the exclusion regions, and then proceed to its optimization, altogether establishing the major benefits of incorporating properly sized exclusion regions in the applicable scheduling algorithms.

This paper presents a framework that enables characterizing analytically the spectral efficiency achievable by device-to-device (D2D) communication integrated with a cellular network. This framework is based on a stochastic geometry formulation with a novel approach to the modeling of interference and with the added possibility of incorporating exclusion regions to protect cellular receivers from excessive interference from active D2D transmitters. To illustrate the potential of the framework, a number of examples are provided. These examples confirm the potential of D2D communication in situations of strong traffic locality, as well as the effectiveness of properly sized exclusion regions.

This paper characterizes the performance of coordinated beamforming with dynamic clustering. A downlink model based on stochastic geometry is put forth to analyze the performance of such a base station (BS) coordination strategy. Analytical expressions for the complementary cumulative distribution function (CCDF) of the instantaneous signal-to-interference ratio (SIR) are derived in terms of relevant system parameters, chiefly the number of BSs forming the coordination clusters, the number of antennas per BS, and the pathloss exponent. Utilizing this CCDF, with pilot overheads further incorporated into the analysis, we formulate the optimization of the BS coordination clusters for a given fading coherence. Our results indicate that: 1) coordinated beamforming is most beneficial to users that are in the outer part of their cells yet in the inner part of their coordination cluster and that 2) the optimal cluster cardinality for the typical user is small and it scales with the fading coherence. Simulation results verify the exactness of the SIR distributions derived for stochastic geometries, which are further compared with the corresponding distributions for deterministic grid networks.

Capitalizing on the analytical potency of stochastic geometry and on some new ideas to model intercell interference, this paper presents analytical expressions that enable quantifying the spectral efficiency of interference alignment (IA) in cellular networks without the need for simulation. From these expressions, the benefits of IA are characterized. Even under favorable assumptions, IA is found to be beneficial only in very specific and relatively infrequent network situations, and a blanket utilization of IA is found to be altogether detrimental. Applied only in the appropriate situations, IA does bring about benefits that are significant for the users involved but relatively small in terms of average spectral efficiency for the entire system.

We present an analytical characterization of the spectral efficiency achievable by D2D communication links overlaid on a cellular network. The analysis relies on a stochastic geometry formulation with a novel approach to the modeling and spatial averaging of interference, which facilitates obtaining compact expressions. These expressions are then applied to gauge the potential of D2D communication, which is found to be very sizeable in situations of strong traffic locality.

This paper presents analytical expressions that enable quantifying the spectral efficiency of antenna-domain IA (interference alignment) in cellular networks. From these expressions, the benefits of such IA are characterized. Under rather favorable assumptions, IA is found to be beneficial only in very specific and relatively infrequent network situations, and a blanket utilization of IA is altogether detrimental. Applied only in the appropriate situations, IA does bring about benefits that are significant for the users involved but small in terms of average spectral efficiency for the entire network.

This paper characterizes the performance of coordinated beamforming with dynamic clustering. A downlink cellular model based on stochastic geometry is put forth to analyze the performance of such base station (BS) coordination strategy. Analytical expressions for the complementary cumulative distribution function (CCDF) of the instantaneous signal-to-interference ratio (SIR) are derived in terms of relevant system parameters, chiefly the number of coordinated BSs, the number of antennas, and the path-loss exponent. Utilizing this CCDF, with pilot overheads further incorporated into the analysis, we formulate the computation of the number of coordinated BSs that maximizes the spectral efficiency for a given fading coherence. The results make precise the intuition that the slower the fading, the more beneficial the coordination. Simulation results verify the exactness of the SIR distribution derived for stochastic geometries, which is further compared with the corresponding distribution for a deterministic grid model.

What will 5G be? What it will not be is an incremental advance on 4G. The previous four generations of cellular technology have each been a major paradigm shift that has broken backward compatibility. Indeed, 5G will need to be a paradigm shift that includes very high carrier frequencies with massive bandwidths, extreme base station and device densities, and unprecedented numbers of antennas. However, unlike the previous four generations, it will also be highly integrative: tying any new 5G air interface and spectrum together with LTE and WiFi to provide universal high-rate coverage and a seamless user experience. To support this, the core network will also have to reach unprecedented levels of flexibility and intelligence, spectrum regulation will need to be rethought and improved, and energy and cost efficiencies will become even more critical considerations. This paper discusses all of these topics, identifying key challenges for future research and preliminary 5G standardization activities, while providing a comprehensive overview of the current literature, and in particular of the papers appearing in this special issue.

This paper presents a new connection between the generalized Marcum-Q function and the confluent hypergeometric function of two variables, Φ3. This result is then applied to the closed-form characterization of the bivariate Nakagami-m distribution and of the distribution of the minimum eigenvalue of correlated noncentral Wishart matrices, both important in communication theory. New expressions for the corresponding cumulative distributions are obtained and a number of communication-theoretic problems involving them are pointed out.

The spectral efficiency achievable by interference alignment (IA) in a K-user multiple-input-multiple-output interference channel is studied in the face of time-selective continuous fading explicitly estimated through pilot-symbol observations. The robustness of IA in such operationally relevant conditions is assessed through a joint optimization of the pilot overhead and the IA update interval, which are characterized - in high-power conditions - as solutions of a fixed-point equation. Variations of the formulation are given for both frequency-division duplexing (FDD) and time-division duplexing, the former requiring explicit feedback of the fading estimates and the latter relying on fading reciprocity. For the FDD variation, analog feedback is considered. In addition to arbitrary numbers of users and antennas and arbitrary temporal fading correlation functions, the derivations accommodate forward and reverse links with asymmetric power levels.

The spectral efficiency achievable by IA (interference alignment) in a K-user interference channel is studied in the face of time-selective fading explicitly estimated through pilot-symbol observations. The robustness of IA is assessed through a joint optimization of the pilot overhead and the IA update interval, which are characterized-in the high-power conditions where IA is relevant-as solutions of a fixed-point equation.

This paper presents a new phase-alignment algorithm for distributed beamforming [1].1This scheme is more energy-efficient than existing solutions, since it requires each node to transmit only once, but it converges at least as fast as all other previously proposed algorithms. In contrast with all other studies of distributed beamforming, where noise had been neglected, here it is explicitly included in the signal model and shown to have a considerable impact on synchronization that cannot be ignored. With and without noise, analytical expressions that characterize the performance of the new algorithm are provided, with emphasis on various limiting regimes of interest.

This paper deals with the ergodic sum-rate of a proportional fair scheduler in wireless systems where both the base stations and the user terminals are equipped with multiantenna transceivers. The scheduling process is allowed to operate jointly over multiple parallel subchannels, e.g., those created by frequency-division multiaccess/multiplexing. Exact expressions are derived for arbitrary numbers of users and antennas, and arbitrary fading distributions. These results are then specialized to Rayleigh fading and, further, to the low- and high-power regimes. Informative expressions are also derived for the regime of large numbers of users.

This paper presents a new deterministic closed-loop phase-alignment algorithm based on quantized feedback from the receiver for distributed beamforming. In contrast with previously proposed methods, which entailed repeated transmissions from all the nodes in the network, this new algorithm requires each node to transmit only once during the synchronization cycle. This drastically reduces the amount of power consumed to achieve phase alignment, yet the new algorithm converges at least as fast as all other existing schemes. In contrast with previous analyses of distributed beamforming based on random phase updates, where noise had been disregarded, here it is explicitly included in the models and shown to have a considerable effect that cannot be ignored. With and without noise, analytical expressions that characterize the performance of the new algorithm are provided, with emphasis on various limiting regimes of interest.

The small carrier wavelength at millimeter-wave (mm-Wave) frequencies enables featuring a large number of co-located antennas. This paper exploits the potential of large antenna arrays to develop a low-complexity directional modulation technique, Antenna Subset Modulation (ASM), for point-to-point secure wireless communication. The main idea in ASM is to modulate the radiation pattern at the symbol rate by driving only a subset of antennas in the array. This results in a directional radiation pattern that projects a sharply defined constellation in the desired direction and expanded further randomized constellation in other directions. Two techniques for implementing ASM are proposed. The first technique selects an antenna subset randomly for every symbol. While randomly switching antenna subsets does not affect the symbol modulation for a desired receiver along the main direction, it effectively randomizes the amplitude and phase of the received symbol for an eavesdropper along a sidelobe. Using a simplified statistical model, an expression for the average uncoded symbol error rate (SER) is derived as a function of the observation angle. To overcome the problem of large sidelobes in random antenna subset switching, the second technique uses an optimized antenna subset selection procedure based on simulated annealing to achieve superior performance compared with random selection. Numerical comparisons of the SER performance and secrecy capacity of the proposed techniques against those of conventional array transmission are presented to highlight the potential of ASM.

Cooperation is viewed as a key ingredient for interference management in wireless networks. This paper shows that cooperation has fundamental limitations. First, it is established that in systems that rely on pilot-assisted channel estimation, the spectral efficiency is upper-bounded by a quantity that does not depend on the transmit powers; in this framework, cooperation is possible only within clusters of limited size, which are subject to out-of-cluster interference whose power scales with that of the in-cluster signals. Second, an upper bound is also shown to exist if the cooperation extends to an entire (large) system operating as a single cluster; here, pilot-assisted transmission is necessarily transcended. Altogether, it is concluded that cooperation cannot in general change an interference-limited network to a noise-limited one. Consequently, the existing literature that routinely assumes that the high-power spectral efficiency scales with the log-scale transmit power provides only a partial characterization. The complete characterization proposed in this paper subdivides the high-power regime into a degree-of-freedom regime, where the scaling with the log-scale transmit power holds approximately, and a saturation regime, where the spectral efficiency hits a ceiling that is independent of the power. Using a cellular system as an example, it is demonstrated that the spectral efficiency saturates at power levels of operational relevance.

Cooperation in a large wireless network with pilotassisted coherent communication is shown to have certain fundamental limitations, namely that even perfect cooperation cannot in general change an interference-limited network to a noise-limited one. Specifically, we demonstrate the existence of a spectral efficiency upper bound that does not grow with the transmit power, when channels are estimated via pilot signals. This is because pilot-assisted channel estimation is only possible within finite cooperation clusters, resulting in out-of-cluster interference that scales with the transmit power. Making the clusters excessively large can actually worsen this effect, nor does sidestepping the pilot-assisted channel estimation via noncoherent demodulation provide an escape. Using a cellular system as an example, it is demonstrated that the spectral efficiency saturates at power levels of operational relevance, indicating that the lackluster gains from cooperation observed in practice may be based on fundamental information-theoretic limitations, rather than current technology imperfections.

Bit and power loading schemes for a single-user multiple-input multiple-output channel using a minimum mean-square error receiver are proposed. Such schemes are derived without the benefit of instantaneous channel state information (CSI) at the transmitter, on the basis of only statistical CSI. They exploit the relationships between the average bit error probability at the receiver and the power allocation at the transmitter, in the context of spatially correlated Rayleigh fading. The performance of the proposed schemes is then benchmarked with respect to both non-adaptive transmission and to an adaptive counterpart with a zero-forcing receiver.

We present a method to compute, quickly and efficiently, the mutual information achieved by an independent identically distributed (IID) complex Gaussian signal on a block Rayleigh-faded channel without side information at the receiver. The method accommodates both scalar and multiple-input multiple-output (MIMO) settings. Operationally, this mutual information represents the highest spectral efficiency that can be attained using Gaussian codebooks. Examples are provided that illustrate the loss in spectral efficiency caused by fast fading and how that loss is amplified when multiple transmit antennas are used. These examples are further enriched by comparisons with the channel capacity under perfect channel-state information at the receiver, and with the spectral efficiency attained by pilot-based transmission.

Interference alignment (IA) is a cooperative transmission strategy that, under some conditions, achieves the interference channel's maximum number of degrees of freedom. Realizing IA gains, however, is contingent upon providing transmitters with sufficiently accurate channel knowledge. In this paper, we study the performance of IA in multiple-input multiple-output systems where channel knowledge is acquired through training and analog feedback. We design the training and feedback system to maximize IA's effective sum-rate: a non-asymptotic performance metric that accounts for estimation error, training and feedback overhead, and channel selectivity. We characterize effective sum-rate with overhead in relation to various parameters such as signal-to-noise ratio, Doppler spread, and feedback channel quality. A main insight from our analysis is that, by properly designing the CSI acquisition process, IA can provide good sum-rate performance in a very wide range of fading scenarios. Another observation from our work is that such overhead-aware analysis can help solve a number of practical network design problems. To demonstrate the concept of overhead-aware network design, we consider the example problem of finding the optimal number of cooperative IA users based on signal power and mobility.

In the context of fading channels it is well established that, with a constrained transmit power, the bit rates achievable by signals that are not peaky vanish as the bandwidth grows without bound. Stepping back from the limit, we characterize the highest bit rate achievable by such non-peaky signals and the approximate bandwidth where that apex occurs. As it turns out, the gap between the highest rate achievable without peakedness and the infinite-bandwidth capacity (with unconstrained peakedness) is small for virtually all settings of interest to wireless communications. Thus, although strictly achieving capacity in wideband fading channels does require signal peakedness, bit rates not far from capacity can be achieved with conventional signaling formats that do not exhibit the serious practical drawbacks associated with peakedness. In addition, we show that the asymptotic decay of bit rate in the absence of peakedness usually takes hold at bandwidths so large that wideband fading models are called into question. Rather, ultrawideband models ought to be used.

We present a method to compute, quickly and efficiently, the mutual information achieved by an IID (independent identically distributed) complex Gaussian input on a block Rayleigh-faded channel without side information at the receiver. The method accommodates both scalar and MIMO (multiple-input multiple-output) settings. Operationally, the mutual information thus computed represents the highest spectral efficiency that can be attained using standard Gaussian codebooks. Examples are provided that illustrate the loss in spectral efficiency caused by fast fading and how that loss is amplified by the use of multiple transmit antennas. These examples are further enriched by comparisons with the channel capacity under perfect channel-state information at the receiver, and with the spectral efficiency attained by pilot-based transmission.

The optimization of the pilot overhead in single-user wireless fading channels is investigated, and the dependence of this overhead on various system parameters of interest (e.g., fading rate, signal-to-noise ratio) is quantified. The achievable pilot-based spectral efficiency is expanded with respect to the fading rate about the no-fading point, which leads to an accurate order expansion for the pilot overhead. This expansion identifies that the pilot overhead, as well as the spectral efficiency penalty with respect to a reference system with genie-aided CSI (channel state information) at the receiver, depend on the square root of the normalized Doppler frequency. It is also shown that the widely-used block fading model is a special case of more accurate continuous fading models in terms of the achievable pilot-based spectral efficiency. Furthermore, it is established that the overhead optimization for multiantenna systems is effectively the same as for single-antenna systems with the normalized Doppler frequency multiplied by the number of transmit antennas.

In a MIMO layered architecture, several codewords are transmitted from a multiplicity of antennas. Although the spectral efficiency is maximized if the rates of these codewords are separately controlled, the feedback rate within the link adaptation loop is reduced if they are constrained to be identical. This poses a direct tradeoff between performance and feedback overhead. This paper provides analytical expressions that quantify the difference in spectral efficiency between both approaches for arbitrary numbers of antennas. Specifically, the characterization takes place in the realm of the low- and highpower regimes via expansions that are shown to have a wide range of validity. In addition, the possibility of adjusting the transmit power of each codeword individually is considered as an alternative to the separate control of their rates. Power allocation, however, turns out to be inferior to rate control within the context of this problem.

A contemporary perspective on transmit antenna diversity and spatial multiplexing is provided. It is argued that, in the context of most modern wireless systems and for the operating points of interest, transmission techniques that utilize all available spatial degrees of freedom for multiplexing outperform techniques that explicitly sacrifice spatial multiplexing for diversity. Reaching this conclusion, however, requires that the channel and some key system features be adequately modeled and that suitable performance metrics be adopted; failure to do so may bring about starkly different conclusions. As a specific example, this contrast is illustrated using the 3GPP long-term evolution system design.

A contemporary perspective on the tradeoff between transmit antenna diversity and spatial multiplexing is provided. It is argued that, in the context of modern cellular systems and for the operating points of interest, transmission techniques that utilize all available spatial degrees of freedom for multiplexing outperform techniques that explicitly sacrifice spatial multiplexing for diversity. Reaching this conclusion, however, requires that the channel and some key system features be adequately modeled; failure to do so may bring about starkly different conclusions. As a specific example, this contrast is illustrated using the 3GPP Long-Term Evolution system design.

For single-user MIMO communication with uncoded and coded QAM signals, we propose bit and power loading schemes that rely only on channel distribution information at the transmitter. To that end, we develop the relationship between the average bit error probability at the output of a ZF linear receiver and the bit rates and powers allocated at the transmitter. This relationship, and the fact that a ZF receiver decouples the MIMO parallel channels, allow leveraging bit loading algorithms already existing in the literature. We solve dual bit rate maximization and power minimization problems and present performance results that illustrate the gains of the proposed scheme with respect to a non-optimized transmission.

The spectral efficiency achievable with joint processing of pilot and data symbol observations is compared with that achievable through the conventional (separate) approach of first estimating the channel on the basis of the pilot symbols alone, and subsequently detecting the data symbols. Studied on the basis of a mutual information lower bound, joint processing is found to provide a non-negligible advantage relative to separate processing, particularly for fast fading. It is shown that, regardless of the fading rate, only a very small number of pilot symbols (at most one per transmit antenna and per channel coherence interval) should be transmitted if joint processing is allowed.

Expressions relating spectral efficiency, power and Doppler spectrum are derived for low-power Rayleigh- faded wireless channels with proper complex signaling. No side information on the state of the channel is assumed at the receiver. Rather, periodic reference signals are postulated in accordance with the functioning of most wireless systems. In contrast with most previous studies, which relied on block-fading channel models, a continuous-fading model is adopted. This embeds the Doppler spectrum directly in the derived expressions thereby imbuing them with practical significance.

This paper studies the fundamental operational limits of a class of Gaussian multicast channels with an interference setting. In particular, the paper considers two base stations multicasting separate messages to distinct sets of users. In the presence of channel state information at the transmitters and at the respective receivers, the capacity region of the Gaussian multicast channel with interference is characterized to within one bit. At the crux of this result is an extension to the multicast channel with interference of the Han-Kobayashi or the Chong-Motani-Garg achievable region for the interference channel.

This paper formulates power allocation policies that maximize the region of mutual informations achievable in multiuser downlink OFDM channels. Arbitrary partitioning of the available tones among users and arbitrary modulation formats, possibly different for every user, are considered. Two distinct policies are derived, respectively for slow fading channels tracked instantaneously by the transmitter and for fast fading channels known only statistically thereby. With instantaneous channel tracking, the solution adopts the form of a multiuser mercury/waterfilling procedure that generalizes the single-user mercury/waterfilling introduced in [1], [2]. With only statistical channel information, in contrast, the mercury/waterfllling interpretation is lost. For both policies, a number of limiting regimes are explored and illustrative examples are provided.

Expressions relating spectral efficiency, power, and Doppler spectrum, are derived for Rayleigh-faded wireless channels with Gaussian signal transmission. No side information on the state of the channel is assumed at the receiver. Rather, periodic reference signals are postulated in accordance with the functioning of most wireless systems. The analysis relies on a well-established lower bound, generally tight and asymptotically exact at low SNR. In contrast with most previous studies, which relied on block-fading channel models, a continuous-fading model is adopted. This embeds the Doppler spectrum directly in the derived expressions, imbuing them with practical significance. Closed-form relationships are obtained for the popular Clarke-Jakes spectrum and informative expansions, valid for arbitrary spectra, are found for the low- and high-power regimes.While the paper focuses on scalar channels, the extension to multiantenna settings is also discussed.

This paper presents a low-complexity iterative algorithm for long-term transmit beamforming in multicast channels. Since it relies only on antenna correlations at the base station, the algorithm requires only infrequent feedback from the users. Multiantenna receivers are not required, but they are transparently accommodated. Simulation results within the context of the UMTS long-term evolution system indicate that, with half-wavelength antenna spacings and a typical power azimuth spectrum, the performance is close to optimal. The specific gains (in average SINR) then depend on the number of transmit antennas and the number of active users.

This paper analyzes the capacity of MIMO channels in the presence of both antenna correlation and co-channel interference. We investigate the optimization of the input covariance, characterize the optimality of beamforming, and study the behavior of the input covariance in the low and high-power regimes. For the special case of separable correlations, we also derive analytical expressions for the key statistical properties of the spectral efficiency achievable with an arbitrary input covariance. Altogether, our analysis enables assessing the joint impact of correlation and interference on the capacity of multiantenna architectures in a cellular system.

This paper formulates the power allocation policy that maximizes the region of ergodic mutual informations achievable in multiuser downlink OFDM channels known only statistically by the base station. Arbitrary partitioning of the available tones among users and arbitrary modulation formats, possibly different for every user, are considered. The derivation relies on the nexus between the mutual information of Gaussian channels and the minimum mean-square error incurred in the nonlinear estimation of the transmit constellation points given their noisy receive observations.

This paper characterizes the marginal probability density function of an unordered eigenvalue of finite-dimensional random matrices of particular interest in MIMO (multiple-input multiple-output) wireless communications. Specifically, a technique is presented for deriving the eigenvalue statistics in one-side correlated Rayleigh-faded channels and in Ricean-faded channels, with or without cochannel interferers. The exact expressions found turn out to be extremely useful in calculating information-theoretic quantities. As an application, we calculate the ergodic mutual information for all the abovementioned channel fading conditions, obtaining a closed form formula for the Rayleigh case and, in turn, a series expression for the Ricean faded one.

The mutual information of independent parallel Gaussian-noise channels is maximized, under an average power constraint, by independent Gaussian inputs whose power is allocated according to the waterfilling policy. In practice, discrete signaling constellations with limited peak-to-average ratios (m-PSK, m-QAM, etc.) are used in lieu of the ideal Gaussian signals. This paper gives the power allocation policy that maximizes the mutual information over parallel channels with arbitrary input distributions. Such policy admits a graphical interpretation, referred to as mercury/waterfilling, which generalizes the waterfilling solution and allows retaining some of its intuition. The relationship between mutual information of Gaussian channels and nonlinear minimum mean-square error (MMSE) proves key to solving the power allocation problem.

We characterize the capacity-achieving input covariance for multi-antenna channels known instantaneously at the receiver and in distribution at the transmitter. Our characterization, valid for arbitrary numbers of antennas, encompasses both the eigenvectors and the eigenvalues. The eigenvectors are found for zero-mean channels with arbitrary fading profiles and a wide range of correlation and keyhole structures. For the eigenvalues, in turn, we present necessary and sufficient conditions as well as an iterative algorithm that exhibits remarkable properties: universal applicability, robustness and rapid convergence. In addition, we identify channel structures for which an isotropic input achieves capacity.

This paper studies the Doppler sensitivity of the link reciprocity provided by TDD in a MIMO (multiple-input multiple-output) system. Assuming perfect channel state information at the receiver, the knowledge at the transmitter is restricted to the channel probability distribution as well as its precise value at the uplink/downlink switch instant. Doppler sensitivity is studied by means of the rate loss within the time window spanned by two consecutive switch instants

In the high-SNR regime, the multi-antenna mutual information behaves as an affine function of SNR|/sub dB/, described by the multiplexing gain, which quantifies the multiplicative increase as function of the number of antennas, and the power offset (zero-order term in dB). The conventional high-SNR analysis that considers only the multiplexing gain is unable to assess the impact of channel features such as the Rician factor since, irrespective thereof, the multiplexing gain equals the minimum of the number of transmit and receive antennas. The impact of the Rician factor at high SNR can be conveniently quantified through the corresponding power offset, which this paper evaluates in closed-form.

For parallel independent Gaussian-noise channels with an aggregate power constraint, independent Gaussian inputs whose powers are allocated according to the waterfilling policy maximize the sum mutual information. In practice, however, discrete signalling constellations such as m-PSK or m-QAM are used in lieu of the ideal Gaussian signals. This paper gives the power allocation policy, referred to as mercury/waterfilling, that maximizes the sum mutual information over parallel channels with arbitrary input constellations

The analysis of the multiple-antenna capacity in the high-SNR regime has hitherto focused on the high-SNR slope (or maximum multiplexing gain), which quantifies the multiplicative increase as a function of the number of antennas. This traditional characterization is unable to assess the impact of prominent channel features since, for a majority of channels, the slope equals the minimum of the number of transmit and receive antennas. Furthermore, a characterization based solely on the slope captures only the scaling but it has no notion of the power required for a certain capacity. This paper advocates a more refined characterization whereby, as a function of SNR|/sub dB/, the high-SNR capacity is expanded as an affine function where the impact of channel features such as antenna correlation, unfaded components, etc., resides in the zero-order term or power offset. The power offset, for which we find insightful closed-form expressions, is shown to play a chief role for SNR levels of practical interest.

This paper applies random matrix theory to obtain analytical characterizations of the capacity of correlated multiantenna channels. The analysis is not restricted to the popular separable correlation model, but rather it embraces a more general representation that subsumes most of the channel models that have been treated in the literature. For arbitrary signal-to-noise ratios (SNR), the characterization is conducted in the regime of large numbers of antennas. For the low- and high-SNR regions, in turn, we uncover compact capacity expansions that are valid for arbitrary numbers of antennas and that shed insight on how antenna correlation impacts the tradeoffs among power, bandwidth, and rate.

Abstract Coexistence of different access technologies, hierarchical cellular deployment, a wide variety of data services, requirements for transparent operation across different technologies, adaptivity to varying network conditions and mobility and quality of service (QoS) constraints introduce a number of challenges in the design of future generation systems and the specification of new air interfaces, such as efficiency and flexibility in the utilization of spectrum, dynamic resource allocation and exploitation of the multiuser diversity and reconfigurable interference management and inter‐cell coordination. In this paper, three critical issues for the design of next generation systems are addressed: (i) duplexing, (ii) scheduling and resource allocation and (iii) interference and inter‐cell coordination. A number of research directions are presented, which constitute promising potential candidates for next generation systems specification. Copyright © 2005 John Wiley & Sons, Ltd.

In single-user MIMO communication, the first-order throughput scaling is determined by the smaller of the number of transmit and receive antennas. This typically renders terminals the constraining bottleneck. In a multiuser downlink, this bottleneck can be bypassed by having the base station communicate with multiple terminals simultaneously, whereby the receive antennas at those terminals are effectively pooled in term of throughput scaling. This, however, requires that the base has instantaneous channel information. Without such information, the structure and statistics of the channel can be exploited to form multiple simultaneous beams towards the various users, but these beams are in general mutually interfering. Focusing on UMTS HSDPA (high-speed downlink packet access), the paper proposes the use of multiuser detection to discriminate the signals conveyed over interfering beams. This approach is formulated and experimentally evaluated on an HSDPA MIMO testbed that involves a commercial base station, multi-antenna terminals, and custom ASICs.

In this paper, the high-SNR multiantenna capacity with coherent receivers on the multiplexing gain, i.e., the multiplicative increase as function of the number of antennas is analyzed. For most channels of interest, such multiplexing gain equals the minimum of the number of transmit and receive antennas. This traditional characterization, however, is unable to quantify the impact of many relevant channel features. As a function of SNR, the capacity is very well approximated, from moderate SNR on, as an affine function. The impact of the various channel features is captured in the power offset (in dB) or zero-order term in the affine expansion.

We characterize the power allocation that maximizes the rate per unit bandwidth supported with arbitrary reliability over single-user multiantenna channels known instantaneously by the receiver and in distribution by the transmitter. The characterization is valid for arbitrary channels and numbers of antennas. Although, in general, it leads to a fixed-point solution, at low and high signal-to-noise it provides explicit allocations. For arbitrary signal-to-noise ratios, we present an iterative algorithm that exhibits remarkable properties: robustness, rapid convergence and universal applicability. Further, when applied to the proper set of signalling eigenvectors, the algorithm converges to the power allocation that attains capacity.

We present an analytical characterization of multi-antenna capacity in the limit of a large number of antennas. In contrast to previous studies, the entries of the channel matrix are not restricted to be identically distributed, thus incorporating diversity mechanisms that are otherwise excluded, such as those based on the use of antennas with distinct polarizations and radiation patterns. In addition to the capacity, first-order expressions in the low- and high-power regimes are also evaluated both asymptotically and non-asymptotically.

This paper provides analytical characterizations of the impact on the multiple-antenna capacity of several important features that fall outside the standard multiple-antenna model, namely: (i) antenna correlation, (ii) Ricean factors, (iii) polarization diversity, and (iv) out-of-cell interference; all in the regime of low signal-to-noise ratio. The interplay of rate, bandwidth, and power is analyzed in the region of energy per bit close to its minimum value. The analysis yields practical design lessons for arbitrary number of antennas in the transmit and receive arrays.

The simultaneous use of multiple transmit and receive antennas can unleash very large capacity increases in rich multipath environments. Although such capacities can be approached by layered multiantenna architectures with per-antenna rate control, the need for short-term feedback arises as a potential impediment, in particular as the number of antennas - and, thus, the number of rates to be controlled - increases. What we show, however, is that the need for short-term feedback in fact vanishes as the number of antennas and/or the diversity order increases. Specifically, the rate supported by each transmit antenna becomes deterministic and a sole function of the signal-to-noise ratio of transmit and receive antennas, and the decoding order, all of which are either fixed or slowly varying. More generally, we illustrate - through this specific derivation - the relevance of some established random code-division multiple-access results to the single-user multiantenna problem.

The improvement in downlink throughput attained by using dual-antenna terminals is quantified within then context of UMTS high-speed packet access. It is shown that, with simple linear combining, the increase in average throughput resulting from the addition of a second terminal antenna ranges within 16-70%. Moreover, since only receiver processing is involved, no standardization is required.

The Bell Labs layered space-time (BLAST) architecture provides high capacity wireless communications in rich scattering environments. Training sequences are transmitted periodically to estimate the channel. We compare the performance without channel tracking to the performance with interpolation-based channel tracking. The optimal training interval, training length and the maximum throughput are found through computer simulations for both schemes as functions of the Doppler frequency and the number of antennas. We show that the throughput is significantly improved with channel tracking for fast continuous fading channels. However channel tracking increases the computation complexity for data detection. A sub-optimal detection algorithm with lower complexity is proposed and investigated.

In emerging mobile systems users must operate very often in the low-power regime. Specifically, almost 40 % of geographical locations experience signal-to-noise ratios (SNR) below 0 dB. Despite its relevance, the multi-antenna low-power regime had not been analyzed in depth until the paper by S. Verdu (see IEEE Trans. on Inform. Theory, p.1319-43, June 2002), where the figure of merit is not the SNR, but rather the normalized energy per information bit, E/sub b//N/sub 0/. This paper expands these findings using a channel model that realistically describes the conditions found in typical wireless systems. The focus is on channels that are known to the receiver, but unknown to the transmitter.

We propose a new Automatic Retransmission reQuest (ARQ) scheme for MIMO systems with multiple transmit and receive antennas. The substreams emitted from various transmit antennas encounter distinct propagation channels and thus have different error statistics. Using per-antenna encoders, separating ARQ processes among the substreams results in a throughput improvement. Moreover, it facilitates the interference cancellation in certain MIMO techniques. Quantitative results from UMTS simulations demonstrate that the proposed multiple ARQ structure yields more than 30% gain in link throughput.

Multiple input multiple output (MIMO) systems can provide high data rate wireless services in a rich scattering environment. We study one of the proposals for MIMO systems, the Bell Labs Layered Space-Time (BLAST) architecture. Channel estimation using training sequences is required for coherent detection in BLAST. We apply the maximum likelihood (ML) channel estimator and the optimal training sequences for block flat fading channels to continuous flat fading channels, and analyze the estimation error. The optimal training length and training interval that maximize the throughput for a given target bit error rate are found by computer simulations as functions of the Doppler frequency and the number of antennas.

Results in information theory have demonstrated the enormous potential of wireless communication systems with antenna arrays at both the transmitter and receiver. To exploit this potential, a number of layered space-time architectures have been proposed. These layered space-time systems transmit parallel data streams, simultaneously and on the same frequency, in a multiple-input multiple-output fashion. With rich multipath propagation, these different streams can be separated at the receiver because of their distinct spatial signatures. However, the analysis of these techniques presented thus far had mostly been strictly narrowband. In order to enable high-data-rate applications, it might be necessary to utilize signals whose bandwidth exceeds the coherence bandwidth of the channel, which brings in the issue of frequency selectivity. In this paper, we present a class of layered space-time receivers devised for frequency-selective channels. These new receivers, which offer various performance and complexity tradeoffs, are compared and evaluated in the context of a typical urban channel with excellent results.

The capacity of wireless communication architectures equipped with multiple transmit and receive antennas and impaired by both noise and cochannel interference is studied. We find a closed-form solution for the capacity in the limit of a large number of antennas. This asymptotic solution, which is a sole function of the relative number of transmit and receive antennas and the signal-to-noise and signal-to-interference ratios (SNR and SIR), is then particularized to a number of cases of interest. By verifying that antenna diversity one can substitute for time and/or frequency diversity at providing ergodicity, we show that these asymptotic solutions approximate the ergodic capacity very closely even when the number of antennas is very small.

The ever growing need for higher capacity in wireless systems has fueled the interest in exploiting the spatial dimension-through the use of antennas arrays-to improve the utilization of the available radio spectrum. As a result, a large number of space-time techniques have been proposed wherein arrays are used to mitigate interference and enhance signal levels. More recently, information theory has shown that, with spatial data multiplexing, very large spectral efficiencies can be attained in multipath channels using transmit and receive antenna arrays. In this paper, the system benefit of using transmit and receive arrays in multicell scenarios is evaluated as a function of both the propagation environment and the number of antennas. Our results confirm the potential for very large system spectral efficiencies associated with the use of transmit and receive arrays, in particular in interference-limited rich-multipath conditions wherein the ability to perform interference mitigation-leading to tighter spectral reuse-and spatial data multiplexing grows with the number of antennas. In environments free of multipath, the potential is smaller but still very significant, associated with interference mitigation and signal enhancement only, since spatial data multiplexing is no longer possible.

Multiple-input-multiple-output (MIMO) systems can provide high data rate wireless services in a rich scattering environment. We study one of the proposals for MIMO systems, the Bell Labs Layered Space-Time (BLAST) architecture. Channel estimation using training sequences is required for coherent detection in BLAST. We apply the maximum-likelihood channel estimator and the optimal training sequences for block flat fading channels to continuous flat fading channels and analyze the estimation error. The optimal training length and training interval that maximize the throughput for a given target bit error-rate are found by computer simulations as functions of the Doppler frequency and the number of antennas.

Using multiple antennas at both the transmitter and receiver, BLAST exploits the multipath characteristics of a wireless channel to enable very large capacities. In an extended V-BLAST, those capacities are approached with single-dimensional coding and rate adaptation. However, such results require infinite precision for rate and power, which is not implementable due to a limited data rate on the feedback channel. We show that discretizing the rates to QAM constellations causes a 3-5 dB penalty. We then propose SQPC (successive quantization with power control), an ad-hoc low-complexity algorithm that reduces that penalty to 1-3 dB. We show that SQPC closely approaches the performance limit attainable with discrete QAM rates, which we find numerically by exhaustive search.

Multiple antennas at the transmitter and receiver can achieve enormous capacities by transmitting on the channel's eigenmodes when the channel realization is known at the transmitter. If the transmitter has no knowledge of the channel, a significant fraction of the eigenmode capacity can be achieved in an open-loop mode, but multi-dimensional coding is required. We show how the open-loop capacity can be achieved with conventional single-dimensional coding using optimum successive decoding (OSD) and simple per-antenna rate control. Using power allocation, the capacity can be further increased, although only slightly.

In BLAST (Bell-Laboratories LAyered Space Time) systems, multiple transmit and receive antennas are employed to achieve very high spectral efficiencies. The ideal detection method for such systems is the maximum-likelihood (ML) algorithm. However, the ML complexity increases exponentially with the number of transmit antennas and the number of bits per modulation symbol. A reduced-complexity detection method has been suggested, using ordered successive interference cancellation. We consider two other suboptimum techniques: channel-based adaptive group detection and multistep reduced-constellation detection. The goal is to reduce the two aforementioned complexity exponentials. The algorithms efficiently combine linear processing with local ML search. We limit the complexity by maintaining small ML searching areas, while maximizing the performance under the complexity constraint by optimizing the front-end linear processing and the selection of the search areas.

Recent information-theory results have shown the enormous capacity potential of wireless techniques that use transmit and receive antenna arrays. As a result, a number of layered space-time (BLAST) architectures have been proposed wherein multiple data streams are transmitted in parallel and separated at the receiver on account of their distinct spatial signatures. While extremely promising, all analysis of BLAST to date were restricted to the context of a single-user link. In this paper, the system-level benefit of using BLAST in multicell scenarios is evaluated in comparison with other directive- and adaptive-array techniques.

Multiple antennas and space-time processing are known to provide large link-level capacity gains. In this paper we investigate the system-level impact of using antenna arrays with up to four elements at both transmitter and receiver in a downlink CDMA system. We consider the following uses for multiple transmit antennas: transmit diversity, BLAST transmission (using the same spreading code to radiate independent data from different antennas), and directive beam steering. Link spectral efficiencies can be calculated as a function of the required signal-to-interference ratio (SIR) assuming capacity-approaching channel coding. System-level simulations provide the probability distribution of received SIR in a multi-cell CDMA system, with and without directive beam steering. The link- and system-level results are then combined to yield the number of equal-rate users that can be supported at some outage level. We show that 10 fold increases in user capacity can be achieved when up to four antenna elements are used on each side of the link (assuming a per-user rate of 1.25 Mbps and a 5-MHz bandwidth).

With the evolution of analog mobile wireless communications systems into digital second- and third-generation systems, there is growing interest in finding more efficient ways of managing the available resources, in particular radio spectrum and power. In the context of time division multiple access (TDMA) systems, this interest has led to the development of a variety of dynamic channel assignment (DCA) and power control (PC) algorithms. Despite the intense activity in both the DCA and the PC arenas and some proposals for combined DCA with PC, not enough work has been devoted to effectively integrating them. In this paper, the integration of DCA and PC is investigated and a family of integrated algorithms is presented. These algorithms, fully distributed and cost adaptive, achieve capacity levels significantly higher than those of a system with only DCA or PC. With respect to a system without any DCA or PC, several-fold capacity increases are obtained. Furthermore, these capacity levels are attained with user mobility included in the analysis.

Mobile stations in high-tier personal communication systems (PCS) are intended for high-mobility users and must thus operate in fast-changing environments. The first generation of high-tier PCS in North-America will be based on existing digital cellular standards including IS-136 TDMA. Moreover dual-band receivers will be needed for extensive roaming. Adaptive equalizers were devised for IS-54/IS-136 at 800 MHz. Achieving similar performance at 1.9 GHz, however, represents a new challenge because of the increased Doppler shifts. We present a robust MISE receiver with per-survivor-processing (PSP) which meets the TIA requirements in both bands and is scalable in terms of performance versus complexity.