In this paper, we develop and evaluate compressed representations of the channels between a massive MIMO base station and user terminals that can serve as attractive proxies in the context of various applications of interest. To create such compressed channel representations (CCRs), channel features are first extracted from wideband massive-MIMO channel data via appropriate preprocessing in the angular-delay domain. PCA-based and UMAP-based embedding algorithms are then applied to the extracted feature sets to generate the CCR embeddings. We evaluate CCRs in the context of two use cases - user localization and beam tracking - using neural networks. Our ray-tracing based simulation reveals that the proposed CCRs are attractive candidates for these tasks, in terms of not only computation and memory usage but also task performance.

Gains in area spectral efficiency have been recently demonstrated in networks of large-antenna arrays by means of fog massive MIMO operation and virtual sector-based processing. In this paper, we adopt such sector-based processing and operation to localize users in the network. We investigate the viability of some widely used supervised-learning methods in estimating user locations by observing across the fog massive MIMO network signals transmitted by the users. In particular, we evaluate linear regression (LR), weighted K-nearest neighbors (WKNN), and neural networks (NN) in the context of a network of massive-antenna remote-radio heads (RRHs) using simulations based on a spatially consistent channel model. As our simulations reveal, NN-based location estimators trained with user-sector channel gains could be a viable approach for providing user location information at the network side.

The decoupling of data and control planes in the forthcoming 5G wireless networks enables the efficient implementation of multitier architectures, where coverage and connectivity are guaranteed by top-tier macro-cells, and at the same time, high throughput and low latency are achieved locally by lower tiers in the hierarchy. This paper considers a new architecture for such lower tiers, dubbed fog massive multi-in multi-out (MIMO), where the user equipment (UE) nodes can connect to a “fog” of the densely deployed remote radio heads (RRHs) in a seamless, user-centric, and opportunistic manner. In the case of dense small cells, traditional cellular architectures inherently give rise to frequent handovers and pilot sequence re-assignments, which typically incur excessive protocol overhead and latency. In the proposed fog massive MIMO architecture, the UEs implicitly associate themselves with the most convenient RRHs in a completely autonomous manner. Each UE makes use of the uplink pilot sequences that contain equal-weight codewords and enable a novel “on-the-fly” pilot contamination control mechanism. We analyze the spectral efficiency and the outage probability of the proposed architecture via stochastic geometry, using some recent results on unique coverage in Boolean models, and provide a detailed comparison with the benchmark represented by massive MIMO cellular system with a genie-aided minimum-distance user-cell association. Our analysis, corroborated by extensive system simulation, reveals that there exists a “sweet spot” of the per pilot user load (number of users per pilot), such that the proposed system achieves a spectral efficiency close to that of the genie-aided cellular system. In these conditions, it is possible to achieve the low protocol overhead and low user RRH association latency promised by the fog massive MIMO at virtually no significant performance cost in terms of system spectral efficiency with respect to the cellular benchmark.

We investigate the viability of using machine-learning techniques for estimating user-channel features at a large-array base station (BS). In the scenario we consider, user-pilot broadcasts are observed and processed by the BS to extract angle-of-arrival (AoA) specific information about propagation-channel features, such as received signal strength and relative path delay. The problem of interest involves using this information to predict the angle-of-departure (AoD) of the dominant propagation paths in the user channels, i.e., channel features not directly observable at the BS. To accomplish this task, the data collected in the same propagation environment are used to train neural networks. Our studies rely on ray-tracing channel data that have been calibrated against measurements from Shinjuku Square, a famous hotspot in Tokyo, Japan. We demonstrate that the observed features at the BS side are correlated with the angular features at the user side. We train neural networks that exploit different combinations of measured features at the BS to infer the unknown parameters at the users. The evaluation based on standard statistical performance metrics suggests that such data-driven methods have the potential to predict unobserved channel features from observed ones.

In this paper we consider a densely deployed phantom cell system providing a low latency network for massive connectivity. Non-orthogonal pilot designs serve as a promising solution to support a large number of users but fast collision detection at the receiver is needed for low latency networks. Recently, a nice solution based on an on-off type non-orthogonal pilot design with collision detection capability has been proposed. It can serve more users than the orthogonal pilot design but at the cost of degraded channel estimation performance compared to the orthogonal optimum pilot design. We propose a new non-orthogonal pilot design with collision detection capability and improved channel estimation performance. An optimum threshold based detection criteria is developed. We further show that a dynamically calculated optimum threshold outperforms a fixed threshold based detection. Next, we investigated non-orthogonal pilot designs for fractional bandwidth allocation. We propose two new non-orthogonal pilot designs for physical resource block (PRB) based resource allocation. Both of the new designs support fast collision detection at the receiver. Performance evaluation results show that the proposed schemes provide equivalent or better channel estimation performance and support much more users than the orthogonal pilots defined in the current 4G standards.

The overheads associated with feedback-based channel acquisition can greatly compromise the achievable rates of FDD based massive MIMO systems. Indeed, downlink (DL) training and uplink (UL) feedback overheads scale linearly with the number of base station (BS) antennas, in sharp contrast to TDD-based massive MIMO, where a single UL pilot trains the whole BS array. In this work, we propose a graph-theoretic approach to reducing DL training and UL feedback overheads in FDD massive MIMO systems. In particular, we consider a single-cell scenario involving a single BS with a massive antenna array serving to single-antenna mobile stations (MSs) in the DL. We assume the BS employs two-stage beamforming in the DL, comprising DFT pre-beamforming followed by MU-MIMO precoding. The proposed graph-theoretic approach exploits knowledge of the angular spectra of the BS-MS channels to construct DL training protocols with reduced overheads. Simulation results reveal that the proposed training-resources allocation method can provide approximately 35% sum-rate performance gain compared to conventional orthogonal training. Our analysis also sheds light into the impact of overhead reduction on channel estimation quality, and, in turn, achievable rates.

We propose a novel scheme for estimating the large- scale gains of the channels between user terminals (UTs) and base stations (BSs) in a cellular system. The scheme leverages TDD operation, uplink (UL) training by means of properly designed non-orthogonal pilot codes, and massive antenna arrays at the BSs. Subject to Q resource elements allocated for UL training and using the new scheme, a BS is able to estimate the large- scale channel gains of K users transmitting UL pilots in its cell and in nearby cells, provided K ≤Q2. Such knowledge of the large-scale channel gains of nearby out-of-cells users can be exploited at the BS to mitigate interference to the out-of-cell users that experience the highest levels of interference from the BS. We investigate the large-scale gain estimation performance provided by a variety of non-orthogonal pilot codebook designs. Our simulations suggest that among all the code designs considered, Grassmannian line-packing type codes yield the best large-scale channel gain estimation performance.

This paper considers a new architecture underpinned by on-the-fly pilot contamination control, termed Fog massive MIMO, where the users are able to establish high-throughput and low-latency data links in a seamless and opportunistic manner, as they travel through a dense fog of high capacity Remote Radio Heads (RRHs). Utilizing some recent results on unique coverage in Boolean models, we analyze the spectral efficiency and outage probability of the proposed architecture via stochastic geometry. Our analysis, supported by extensive system simulation, reveals that there exists a “sweet spot” of the per-pilot user load (number of users per pilot), such that the proposed system achieves spectral efficiency close to that of the ideal cellular massive MIMO baseline, while exhibiting the simplicity and low-latency of completely user-centric operations.

In this paper, we consider a device-to-device communication network in which K transmitter-receiver pairs are sharing spectrum with each other. We propose a novel but simple binary scheduling scheme for this network to maximize the average sum rate of the pairs. According to the scheme, each receiver predicts its Signal-to-Interference-plus-Noise Ratio (SINR), assuming all other user pairs are active, and compares it to a preassigned threshold to decide whether its corresponding transmitter to be activated or not. For our proposed scheme, the optimal threshold that maximizes the expected sum rate is obtained analytically for the two user-pair case and empirically in the general K user-pair case. Simulation results reveal that our proposed SINR-threshold scheduling scheme outperforms ITLinQ [1], FlashLinQ [2] and the method presented in [3] in terms of the expected sum rate (network throughput). In addition, the computational complexity of the proposed scheme is O(K), outperforming both ITLinQ and FlashLinQ that have O(K^2) complexity requirements. Moreover, we also discuss the application of our proposed new scheme into an operator-assisted cellular D2D heterogeneous network.

We consider a single-cell scenario involving a single base station (BS) with a massive array serving multi-antenna terminals in the downlink of a mmWave channel. We present a class of multiuser MIMO schemes, which rely on uplink training from the user terminals, and on uplink/downlink channel reciprocity. The BS employs virtual sector-based processing according to which, user-channel estimation and data transmission are performed in parallel over non-overlapping angular sectors. The uplink training schemes we consider are non-orthogonal, that is, we allow multiple users to transmit pilots on the same pilot dimension (thereby potentially interfering with one another). Elementary processing allows each sector to determine the subset of user channels that can be resolved on the sector (effectively pilot contamination free) and, thus, the subset of users that can be served by the sector. This allows resolving multiple users on the same pilot dimension at different sectors, thereby increasing the overall multiplexing gains of the system. Our analysis and simulations reveal that, by using appropriately designed directional training beams at the user terminals, the sector-based transmission schemes we present can yield substantial spatial multiplexing and ergodic user-rates improvements with respect to their orthogonal-training counterparts.

Hybrid preprocessing, where an analogue preprocessing matrix is used to feed a large antenna array to fewer up/down-conversion chains, helps reduce hardware cost of massive Multiple-Input-Multiple-Output systems. Here, we analyze a variant of hybrid preprocessing, namely hybrid preprocessing with selection (HPwS), as an attractive solution to reduce this hardware cost while retaining good performance. In HPwS, the preprocessing matrix, built from radio frequency (RF) hardware, has a larger number of input ports L than the number of up/down-conversion chains K. A bank of RF switches connects the instantaneously best K input ports to the up/down-conversion chains. The preprocessor is designed based on average channel statistics and therefore needs to be updated only infrequently. This allows for a higher diversity-order and/or simpler RF hardware than some conventional hybrid preprocessing systems. In this paper, we propose a generic architecture of HPwS with a possibly non-unitary rectangular preprocessing matrix. A novel preprocessor is designed that maximizes a capacity lower bound for channels with isotropic scattering. In addition, we study how L, the number of users, and the use of low complexity RF hardware, such as discrete phase-shifters, impact system performance. We also present a method to extend the preprocessor design to anisotropic channels.

Technologies for millimeter wave (mmWave) communication are at the forefront of investigations in both industry and academia, as the mmWave band offers the promise of orders of magnitude additional available bandwidths to what has already been allocated to cellular networks. The much larger number of antennas that can be supported in a small footprint at mmWave bands can be leveraged to harvest massive-MIMO type beamforming and spatial multiplexing gains. Similar to Long-Term Evolution (LTE) systems, two prerequisites for harvesting these benefits are detecting users and acquiring user channel state information (CSI) in the training phase. However, due to the fact that mmWave channels encounter much harsher propagation and decorrelate much faster, the tasks of user detection and CSI acquisition are both imperative and much more challenging than in LTE bands. In this paper, we investigate the problem of fast user detection and CSI acquisition in the downlink of small cell mmWave networks. We assume Time Division Duplex (TDD) operation and channel-reciprocity based CSI acquisition. To achieve densification benefits we propose pilot designs and channel estimators that leverage a combination of aggressive pilot reuse with fast user detection at the base station and compressed sensing channel estimation. As our simulations show, the number of users that can be simultaneously served by the entire mmWave-band network with the proposed schemes increases substantially with respect to traditional compressed sensing based approaches with conventional pilot reuse.

Dense large-scale antenna deployments are one of the most promising technologies for delivering very large throughputs per unit area in the downlink (DL) of cellular networks. We consider such a dense deployment involving a distributed system formed by multi-antenna remote radio head (RRH) units connected to the same fronthaul serving a geographical area. Knowledge of the DL channel between each active user and its nearby RRH antennas is most efficiently obtained at the RRHs via reciprocity based training, that is, by estimating a user's channel using uplink (UL) pilots transmitted by the user, and exploiting the UL/DL channel reciprocity. We consider aggressive pilot reuse across an RRH system, whereby a single pilot dimension is simultaneously assigned to multiple active users. We introduce a novel coded pilot approach, which allows each RRH unit to detect pilot collisions, i.e., when more than a single user in its proximity uses the same pilot dimensions. Thanks to the proposed coded pilot approach, pilot contamination can be substantially avoided. As shown, such a strategy can yield densification benefits in the form of increased multiplexing gains per UL pilot dimension with respect to conventional reuse schemes and some recent approaches assigning pseudorandom pilot vectors to the active users.

For the development of new 5G systems to operate in bands up to 100 GHz, there is a need for accurate radio propagation models at these bands that currently are not addressed by existing channel models developed for bands below 6 GHz. This document presents a preliminary overview of 5G channel models for bands up to 100 GHz. These have been derived based on extensive measurement and ray tracing results across a multitude of frequencies from 6 GHz to 100 GHz, and this document describes an initial 3D channel model which includes: 1) typical deployment scenarios for urban microcells (UMi) and urban macrocells (UMa), and 2) a baseline model for incorporating path loss, shadow fading, line of sight probability, penetration and blockage models for the typical scenarios. Various processing methodologies such as clustering and antenna decoupling algorithms are also presented.

Two key traits of 5G cellular networks are much higher base station (BS) densities-especially in the case of low-power BSs-and the use of massive MIMO at these BSs. This paper explores how massive MIMO can be used to jointly maximize the offloading gains and minimize the interference challenges arising from adding small cells. We consider two interference management approaches: joint transmission (JT) with local precoding, where users are served simultaneously by multiple BSs without requiring channel state information exchanges among cooperating BSs, and resource blanking, where some macro BS resources are left blank to reduce the interference in the small cell downlink. A key advantage offered by massive MIMO is channel hardening, which enables to predict instantaneous rates a priori. This allows us to develop a unified framework, where resource allocation is cast as a network utility maximization (NUM) problem, and to demonstrate large gains in cell-edge rates based on the NUM solution. We propose an efficient dual subgradient based algorithm, which converges towards the NUM solution. A scheduling scheme is also proposed to approach the NUM solution. Simulations illustrate more than 2x rate gain for 10th percentile users vs. an optimal association without interference management.

Massive MIMO is one of the most promising approaches for coping with the predicted wireless data traffic explosion. Future deployment scenarios will involve dense heterogeneous networks, comprised of massive MIMO base stations with different powers, numbers of antennas and multiplexing gain capabilities, and possibly highly nonhomogeneous user density (hot-spots). In such dense irregularly deployed networks, it will be important to have mechanisms for associating users to base stations so that the available wireless infrastructure is efficiently used. In this paper, we consider the optimal user-cell association problem for massive MIMO heterogeneous networks and illustrate how massive MIMO can also provide nontrivial advantages at the system level. Unlike previous treatments that rely on integer program problem formulations and their convex relaxations, the user-cell association problem is formulated directly as a convex network utility maximization and solved efficiently by a centralized subgradient algorithm. As we show, the globally optimal solution is physically realizable, in that there exists a sequence of integer-valued associations approaching arbitrarily closely the optimal fractional association. We also consider simple decentralized user-centric association schemes, where each user individually and selfishly connects to the base station with the highest promised throughput. Such user-centric schemes where users make local association decisions in a probabilistic manner can be viewed as games and are known to converge to Nash equilibria. Surprisingly, as we show, under certain conditions, the globally optimal solution is close to these Nash equilibria. Such decentralized approaches are, therefore, attractive not only for their simplicity, but also because they operate near the system social optimum. Our theoretical results are confirmed by extensive simulations with realistic LTE-like network parameters.

Many practical communication systems have some form of restricted precoding or postcoding, such as antenna selection, selection combining, beam selection, and limited feedback precoding, to name a few. The capacity analysis of such systems is, in general, difficult and previous works in the literature provide results only for certain simplified cases. This paper derives a novel approach to analyze the capacity for such systems under a very generic setting. The results are based on asymptotic closed-form expressions for the second-order statistics and joint distributions of eigenvalues for a set of coupled, doubly correlated Wishart matrices. A tight approximation to the joint distribution of the eigenvalues in the non-asymptotic regime is also proposed. These results are then used to show that the system capacity can be approximated as the largest element of a correlated Gaussian vector. Showing that this is equivalent to the problem of finding the distribution of sum of lognormals, we propose a novel approach to characterize its distribution. As an application, the capacity for an antenna selection system and a limited feedback precoding system is compared with their respective approximations. This paper also demonstrates how the results can be used to design the precoding codebook in limited feedback systems.

Multi-tier networks with large-array base stations (BSs) that are able to operate in the "massive MIMO" regime are envisioned to play a key role in meeting the exploding wireless traffic demands. Operated over small cells with reciprocity-based training, massive MIMO promises large spectral efficiencies per unit area with low overheads. Also, near-optimal user-BS association and resource allocation are possible in cellular massive MIMO HetNets using simple admission control mechanisms and rudimentary BS schedulers, since scheduled user rates can be predicted a priori with massive MIMO. Reciprocity-based training naturally enables coordinated multi-point transmission (CoMP), as each uplink pilot inherently trains antenna arrays at all nearby BSs. In this paper we consider a distributed-MIMO form of CoMP, which improves cell-edge performance without requiring channel state information exchanges among cooperating BSs. We present methods for harmonized operation of distributed and cellular massive MIMO in the downlink that optimize resource allocation at a coarser time scale across the network. We also present scheduling policies at the resource block level which target approaching the optimal allocations. Simulations reveal that the proposed methods can significantly outperform the network-optimized cellular-only massive MIMO operation (i.e., operation without CoMP), especially at the cell edge.

Large-scale distributed Multiuser MIMO (MU-MIMO) is a promising wireless network architecture that combines the advantages of "massive MIMO" and "small cells." It consists of several Access Points (APs) connected to a central server via a wired backhaul network and acting as a large distributed antenna system. We focus on the downlink, which is both more demanding in terms of traffic and more challenging in terms of implementation than the uplink. In order to enable multiuser joint precoding of the downlink signals, channel state information at the transmitter side is required. We consider Time Division Duplex (TDD), where the downlink channels can be learned from the user uplink pilot signals, thanks to channel reciprocity. Furthermore, coherent multiuser joint precoding is possible only if the APs maintain a sufficiently accurate relative timing and phase synchronization. AP synchronization and TDD reciprocity calibration are two key problems to be solved in order to enable distributed MU-MIMO downlink. In this paper, we propose novel over-the-air synchronization and calibration protocols that scale well with the network size. The proposed schemes can be applied to networks formed by a large number of APs, each of which is driven by an inexpensive 802.11-grade clock and has a standard RF front-end, not explicitly designed to be reciprocal. Our protocols can incorporate, as a building block, any suitable timing and frequency estimator. Here we revisit the problem of joint ML timing and frequency estimation and use the corresponding Cramer-Rao bound to evaluate the performance of the synchronization protocol. Overall, the proposed synchronization and calibration schemes are shown to achieve sufficient accuracy for satisfactory distributed MU-MIMO performance.

Recently, a novel class of high-performance multi-round MU- MIMO schemes were developed by Yang et al. [1] for the practically attractive scenario in which the transmitter has available partial channel state information (CSI) regarding the current state of the channel, but also full delayed CSI. We study methods for combining user scheduling with these multi-round transmission schemes, in order to realize some of their multiplexing gains in the form of high-rates in the SNR regime of practical interest. In these multi-round schemes several differently encoded signals are transmitted to each user over many time-dispersed slots. The challenge in designing schedulers is that certain signals intended for a given user are transmitted in different forms and combinations with other-user signals over multiple slots, occurring at distinctly different times with distinctly different channels. We develop a simple and novel scheduling framework based on appropriate resource division between transmission rounds and round-specific scheduling criteria. Simulation-based performance analysis of the proposed scheduling algorithms, operated on the 2-user transmission scheme of Yang et al. [1] and a 3-user extension suggest that these schemes can harvest significant scheduling benefits in some scenarios.

Time-Division Duplexing (TDD) allows to estimate the downlink channels for an arbitrarily large number of base station antennas from a finite number of orthogonal uplink pilot signals, by exploiting channel reciprocity. Based on this observation, a recently proposed "Massive MIMO" scheme was shown to achieve unprecedented spectral efficiency in realistic conditions of distance-dependent pathloss and channel coherence time and bandwidth. The main focus and contribution of this paper is an improved Network-MIMO TDD architecture achieving spectral efficiencies comparable with "Massive MIMO", with one order of magnitude fewer antennas per active user per cell (roughly, from 500 to 50 antennas). The proposed architecture is based on a family of Network-MIMO schemes defined by small clusters of cooperating base stations, zero-forcing multiuser MIMO precoding with suitable inter-cluster interference mitigation constraints, uplink pilot signals allocation and frequency reuse across cells. The key idea consists of partitioning the users into equivalence classes, optimizing the Network-MIMO scheme for each equivalence class, and letting a scheduler allocate the channel time-frequency dimensions to the different classes in order to maximize a suitable network utility function that captures a desired notion of fairness. This results in a mixed-mode Network-MIMO architecture, where different schemes, each of which is optimized for the served user equivalence class, are multiplexed in time-frequency. In order to carry out the performance analysis and the optimization of the proposed architecture in a systematic and computationally efficient way, we consider the large-system regime where the number of users, the number of antennas, and the channel coherence block length go to infinity with fixed ratios.

Fundamentally, benefits of Multiple Input Multiple Output (MIMO) in wireless systems rest on increasing the number of Degrees of Freedom (DoFs) that can be exploited for transmission. DoFs can be used to increase spectral efficiency and/or provide increased diversity. However, exploiting DoFs in MIMO often requires knowledge of Channel State Information (CSI) at the transmitter and/or receiver. Obtaining CSI creates overheads due to the required use of wireless resources for CSI-estimation pilots and/or CSI feedback. This can create a fundamental "dimensionality bottleneck" that limits the growth in spectral efficiency for some conventional MIMO systems. Recently a new class of techniques known as "Blind Interference Alignment" (BIA) has demonstrated the possibility to grow DoFs without many of the CSI overheads of conventional systems. BIA therefore has the potential to relieve the "dimensionality bottleneck". However, BIA can have limited applicability to many users in a cellular environment given that it requires a high Signal to Noise Ratio (SNR) to operate efficiently. By examining BIA, we present a method to reduce the SNR requirements and improve the application of BIA through power allocation and cluster-based transmission. We show that BIA can be applied successfully in cellular without the need for joint processing between base-stations, and can improve performance over conventional MIMO for even cell-edge users in some scenarios.

User scheduling and multiuser multi-antenna (MU-MIMO) transmission are at the core of high-rate data-oriented downlink schemes of the next-generation of cellular systems (e.g., LTE-Advanced). Scheduling selects groups of users according to their channels vector directions and SINR levels. However, when scheduling is applied independently in each cell, the inter-cell interference (ICI) power at each user receiver is not known in advance since it changes at each new scheduling slot depending on the scheduling decisions of all interfering base stations. In order to cope with this uncertainty, we consider the joint operation of scheduling, MU-MIMO beamforming and Automatic Repeat reQuest (ARQ). We develop a game-theoretic framework for this problem and build on stochastic optimization techniques in order to find optimal scheduling and ARQ schemes. Particularizing our framework to the case of "outage service rates", we obtain a scheme based on adaptive variable-rate coding at the physical layer, combined with ARQ at the Logical Link Control (ARQ-LLC). Then, we present a novel scheme based on incremental redundancy Hybrid ARQ (HARQ) that is able to achieve a throughput performance arbitrarily close to the "genie-aided service rates", with no need for a genie that provides non-causally the ICI power levels. The novel HARQ scheme is both easier to implement and superior in performance with respect to the conventional combination of adaptive variable-rate coding and ARQ-LLC.

In next-generation cellular systems, Multi-User MIMO (MU-MIMO) schemes are expected to achieve very high downlink throughput, provided that issues such as Inter-Cell Interference (ICI), user scheduling, etc. are properly handled. In order to accomplish this goal, we consider a joint scheduling and cell clustering scheme where groups of adjacent cells (clusters) are jointly controlled and effectively act as a distributed multi-antenna transmitter. An ``overlapped'' clustering scheme is used whereby different clustering patterns operate on different frequency subchannels. The arrangement is such that each user in the system is located away from the cluster-edge and sees a favorable ICI level on one or more subchannels. This leads to significant per-user and per-cell performance improvements over single-arrangement clustering schemes that force a user to use only one cluster arrangement. Critical to realizing such benefits in an overlapped cluster arrangement is the joint operation of downlink opportunistic scheduling across all cluster patterns, i.e., across the frequency bands. Indeed, joint scheduling enables users to optimally share and balance the use multiple subchannels (i.e, multiple clustering patterns). Such a balance does not trivially schedule users close to cluster centers. We demonstrate, under the popular Proportionally Fair Scheduling (PFS) criterion, that the joint system operation of scheduling and overlapped arrangements yields significant improvements to ``cell-edge'' user rates while also increasing overall per-cell throughput.

User scheduling and multiuser multi-antenna (MU-MIMO) transmission are at the core of high-rate data-oriented downlink schemes foreseen in the next-generation cellular systems (e.g., LTE-Advanced). Scheduling exploits the knowledge of the users' instantaneous achievable coding rates. However, when it is applied independently in each cell, the level of inter-cell interference (ICI) experienced by each user is not known a priori at the cell scheduler as it depends on the local scheduling decisions at all other base stations. As a result, the instantaneous achievable rates are random variables, even under the optimistic assumption of perfect transmitter channel state information. In order to cope with this problem, we consider the joint design and operation of scheduling, MU-MIMO beamforming and Automatic Repeat reQuest (ARQ). We present a systematic method for joint scheduler/ARQ design and show that properly designed incremental-redundancy hybrid ARQ schemes can achieve (with finite average decoding delay) any desired fraction of the "optimistic" throughputs, i.e., of the throughputs that would be achieved if the transmitter knew the instantaneous user ICI and transmitted to each scheduled user at the maximum possible instantaneous rate.

User scheduling and multiuser multi-antenna (MU-MIMO) transmission are at the core of high-rate data-oriented downlink schemes of the next-generation of cellular systems (e.g., LTE-Advanced). Scheduling selects groups of users according to their channels vector directions and channel quality indicator (CQI). However, when scheduling is applied independently in each cell, the inter-cell interference (ICI) power at each user receiver is random and not known in advance since it changes from slot to slot depending on the scheduling decisions of all interfering base stations. In order to cope with this uncertainty, we consider the joint operation of scheduling, MU-MIMO beamforming and Automatic Repeat reQuest (ARQ). We develop a game-theoretic framework for this multi-cell decentralized scheduling problem for which we prove existence of Nash equilibria, and we build on stochastic optimization techniques in order to devise good joint scheduling and ARQ schemes. We also find “extremal” ICI power statistics that provide uniform lower and upper bounds to the values of the network utility function at all Nash equilibria of the decentralized scheduling game.

The optimization of the transmit parameters (power allocation and steering vectors) for the MIMO BC under general linear constraints is treated under the optimal DPC coding strategy and the simple suboptimal linear zero-forcing beamforming strategy. In the case of DPC, we show that ¿SINR duality¿ and ¿min-max duality¿ yield the same dual MAC problem, and compare two alternatives for its efficient solution. In the case of zero-forcing beamforming, we provide a new efficient algorithm based on the direct optimization of a generalized inverse matrix. In both cases, the algorithms presented here address the problems in the most general form and can be applied to special cases previously considered, such as per-antenna and per-group of antennas power constraints, ¿forbidden interference direction¿ constraints, or any combination thereof.

We present a new class of reduced-complexity high-performance iterative receivers for single-user MIMO/OFDM systems with bit-interleaved coded modulation (BICM). The proposed inner-outer decoder structures rely on joint demappers which generate soft-output via an M-algorithm limited tree-search. A key element of the proposed designs is the way the M-algorithm chooses its survivor candidates during the tree search. In particular, at every depth in the tree the survivor list comprises the best M candidates as well as additional good candidates which are chosen to enable high-quality soft-output information for every bit. In the context of iterative inner/outer decoders, simulations show that the proposed survivor selection criteria result in superior performance/complexity trade-offs as compared to a conventional M-algorithm search.

In this paper we present reduced-complexity high-performance iterative receivers for single-user MIMO systems that employ OFDM with bit-interleaved coded modulation. The proposed inner-outer decoder structures exploit the Soft-Output M algorithm (SOMA) to calculate bit-likelihood estimates via a limited search on a tree. A key element of the proposed designs is the way the SOMA front-end adapts at each iteration to new extrinsic information. This front-end defines the tree, both in terms of the order in which symbols are considered, as well as in terms of the subspaces used in the likelihood calculations at each depth in the tree. The proposed SOMA front-ends are different from the conventional QR-decomposition front-ends and provide a systematic way to adapt both the order and the subspaces at each iteration in an iterative decoding structure. Simulations for 4 × 4 16 QAM systems show that the proposed iterative receivers can yield significant performance gains over receivers using non-adaptive conventional front-ends.

We consider random beamforming and user scheduling strategies in a multi-cell environment for downlink wireless communications. Our focus is on using combined scheduling and beamforming to mitigate performance losses due to inter-cell interference. Of interest are strategies requiring minimal intra-cell and inter-cell system information exchange. Indeed the acquisition and inter-cell exchange of such information can be significant overheads in a practical deployment. Random beamforming requires substantially less channel state information than linear zero forcing strategies to schedule transmissions. It is therefore of practical interest despite some limitations in controlling intra-cell interference. We consider how one can use a minimal exchange of information, in a multi-cell multistep pattern of exchanges, to improve the overall performance of random beamforming in a multi-cell environment.

We consider forward-link multiuser MIMO transmission, whereby K users are served by a base station with a large number of (potentially distributed) transmit antennas. We address the problem of channel state information (CSI) mismatch between the transmitter and the receivers with a two-way training scheme. In a quasi-static setting, the proposed scheme requires only K pilot symbols for uplink training and as little as one symbol for downlink training. In particular, we show that the performance of the system where the receiver has perfect channel knowledge can be approached with a small value of downlink training symbols. We consider two variants of linear MMSE precoders that take into account the quality of the channel state information at the transmitter and demonstrate that robust high sum-rate systems can be designed that rely on single-antenna receivers, provided that a large enough number of antennas is used for transmission. Finally, we sketch an example of implementations for the equal rate case.

In this paper we present reduced-complexity high- performance receivers for single-user MIMO systems that employ coded OFDM transmission with bit-interleaved coded modulation (BICM). High data-rate versions of these systems have been proposed for 4G. The near-optimum inner-outer decoder structures for these MIMO/BICM/OFDM/QAM systems have complexity that grows exponentially fast with the number of multiplexed streams (number of transmit antennas) and the number of bits represented by the QAM constellations. We focus on reduced-complexity inner-outer decoder structures that exploit adaptive soft-output M algorithms for soft inner decoding. Our simulations show that these receivers can be systematically optimized to yield substantial complexity reductions without significant loss in performance.

We consider space-time coding methods for cooperative narrowband and wideband downlink transmission from multiple base stations. The communication channels in multi base-station signaling differ from those involving collocated transmit-antenna systems, and, if properly used, can provide improved resistance to shadowing and extended range. One challenge, however, that arises in this context is asynchronous reception of the signal elements. In the narrowband case the proposed designs generate asynchrony robust space-time block codes (STBCs) via transformations of existing orthogonal STBCs. Subject to a maximum allowable relative delay between signals from distinct transmit antennas, the resulting received signal can be equivalently modeled as arising from synchronous STBC transmission with orthogonal or diagonal STBCs, implying that these designs provide full space diversity with low-complexity decoding in asynchronous settings. We also describe three system approaches for wideband transmission with throughput diversity receiver-complexity trade-offs. Common to all these systems are elements such as OFDM- type signaling, bit-interleaved coded modulation, and iterative decoding. The lowest data-rate lowest complexity system employs an inner orthogonal space-time block code with embedded OFDM-type transmission, while the highest data-rate highest complexity scheme does not employ an inner code. The latter group of schemes can also conveniently provide high data rates at the cost of reduced space diversity. All schemes can be readily extended to provide flexible unequal error protection for media transport.

We present techniques for simulcasting low-power digital data and analog FM over fading channels. Our methods have strong connections to what are referred to as dirty paper coding techniques in that a low-power digital data signal is modulo-added to the host analog FM signal. Due to the low power levels of the digital data and the suppression capability of analog FM, a standard analog FM receiver can be used to reliably recover the analog audio signal. We develop digital receivers that work reliably over fading channels given imperfect channel state information. Spread- response preceding is exploited to not only provide rate-1 temporal diversity benefits, but also to simplify the design of the modulo-precanceler and the receiver. Our theoretical and simulation-based performance analysis of the digital receivers and our MSE-based analog FM distortion analysis suggest that precoded modulo-precanceling schemes provide substantially higher data rates than existing precanceling strategies at the cost of transmission delay and transmitter and receiver complexity. The systems we present are also readily compatible with conventional channel coding techniques as well as with analog FM postcancelers, which can improve the digital receiver bit-error-rate performance at the expense of receiver complexity.

We present a method for network coding over time-varying networks with attractive tradeoffs between throughput and decoding-delay. We model the time-varying networks as a sequence of topology graphs. The method relies on designing a deterministic network code for a fixed virtual graph derived from the actual sequence of topologies, and on exploiting proper buffering in order to implement the code over the actual network. We show that, under certain conditions, this approach can achieve the maximum achievable throughput over the given time- varying network. We also present simulation results that validate the theoretical findings.

We present techniques for simulcasting low-power digital data and analog FM over fading channels. Our methods have strong connections to "writing on dirty paper" techniques in that a low-power digital data signal is modulo-added to the host analog FM signal, so as to minimize the distortion on the analog FM signal. We develop digital receivers that work reliably over fading channels given partial channel state information. Spread-response preceding is exploited to provide rate-1 temporal diversity benefits and simplify the digital receiver design. Our theoretical and simulation-based performance analysis of the digital receivers and our MSE-based analog FM distortion analysis suggest that the proposed schemes provide higher data rates than existing precanceling strategies at the cost of transmission delay and transceiver complexity.

In this paper, we develop algorithms for distributed computation of averages of the node data over networks with arbitrary but fixed connectivity. The algorithms we develop are linear dynamical systems that generate sequences of improving approximations to the desired computation at each node, via iterative processing and broadcasting. The algorithms are locally constructed at each node by exploiting only locally available and macroscopic information about the network topology. We present methods for optimizing the convergence rates of these algorithms to the desired computation, and evaluate their performance characteristics in the context of a problem of signal estimation from multinode noisy observations. By conducting simulations based on simple power-loss propagation models, we perform a preliminary comparison of the algorithms we develop against other types of distributed algorithms for computing averages, and identify transmit-power optimized algorithmic implementations as a function of the size and density of the sensor network.

We consider chaotic spread spectrum (DS/SS) systems for secure communication over fading channels, whereby a symbol stream is linearly modulated on a spreading sequence generated by iterating an initial condition through a suitably chosen chaotic map. For a class of these systems, we develop methods for quantifying the uncoded probability of error, Pr(/spl epsiv/), of unintended receivers that do not know the initial condition. We show that the Pr(/spl epsiv/) of unintended receivers exploiting K degrees of diversity decays as 1//spl radic/(SNR), in contrast to the intended receiver Pr(/spl epsiv/) that decays as 1/(SNR)/sup K/, demonstrating that these systems can provide reliable and private communication over fading channels.

We study the physical-layer secrecy potential of a class of chaotic direct sequence spread spectrum (DS/SS) systems in additive white Gaussian noise, whereby a symbol stream is linearly modulated on a sequence generated by iterating an initial condition through a suitably chosen chaotic map. We present iteratively constructed sequences of chaotic DS/SS systems and develop methods for quantifying and comparing the uncoded probability of error (Pr(ϵ)) of unintended receivers that do not know the initial condition, against that of intended receivers. For a subclass of these systems, we show that the unintended receiver Pr(ϵ) decays as 1/ √ SNR, and develop metrics that accurately predict the relation between the unintended receiver Pr(ϵ) and system parameters.

In this paper we develop algorithms for distributed computation of a broad range of estimation and detection tasks over networks with arbitrary but fixed connectivity. The distributed algorithms we develop are linear dynamical systems that generate sequences of approximations to the desired computation. The algorithms are locally constructed at each node by exploiting only locally available and macro-scopic information about the network topology. We present methods for designing these distributed algorithms so as to optimize the convergence rates to the desired computation and demonstrate their performance characteristics in the context of a problem of signal estimation from multi-node signal observations in Gaussian noise.

We study a class of pseudo-chaotic spread spectrum systems for secure communication over additive white Gaussian noise channels, whereby a symbol stream is linearly modulated on a sequence generated by iterating an initial condition through a suitably chosen chaotic map. We compare the optimal uncoded probability of error (Pr(/spl epsi/)) attainable by intended receivers that know the initial condition, to the associated Pr(/spl epsi/) of unintended receivers that know the modulation scheme but not the initial condition. The sensitive dependence of chaotic sequences on initial conditions together with the presence of channel noise can be exploited to provide Pr(/spl epsi/) benefits to intended receivers. We develop bounds on the best Pr(/spl epsi/)performance of intended and unintended receivers. In the process, we identify properties of the chaotic maps that affect the secrecy benefits and develop methods for designing maps that meet a required level of secrecy.

We consider simultaneous broadcasting of low-power digital data and analog FM and present reliable receivers for the digital data. Due to the relatively low power level of the digital data and the interference suppression capability of analog FM, standard analog FM receivers can reliably recover the analog FM audio signal. To recover the digital data, an extended Kalman filter front end is developed that exploits the capture capability of analog FM to reconstruct and postcancel the analog FM component from the received composite signal. Simulations are conducted with artificial analog FM signals, suggesting that postcanceling schemes can provide higher data rates than their precanceling counterparts, at a lower transmission delay penalty but higher digital receiver complexity. For analog FM to digital signal power level ratios in the range of 30-50 dB, the postcanceler digital signal recovery appears fairly robust, providing digital signal-to-noise ratios of 2-7.5 dB. The corresponding uncoded bit error rates strongly depend on the power level difference between the host analog FM and the digital data signal. In particular, at 260 kb/s and E/sub b//N/sub o/=10 dB they range between 1% to about 15%, and can be reduced to acceptable levels using standard channel coding techniques.

We develop algorithms for sequential signal encoding from sensor measurements, and for signal estimation via fusion of channel-corrupted versions of these encodings. For signals described by state space models, we present optimized sequential binary-valued encodings constructed via threshold-controlled scalar quantization of a running Kalman filter signal estimate from the sensor measurements. We also develop methods for robust fusion from observations of these encodings corrupted by binary symmetric channels.

Signal estimation from a sequential encoding in the form of quantized noisy measurements is considered. As an example context, this problem arises in a number of remote sensing applications, where a central site estimates an information-bearing signal from low-bandwidth digitized information received from remote sensors, and may or may not broadcast feedback information to the sensors. We demonstrate that the use of an appropriately designed and often easily implemented additive control input before signal quantization at the sensor can significantly enhance overall system performance. In particular, we develop efficient estimators in conjunction with optimized random, deterministic, and feedback-based control inputs, resulting in a hierarchy of systems that trade performance for complexity.

Low-complexity schemes for digital encoding of a noise-corrupted signal and associated signal estimators are presented. This problem arises in wireless distributed sensor networks where an environmental signal of interest is to be estimated at a central site from low-bandwidth digitized information received from collections of remote sensors. We show that the use of a properly designed and often easily implemented additive control input before signal quantization can significantly enhance overall system performance. In particular, efficient estimators can be constructed and used with optimized pseudo-noise, deterministic, and feedback-based control inputs, resulting in a hierarchy of practical systems with very attractive performance-complexity characteristics.

A reliable method for broadcasting digital audio simultaneously with existing analog FM radio is presented. According to this linear combining scheme, the digital data sequence is adaptively modulated so as to achieve high data rates while ensuring that the interference incurred on the analog FM signal remains at acceptably low levels. The digital transmission is based on adaptive orthogonal frequency division multiplexing. Implementable, near optimum algorithms for insertion of the maximum number of carriers at all times are presented, which incur negligible distortion on the analog FM signal. The digital receiver response of the analog FM signal (known to the transmitter) is computed and canceled from the aggregate signal prior to signal transmission. As a result of this precanceling process the digital transmission is free from analog FM interference. We present simulation results on acoustic signals where the average digital data use up to 50% of the 200 kHz power spectrum with digital signal power levels 25-35 dB below the analog signal. A control channel is used for the positions of the modem carriers.

A method for broadcasting digital audio simultaneously with existing analog frequency modulation (FM) radio is presented. The method is based on precomputing the response of the host analog FM signal at the digital receiver and precanceling it at the transmitter. As a result, the digital transmission is free from interference from analog FM. We select the rate and power level of the digital transmission in a manner that the interference the digital data incur on the analog FM signal remains at acceptably low levels. The digital transmission is based on adaptive orthogonal frequency-division multiplexing (OFDM) (adaptive multicarrier). The frequencies and number of carriers of the digital multicarrier modem are judiciously selected in a time-varying fashion so as to cause a negligible distortion in a standard receiver for analog FM. Simulations based on conservative nonoptimized signal design indicate that data rates up to about 130 kb/s inside the 200-kHz FM channel are achievable for acoustic test signals. We present a number of numerical examples where the average digital data use up to 50% of the 200-kHz power spectrum with digital signal power levels 25-35 dB below the analog signal. Due to the resulting variable-rate digital transmission, a control channel is required. A method of precanceling with multiple orthogonal direct-sequence spread-spectrum schemes is also presented.

The class of stochastic resonance systems based on M-level quantizer maps is developed. We derive expressions for the output invariant density and autocorrelation function of these maps when they are driven by a square wave in noise. These systems are shown to provide signal-to-noise ratio enhancement and robustness to noise characteristics. These properties render the quantizer maps potentially appealing for a wide range of signal processing applications such as interference suppression and robust communication. A framework for the analysis of more general discrete-time stochastic resonance systems is also presented, which is based on approximating these systems via quantizer maps.

The chaotic sequences corresponding to tent map dynamics are potentially attractive in a range of engineering applications. Optimal estimation algorithms for signal filtering, prediction, and smoothing in the presence of white Gaussian noise are derived for this class of sequences based on the method of maximum likelihood. The resulting algorithms are highly nonlinear but have convenient recursive implementations that are efficient both in terms of computation and storage. Performance evaluations are also included and compared with the associated Cramer-Rao bounds.>