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Current research on multi-antenna architectures is trending towards increasing the amount of antennas in the base stations (BSs) so as to increase the spectral efficiency. As a result, the interconnection bandwidth and computational complexity required to process the data using centralized architectures is becoming prohibitively high. Decentralized architectures can reduce these requirements by pre-processing the data before it arrives at a central processing unit (CPU). However, performing decentralized processing introduces also cost in complexity/interconnection bandwidth at the antenna end which is in general being ignored. This paper aims at studying the interplay between level of decentralization and the associated complexity/interconnection bandwidth requirement at the antenna end. To do so, we propose a general framework for centralized/decentralized architectures that can explore said interplay by adjusting some system parameters, namely the number of connections to the CPU (level of decentralization), and the number of multiplications/outputs per antenna (complexity/interconnection bandwidth). We define a novel matrix decomposition, the WAX decomposition, that allows information-lossless processing within our proposed framework, and we use it to obtain the operational limits of the interplay under study. We also look into some of the limitations of the WAX decomposition., Comment: 14 pages, 9 figures, submitted to IEEE Transactions on Signal Processing. arXiv admin note: text overlap with arXiv:2003.01961

Network modeling is a key enabler to achieve efficient network operation in future self-driving Software-Defined Networks. However, we still lack functional network models able to produce accurate predictions of Key Performance Indicators (KPI) such as delay, jitter or loss at limited cost. In this paper we propose RouteNet, a novel network model based on Graph Neural Network (GNN) that is able to understand the complex relationship between topology, routing, and input traffic to produce accurate estimates of the per-source/destination per-packet delay distribution and loss. RouteNet leverages the ability of GNNs to learn and model graph-structured information and as a result, our model is able to generalize over arbitrary topologies, routing schemes and traffic intensity. In our evaluation, we show that RouteNet is able to predict accurately the delay distribution (mean delay and jitter) and loss even in topologies, routing and traffic unseen in the training (worst case MRE=15.4%). Also, we present several use cases where we leverage the KPI predictions of our GNN model to achieve efficient routing optimization and network planning., Comment: 12 pages

Emerging applications such as the metaverse, telesurgery or cloud computing require increasingly complex operational demands on networks (e.g., ultra-reliable low latency). Likewise, the ever-faster traffic dynamics will demand network control mechanisms that can operate at short timescales (e.g., sub-minute). In this context, Traffic Engineering (TE) is a key component to efficiently control network traffic according to some performance goals (e.g., minimize network congestion). This paper presents Routing By Backprop (RBB), a novel TE method based on Graph Neural Networks (GNN) and differentiable programming. Thanks to its internal GNN model, RBB builds an end-to-end differentiable function of the target TE problem (MinMaxLoad). This enables fast TE optimization via gradient descent. In our evaluation, we show the potential of RBB to optimize OSPF-based routing ($\approx$25\% of improvement with respect to default OSPF configurations). Moreover, we test the potential of RBB as an initializer of computationally-intensive TE solvers. The experimental results show promising prospects for accelerating this type of solvers and achieving efficient online TE optimization., NOTE:This work has been accepted for presentation in the International Conference on Network and Service Management (CNSM)

Network modeling is a fundamental tool in network research, design, and operation. Arguably the most popular method for modeling is Queuing Theory (QT). Its main limitation is that it imposes strong assumptions on the packet arrival process, which typically do not hold in real networks. In the field of Deep Learning, Graph Neural Networks (GNN) have emerged as a new technique to build data-driven models that can learn complex and non-linear behavior. In this paper, we present RouteNet-Erlang, a pioneering GNN architecture designed to model computer networks. RouteNet-Erlang supports complex traffic models, multi-queue scheduling policies, routing policies and can provide accurate estimates in networks not seen in the training phase. We benchmark RouteNet-Erlang against a state-of-the-art QT model, and our results show that it outperforms QT in all the network scenarios. This publication is part of the Spanish I+D+i project TRAINER-A (ref.PID2020-118011GB-C21), funded by MCIN/ AEI/10.13039/501100011033. This work is also partially funded by the Catalan Institution for Research and Advanced Studies (ICREA) and the Secretariat for Universities and Research of the Ministry of Business and Knowledge of the Government of Catalonia and the European Social Fund.

Algorithms for Massive MIMO uplink detection and downlink precoding typically rely on a centralized approach, by which baseband data from all antenna modules are routed to a central node in order to be processed. In the case of Massive MIMO, where hundreds or thousands of antennas are expected in the base-station, said routing becomes a bottleneck since interconnection throughput is limited. This paper presents a fully decentralized architecture and an algorithm for Massive MIMO uplink detection and downlink precoding based on the Stochastic Gradient Descent (SGD) method, which does not require a central node for these tasks. Through a recursive approach and very low complexity operations, the proposed algorithm provides a good trade-off between performance, interconnection throughput and latency. Further, our proposed solution achieves significantly lower interconnection data-rate than other architectures, enabling future scalability., Manuscript accepted for publication in IEEE Transactions on Signal Processing

We analyze the downlink of a massive multiuser multiple input, multiple output (MIMO) system where antenna units at the base station are connected in a daisy chain without a central processing unit and only possess local channel knowledge. For this setup, we develop and analyze a linear precoding algorithm for suppressing interuser interference. It is demonstrated that the algorithm is close to zero-forcing precoding in terms of performance for a large number of antennas. Moreover, we show that with careful scheduling of processing across antennas, requirements for interconnection throughput are reduced compared with the fully centralized solution. Favorable tradeoff between performance and interconnection throughput makes the daisy chain a viable candidate topology for real-life implementations of base stations in MIMO systems where the number of antennas is very large.

In this paper, we consider the potential of data-transmission in a system with a massive number of radiating and sensing elements, thought of as a contiguous surface of electromagnetically active material. We refer to this as a large intelligent surface (LIS). The "LIS" is a newly proposed concept, which conceptually goes beyond contemporary massive MIMO technology, that arises from our vision of a future where man-made structures are electronically active with integrated electronics and wireless communication making the entire environment "intelligent". We consider capacities of single-antenna autonomous terminals communicating to the LIS where the entire surface is used as a receiving antenna array. Under the condition that the surface-area is sufficiently large, the received signal after a matched-filtering (MF) operation can be closely approximated by a sinc-function-like intersymbol interference (ISI) channel. We analyze the capacity per square meter (m^2) deployed surface, \hat{C}, that is achievable for a fixed transmit power per volume-unit, \hat{P}. Moreover, we also show that the number of independent signal dimensions per m deployed surface is 2/\lambda for one-dimensional terminal-deployment, and \pi/\lambda^2 per m^2 for two and three dimensional terminal-deployments. Lastly, we consider implementations of the LIS in the form of a grid of conventional antenna elements and show that, the sampling lattice that minimizes the surface-area of the LIS and simultaneously obtains one signal space dimension for every spent antenna is the hexagonal lattice. We extensively discuss the design of the state-of-the-art low-complexity channel shortening (CS) demodulator for data-transmission with the LIS., Comment: Submitted to IEEE Trans. on Signal Process., 30 pages, 12 figures

We consider designing demodulators for linear vector channels that use reduced-size trellis descriptions for the received signal. We assume an iterative receiver and use interference cancellation (IC) with soft-information provided by an outer decoder to mitigate the signal part that is not covered by a reduced-size trellis description. In order to reach a trellis description, a linear filter is applied as a front end to compress the signal structure into a small trellis. This process requires three parameters to be designed: (1) the front-end filter; (2) the feedback filter through which the IC is done; and (3) a target response which specifies the trellis. Demodulators of this form have been studied before under the name channel shortening (CS), but the interplay between CS, IC, and the trellis-search processes has not been adequately addressed in the literature. In this paper, we analyze two types of CS demodulators that are based on the Forney and Ungerboeck detection models, respectively. The parameters are jointly optimized with a generalized mutual information (GMI) function. We also introduce a third type of CS demodulator that is, in general, suboptimal, which has closed-form solutions. Furthermore, signal-to-noise ratio asymptotic properties are analyzed, and we show that the third CS demodulator asymptotically converges to the optimal CS demodulator in the sense of GMI maximization.

This paper presents a mutual coupling based calibration method for time-division-duplex massive MIMO systems, which enables downlink precoding based on uplink channel estimates. The entire calibration procedure is carried out solely at the base station (BS) side by sounding all BS antenna pairs. An Expectation-Maximization (EM) algorithm is derived, which processes the measured channels in order to estimate calibration coefficients. The EM algorithm outperforms current state-of-the-art narrow-band calibration schemes in a mean squared error (MSE) and sum-rate capacity sense. Like its predecessors, the EM algorithm is general in the sense that it is not only suitable to calibrate a co-located massive MIMO BS, but also very suitable for calibrating multiple BSs in distributed MIMO systems. The proposed method is validated with experimental evidence obtained from a massive MIMO testbed. In addition, we address the estimated narrow-band calibration coefficients as a stochastic process across frequency, and study the subspace of this process based on measurement data. With the insights of this study, we propose an estimator which exploits the structure of the process in order to reduce the calibration error across frequency. A model for the calibration error is also proposed based on the asymptotic properties of the estimator, and is validated with measurement results., Comment: Submitted to IEEE Transactions on Wireless Communications, 21/Feb/2017

In this paper, we propose a soft-output detector for multiple-input multiple-output (MIMO) channels that utilizes achievable information rate (AIR) based partial marginalization (PM). The proposed AIR based PM (AIR-PM) detector has superior performance compared to previously proposed PM designs and other soft-output detectors such as K-best, while at the same time yielding lower computational complexity, a detection latency that is independent of the number of transmit layers, and straightforward inclusion of soft input information. Using a tree representation of the MIMO signal, the key property of the AIRPM is that the connections among all child layers are broken. Therefore, least-square (LS) estimates used for marginalization are obtained independently and in parallel, which have better quality than the zero-forcing decision feedback (ZF-DF) estimates used in previous PM designs. Such a property of the AIRPM detector is designed via a mismatched detection model that maximizes the AIR. Furthermore, we show that the chain rule holds for the AIR calculation, which facilitates an information theoretic characterization of the AIR-PM detector. (Less)

We consider optimal precoder design for multiuser multiple-input multiple-output broadcasting channels in single-carrier systems. Instead of linear detection, we assume that the advanced nonlinear channel shortening detectors are utilized at the receivers. Such a scenario is challenging for precoder design as the uplink–downlink duality is inapplicable. The target of our linear precoder design is to maximize the sum of the achievable information rate (sum-AIR), with AIR of each user being explicitly derived. We analyze such a precoder design in general, and provide an efficient per-user based optimization algorithm for the design of block-diagonalization precoder.

Optimal data detection of data transmitted over a linear channel can always be implemented through the Viterbi algorithm (VA). However, in many cases of interest the memory of the channel prohibits direct application of the VA. A popular and conceptually simple method in this case, studied since the early 1970s, is to first filter the received signal in order to shorten the memory of the channel, and then to apply a VA that operates with the shorter memory. We shall refer to this general concept as a channel shortening (CS) receiver. Although studied for almost four decades, an information theoretic understanding of what such a simple receiver solution is actually doing is not available. In this paper, we show that an optimized CS receiver has a direct correspondance to the chain rule of mutual information. Furthermore, we show that the tools for analyzing the ensuing achievable rates from an optimized CS receiver are precisely the same as those used for analyzing the achievable rates of a minimum mean-square-error (MMSE) receiver.

We consider detection over intersymbol interference channels that are unknown at the receiver. For such a kind of systems, we consider an alternative approach to the design of a reduced-complexity receiver based on channel shortening without the explicit estimation of the channel impulse response. The proposed solution allows a simplification of the receiver architecture and a faster convergence for channels with a long memory and a few number of poles.

This article discusses opportunities and challenges of physical layer security integration in massive multiple-input multiple-output (MaMIMO) systems. Specifically, we first show that MaMIMO itself is robust against passive eavesdropping attacks. We then review a pilot contamination scheme which actively attacks the channel estimation process. This pilot contamination attack is not only dramatically reducing the achievable secrecy capacity but is also difficult to detect. We proceed by reviewing some methods from literature that detect active attacks on MaMIMO. The last part of the paper surveys the open research problems that we believe are the most important to address in the future and give a few promising directions of research to solve them., Comment: 5 figures, to appear in IEEE Communications Magazine 2015, special issue on Wireless Physical Layer Security

It has been about 40 years since Gottfried Ungerboeck published his paper [1] on an alternative maximum-likelihood (ML) detector for intersymbol interference (ISI) channels. The ISI model used by Ungerboeck is commonly referred to as the Ungerboeck model. Ungerboeck?s ML detector has equivalent performance compared to Forney?s detector, which was published two years earlier in [2], but received lesser considerations. Perhaps the best example of this is the fact that a BCJR algorithm [3] operating on the Ungerboeck model was derived as late as 2005 [4]. However, the Ungerboeck model has many strong aspects and has therefore been rediscovered over the last few decades.

In Space Division Multiple Access (SDMA) systems, users are separated based on their positions. The base-station (BS) signals typically reach the users through multipath clusters. The goal of this work is to find the probability that a randomly selected user will receive the signal from the BS via at least one non-shared (by any other user) multipath cluster. This probability, referred to as ${\rm P}_{\rm Success} $ , provides (under some conditions) the limit of the probability that the BS is able to communicate successfully with a randomly selected user using SDMA without causing significant interference to other users. To find ${\rm P}_{\rm Success} $ , we first derive a model describing the probability distribution function (pdf) of the non-overlapping area of a set of circles, that have the same radius and are spatially distributed according to a Poisson distribution. Then, the proposed model for this pdf is used to express ${\rm P}_{\rm Success} $ as a function of the user's intensity and the clusters' intensity. Simulations, using the COST 2100 model, for outdoor and indoor scenarios, are used to validate the derived expression for ${\rm P}_{\rm Success} $ with a very good agreement, where the maximum error was 0.04.

This paper investigates linear precoding over nonsingular linear channels with additive white Gaussian noise, with lattice-type inputs. The aim is to maximize the minimum distance of the received lattice points, where the precoder is subject to an energy constraint. It is shown that the optimal precoder only produces a finite number of different lattices, namely perfect lattices, at the receiver. The well-known densest lattice packings are instances of perfect lattices, but are not always the solution. This is a counter-intuitive result as previous work in the area showed a tight connection between densest lattices and minimum distance. Since there are only finite many different perfect lattices, they can theoretically be enumerated offline. A new upper bound on the optimal minimum distance is derived, which significantly improves upon a previously reported bound, and is useful when actually constructing the precoders.

Fifth-generation (5G) cellular communications promise to deliver the gigabit experience to mobile users, with a capacity increase of up to three orders of magnitude with respect to current long-term evolution (LTE) systems. There is widespread agreement that such an ambitious goal will be realized through a combination of innovative techniques involving different network layers. At the physical layer, the orthogonal frequency division multiplexing (OFDM) modulation format, along with its multiple-access strategy orthogonal frequency division multiple access (OFDMA), is not taken for granted, and several alternatives promising larger values of spectral efficiency are being considered. This article provides a review of some modulation formats suited for 5G, enriched by a comparative analysis of their performance in a cellular environment, and by a discussion on their interactions with specific 5G ingredients. The interaction with a massive multiple-input, multiple-output (MIMO) system is also discussed by employing real channel measurements.

In this letter, a new model for generating SVC-like traffic and estimating its quality is introduced. Comprising two independent components - i.e., the traffic generator and the quality estimator - the model can significantly speed up the development of SVC applications in various use-cases by relying on approximated properties of streams instead of real traces. The model is verified based on a set of video streams and the performance of its components is assessed.

We consider channels affected by intersymbol interference with reduced-complexity, mutual information optimized, channel-shortening detection. For such settings, we optimize the transmit filter, taking into consideration the reduced receiver complexity constraint. As figure of merit, we consider the achievable information rate of the entire system and with functional analysis, we establish a general form of the optimal transmit filter, which can then be optimized by standard numerical methods. As a corollary to our main result, we obtain some insight of the behavior of the standard waterfilling algorithm for intersymbol interference channels. With only some minor changes, the general form we derive can be applied to multiple-input multiple-output channels with intersymbol interference. To illuminate the practical use of our results, we provide applications of our theoretical results by deriving the optimal shaping pulse of a linear modulation transmitted over a bandlimited additive white Gaussian noise channel which has possible applications in the faster-than-Nyquist/time packing technique.

In this paper, we survey faster-than-Nyquist (FTN) signaling, an extension of ordinary linear modulation in which the usual data bearing pulses are simply sent faster, and consequently are no longer orthogonal. Far from a disadvantage, this innovation can transmit up to twice the bits as ordinary modulation at the same bit energy, spectrum, and error rate. The method is directly applicable to orthogonal frequency division multiplex (OFDM) and quadrature amplitude modulation (QAM) signaling. Performance results for a number of practical systems are presented. FTN signaling raises a number of basic issues in communication theory and practice. The Shannon capacity of the signals is considerably higher.

We design of an efficient channel shortener for nonlinear satellite channels. When the memory of the channel is too large to be taken into account by a full complexity detector, excellent performance can be achieved by properly filtering the received signal followed by a reduced-state detector. This letter derives closed-form expressions for the front-end filter and the target response of the reduced-state detector.

In this paper, a number of channel estimation algorithms for iterative receivers are compared for the case of an up-link orthogonal frequency division multiplexing interleave division multiple access (OFDM-IDMA) system. Both pilot based algorithms, used to obtain an initial estimate, as well as semi-blind decision-directed algorithms working as a component of the iterative receiver are considered. Algorithms performing either joint minimum mean square error (MMSE) channel estimation, or iterative estimation using space-alternating expectation maximization (SAGE), are evaluated. The considered algorithms differ in terms of complexity, as well as performance. The main contribution of this paper is to give an overview of different channel estimation approaches for OFDM-IDMA, where the complexity versus performance tradeoff is at the focal point. There is no single channel estimator providing the best tradeoff and our analysis shows how the system load (number of users) and the SNR influence the estimator choice.

Consider receivers that do not exploit any knowledge of the channel distribution in non-coherent block-fading multiple-input multiple-output (MIMO) channels. The main contribution of this letter is to derive a semi-analytical semi-numerical lower bound to the achieveble rate of such receivers. The bound is of operational meaning since practical receivers can achieve the lower bound. Several numerical examples with Gaussian block fading channels, which illuminate that the lack of channel distribution at the receiver imposes a significant penalty if the coherence time (counted in symbol times) is not much larger than the number of transmit antennas, are provided.

We consider the problem of bounding the information rate of intersymbol interference channels via simulation-based algorithms. The adopted approach, which is based on a general class of reduced-complexity receivers that includes several previously studied receivers as special cases, leads to provable upper and lower bounds on the information rate of interest. As a by-product of the information-theoretic investigations, novel insights on the design of efficient reduced-complexity receivers are also provided, since the proposed lower bounds are known to be achievable by practical receivers. In many scenarios, our novel approach significantly outperforms the existing ones, for all practical values of the signal-to-noise ratio.

We deal with the construction of optimal channel shortening, also known as combined linear Viterbi detection, algorithms for ISI and MIMO channels. In the case of MIMO channel shortening, the tree structure to represent MIMO signals is replaced by a trellis. The optimization is performed from an information theoretical perspective and the achievable information rates of the shortened models are derived and optimized. Closed form expressions for all components of the optimal detector of the class are derived. Furthermore, we show that previously published channel shortening algorithms can be seen as special cases of the derived model.

We perform a minimum distance analysis of a class of two-dimensional intersymbol interference (ISI) channels applicable to multitrack magnetic recording and orthogonal frequency division multiplex transmission systems. Exact minimum distance for a wide class of ISI responses is derived. The fundamental analytical technique is to transform the channel into an equivalent minimum phase channel. The results improve upon the prior work of Soljanin and Georghiades.

This paper evaluates the hardware aspects of multicarrier faster-than-Nyquist (FTN) signaling transceivers. The choice of time-frequency spacing of the symbols in an FTN system for improved bandwidth efficiency is targeted towards efficient hardware implementation. This work proposes a hardware architecture for the realization of iterative decoding of FTN multicarrier modulated signals. Compatibility with existing systems has been considered for smooth switching between the faster-than-Nyquist and orthogonal signaling schemes. One such being the use of fast Fourier transforms (FFTs) for multicarrier modulation. The performance of the fixed point model is very close to that of the floating point representation. The impact of system parameters such as number of projection points, time-frequency spacing, finite wordlengths and their design tradeoffs for reduced complexity iterative decoders in FTN systems have been investigated. The FTN decoder has been designed and synthesized in both 65 nm CMOS and FPGA. From the hardware resource usage numbers it can be concluded that FTN signaling can be used to achieve higher bandwidth efficiency with acceptable complexity overhead.