This study investigates a virtual multiuser multiple-input multiple-output (MU-MIMO) system with PSK modulation, realized with a reconfigurable intelligent surface (RIS)-based transmitter. The study focuses on minimizing transmit power under quality-of-service (QoS) constraints while addressing the associated computational complexity. A discrete phase-shift RIS model is considered, and the power minimization problem is formulated in two scenarios. First, for QPSK user data, the symbol-error probability (SEP) is adopted as the QoS criterion. Second, for generalM-PSK modulation, the union-bound SEP (UBSEP) is used to define the QoS constraints. Based on the considered formulations, a partial branch-and-bound (PBB) approach is developed, which improves on full branch-and-bound (FBB) methods in the sense of allowing for favorable complexity performance trade-offs. For the special case of high-resolution RIS, the discrete phase-shift set is approximated by its continuous counterpart, enabling the reformulation of the original problems as constrained optimizations on an oblique manifold, which are solved with reduced computational complexity with the proposed bisection method. Numerical results demonstrate the effectiveness of the proposed approaches in minimizing the transmit power for different SEP requirements and showcase the balance between power efficiency and computational complexity.

Cascaded or stacked intelligent metasurfaces (SIMs) have emerged as a promising technology to overcome the physical limitations of single-layer reconfigurable intelligent surfaces (RISs) in wideband wireless communication. By intelligently manipulating electromagnetic waves, SIMs enhance signal propagation in complex environments and offer additional degrees of freedom for beamforming. This paper proposes a coupling-aware, wideband, circuit-based framework that captures frequency-dependent mutual coupling and wideband channel responses over multiple subbands. Based on this model, we formulate a joint active and passive beamforming design that optimizes the base-station precoder to enable carrier aggregation across frequency-selective subbands, together with metasurface phase shifts, to maximize spectral efficiency. Simulation results reveal the importance of accounting for coupling and wideband effects, and show that performance depends strongly on operating conditions. Single-layer RIS configurations can be favorable in narrowband and/or low-SNR regimes, whereas SIMs can significantly outperform under wideband multi-user conditions by mitigating coupling-induced distortion and maintaining a more consistent phase response across frequencies. The results provide physical insights into design trade-offs between structural simplicity and wideband adaptability, highlighting SIMs as a scalable solution for future-generation wideband multi-user MIMO systems. We further show that partially reconfigurable SIM architectures achieve near-optimal performance with reduced complexity.

We investigate the uplink scenario in massive multiple input multiple output (MIMO) communication systems using dense uniform linear arrays (ULAs) of antenna elements that are tightly packed within a confined space and equipped with low-resolution analog-to-digital converters (ADCs). We tackle the problem of power consumption reduction and hardware simplification while simultaneously improving the performance of quantized systems by exploring spatial oversampling. Due to the subwavelength inter-element spacing in dense ULAs, extrinsic spatial thermal noise correlations arise from the significant coupling between adjacent antenna terminals. In addition to this correlated extrinsic noise, the noise figure caused by hardware imperfections profoundly impacts signal recovery and cannot be simply neglected in system performance analysis. We propose a low-resolution multi-user detection method based on a modified version of the vector approximate message passing (VAMP) framework. We also conduct a state evolution analysis to characterize the asymptotic behaviour of the proposed algorithm. We demonstrate that spatial oversampling in the context of low-resolution communication substantially enhances system performance, bringing it closer to the ideal scenario with infinite-resolution ADCs. This reveals the benefits of spatial oversampling as an effective strategy for enhancing the performance of low-resolution massive MIMO systems. We also thoroughly analyze the impact of noise figure on signal recovery under spatial oversampling, thereby highlighting its significance in system design considerations1.

Mutual Coupling (MC) emerges as an inherent feature in Reconfigurable Intelligent Surface (RIS) structures, particularly when they are fabricated with sub-wavelength inter-element spacing. Hence, their realistic modeling and efficient optimization need to accurately incorporate MC-induced effects. In addition, the design of electromagnetics-compliant transmit/receive radiation patterns constitutes another critical factor for efficient RIS operation. These radiation patterns together with MC naturally lead to the emergence of non-local RIS structures, whose operation can be effectively described via non-diagonal phase configuration matrices. In this paper, we present a physically-consistent joint optimization framework for the MC and the radiation patterns of non-local RIS structures for the case of RIS-assisted multi-user Multiple-Input Single-Output (MISO) communication systems. Both conventional reflective as well as transmissive RIS setups are considered. Assuming the availability of statistical properties of the wireless environment for the targeted RIS deployment, we particularly devise a novel offline optimization approach for the static scattering S-parameters of the RIS, which is followed by a dynamic, per-channel-realization optimization of the metasurface’s response-tunable elements and the transmitter’s active precoder. Our extensive simulation results, using both parametric and geometric channel models, showcase the validity of the proposed two-step optimization framework over benchmark schemes, indicating that improved performance can be achievable without the need for optimizing the MC and the radiation patterns of the RIS on the fly, which can be rather cumbersome.

Massive multiple-input multiple-output (mMIMO) antenna systems and inter-band carrier aggregation (CA)-enabled multi-band communication are two key technologies to achieve very high data rates in beyond fifth generation (B5G) wireless systems. We propose a joint optimization framework for such systems where the mMIMO antenna spacing selection, pre-coder optimization, optimum subcarrier selection and optimum power allocation are carried out simultaneously. We harness the bandwidth gain existing in a tightly coupled base station mMIMO antenna system to avoid sophisticated, non-practical antenna systems for multi-band operation. In particular, we analyze a multi-band communication system using a circuit-theoretic model to consider physical characteristics of a tightly coupled antenna array, and formulate a joint optimization problem to maximize the sum-rate. As part of the optimization, we also propose a novel block iterative water-filling-based subcarrier selection and power allocation optimization algorithm for the multi-band mMIMO system. A novel subcarrier windowing-based subcarrier selection scheme is also proposed which considers the physical constraints (hardware limitation) at the mobile user devices. We carry out the optimizations in two ways: (i) to optimize the antenna spacing selection in an offline manner, and (ii) to select antenna elements from a dense array dynamically. Via computer simulations, we illustrate superior bandwidth gains present in the tightly-coupled colinear and rectangular planar antenna arrays, compared to the loosely-coupled or tightly-coupled parallel arrays. We compare the optimum sum-rate performance of the proposed optimizationbased framework under various power allocation schemes and various user capability scenarios. Also, we show that the proposed optimization framework is superior to the existing joint optimization frameworks in terms of sum-rate performance and we verify the convergence of the proposed iterative optimization algorithms.

The rapid growth of Internet of Things (IoT) applications, such as smart cities and industrial automation, necessitates efficient massive machine-type communication (mMTC) solutions for sixth-generation (6G) networks. Traditional access protocols struggle to accommodate many devices with sporadic activity and low data volumes, leading to increased latency and collisions. This paper proposes a novel grant-free random access (RA) protocol that leverages Reconfigurable Intelligent Surfaces (RIS) to enhance connectivity and channel conditions in mMTC scenarios. The protocol consists of two stages: first, the base station transmits downlink (DL) pilots while the RIS sweeps its reflection configurations, enabling devices to identify optimal transmission opportunities. In the second stage, devices utilize these opportunities to transmit data, minimizing collisions and improving throughput. By employing a predefined codebook of reflection configurations, previously optimized to thoroughly scan the covered space in a few rounds of multiple narrow beams, the protocol reduces overhead and meets a maximum latency constraint of 20 ms under certain reliability constraints, demonstrating significant performance improvements over existing methods. This approach enhances network efficiency while supporting a vast number of devices and encourages the deployment of RIS technology in future wireless communication systems.

This paper explores the idea of usingredirectivereconfigurable intelligent surfaces (RedRIS) to overcome many of the challenges associated with the conventionalreflectiveRIS. We develop a framework for jointly optimizing the switching matrix of the lens-type RedRIS ports along with the active precoding matrix at the base station (BS) and the receive scaling factor. A joint non-convex optimization problem is formulated under the minimum mean-square error (MMSE) criterion with the aim to maximize the spectral efficiency of each user. In the single-cell scenario, the optimum active precoding matrix at the multi-antenna BS and the receive scaling factor are found in closed-form by applying Lagrange optimization, while the optimal switching matrix of the lens-type RedRIS is obtained by means of a newly developed alternating optimization algorithm. We then extend the framework to the multi-cell scenario with single-antenna base stations that are aided by the same lens-type RedRIS. We further present two methods for reducing the number of effective connections of the RedRIS ports that result in appreciable overhead savings while enhancing the robustness of the system. The proposed RedRIS-based schemes are gauged against conventional reflective RIS-aided systems under both perfect and imperfect channel state information (CSI). The simulation results show the superiority of the proposed schemes in terms of overall throughput while incurring much less control overhead.

Approximate message passing (AMP) algorithms are devised under the Gaussianity assumption of the measurement noise vector. In this work, we relax this assumption within the vector AMP (VAMP) framework to arbitrary independent and identically distributed (i.i.d.) noise priors. We do so by rederiving the linear minimum mean square error (LMMSE) to accommodate both the noise and signal estimations within the message passing steps of VAMP. Numerical results demonstrate how our proposed algorithm handles non-Gaussian noise models as compared to VAMP. This extension to general noise priors enables the use of AMP algorithms in a wider range of engineering applications where non-Gaussian noise models are more appropriate.

This paper investigates distributed estimation problems with factorized structures over factor graphs. By building upon the recent progress in the approximate message passing (AMP) paradigm, this paper extends the vector AMP (VAMP) algorithm to the distributed scenario where multiple agents collaboratively estimate the same signal using different measurement channels. We do so by deriving the new collaborative linear minimum mean square error (LMMSE) messages within the estimation steps through message passing. The new algorithm — coined D-VAMP — allows distributed agents to be heterogeneous thereby handling a broader class of practical applications. Our numerical results demonstrate the trade-off between the reconstructed accuracy and the level of heterogeneity measured in terms of the number of correlated agents and signal-to-noise ratio.

Machine learning (ML)-aided wireless communication studies are initiating the investigation of the domain generalization capabilities of deep neural networks (DNNs) when applied to communication problems. They do so by adopting the out-of-distribution (OOD) performance evaluation by comparing it to the in-distribution (ID) performance as usually done within the ML community. In this paper, we demonstrate that such blind adoption can yield a misleading OOD performance analysis of DNNs unless wireless communication metrics are involved in the OOD evaluation. By analyzing the OOD performance of an end-to-end (E2E) ML communication system over additive white Gaussian noise (AWGN) channels in terms of bit error rate (BER), we show that smaller (resp. larger) BER gaps between ID and OOD performance do not necessarily translate into a high (resp. low) reconstruction accuracy. Our results suggest that the comparison between ID and OOD performances is not enough to judge whether the OOD performance is acceptable or not. The ID and OOD performances of E2E communication systems should instead be carried out based on wireless metrics.

The present study considers a virtual multiuser multiple-input multiple-output system with PSK modulation realized via the reconfigurable intelligent surface-based passive transmitter setup. The study considers a discrete phase shift reconfigurable intelligent surface model where the reflecting elements' coefficients are restricted to a discrete set. With this, a symbol-level precoding power minimization problem under the condition that the union-bound symbol-error probability is below a given requirement is formulated. The corresponding mixed integer problem is solved via the utilization of a sophisticated branch-and-bound strategy. Numerical results show that the proposed power minimization approach yields reduced average transmit power when compared to state-of-the-art techniques.

Channel state information (CSI) estimation is a critical issue in the design of modern massive multiple-input multiple-output (mMIMO) networks. With the increasing number of users, assigning orthogonal pilots to everyone incurs a large overhead that strongly penalizes the spectral efficiency (SE) of the system. It becomes thus necessary to reuse pilots, giving rise to pilot contamination, a vital performance bottleneck of mMIMO networks. Reusing pilots among the users of the same cell is a very desirable operation condition from the perspective of reducing training overheads; however, the intra-cell pilot contamination might become even worse due to the users’ proximity. Reconfigurable intelligent surfaces (RISs), which are capable of smartly controlling the wireless channel, can be leveraged to achieve intra-cell pilot reuse. In this paper, our main contribution is an RIS-aided approach for intra-cell pilot reuse and the corresponding channel estimation method. Relying upon the knowledge of only statistical CSI, we then optimize the RIS phase-shifts based on a manifold optimization framework and the RIS positioning based on a deterministic approach. The extensive numerical results highlight the remarkable performance improvements achieved by the proposed scheme (for both uplink and downlink transmissions) compared to other alternatives.

Channel estimation in reconfigurable intelligent surfaces (RIS)-aided systems is crucial for optimal configuration of the RIS and various downstream tasks such as user localization. In RIS-aided systems, channel estimation involves estimating two channels for the user-RIS (UE-RIS) and RIS-base station (RIS-BS) links. In the literature, two approaches are proposed: (i) cascaded channel estimation where the two channels are collapsed into a single one and estimated using training signals at the BS, and (ii) separate channel estimation that estimates each channel separately either in a passive or semi-passive RIS setting. In this work, we study the separate channel estimation problem in a fully passive RIS-aided millimeter-wave (mmWave) single-user single-input multiple-output (SIMO) communication system. First, we adopt a variational-inference (VI) approach to jointly estimate the UE-RIS and RIS-BS instantaneous channel state information (I-CSI). In particular, auxiliary posterior distributions of the I-CSI are learned through the maximization of the evidence lower bound. However, estimating the I-CSI for both links in every coherence block results in a high signaling overhead to control the RIS in scenarios with highly mobile users. Thus, we extend our first approach to estimate the slow-varying statistical CSI of the UE-RIS link overcoming the highly variant I-CSI. Precisely, our second method estimates the I-CSI of RIS-BS channel and the UE-RIS channel covariance matrix (CCM) directly from the uplink training signals in a fully passive RIS-aided system. The simulation results demonstrate that using maximum a posteriori channel estimation using the auxiliary posteriors can provide a capacity that approaches the capacity with perfect CSI. Leveraging the UE-RIS CCM enhances spectral efficiency by minimizing the training overhead required to control the RIS, and exploiting its low-rank structure reduces training overhead compared to the maximum likelihood estimator.

We examine the problem of joint active and passive beamforming in a controllable multi-user reconfigurable intelligent surface (RIS)-assisted downlink and uplink wireless communication system, considering the mutual coupling among RIS elements. Due to the sub-wavelength structure, mutual coupling among RIS elements is unavoidable, and it inherently leads to multiple reflection effects that are ignored in conventional (approximative) RIS models. We formulate a joint non-convex problem under the MMSE criterion and use alternative optimization to convert the non-convex problem into two sub-problems for downlink and uplink transmissions separately. In both transmissions, one sub-problem involves optimizing the phase-shift matrix of RIS. In downlink, the other sub-problem is the optimization of active precoding for the base station (BS), while the equivalent sub-problem in uplink is the optimization of the linear receiver matrix. We optimize the phase shift matrix under a physically-consistent model using the gradient descent algorithm for both transmissions. We use the Lagrange multiplier method to optimize active precoding in the downlink and apply the First Order Necessary Condition (FONC) to optimize the linear receiver in the uplink. Simulation results are represented for both lossless and lossy RIS scenarios under perfect and imperfect channel state information. We discuss the impact of changing the number of RIS elements and the RIS element spacing on system performance. The results show that, with optimized phase shifts and active precoding, the inherent multiple reflection effect can improve the performance of RIS-aided wireless communications systems.

This paper develops a linear minimum mean-square error (LMMSE) channel estimator that takes advantage of the mutual coupling in antenna arrays. We model the mutual coupling through multiport networks and express the singleuser multiple-input multiple-output (MIMO) communication channel in terms of the impedance and scattering parameters of the antenna arrays. It is shown that appropriately accounting for mutual coupling through the developed physically consistent model leads to remarkable improvements in terms of channel estimation performance. We demonstrate the gains in our algorithm in a rich-scattering environment using a connected array of slot antennas both at the transmitter and receiver sides.

We consider a controllable multi-user wireless communications system based on reconfigurable intelligent surface (RIS) and investigate the problem of optimizing active and passive beamforming jointly, with the presence of mutual coupling effects. Due to the sub-wavelength structure, mutual coupling between RIS elements is unavoidable. We investigate the effect of mutual coupling among RIS elements resulting in multiple reflection effects which are ignored in conventional (approximative) RIS models. We propose a novel method to optimize the RIS phase shifters considering the effect of multiple reflections using a more physically-consistent model (exact RIS model). Numerical results show that the inherent multiple reflection effects along with the optimized phase shifters and the active precoder can improve the performance of RIS-aided wireless communications systems.

With the proliferation of deep learning techniques for wireless communication, several works have adopted learning-based approaches to solve the channel estimation problem. While these methods are usually promoted for their computational efficiency at inference time, their use is restricted to specific stationary training settings in terms of communication system parameters, e.g., signal-to-noise ratio (SNR) and coherence time. Therefore, the performance of these learning-based solutions will degrade when the models are tested on different settings than the ones used for training. This motivates our work in which we investigate continual supervised learning (CL) to mitigate the shortcomings of the current approaches. In particular, we design a set of channel estimation tasks wherein we vary different parameters of the channel model. We focus on Gauss-Markov Rayleigh fading channel estimation to assess the impact of non-stationarity on performance in terms of the mean square error (MSE) criterion. We study a selection of state-of-the-art CL methods and we showcase empirically the importance of catastrophic forgetting in continuously evolving channel settings. Our results demonstrate that the CL algorithms can improve the interference performance in two channel estimation tasks governed by changes in the SNR level and coherence time.

We present a unified model for connected antenna arrays with a large number of tightly integrated (i.e., coupled) antennas in a compact space within the context of massive multiple-input multiple-output (MIMO) communication. We refer to this system as tightly-coupled massive MIMO. From an information-theoretic perspective, scaling the design of tightly-coupled massive MIMO systems in terms of the number of antennas, the operational bandwidth, and form factor was not addressed in prior art. We investigate this open research problem using a physically consistent modeling approach for far-field (FF) MIMO communication based on multi-port circuit theory. In doing so, we turn mutual coupling (MC) from a foe to a friend of MIMO systems design, thereby challenging a basic percept in antenna systems engineering that promotes MC mitigation/compensation. We show that tight MC widens the operational bandwidth of antenna arrays thereby unleashing a missing MIMO gain that we coin “bandwidth gain”. Furthermore, we derive analytically the asymptotically optimum spacing-to-antenna-size ratio by establishing a condition for tight coupling in the limit of large-size antenna arrays with quasi-continuous apertures. We also optimize the antenna array size while maximizing the achievable rate under fixed transmit power and inter-element spacing. Then, we study the impact of MC on the achievable rate of MIMO systems under line-of-sight (LoS) and Rayleigh fading channels. These results reveal new insights into the design of tightly-coupled massive antenna arrays as opposed to the widely-adopted “disconnected” designs that disregard MC by putting faith in the half-wavelength spacing rule.

We propose a variational inference-based channel estimation method in fully passive reconfigurable intelligent surface (RIS)-aided mmWave single-user single-input multiple-output (SIMO) communication systems. The main goal is to jointly estimate the user equipment (UE)-to-RIS (UE-RIS) and RIS-to-base station (RIS-BS) channels using uplink training signals in a passive RIS setup. Specifically, by using a variational inference framework, we approximate the posterior of the channels with convenient distributions given the received uplink training signals. The parameters of the approximated distributions are generated by deep neural networks trained using variational loss functions derived using a lower bound on the log-likelihood of the received signal. Then, the learned distributions, which are close to the true posterior distributions in terms of Kullback Leibler divergence, are leveraged to obtain the maximum a posteriori (MAP) estimation of the UE-RIS and RIS-BS channels. We evaluate the proposed channel estimation solution under two channel priors. The first channel prior models Rayleigh fading channels with Gaussian prior, whereas the second one represents sparse channels in the angular domain with Laplace prior. The simulation results demonstrate that MAP channel estimates using the approximated posteriors yield a capacity which is close to the one achieved with the true posteriors, thus demonstrating the effectiveness of the proposed method.

We present a unified model for connected antenna arrays with a large number of tightly integrated (i.e., coupled) antennas in a compact space within the context of massive multiple-input multiple-output (MIMO) communication. We refer to this system as tightly-coupled massive MIMO. From an information-theoretic perspective, scaling the design of tightly-coupled massive MIMO systems in terms of the number of antennas, the operational bandwidth, and form factor was not addressed in prior art. We investigate this open research problem using a physically consistent modeling approach for far-field (FF) MIMO communication based on multi-port circuit theory. In doing so, we turn mutual coupling (MC) from a foe to a friend of MIMO systems design, thereby challenging a basic percept in antenna systems engineering that promotes MC mitigation/compensation. We show that tight MC widens the operational bandwidth of antenna arrays thereby unleashing a missing MIMO gain that we coin “bandwidth gain”. Furthermore, we derive analytically the asymptotically optimum spacing-to-antenna-size ratio by establishing a condition for tight coupling in the limit of large-size antenna arrays with quasi-continuous apertures. We also optimize the antenna array size while maximizing the achievable rate under fixed transmit power and inter-element spacing. Then, we study the impact of MC on the achievable rate of MIMO systems under line-of-sight (LoS) and Rayleigh fading channels. These results reveal new insights into the design of tightly-coupled massive antenna arrays as opposed to the widely-adopted “disconnected” designs that disregard MC by putting faith in the half-wavelength spacing rule.

This study focuses on the development of low-resolution symbol-level precoding techniques for multiuser MIMO downlink systems with PSK modulation. While for QPSK the established minimum symbol error probability criterion is used, a criterion for PSK modulation, in general, is proposed based on the minimum union-bound symbol-error probability. Based on these criteria different low-resolution precoding approaches are proposed. First, suboptimal solutions are computed via a partial greedy search method. Then the suboptimal solutions are utilized as initialization for a novel branch-and-bound algorithm that can exploit knowledge of the system’s quality-of-service demands. Different than existing branch-and-bound approaches the proposed quality-of-service branch-and-bound method searches for a solution that attains a target symbol-error probability while going in the direction of the global optimal solution. In this sense, the proposed branch-and-bound method allows for tunable complexity performance trade-offs. Numerical results confirm that the proposed quality-of-service branch-and-bound algorithm yields reduced symbol-error probability with significantly smaller computational complexity than other state-of-the-art branch-and-bound designs.

This paper introduces a novel information-theoretic approach for studying the effects of mutual coupling (MC), between the transmit and receive antennas, on the overall performance of single-input-single-output (SISO) near-field communications (NFC). By incorporating the finite antenna size constraint using Chu’s theory and under the assumption of canonical-minimum scattering (CMS), we derive the MC between two radiating volumes of fixed sizes. Expressions for the self and mutual impedances are obtained by the use of the reciprocity theorem. Based on a circuit-theoretic two-port model for SISO radio communication systems, we first establish its input-output relationship where the noise depends on the self/mutual impedances of the antennas, unlike the conventional assumption of independent additive white Gaussian noise. We then characterise the achievable data rate for a given pair of transmit and receive antenna sizes, thereby providing an upper bound on the system performance under physical size constraints. Through the lens of these findings, we shed new light on the influence of MC on the information-theoretic limits of near-field communications using compact antennas.

High-resolution fully digital transceivers are infeasible at millimeter-wave (mmWave) due to their increased power consumption, cost, and hardware complexity. The use of low-resolution converters is one possible solution to realize fully digital architectures at mmWave. In this paper, we consider a setting in which a fully digital base station with constant envelope quantized (CEQ) digital-to-analog converters on each radio frequency chain communicates with multiple single antenna users with individual signal-to-quantization-plus-interference-plus-noise ratio (SQINR) constraints over frequency selective channels. We first establish uplink downlink duality for the system with CEQ hardware constraints and OFDM-based transmission considered in this paper. Based on the uplink downlink duality principle, we present a solution to the multi-user multi-carrier beamforming and power allocation problem that maximizes the minimum SQINR over all users and sub-carriers. We then present a per sub-carrier version of the originally proposed solution that decouples all sub-carriers of the OFDM waveform resulting in smaller sub-problems that can be solved in a parallel manner. Our numerical results based on 3GPP channel models generated from Quadriga demonstrate improvements in terms of ergodic sum rate and ergodic minimum rate over state-of-the-art linear solutions. We also show improved performance over non-linear solutions in terms of the coded bit error rate with the increased flexibility of assigning individual user SQINRs built into the proposed framework.

This paper introduces a new algorithmic solution to the massive unsourced random access (mURA) problem. The proposed uncoupled compressed sensing (UCS)-based scheme relies on slotted transmissions and takes advantage of the inherent coupling provided by the users’ spatial signatures in the form of channel correlations across slots to completely eliminate the need for concatenated coding. As opposed to all existing methods, the proposed solution combines the steps of activity detection, channel estimation, and data decoding into a unified mURA framework. It capitalizes on the bilinear vector approximate message passing (Bi-VAMP) algorithm, tailored to fit the inherent constraints of mURA. Exhaustive computer simulations demonstrate that the proposed scheme outperforms recent coupled and uncoupled mURA schemes in massive connectivity/MIMO setup.

This paper introduces a novel information-theoretic approach for studying the effects of mutual coupling (MC), between the transmit and receive antennas, on the overall performance of single-input-single-output (SISO) near-field communications. By incorporating the finite antenna size constraint using Chu’s theory and under the assumption of canonical-minimum scattering, we derive the MC between two radiating volumes of fixed sizes. Expressions for the self and mutual impedances are obtained by the use of the reciprocity theorem. Based on a circuit-theoretic two-port model for SISO radio communication systems, we establish the achievable rate for a given pair of transmit and receive antenna sizes, thereby providing an upper bound on the system performance under physical size constraints. Through the lens of these findings, we shed new light on the influence of MC on the information-theoretic limits of near-field communications using compact antennas.

We derive an achievability bound in an uplink setting where N single-antenna devices, of which a random subset of Kausers are active in each transmission period, attempt to update a base-station (BS), equipped with M antennas, with their status packets. Motivated by emerging applications of massive connectivity we consider the asymptotic scenario where both the total number of users and the number of antennas at the BS grow large at a fixed ratio $\zeta = \frac{M}{N}$. Under maximal-ratio combining and perfect channel state information at the receiver, we find that the achievable rate approaches ${\log _2}\left( {1 + \frac{M}{{{K_a}}}} \right)$ in the large system limit. We explore the trade-offs between this achievable rate and the freshness of the status packets using the age of information (AoI) metric. In the limiting regime, we find that the penalty one pays for increasing the data rate is a rise in the minimum AoI obtainable. Finally, we compare recent massive unsourced random access (URA) schemes against the newly established bound.

This paper introduces a novel approach of utilizing the reconfigurable intelligent surface (RIS) for joint data modulation and signal beamforming in a multi-user downlink cellular network by leveraging the idea of backscatter communication. We present a general framework in which the RIS, referred to as modulating intelligent surface (MIS) in this paper, is used to:$i$) beamform the signals for a set of users whose data modulation is already performed by the base station (BS), and at the same time,$ii$) embed the data of a different set of users by passively modulating the deliberately sent carrier signals from the BS to the RIS. To maximize each user’s spectral efficiency, a joint non-convex optimization problem is formulated under the sum minimum mean-square error (MMSE) criterion. Alternating optimization is used to divide the original joint problem into two tasks of:$i$) separately optimizing the MIS phase-shifts for passive beamforming along with data embedding for the BS- and MIS-served users, respectively, and$ii$) jointly optimizing the active precoder and the receive scaling factor for the BS- and MIS-served users, respectively. While the solution to the latter joint problem is found in closed-form using traditional optimization techniques, the optimal phase-shifts at the MIS are obtained by deriving the appropriate optimization-oriented vector approximate message passing (OOVAMP) algorithm. Moreover, the original joint problem is solved under both ideal and practical constraints on the MIS phase shifts, namely, the unimodular constraint and assuming each MIS element to be terminated by a variable reactive load. The proposed MIS-assisted scheme is compared against state-of-the-art RIS-assisted wireless communication schemes and simulation results reveal that it brings substantial improvements in terms of system throughput while supporting a much higher number of users.

As larger bandwidths are used in multiple antenna wireless systems, the frequency selectivity of the antenna arrays starts to impact rate. Motivated by optimizing the achievable rate in compact antenna arrays, we present a system model that incorporates the effects of mutual coupling (MC) of wideband physically realizable single-input multiple-output (SIMO) and multiple-input single-output (MISO) antenna systems. For the SIMO system setup, we extract the noise correlation matrices for two different antenna array configurations (parallel and co-linear). We optimize the inter-element spacing in each alignment while maximizing the achievable rate and fixing the transmit power. Then, we compare the two compact antenna designs to a perfectly matched single omni-directional antenna while accounting for MC. Likewise, for the MISO antenna system, we derive the optimal beamformer that maximizes the achievable rate using the same antenna configurations as the SIMO system. Then, we study the impact of MC and develop a new single-port matching technique for wideband antenna arrays. Finally, we provide reciprocity plots to compare the performance of the SIMO-MISO systems using different channel models.

Using ideas from Chu and Bode/Fano theories, we characterize the maximum achievable rate over the single-input single-output wireless communication channels under a restriction on the antenna size at the receiver. By employing circuit-theoretic multiport models for radio communication systems, we derive the information-theoretic limits of compact antennas. We first describe an equivalent Chu’s antenna circuit under the physical realizability conditions of its reflection coefficient. Such a design allows us to subsequently compute the achievable rate for a given receive antenna size thereby providing a physical bound on the system performance that we compare to the standard size-unconstrained Shannon capacity. We also determine the effective signal-to-noise ratio (SNR) which strongly depends on the antenna size and experiences an apparent finite-size performance degradation where only a fraction of Shannon capacity can be achieved. We further determine the optimal signaling bandwidth which shows that impedance matching is essential in both narrowband and broadband scenarios. We also examine the achievable rate in presence of interference showing that the size constraint is immaterial in interference-limited scenarios. Finally, our numerical results of the derived achievable rate as function of the antenna size and the SNR reveal new insights for the physically consistent design of radio systems.

We investigate the age-limited capacity of the Gaussian many channel with total$N$users, out of which a random subset of$K_{a}$users are active in any transmission period, and a large-scale antenna array at the base station (BS). In an uplink scenario where the transmission power is fixed among the users, we consider the setting in which both the number of users,$N$, and the number of antennas at the BS,$M$, are allowed to grow large at a fixed ratio$\zeta = {M}/{N}$. Assuming perfect channel state information (CSI) at the receiver, we derive the achievability bound under maximal ratio combining. As the number of active users,$K_{a}$, increases, the achievable spectral efficiency is found to increase monotonically to a limit$\log _{2}\left ({1+\frac {M}{K_{a}}}\right)$. Further extensions of the analysis to the zero-forcing receiver as well as imperfect CSI are provided, demonstrating the channel estimation penalty in terms of the mean squared error in estimation. Using the age of information (AoI) metric, first coined by Kaul et al., as our measure of data timeliness or freshness, we investigate the trade-offs between the AoI and spectral efficiency in the context massive connectivity with large-scale receiving antenna arrays. As an extension of Liu and Yu, based on our large system analysis, we provide an accurate characterization of the asymptotic (finite system size) spectral efficiency as a function of the number of antennas and the number of users, the attempt probability, and the AoI. It is found that while the spectral efficiency can be made large, the penalty is an increase in the minimum AoI obtainable. The proposed achievability bound is further compared against recent massive MIMO-based massive unsourced random access (URA) schemes.

Nonlinear precoding and pulse shaping are jointly considered in multi-user massive multiple-input multiple-output (MIMO) systems with low resolution D/A-converters (DACs) in terms of algorithmic approach as well as large system performance. Two design criteria are investigated: the mean squared error (MSE) with active constellation extension (ACE) and the symbol error rate (SER). Both formulations are solved based on a modified version of the generalized approximate message passing (GAMP) algorithm. Furthermore, theoretical performance results are derived based on the state evolution analysis of the GAMP algorithm. The MSE based technique is extended to jointly perform over-the-air (OTA) spectral shaping and precoding for frequency-selective channels, in which the spectral performance is characterized at the transmitter and at the receiver. Simulation and analytical results demonstrate that the MSE based approach yields the same performance as the SER based formulation in terms of uncoded SER. The analytical results provide good performance predictions up to medium SNR. Substantial improvements in detection, as well as spectral performance, are obtained from the proposed combined pulse shaping and precoding approach compared to standard linear methods.

We put forward a new algorithmic solution to the massive unsourced random access (URA) problem, by leveraging the rich spatial dimensionality offered by large-scale antenna arrays. This paper makes an observation that spatial signature is key to URA in massive connectivity setups. The proposed scheme relies on a slotted transmission framework but eliminates the need for concatenated coding that was introduced in the context of the coupled compressive sensing (CCS) paradigm. Indeed, all existing works on CCS-based URA rely on an inner/outer tree-based encoder/decoder to stitch the slot-wise recovered sequences. This paper takes a different path by harnessing the nature-provided correlations between the slot-wise reconstructed channels of each user in order to put together its decoded sequences. The required slot-wise channel estimates and decoded sequences are first obtained through the hybrid generalized approximate message passing (HyGAMP) algorithm which systematically accommodates the multiantenna-induced group sparsity. Then, a channel correlation-aware clustering framework based on the expectation-maximization (EM) concept is used together with the Hungarian algorithm to find the slot-wise optimal assignment matrices by enforcing two clustering constraints that are very specific to the problem at hand. Stitching is then accomplished by associating the decoded sequences to their respective users according to the ensuing assignment matrices. Exhaustive computer simulations reveal that the proposed scheme can bring performance improvements, at high spectral efficiencies, as compared to a state-of-the-art technique that investigates the use of large-scale antenna arrays in the context of massive URA.

This paper tackles the problem of joint active and passive beamforming optimization for an intelligent reflective surface (IRS)-assisted multi-user downlink multiple-input multiple-output (MIMO) communication system under both ideal and practical IRS phase shifts. We aim to maximize the spectral efficiency of the users by minimizing the sum mean square error (MSE) of the users’ received symbols. For this, a joint non-convex optimization problem is formulated under the sum minimum mean square error (MMSE) criterion. Alternating minimization is used to break the original joint optimization problem into the separate optimization of the active precoding matrix for the base station (BS) and the matrix of phase shifts for the IRS. While the MMSE active precoder is obtained in closed-form, the IRS phase shifts are optimized iteratively using a modified version (developed in this paper) of the vector approximate message passing (VAMP) algorithm. Moreover, the underlying joint optimization problem is solved under two different models for the IRS phase shifts, namely by assuming$i$) a unimodular (i.e., ideal) constraint on the reflection coefficients and$ii$) a more practical reflection elements termination by a variable reactive load (which inherently introduces the phase-dependent amplitude attenuation in the IRS phase shifts). Simulation results are presented to illustrate the performance of the proposed method under both perfect and imperfect channel state information (CSI) and to show the effect of the practical constraint on the system throughput. The results validate the superiority of the proposed method over the state-of-the-art techniques both in terms of throughput and computational complexity.

A fully-digital mmWave wideband JCR places difficult demands of power consumption and hardware complexity on the receivers' analog-to-digital converters (ADCs). To address these concerns, we present a low-complexity proof-of-concept (PoC) development for a wideband MIMO JCR that uses a mmWave communications waveform and low-resolution ADCs, while maintaining a separate radio-frequency chain per antenna. To accurately characterize the radar performance of our developed PoC tested, we conduct experiments using a trihedral corner reflector and apply traditional as well as advanced receiver processing algorithms. The results demonstrate that our MIMO PoC platform with a fully-digital JCR waveform at 73 GHz carrier frequency, 2 GHz bandwidth, and 1-bit ADCs achieves high range/direction estimation accuracy as well as high detection capability with a wide field of view.

We analyze the performance of multiple-input multiple-output (MIMO) links with one-bit output quantization in terms of achievable rates and characterize their performance loss compared to unquantized systems for general channel statistical models and general channel state information (CSI) at the receiver. One-bit ADCs are particularly suitable for large-scale millimeter wave MIMO Communications (massive MIMO) to reduce the hardware complexity. In such applications, the signal-to-noise ratio per antenna is rather low due to the propagation loss. Thus, it is crucial to analyze the performance of MIMO systems in this regime by means of information-theoretical methods. Since an exact and general information-theoretic analysis is not possible, we resort to the derivation of a general asymptotic expression for the mutual information in terms of a second-order expansion around zero SNR. We show that up to second order in the SNR, the mutual information of a system with two-level (sign) output signals incorporates only a power penalty factor of π/2 (1.96 dB) compared to systems with infinite resolution for all channels of practical interest with perfect or statistical CSI. An essential aspect of the derivation is that we do not rely on the common pseudo-quantization noise model.

In this paper we present a novel capacity optimization methodology for a two-element multi-band single-input multiple-output (SIMO) antenna. We first introduce a system model that incorporates antenna mutual coupling in the wideband regime. Then, we extract the noise correlation matrices from two different antenna configurations (parallel and co-linear). We optimize the spacing in each scenario while maximizing the Shannon capacity under transmit power constraint. Consequently, we compare the two compact planar antenna designs to the AWGN capacity baseline at different inter- element spacing while accounting for mutual coupling. Finally, we introduce a matching network that maximizes the capacity of the wideband SIMO system.

Millimeter wave (mmWave) systems can enable high data rates if the link between the transmitting and receiving radios is configured properly. Fast configuration of mmWave links, however, is challenging due to the use of large antenna arrays and hardware constraints. For example, a large amount of training overhead is incurred by exhaustive search-based beam alignment in typical mmWave phased arrays. In this paper, we present a framework called FALP for Fast beam Alignment with Low-resolution Phase shifters. FALP uses an efficient set of antenna weight vectors to acquire channel measurements, and allows faster beam alignment when compared to exhaustive scan. The antenna weight vectors in FALP can be realized in ultra-low power phase shifters whose resolution can be as low as one-bit. From a compressed sensing (CS) perspective, the CS matrix designed in FALP satisfies the restricted isometry property and allows CS algorithms to exploit the fast Fourier transform. The proposed framework also establishes a new connection between channel acquisition in phased arrays and magnetic resonance imaging.

In this contribution, we investigate a coarsely quantized multi-user multiple-input single-output downlink communication system, where we assume 1-bit digital-to-analog converters at the base station antennas. First, we analyze the achievable sum rate lower-bound using the Bussgang decomposition under new assumptions. In the presence of the non-linear quantization, our analysis indicates the potential merit of reconsidering traditional signal processing techniques in coarsely quantized systems, i.e., reconsidering transmit covariance matrices whose rank is equal to the rank of the channel. Furthermore, in the latter part of this paper, we propose a linear precoder design that achieves the predicted increase in performance compared with a state-of-the-art linear precoder design. Moreover, our linear signal processing algorithm allows for higher order modulation schemes to be employed.

We consider a multipair half-duplex relay communication network, where the relay is deployed with one-analog-to-digital converters and one-bit digital-to-analog converters. To suppress the interpair interference and quantization artifacts, we propose nonlinear precoding schemes to forward the quantized signals at the relay. We first present a technique based on gradient projection, and then show how to refine the solution using ordered quantization and perturbation methods. For the single-user case with BPSK symbols, we obtain a closed-form solution for the optimal transmit vector. Numerical results verify that the proposed precoding design significantly outperforms quantized linear precoding strategies.

Coarsely quantized massive multiple-input multiple-output (MIMO) systems are gaining more interest due to their power efficiency. We present a new precoding technique to mitigate the multi-user interference and the quantization distortions in a downlink multi-user MIMO system with coarsely quantized constant envelope (QCE) signals at the transmitter. The transmit signal vector is optimized for every desired received vector taking into account a relaxed version of the QCE constraint. The optimization is based on maximizing the safety margin to the decision thresholds of the receiver constellation modulation. Due to the linear property of the objective function and the constraints, the optimization problem is formulated as a linear programming problem. The simulation results show a significant gain in terms of the uncoded bit error rate compared to the existing precoding techniques.

In this contribution, we provide an information theoretical analysis of coarsely-quantized downlink Single-User (SU)- Multiple Input Single Output (MISO) communication systems. We address the question of whether traditional signal processing techniques, i.e., proper signaling and channel rank transmit covariance matrices, are still optimal with respect to maximizing the data rate. We investigate the mutual information lower bound based on the Bussgang theorem, in the SU-MISO downlink scenario, where we assume 1-bit quantized Digital-to-Analog Converters (DACs) in the transmit antennas at the Base Station (BS). We prove that at low Signal-to-Noise Ratio (SNR), existing signal processing techniques maximize the data rate. However, at higher SNR we show, using counter examples, that the data rates can be improved using different signal processing techniques. These results show the potential merit of reconsidering signal processing techniques in coarsely- quantized SU-MISO downlink scenarios.

We study the impact of using one-bit analog-to-digital and digital-to-analog converters in a multipair amplify-and-forward MIMO relaying system. The relay estimates the channel state information using training data, and then uses the channel estimate to perform maximum ratio combining and maximum ratio transmission. An exact achievable rate is derived for the system under general assumptions on the quantization noise, and then a closed-form asymptotic approximation is derived, which enables efficient evaluation of the impact of key parameters on system performance. Contrary to the conventional unquantized systems, the performance is seen to depend on the specific pilot sequences that are employed. In addition, the sum rate gap between the double quantized relay system and an ideal unquantized system is shown to be a factor of 4/π2in the low source power regime.

Linear precoders have been shown to perform reasonably well at low SNR when the basestation of a MIMO downlink employs one-bit digital-to-analog converters to quantize the precoder outputs. However, at medium-to-high SNRs, an error floor is encountered due to the coarse quantization. This paper examines methods for slightly perturbing the transmitted signal prior to quantization in an effort to improve downlink performance at higher SNRs. The perturbation is performed with the goal of minimizing the worst-case probability of error among the user terminals, and assumes that the symbols to be transmitted are drawn from a finite alphabet constellation. Two different types of perturbations are studied, and it is found via simulation that the methods can provide dramatic gains in downlink performance.

This paper considers training-based transmissions in massive multi-input multi-output (MIMO) systems with one-bit analog-to-digital converters (ADCs). We assume that each coherent transmission block consists of a pilot training stage and a data transmission stage. The base station (BS) first employs the linear minimum mean-square-error (LMMSE) method to estimate the channel and then uses the maximum-ratio combining (MRC) receiver to detect the data symbols. We first obtain an approximate closed-form expression for the uplink achievable rate in the low SNR region. Then based on the result, we investigate the optimal training length that maximizes the sum spectral efficiency for two cases: i) The training power and the data transmission power are both optimized; ii) The training power and the data transmission power are equal. Numerical results show that, in contrast to conventional massive MIMO systems, the optimal training length in one-bit massive MIMO systems is greater than the number of users and depends on various parameters such as the coherence interval and the average transmit power. Also, unlike conventional systems, it is observed that in terms of sum spectral efficiency, there is relatively little benefit to separately optimizing the training and data power.

We propose a novel linear minimum-mean-squared-error (MMSE) precoder design for a downlink (DL) massive multiple-input-multiple-output (MIMO) scenario. For economical and computational efficiency reasons low resolution 1-bit digital-to-analog (DAC) and analog-to-digital (ADC) converters are used. This comes at the cost of performance gain that can be recovered by the large number of antennas deployed at the base station (BS) and an appropriate pre-coder design to mitigate the distortions due to the coarse quantization. The proposed precoder takes the quantization non-linearities into account and is split into a digital precoder and an analog precoder. We formulate the two-stage precoding problem such that the MSE of the users is minimized under the 1-bit constraint. In the simulations, we compare the new optimized precoding scheme with previously proposed linear precoders in terms of uncoded bit error ratio (BER).

The mitigation of nonlinear distortion caused by power amplifiers (PA) in Orthogonal Frequency Division Multiplexing (OFDM) systems is an essential issue to enable energy efficient operation. We propose a new algorithm for receiver-based clipping estimation in OFDM systems that combines the existing Iterative Hard Thresholding method with a novel Bayesian framework to estimate clipping parameters at the receiver. We avoid the use of pilots and formulate the recovery problem solely on reliably detected sub-carriers. We also develop a new criterion for selecting these reliable carriers that takes into account the channel code. Through simulations, we show that the proposed technique outperforms the existing methods both in terms of BER and speed.

We consider the downlink of a cellular network with multiple antenna base stations and single antenna user terminals. In cellular networks with and without cooperation, the problem of instationary intercell interference arises. Some base stations change their beamforming unpredictably and the signal to interference plus noise ratios of the served user terminals are unknown at the base station. Consequently, the precoding and link rate adaption are outdated and the transmission might fail. Hybrid automatic repeat request can be used to mitigate the risk of such a fail. We propose to optimize the precoders at the base stations based on the expectation of the rate, where we include the effects of soft combining in the optimization.

The problem of optimum analog receive filtering for digital signal detection and parameter estimation is considered. Here the case of a signal source with bandwidth Btand a receiver with fixed sampling rate fsis discussed under the assumption that 2Bt> fs. We investigate the impact of adjusting the receive bandwidth Brof the analog pre-filter, which is applied prior to the sampler, with respect to the deflection coefficient or the Fisher information measure. This reveals that the design rule 2Brs, known as the sampling theorem, does not necessarily lead to optimum system performance. Studying the two analytical information measures under a fix sampling rate fsand an arbitrary choice of Br, we provide an example where receive setups with 2Br> fsachieve higher detection and parameter estimation performance.

To communicate over a priori unknown channels, pilot sequences can be exploited to assist the receiver in obtaining the channel state information. The analog-to-digital converter (ADC) at the front end of the receiver samples and quantizes the input signal, including both pilot and data symbols. This results in a reduction of the receive signal-to-noise ratio (SNR) as well as deteriorated quality of channel estimation, which depends quantitatively on the bit resolution used by the ADC. In this work, we consider the point-to-point, training based communication between a single-antenna transmitter and a multi-antenna receiver over a Rayleigh block fading channel, and take into account the impact of the ADC for a joint optimization of the training length, the average receive SNR, the number of receive antennas, and the bit resolution of the ADC. Goal of the optimization is to minimize the energy per bit metric, where we include both transmit power and power dissipation of the ADC into the energy consumption model, and employ a capacity lower bound which depends on all aforementioned design parameters. Results from numerical simulations are demonstrated and analyzed, leading to a number of insightful observations and conclusions which are important for the energy efficient operation of the system.

The problem of parameter estimation from large noisy data is considered. If the observation size N is large, the calculation of efficient estimators is computationally expensive. Further, memory can be a limiting factor in technical systems where data is stored for later processing. Here we follow the idea of reducing the size of the observation by projecting the data onto a subspace of smaller dimension M ≪ N, but with the highest possible informative value regarding the estimation problem. Under the assumption that a prior distribution of the parameter is available and the output size is fixed to M, we derive a characterization of the Pareto-optimal set of linear transformations by using a weighted form of the Bayesian Cramér-Rao lower bound (BCRLB) which stands in relation to the expected value of the Fisher information measure. Satellite-based positioning is discussed as a possible application. Here N must be chosen large in order to compensate for low signal-to-noise ratios (SNR). For different values of M, we visualize the information-loss and show by simulation of the MAP estimator the potential accuracy when operating on the reduced data.

The problem how to approximately determine the value of the Fisher information measure for a general parametric probabilistic system is considered. Having available the first and second moment of the system output in a parametric form, it is shown that the information measure can be bounded from below through a replacement of the original system by a Gaussian system with equivalent moments. The presented technique is applied to a system of practical importance and the potential quality of the bound is demonstrated.

The available channel state information (CSI) in a limited feedback system like the frequency division duplex (FDD) downlink is not perfect since it is subject to estimation, quantization and feedback errors, and in addition, can be outdated. Despite the fact that the capacity of limited feedback systems is unknown in general, we derive a novel lower bound on the capacity of single user limited feedback systems with imperfect CSI. Based on this bound, we propose the design of an FDD system by finding the optimum training and number of feedback bits. To this end we also take the FDD uplink into account, since practical FDD systems represent two-way systems. We also provide closed-form approximations for the optimum downlink training, uplink training and number of feedback bits which basically maximize lower bounds on the FDD downlink and uplink capacity with imperfect CSI.

Using coarse resolution analog-to-digital conversion (ADC) offers the possibility to reduce the complexity of digital receive systems but introduces a loss in effective signal-to-noise ratio (SNR) when comparing to ideal receivers with infinite resolution ADC. Therefore, here the problem of signal parameter estimation from a coarsely quantized receive signal is considered. In order to increase the system performance, we propose to adjust the analog radio front-end to the quantization device in order to reduce the quantization-loss. By optimizing the bandwidth of the analog filter with respect to a weighted form of the Cramér-Rao lower bound (CRLB), we show that for low SNR and a 1-bit hard-limiting device it is possible to significantly reduce the quantizationloss of initially -1.96 dB. As application, joint carrier-phase and time-delay estimation for satellite-based positioning and synchronization is discussed. Simulations of the maximum-likelihood estimator (MLE) show that the optimum estimator achieves the same quantization-loss reduction as predicted by the performance bound of the optimized system.

In this paper, we analyze the fundamental trade-off between information transfer and power gain by means of an information-theoretic framework in communications circuits. This analysis is of interest as many of today's applications require that maximum information and maximum signal power are extracted (or transferred) through the circuit at the same time for further processing so that a compromise concerning the signal spectral shape as well as the matching network has to be found. To this end, the optimization framework is applied to a two-port circuit, which is used as an abstraction for a broadband amplifier. Thereby, we characterize the involved Pareto bound by considering different optimization problems. The first one aims at optimizing the input power spectral density (PSD) as well as the source and load admittances, whereas the second approach assumes the PSD to be fixed and uniformly distributed within a fixed bandwidth and optimizes the source and load admittances only. Moreover, we will show that additional matching networks may help to improve the trade-off.

For the Multiuser Multiple-Input Single-Output (MUMISO) system in a downlink (DL) scenario, we design a non-linear precoding scheme, namely the spatial Tomlinson-Harashima precoder (S-THP) based on the total mean squared error (MSE) criterion. The channel state information (CSI) available at the transmitter for the precoding is the quantized channel direction information (CDI) relayed back in a frequency division duplex (FDD) system from each receiver using B feedback bits received erroneously and quasi-instantaneous. The proposed MMSE-STHP with a suboptimum precoding order clearly outperforms the linear precoding scheme given in starting from a certain Signal-to-Noise Ratio (SNR) and a number of feedback bits in terms of Bit Error Ratio (BER).

In this contribution we propose a simple method of obtaining a perfect reconstruction (PR) FIR prototype filter starting from a given near-perfect reconstruction (NPR) prototype filter for complex modulated filter banks. Our method consists of two steps. First, we calculate the product of a pair of polyphase components that fulfill the PR conditions, and second, we search for spectral factors that maximize the correlation coefficients between the impulse response of the new polyphase components and of the original ones. Our preliminary numerical results show that the price to be payed to satisfy the PR conditions is some loss in the spectral characteristics.

We consider the minimization of the overall power consumption when communicating over a noisy single-input single-output channel while satisfying a certain throughput and error rate constraint. The total power dissipation includes the radiated power as well as the circuit power consumption. In the context of battery operated short range communication, where low power, low cost and small size are key requirements (e.g. standard IEEE 802.15.4), this circuit aware system optimization is crucial given the growing importance of “Green Communication”. In fact, the power dissipation of certain analog and digital components along the signal path reaches values in the order of or is even higher than the transmit power in such applications. Using an appropriate information-theoretic framework we derive the optimal bit-resolution of the analog-to-digital converter (ADC), the optimal choice of the noise figure for the low noise amplifier (LNA), the optimal operating input back-off (IBO) of the power amplifier (PA), as well as the optimum decoding (DEC) strategy as a function of the path-loss (i.e. the communication distance), that guarantee a certain net data rate R under a certain error probability Pe. This work is an extension to the work [1], where only the power of the analog components was considered.

The availability of the Channel State Information (CSI) at the transmitter is crucial for the precoder design in Multi-user Multiple Input Single Output (MU-MISO) systems. In Frequency Division Duplex (FDD) systems, CSI can be just available at the transmitter through a limited feedback channel [1], where we assume that each user estimates, normalizes, and finally quantizes its channel direction with a finite number of quantization bits relayed back error-free and quasi-instantaneous. In this paper, we consider a simple sequential uniform scalar quantization (SQ) scheme of the individual components (real and imaginary parts) of the Channel Direction Information (CDI). Although vector quantization (VQ) schemes [2], [3] still outperform this scalar scheme in terms of quantization error and Bit Error Rate (BER), the former scheme suffers from an exponential search complexity and high storage requirements at the receiver for high number of feedback bits.

We present a channel-matched coding method for the compensation of dispersion in coarsely quantized coherent optical communication systems. The approach consists in optimizing a subset of equiprobable input symbols for each channel state, which is robust against dispersion and coarse quantization. This codebook optimization is solved efficiently using an iterative algorithm. The performance of the channel-matched codes is simulated for a 42.8Gbit/s dual-polarization coherent optical fiber system with QPSK modulation and single-bit analog-to-digital (A/D) conversion. Apart from the optimization, the proposed coding method does not increase the encoding/decoding complexity, but yields a considerable coding gain.

We consider the maximization of the energy efficiency when communicating over a noisy single-input single-output channel, taking into account the transmit power as well as the power consumption of the analog-to-digital converter (ADC). This analysis is of interest in the context of energy constrained short range communication where low power, low cost and small size are key requirements (e.g. standard IEEE 802.15.4). In fact, the transmit power in such applications become smaller and reaches values in the order of the conversion or the processing power. Using an appropriate information-theoretic framework we show that the analog-to-digital converters (ADCs) for short range communication should be low-resolution, in order to reduce the overall power consumption. In addition we derive the optimal operating bit-resolution and signal-to-noise ratio (SNR) as function of the path-loss (i.e. the communication distance).

In multiple-antenna communications, as bandwidth and modulation order increase, system components must work with demanding tolerances. In particular, high resolution and high sampling rate analog-to-digital converters (ADCs) are often prohibitively challenging to design. Therefore ADCs for such applications should be low-resolution. This paper provides new insights into the problem of optimal signal detection based on quantized received signals for multiple-input multiple-output (MIMO) channels. It capitalizes on previous works which extensively analyzed the unquantized linear vector channel using graphical inference methods. In particular, a “loopy” belief propagation-like (BP) MIMO detection algorithm, operating on quantized data with low complexity, is proposed. In addition, we study the impact of finite receiver resolution in fading channels in the large-system limit by means of a state evolution analysis of the BP algorithm, which refers to the limit where the number of transmit and receive antennas go to infinity with a fixed ratio. Simulations show that the theoretical findings might give accurate results even with moderate number of antennas.

We consider general multi-antenna fading channels with coarsely quantized outputs, where the channel is unknown to the transmitter and receiver. This analysis is of interest in the context of sensor network communication where low power and low cost are key requirements (e.g. standard IEEE 802.15.4 applications). This is also motivated by highly energy constrained communications devices where sampling the signal may be more energy consuming than processing or transmitting it. Therefore the analog-to-digital converters (ADCs) for such applications should be low-resolution, in order to reduce their cost and power consumption. In this paper, we consider the extreme case of only 1-bit ADC for each receive signal component. We derive asymptotics of the mutual information up to the second order in the signal-to-noise ratio (SNR) under average and peak power constraints and study the impact of quantization. We show that up to second order in SNR, the mutual information of a system with two-level (sign) output signals incorporates only a power penalty factor of almost ¿/2 (1.96 dB) compared to the system with infinite resolution for all channels of practical interest. This generalizes a recent result for the coherent case.

We study the joint optimization of the quantizer and the spatial decision feedback equalizer (DFE) for the flat multi-input multi-output (MIMO) channel with quantized outputs. Our design is based on a minimum mean square error (MMSE) approach, taking into account the effects of quantization. Our derivation does not make use of the assumption of uncorrelated white quantization errors and considers the correlations of the quantization error with the other signals of the system. Through simulation, we compare the new DFE to the conventional spatial DFE operating on quantized data in terms of uncoded BER.

We consider multi-input multi-output (MIMO) Rayleigh-fading channels with coarsely quantized outputs, where the channel is unknown to the transmitter and receiver. This analysis is of interest in the context of sensor network communication with low cost devices. The key point is that the analog-to-digital converters (ADCs) for such applications should be low-resolution, in order to reduce their cost and power consumption. In this paper, we consider the extreme case of only 1-bit ADC for each receive signal component. We elaborate on some properties of the mutual information compared to the unquantized case. For the SISO case, we show that on-off QPSK signaling is the capacity achieving distribution. To our knowledge, the block-wise Rayleigh-fading channel with mono-bit detection was not studied in the literature.

We study the performance of multi-input multi-output (MIMO) channels with coarsely quantized outputs in the low signal-to-noise ratio (SNR) regime, where the channel is perfectly known at the receiver. This analysis is of interest in the context of ultra-wideband (UWB) communications from two aspects. First the available power is spread over such a large frequency band, that the power spectral density is extremely low and thus the SNR is low. Second the analog-to-digital converters (ADCs) for such high bandwidth signals should be low-resolution, in order to reduce their cost and power consumption. In this paper we consider the extreme case of only 1-bit ADC for each receive signal component. We compute the mutual information up to second order in the SNR and study the impact of quantization. We show that, up to first order in SNR, the mutual information of the 1-bit quantized system degrades only by a factor of 2/pi compared to the system with infinite resolution independent of the actual MIMO channel realization. With channel state information (CSI) only at receiver, we show that QPSK is, up to the second order, the best among all distributions with independent components. We also elaborate on the ergodic capacity under this scheme in a Rayleigh flat-fading environment.