This letter addresses the quadratic equality constrained least squares (QEC-LS) problem, a class of non-convex optimization problems that arise in various signal processing and communication applications. We revisit the alternating direction method of multipliers (ADMM) approach to QEC-LS problem and investigate its convergence and efficiency. Despite the inherent non-convexity, the proposed ADMM algorithm is proved to converge globally only requiring the quadratic term equal to a positive constant. Numerical results demonstrate that our method achieves global optimality with significantly reduced complexity compared to existing approaches such as semidefinite relaxation and primal-dual methods.

Code-switching is a widely prevalent linguistic phenomenon in multilingual societies like India. Building speech-to-text models for code-switched speech is challenging due to limited availability of datasets. In this work, we focus on the problem of spoken translation (ST) of code-switched speech in Indian languages to English text. We present a new end-to-end model architecture CoSTA that scaffolds on pretrained automatic speech recognition (ASR) and machine translation (MT) modules (that are more widely available for many languages). Speech and ASR text representations are fused using an aligned interleaving scheme and are fed further as input to a pretrained MT module; the whole pipeline is then trained end-to-end for spoken translation using synthetically created ST data. We also release a new evaluation benchmark for code-switched Bengali- English, Hindi-English, Marathi-English and Telugu-English speech to English text. CoSTA significantly outperforms many competitive cascaded and end-to-end multimodal baselines by up to 3.5 BLEU points.

This paper presents an approach to target detection in automotive radar systems, where the highly dynamic nature of the sensor platform and environment, along with challenges such as hardware cost and installation constraints, necessitates a general sensor configuration that integrates widely separated, colocated MIMO, and distributed aperture radar (DAR). A joint Doppler processing and MIMO transmit demodulation technique is proposed, utilizing arbitrary DDM, TDM, or BPM precoding matrices with a lower-dimensional antenna steering matrix as the detection input. The signal model incorporates environmental information, such as the cell under test (CUT) range and line-of-sight regions, to enhance detection performance. A distributed Generalized Likelihood Ratio Test (GLRT) detector is derived using secondary data with CFAR property, and the robustness of the proposed detectors is evaluated under mismatch conditions using mesa plots. Simulation results demonstrate the effectiveness of the proposed approach, evaluated using key performance metrics such as probability of detection, probability of false alarm.

Environmental sensing can be achieved using Wireless Sensor Networks (WSNs) with low-complexity sensors. Integrated sensing and communication (ISAC) allows simultaneous sensing and data transmission through optimized waveform, architecture, and protocol design. Given the multiple sensors in WSNs, minimizing interference and ensuring Doppler-tolerant pulses for detecting dynamic targets is crucial. This paper proposes a Doppler-tolerant waveform design that embeds multiple communication symbols within each pulse of a coherent processing interval (CPI). We formulate a Sum-of-Squares (SoS) optimization problem to minimize the cross-correlation integrated sidelobe level (CISL) for a MIMO sensor arrangement, constrained by unwrapped quadratic phase behavior. Simulation results confirm the effectiveness of the proposed approach.

This paper investigates the integration of Internet of Things (IoT) networks with modern massive multiple-input multiple-output (MIMO) wireless systems to enable various new use cases. Given the dynamic nature of parameters monitored by IoT nodes, efficient techniques for tracking these time-varying parameters are required. In a typical IoT networks, each IoT node linearly precodes its observations and transmits them over a coherent multiple access channel to a fusion center (FC). These IoT networks are power and bandwidth constrained in nature. Therefore, designing transmit precoders for the IoT nodes and a combiner for the FC is essential. This work proposes online linear receive combiner and transmit precoder designs that minimize the mean square error (MSE) under transmit power constraints. Using an alternating optimization technique, we derive closed-form solutions for the combiners and transmit precoders. Our numerical results validate the effectiveness of the proposed algorithms.

A scenario with multiple radars connected to a fusion centre and tracking a target endowed with cognitive abilities is considered. The aim of the target is to degrade the performance of the radar network using its cognitive abilities. In the embodiment considered in this paper, the target injects interference that perturbs the measurements at the different radars. The injected interference is designed to maximize the trace of the error covariance matrix in each instance of the extended Kalman filter iterations used at the fusion centre. The optimal interference in such a setting is formulated as a convex problem and its structure reveals a low-rank correlated structure unlike the intuitive additive white noise. Relation to water-filling is drawn and the impact of such an interference is subsequently analysed using numerical simulations.

Automotive radar sensors are vital for enhancing vehicle safety and autonomy, enabling functionalities such as adaptive cruise control and collision avoidance. The performance of these radar systems is highly dependent on the selected waveform. There are several waveform options for automotive radars, including Frequency Modulated Continuous Wave (FMCW), Phase Modulated Continuous Wave (PMCW), Orthogonal Frequency Division Multiplexing (OFDM), and Orthogonal Time Frequency Space (OTFS). Each offers unique benefits and tradeoffs concerning range resolution, Doppler resolution, resistance to multipath fading, and complexity. Notably, OTFS, as a promising technique in Integrated Sensing And Communication (ISAC), outperforms OFDM in high-mobility scenarios for communication systems. In this paper, we design a phase-perturbation matrix using the Coordinate Descent (CD) framework, to optimize the range Integrated Side-lobe Level (ISL) of OTFS waveform in automotive radar applications. We also examine the Ambiguity Function (AF) characteristics of OTFS compared to OFDM, PMCW, and FMCW. The simulation results demonstrate an improvement in the ISL values of the designed OTFS waveforms.

The evolution of array signal processing technologies is progressing toward the deployment of compact, densely arranged sensors to form extremely large aperture arrays (ELAA), aiming to significantly improve angular resolution and beamforming gain. In this paper, we propose a wideband near-field sensing system that combines orthogonal frequency-division multiplexing (OFDM) signaling with ELAA technology. The ELAA transmitter emits wideband signals, while the radar receiver processes the echoes to estimate critical target parameters, including location, velocity, and radar cross-section. We then derive the generalized Cramér-Rao lower bound (CRB) to assess the OFDM system’s estimation performance. Numerical experiments reveal that the proposed wideband near-field sensing system outperforms its far-field and narrowband counterparts by achieving lower CRB values.

A key challenge in Integrated Sensing and Communications (ISAC), especially in Full-Duplex (FD) Dual-Functional Radar-Communication (DFRC) systems, is self-interference (SI) caused by signal leakage from the transmitter to the receiver, impairing sensing tasks. Reconfigurable Intelligent Surface (RIS) can manipulate signal reflections with minimal power, making them promising for enhancing 6G communication, yet its potential for SI mitigation in DFRC systems is underexplored. This paper proposes a novel RIS-enabled approach for SI elimination in mono-static full-duplex DFRC systems. Our method decomposes the RIS function into spatial beam-forming and temporal modulation to ensure sufficient target illumination and orthogonality between reflected and transmitted waveforms, effectively eliminating SI without the need for SI channel information. Simulations confirm the effectiveness of the proposed approach in SI elimination and sensing performance.

In dual-function radar-communication (DFRC) systems, precise and concurrent estimations of channel state information (CSI) and target directions are imperative to ensure simultaneous communications and sensing. This paper delves into a massive MIMO system employing hybrid beamforming (HBF), identified as a viable solution for achieving significant antenna gains while maintaining manageable hardware costs. The study focuses on a MIMO-DFRC system with a subarray-connection HBF architecture, proposing a preamble-by-preamble methodology to enable simultaneous wireless communications and target tracking on a per-frame basis. In the initial frames, we exploit the Doppler discrepancies between communication paths and fast-moving targets to segregate them. This sets the stage for iterative refinements of Doppler frequencies, angles of departure (AoDs), and angles of arrival (AoAs) estimations in the least square manner. Utilizing these estimations accrued from previous frames, the hybrid precoder and combiner of the subsequent frames are designed to boost a weighted signal-to-noise ratio (SNR), safeguarding the target tracking accuracy while concurrently refining the CSI estimation. Numerical simulations validate the proposed algorithm, demonstrating effective tracking performance with low overhead in MIMO-DFRC systems.

Indoor sensing is challenging because of the multi-bounce effect, spherical wavefront, and spatial nonstationarity (SNS) of the near-field effect. This paper addresses radio-based environment sensing considering these issues. Specifically, graph theory (GT) is used to model the multi-bounce propagation of the near field. In this manner, indoor reflectors/scatterers are modeled as vertices in a propagation graph, the multi-bounce paths are modeled by the edges linking the vertices. Besides, the coupled multipath parameters in the near field, i.e., range and angles, are denoted directly by the coordinates of vertices. Then, the space-alternating generalized expectation-maximization (SAGE) algorithm is adapted to the proposed GT-based dictionary-aided multi-bounce SAGE (GM-SAGE), where the searching parameters including range and angle of departure/arrival (AoD/AoA) are transformed to the coordinates of vertices in the graph. To accelerate the two-bounce sensing, a recursive strategy is proposed. Furthermore, geometric information-based radio-sensing ambiguities are analyzed. The proposed algorithm is validated through a synthetic scattering channel and realistic ray tracing (RT) in a complex indoor office. The results demonstrate that the proposed GM-SAGE can deal with multi-bounce channels, where the one-bounce paths and near-field two-bounce paths are used to reconstruct the scatterers in the environment, the residual higher-bounce paths are identified and hence avoid ghost/mirror detection. Numerical simulations also show the influence of signal-noise ratio (SNR), grid size of vertices, aperture size, and near-far-field effects.

We introduce a simple and effective prompt ing technique called incontext mixing (ICM) for effective incontext learning (ICL) with multilingual large language models (MLLMs).With ICM, we modify the fewshot examples within ICL prompts to be intrasententially codemixed by randomly swapping content words in the target languages with their English translations.We observe that ICM prompts yield superior performance in NLP tasks such as disfluency correction, grammar error cor rection and text simplification that demand a close correspondence between the input and out put sequences.Significant improvements are observed mainly for lowresource languages that are underrepresented during the pretrain ing and finetuning of MLLMs.We present an extensive set of experiments to analyze when ICM is effective and what design choices con tribute towards its effectiveness.ICM works consistently and significantly better than other prompting techniques across models of varying capacity such as mT0XXL, BloomZ and GPT 4. Code, prompts and datasets are available here.

In this paper, we present a generic signal model applicable to various distributed radar setups, encompassing both phased array (PA) and MIMO radar configurations. We consider a range of waveform modulation methods, including TDM, BPM, DDM, and fast time CDM. We devise a GLRT based detector for scenarios where the interference consists of colored noise plus a signal in a low-rank subspace and prove that the designed detector is CFAR. We demonstrate that when the CPI time is similar for the systems, the PA radar system exhibits better detection performance than MIMO, irrespective of the waveform modulation approach adopted. However, if the CPI time of the PA system is divided to the number of transmit waveforms utilized in the MIMO radar case (to account for the time needed for a PA radar to scan all angles), then in the presence of non-uniform interference, MIMO techniques, except TDM, surpass the performance of PA. Conversely, in cases of uniform interference, the performance of both MIMO techniques and PA are equivalent.

Space debris detection and tracking, a key enabler for Space Situational Awareness (SSA), poses two inherent challenges: (1) small-sized targets (e.g., 1 − 10 cm) posing detection difficulties for conventional ground-based radars (GBRs) and optical measurements; (2) large number resulting in a costly tracking exercise. To address these, this work utilizes intersatellite link (ISL) in the emerging low earth orbit (LEO) constellations to opportunistically sense debris. The spatially dense-distributed debris is modeled as a cluster to reduce the number of quantities estimated. Using a stochastic geometry-based channel model, a nested expectationbased SAGE2is proposed, building on space-alternativegeneration-estimation-maximization (SAGE) to estimate the cluster-based channel parameters. Finally, the debris clusters are localized using the ISL forming a bistatic sensing setup. Simulation results validate the proposed approach and show the proposed SAGE2is faster than the conventional SAGE in clustered multipath channels.

This work considers an IoT network comprising of several IoT sensor nodes (SNs), a passive intelligent reflecting surface (IRS), and a fusion center (FC). Each IoT SN observes multiple physical phenomena, and transmits its observations to the FC for post processing. This necessitates the need for efficient preprocessing of each SN’s observations to combat wireless fading effects and optimize transmit power utilization. In this context, this paper presents a novel approach that jointly designs the transmit precoding matrix (TPM) for IoT SNs and optimizes the phase reflection matrix (PRM) for the IRS. The resulting non-convex optimization problem is tackled through an alternating optimization framework, where the individual TPM and PRM design subproblems are further addressed using the majorization minimization (MM) framework. Notably, the proposed solution yields closed-form expressions for TPM and PRM in each MM iteration, making it particularly suitable for low-cost IoT SNs. Numerical results demonstrate the efficacy of the proposed approach by showcasing significant enhancements in estimation performance compared to IoT networks lacking an IRS component.

This paper explores multi-connectivity (MC) techniques to enhance spectral efficiency (SE) of multi-tiered non-terrestrial networks (NTNs) in a pay-as-you-go (PAYG) service model, in which subscribers pay based on the volume of data consumed. The key objective is to maximize SE, and a resource allocation architecture is proposed to incorporate a multi-tier NTN with a hybrid gateway station (HGS) that manages the orbital satellites through co-located gateway antennas. This architecture operates with two different waveforms: 5G New Radio (NR) and DVB-S2X, both adapted into the 3rd Generation Partnership Project (3GPP) protocol stack, allowing for carrier capacity merging from all links with varying waveforms at the receiving user. To this end, a non-convex combinatorial opti-mization problem is formulated with inequality constraints and solved using a multi-agent reinforcement learning (MARL) aided resource allocation algorithm. This algorithm functions using the channel quality indicator (CQI) obtained for the different waveforms and under two channel conditions of clear sky (CS) and rain fading (RFD), to intelligently configure a resource allocation pattern which maximizes SE. The proposed algorithm is compared to proportional fairness (PF) and bottleneck max fairness (BMF) algorithms, and it outperforms in terms of SE by 11.16% and 24.15%, respectively.

Driven by emerging applications, mmWave radars are increasingly being integrated into indoor scene monitoring systems due to their ability to provide high accuracy range, velocity, and angle information of the objects. This paper addresses the problem of moving target detection in a connected, distributed Multiple-Input Multiple-Output (MIMO) radar sensor network designed as an indoor scene monitoring system. We propose a general signal model for distributed connected MIMO radar sensors that collect unwanted and interference signals in a low-rank subspace based on their angle and Doppler frequency spread with different subspace coefficients. We use Generalized Likelihood Ratio Test (GLRT) for moving target detection and find the best detector while demonstrating that it has a constant false alarm rate. The performance of the proposed detector is validated by Monte-Carlo simulation.

In this paper, the trade-off between the quantization noise and the dynamic range of ADCs used to acquire radar signals is revisited using the Unlimited Sensing Framework (USF) in a practical setting. Trade-offs between saturation and resolution arise in many applications, like radar, where sensors acquire signals which exhibit a high degree of variability in amplitude. To solve this issue, we propose the use of the co-design approach of the USF which acquires folded version of the signal of interest and leverages its structure to reconstruct it after its acquisition. We demonstrate that this method outperforms other standard acquisition methods for Doppler radars. We show this theoretically by providing mathematical insights on why the perfect reconstruction of Doppler signals from their folded measurements is possible. Our findings are corroborated via numerical simulations. Taking our theory all the way to practice, we develop a prototype USF-enabled Doppler Radar and show the clear benefits of our method. In each experiment, we show that using the USF increases sensitivity compared to a classic acquisition approach.

In this paper, we look at a waveform design problem for colocated Multiple-Input Multiple-Output (MIMO) radar systems. Under continuous phase constraint, we aim to minimize the range-Integrated Sidelobe Level (ISL) with a compatible spectral response. In this regard, we define the range-ISL function in the time domain first, and then express it in the frequency domain using the Parseval relation. Following that, we incorporate weights on the range-ISL in the frequency domain to apply spectral compatibility. As a result, we have a multi-variable, non-convex, NP-hard optimization problem. We proposed an iterative algorithm based on the Coordinate Descent (CD) method to obtain a local optimum solution. We show the performance of the proposed method and compare it to the counterparts in the numerical results.

Dual-function radar-communications (DFRC) systems generally employ reconfigurable intelligent surface (RIS) as a reflector in the wireless media to enable non-line-of-sight (NLoS) sensing and communications. Different from RIS, reconfigurable holographic surface (RHS) are the surfaces with an embedded feed. These surfaces are deployed at the transceiver thereby leading to a lightweight design and greater control of the radiation amplitude. In this paper, we propose a novel frequency-selective RIS-assisted wideband DFRC system that is also equipped with a RHS at the transceiver. Our goal is to jointly design the digital, holographic, and passive beam-formers to maximize the radar signal-to-interference-plus-noise ratio (SINR) while ensuring the communication SINR among all users. The resulting nonconvex optimization problem involves maximin objective and difference of convex constraint. We develop an alternating maximization framework to decouple and iteratively solve these subproblems. Numerical experiments demonstrate that the proposed method achieves better radar performance than non-RHS, non-RIS, and randomly-configured RIS-aided DFRC systems.

In the symbol-level precoding (SLP) based wireless systems, the reconfigurable intelligent surface (RIS) is usually configured on a block level, which causes a mismatch to the SLP design in terms of update rate. Although it is expected that updating both the RIS and precoding on the symbol level could boost the system performance, the requirements for synchronization and system overhead will become demanding inevitably. In this paper, we consider the RIS-aided dual-function radar-communication (DFRC) system and investigate the benefit of increasing the RIS updating frequency. We jointly design the SLP and RIS to maximize the target illumination power while satisfying the power budget and the multiuser multiple input single output (MU-MISO) communication quality of service (QoS) requirements, where the RIS is updated multiple times in a block (at a sub-block level). Through the simulation results, we demonstrate the optimized trade-off between system performance and RIS update rate.

Intelligent reflecting surface (IRS) is recognized as an enabler of future dual-function radar-communications (DFRC) by improving spectral efficiency, coverage, parameter estimation, and interference suppression. Prior studies on IRS-aided DFRC focus either on narrowband processing, single-IRS deployment, static targets, non-clutter scenario, or on the under-utilized line-of-sight (LoS) and non-line-of-sight (NLoS) paths. In this paper, we address the aforementioned shortcomings by optimizing a wideband DFRC system comprising multiple IRSs and a dual-function base station that jointly processes the LoS and NLoS wideband multi-carrier signals to improve both the communications SINR and the radar SINR in the presence of a moving target and clutter. We formulate the transmit, receive and IRS beamformer design as the maximization of the worst-case radar signal-to-interference-plus-noise ratio (SINR) subject to transmit power and communications SINR. We tackle this nonconvex problem under the alternating optimization framework, where the subproblems are solved by a combination of Dinkelbach algorithm, consensus alternating direction method of multipliers, and Riemannian steepest decent. Our numerical experiments show that the proposed multi-IRS-aided wideband DFRC provides over 4 dB radar SINR and 31.7% improvement in target detection over a single-IRS system.

In millimeter-wave (mmWave) dual-function radar-communication (DFRC) systems, hybrid beamforming (HBF) is recognized as a promising technique utilizing a limited number of radio frequency chains. In this work, in the presence of extended target and clutters, a HBF design based on the subarray connection architecture is proposed for a multiple-input multiple-output (MIMO) DFRC system. In this HBF, the double-phase-shifter (DPS) structure is embedded to further increase the design flexibility. We derive the communication spectral efficiency (SE) and radar signal-to-interference-plus-noise-ratio (SINR) with respect to the transmit HBF and radar receiver, and formulate the HBF design problem as the SE maximization subjecting to the radar SINR and power constraints. To solve the formulated nonconvex problem, the joinT Hybrid bEamforming and Radar rEceiver OptimizatioN (THEREON) is proposed, in which the radar receiver is optimized via the generalized eigenvalue decomposition, and the transmit HBF is updated with low complexity in a parallel manner using the consensus alternating direction method of multipliers (consensus-ADMM). Furthermore, we extend the proposed method to the multi-user multiple-input single-output (MU-MISO) scenario. Numerical simulations demonstrate the efficacy of the proposed algorithm and show that the solution provides a good trade-off between number of phase shifters and performance gain of the DPS HBF.

Direction-of-arrival (DOA) estimation can be represented as a sparse signal recovery problem and effectively solved by sparse Bayesian learning (SBL). For the DOA estimation in active sensing, the SBL-based estimation error is related to the transmitted probing waveform. Therefore, it is expected to improve the estimation by waveform optimization. In this paper, we propose a recurrent scheme of waveform design by sequentially leveraging on the previous-round SBL estimates. Within this scheme, we formulate the waveform design problem as a minimization of the SBL estimation variance, which is non-convex and then solved by a majorization-minimization based algorithm. The simulations demonstrate the efficacy of the proposed design scheme in terms of avoiding incorrect detection and accelerating the DOA estimation convergence. Further, the results indicate that the waveform design is essentially a beampattern shaping methodology.

Pulse compression can enhance both the performance in range resolution and sensitivity for weather radar. However, it will introduce the issue of high sidelobes if not delicately implemented. Motivated by this fact, we focus on the pulse compression design for weather radar in this paper. Specifically, we jointly design both the subpulse codes and extended mismatch filter based on the alternating direction method of multipliers (ADMM). This joint design will yield a pulse compression with low sidelobes, which equivalently implies a high signal-to-interference-plus-noise ratio (SINR) and a low estimation error on meteorological reflectivity. The experiment results demonstrate the efficacy of the proposed pulse compression strategy since its achieved meteorological reflectivity estimations are highly similar to the ground truth.

Abstract The joint design problem for the coexistence of multiple‐input multiple‐output (MIMO) radar and multi‐user multiple‐input‐single‐output (MU‐MISO) communication is investigated. Different from the conventional design schemes, which require defining the primary function, we consider designing the transmit waveform, precoding matrix and receive filter to maximize the radar SINR and the minimal SINR of communication users, simultaneously. By doing so, the promising overall performance for both sensing and communication is achieved without requiring parameter tuning for the threshold of communication or radar. However, the resulting optimization problem which contains the maximin objective function and the unit sphere constraint, is highly nonconvex and hence difficult to attain the optimal solution directly. Towards this end, the epigraph‐form reformulation is first adopted, and then an alternating maximisation (AM) method is devised, in which the Dinkelbach’s algorithm is used to tackle the nonconvex fractional‐programing subproblem. Simulation results indicate that the proposed method can achieve improved performance compared with the benchmarks.

Optimal allocation of shared resources is key to deliver the promise of jointly operating radar and communications systems. In this paper, unlike prior works which examine synergistic access to resources in colocated joint radar-communications or among identical systems, we investigate this problem for a distributed system comprising heterogeneous radars and multi-tier communications. In particular, we focus on resource allocation in the context of multi-target tracking (MTT) while maintaining stable communications connections. By simultaneously allocating the available power, dwell time and shared bandwidth, we improve the MTT performance under a Bayesian tracking framework and guarantee the communications throughput. Our${a}$lter${n}$ating allo${c}$ation of${h}$eterogene${o}$us${r}$esources (ANCHOR) approach solves the resulting non-convex problem based on the alternating optimization method that monotonically improves the Bayesian Cramér-Rao bound. Numerical experiments demonstrate that ANCHOR significantly improves the tracking error over two baseline allocations and stability under different target scenarios and radar-communications network distributions.

This paper studies the joint multislot design of user scheduling and precoding to minimize the time needed to serve all the users for unicast and multicast transmission in single-cell multiuser MISO downlink systems. In the literature, the joint design of scheduling and precoding is typically undertaken based on feedback from previous slots. In a system with time-varying channels and QoS requirements, joint multislot designs can achieve better performance since they have the flexibility to schedule users over multiple slots and also can split users across slots efficiently. Further, a joint multislot design can provide a feasible solution even when the sequential design fails. In this paper, scheduling is represented by a binary matrix where the rows represent users, columns represent slots and entries represent scheduling of users in the slots. Noticing that the users may not be permuted across slots for time-varying channels, service time needed for scheduling is rendered as the highest column index corresponding to non-zero columns. With the help of binary scheduling matrix, service time minimization is formulated as a structured mixed-Boolean fractional programming. Further, by exploiting the hidden convex-concave structure in the problem, a convex-concave procedure-based iterative algorithm is proposed. Finally, we vindicate the necessity and illustrate the superiority in performance of joint multislot design over the sequential solution through Monte-Carlo simulations.

In large-scale antenna array (LSAA) wireless communication systems employing analog beamforming architectures, the placement or selection of a subset of antennas can significantly reduce the power consumption and hardware complexity. In this work, we propose a joint design of analog beamforming with antenna selection (AS) or antenna placement (AP) for an analog beamforming system. We approach this problem from a beampattern matching perspective and formulate a sparse unit-modulus least-squares (SULS) problem, which is a nonconvex problem due to the unit-modulus and the sparsity constraints. To that end, we propose an efficient and scalable algorithm based on the majorization-minimization (MM) framework for solving the SULS problem. We show that the sequence of iterates generated by the algorithm converges to a stationary point of the problem. Numerical results demonstrate that the proposed joint design of analog beamforming with AS outperforms conventional array architectures with fixed inter-antenna element spacing.

This paper shows the enhancement in detection performance in an automotive scenario by leveraging the backscattered communication signals from vehicles at the target scene. A sensor fusion algorithm is proposed to benefit from the information from radar and communication to improve the final range estimates. We demonstrate theoretically and illustrate through simulation that our proposed scheme enhances the radar detection performance. Thus the proposed scheme offers a solution for augmenting existing sensing capabilities to enhance detecting capabilities in a dynamic automotive scenario.

Co-array-based Direction of Arrival (DoA) estimation using Sparse Linear Arrays (SLAs) has recently gained considerable attention in array processing thanks to its capability of providing enhanced degrees of freedom for DoAs that can be resolved. Additionally, deployment of one-bit Analog-to-Digital Converters (ADCs) has become an important topic in array processing, as it offers both a low-cost and a low-complexity implementation. Although the problem of DoA estimation form one-bit SLA measurements has been studied in some prior works, its analytical performance has not yet been investigated and characterized. In this paper, to provide valuable insights into the performance of DoA estimation from one-bit SLA measurements, we derive an asymptotic closed-form expression for the performance of One-Bit Co-Array-Based MUSIC (OBCAB-MUSIC). Further, numerical simulations are provided to validate the asymptotic closed-form expression for the performance of OBCAB-MUSIC and to show an interesting use case of it in evaluating the resolution of OBCAB-MUSIC.

Beampattern synthesis is a key problem in many wireless applications. With the increasing scale of MIMO antenna array, it is highly desired to conduct beampattern synthesis on a sparse array to reduce the power and hardware cost. In this paper, we consider conducting beampattern synthesis and sparse array construction jointly. In the formulated problem, the beam-pattern synthesis is designed by minimizing the matching error to the beampattern template, and the Shannon entropy function is first introduced to impose the sparsity of the array. Then, for this nonconvex problem, an iterative method is proposed by leveraging on the alternating direction multiplier method (ADMM) and the majorization minimization (MM). Simulation results demonstrate that, compared with the benchmark, our approach achieves a good trade-off between array sparsity and beampattern matching error with less runtime.

This letter studies the problem of Direction of Arrival (DoA) estimation from low-resolution few-bit quantized data collected by Sparse Linear Array (SLA). In such cases, contrary to the one-bit quantization case, the well known arcsine law cannot be employed to estimate the covaraince matrix of unquantized array data. Instead, we develop a novel optimization-based framework for retrieving the covaraince matrix of unquantized array data from low-resolution few-bit measurements. The MUSIC algorithm is then applied to an augmented version of the recovered covariance matrix to find the source DoAs. The simulation results show that increasing the sampling resolution to 2 or 4 bits per samples could significantly increase the DoA estimation performance compared to the one-bit sampling regime while the power consumption and implementation costs is still much lower in comparison to the high-resolution sampling implementations.

In conventional polar format algorithm (PFA), the effective imaging scene is bounded to a limited region near the reference point unless postprocessing is utilized. In a previous paper, refocusing and zoom-in PFA (RZPFA) for curvilinear spotlight SAR imaging were proposed to produce a refocused image for an arbitrary region of interest (ROI) with constant elevation. However, for certain applications, the residual distortion and defocus caused by rugged terrain could not be ignored. In this article, RZPFA is adapted to incorporate the known digital elevation model (DEM) into the imaging process, which is named as orthorectified PFA (OPFA). With just little additional computations than RZPFA, OPFA can realize georeferenced orthorectified imaging via a nonuniform fast Fourier transform of type 3 (NuFFT-3) without the need of postprocessing. The quantitative metrics for the residual DEM distortion and residual DEM defocus were also derived to determine the effective imaging extent of OPFA. Within the effective extent, the proposed OPFA can obtain an orthorectified image efficiently, and the image has a very high quality comparable to backprojection (BP). The imaging results of measured echo and DEM data demonstrated the effectiveness of the proposed algorithm.

In this paper, we provide a framework for the direction of arrival (DOA) estimation using a single moving sensor and evaluate performance bounds on estimation. We introduce a signal model which captures spatio-temporal incoherency in the received signal due to sensor motion in space and finite bandwidth of the signal, hitherto not considered. We show that in such a scenario, the source signal covariance matrix becomes a function of the source DOA, which is usually not the case. Due to this unknown dependency, traditional subspace techniques cannot be applied and conditions on source covariance needs to imposed to ensure identifiability. This motivates us to investigate the performance bounds through the Cramer-Rao Lower Bounds (CRLBs) to set benchmark performance for future estimators. This paper exploits the signal model to derive an appropriate CRLB, which is shown to be better than those in relevant literature.

Single waveform design for automotive joint radar-communications (JRC) is being increasingly considered recently, as it addresses the problem of spectrum sharing between the two systems. The paper addresses the challenge of designing a waveform in MIMO-radar MIMO-communications (MRMC) set-up in a broadcast environment to ensure certain performance of the two systems is guaranteed. It develops an optimization problem to enhance mutual information metrics for radar and communications considering the worst case Doppler/angle at a range bin. The intractable optimization problem is decomposed and relaxed into two convex sub-problems, which are subsequently solved through an iterative method. The benefits of the proposed waveform are illustrated through numerical simulations.

In this work, an intelligent and reconfigurable ultra-wideband angular sensing (UWAS) framework is proposed which is independent of the maximum number of active transmissions in a wideband spectrum unlike the existing UWAS methods. To perform the above task, we propose a sub-Nyquist sampling and sparse ruler based multi-antenna array receiver architecture. By characterizing and selecting a set of frequency bands via a learning algorithm, the proposed receiver allows sensing of more active transmissions than the number of antenna over an unlimited bandwidth. The simulation results show that due to the learning based approach, the proposed UWAS outperforms when compared to non-learning based UWAS method.

We investigate waveform design for coexistence between a multiple-input multiple-output (MIMO) radar and MIMO communications (MRMC), with a radar-centric criterion that leads to a minimal interference in the communications system. The communications use the traditional mode of operation in Long Term Evolution (LTE)/Advanced (FDD), where we formulate the design problem based on information-theoretic criterion with the discrete phase constraint at the design stage. The optimization problem, is non-convex, multi-objective and multi-variable, where we propose an efficient algorithm based on the coordinate descent (CD) framework to simultaneously improve radar target detection performance and the communications rate. The numerical results indicate the effectiveness of the proposed algorithm in designing discrete phase set of sequences, potentially binary.

While the use of weather radars to continuously monitor the spatiotemporal dynamics of precipitation has grown in recent years, these systems are expensive and sparsely deployed across the world. In this context, densely located ground-based terminals for interactive satellite services have the potential for dual-use as weather sensors because they measure rain-attenuated power of the downlink signal. Although in the millimeter-wave regime, the rain rate has almost a linear relationship with specific attenuation, lack of other weather radar observables at satellite terminals imposes a daunting task of extracting rainfall rate from these highly attenuated signals. We address this problem by designing a deep convolutional neural network (CNN) that learns the relationship between the signal attenuation and rainfall rate observed by weather radars and rain gauges at a given location. During the prediction stage, the CNN accepts downlink attenuation as input and classifies the rain intensity which is then used to apply an appropriate rainfall estimator. Our experiments with real data show that, despite severe attenuation, CNN-based downlink rainfall accumulations closely follow the nearest C-band German weather service Deutscher Wetterdienst (DWD) radar.

This paper is focused on designing a set of constant modulus waveform for cognitive Multiple-Input Multiple-Output (MIMO) radar systems. The aim is to shape the beam-pattern in transmitter to minimize the Integrated Side-lobe Level (ISL) in spatial domain in a cognitive paradigm. This minimization leads to a NP-hard and non-convex optimization problem. To address this, the problem is formulated based on Coordinate Descent (CD) framework with constant modulus constraint. Subsequently, an low-complexity and fast method based on Discrete Fourier Transform (DFT) is proposed which monotonically decreases the spatial ISL. Finally, we show some numerical results and assess the performance of the proposed technique.

Parameter estimation from noisy and one-bit quantized data has become an important topic in signal processing, as it offers low cost and low complexity in the implementation. On the other hand, Direction-of-Arrival (DoA) estimation using Sparse Linear Arrays (SLAs) has recently gained considerable interest in array processing due to their attractive capability of providing enhanced degrees of freedom. In this paper, the problem of DoA estimation from one-bit measurements received by an SLA is considered and a novel framework for solving this problem is proposed. The proposed approach first provides an estimate of the received signal covariance matrix through minimization of a constrained weighted least-squares criterion. Then, MUSIC is applied to the spatially smoothed version of the estimated covariance matrix to find the DoAs of interest. Several numerical results are provided to demonstrate the superiority of the proposed approach over its counterpart already propounded in the literature.

The average performance of the MISO downlink channel, with a large number of users compared to transmit antennas of the base station, depends on the interference management which necessitates the joint design of scheduling and precoding. Unlike the previous works which do not offer a truly joint design, this paper focuses on formulating a problem amenable for the joint update of scheduling and precoding. Novel optimization formulations are investigated to reveal the hidden difference of convex/ concave structure for three classical criteria (weighted sum rate, max-min signal-to-interference plus noise ratio, and power minimization) and associated constraints are considered. Thereafter, we propose a convex-concave procedure framework based iterative algorithm where scheduling and precoding variables are updated jointly in each iteration. Finally, we show the superiority in performance of joint solution over the state-of-the-art designs through Monte-Carlo simulations.

In a mmWave multiple-input multiple-output (MIMO) communication system employing a large-scale antenna array (LSAA), the hybrid transceivers are used to reduce the power consumption and the hardware cost. In a hybrid analog-digital (A/D) transceiver, the pre/post-processing operation splits into a lower-dimensional baseband (BB) pre/postcoder, followed by a network of analog phase shifters. Primarily two kinds of hybrid architectures are proposed in the literature to implement hybrid transceivers namely, the fully- connected and the partially-connected. Implementation of fully-connected architecture has higher hardware complexity, cost and power consumption in comparison with partially- connected. In this paper, we focus on partially- connected hybrid architecture and develop a low- complexity algorithm for transceiver design for a single user point-to-point mmWave MIMO system. The proposed algorithm utilizes the variable elimination (projection) and the minorization- maximization (MM) frameworks and has convergence guarantees to a stationary point. Simulation results demonstrate that the proposed algorithm is easily scalable for LSAA systems and achieves significantly improved performance in terms of the spectral efficiency (SE) of the system compared to the state-of-the-art solution.

Recent satellite standards enforce the coding of multiple users’ data in a frame. This transmission strategy mimics the well-known physical layer multigroup multicasting (MGMC). However, typical beam coverage with a large number of users and limited frame length lead to the scheduling of only a few users. Moreover, in emerging aggressive frequency reuse systems, scheduling is coupled with precoding. This is addressed in this work, through the joint design of scheduling and precoding for frame-based MGMC satellite systems. This aim is formulated as the maximization of the sum- rate under per beam power constraint and minimum SINR requirement of scheduled users. Further, a framework is proposed to transform the non-smooth SR objective with integer scheduling and nonconvex SINR constraints as a difference- of-convex problem that facilitates the joint update of scheduling and precoding. Therein, an efficient convex-concave procedure based algorithm is proposed. Finally, the gains (up to 50%) obtained by the jointed design over state- of-the-art methods is shown through Monte-Carlo simulations.

This paper designs dynamic onboard signal processing schemes in a multiple gateway multi-beam satellite system where full frequency reuse pattern is considered among the beams and feeds. In particular, we deploy on-board Joint Precoding, Feed selection and Signal switching mechanism (JPFS) so that the following advantages are realized, I) No need of Channel State Information (CSI) exchange among the gateways and satellite, since the performance of precoding is highly sensitive to the quality of CSI, II) In case one gateway fails, rerouting signals through other gateways can be applied without any extra signal processing, III) Properly selecting on-board feed/s to serve each user which generates maximum gain toward corresponding user, IV) Flexibly switching the signals received from the gateways to requested users where each user can dynamically request traffic from any gateway, and V) Multiple users with multiple traffic streams can be dynamically served at each beam. However, deploying such JPFS architecture imposes high complexity to the satellite payload. To tackle this issue, this study aims at deploying JPFS that can provide affordable complexity at the payload. In addition, while increasing the data demand imposes extensive bandwidth resources requirement in the feeder link, the proposed JPFS design works efficiently with available feeder link resources even if the data demand increases. The proposed design is evaluated with a close-to-real beam pattern and the latest broadband communication standard for satellite communications.

Single waveform design for automotivejoint radar-communications (JRC) is being increasingly considered of late. This paper formulates the JRC design as an optimization problem exploiting the co-location of the two systems and investigates the trade-off between them. We propose an algorithm to maximize the performance of communication and radar receivers (e.g., BER and probability of detection, respectively). This intractable optimization problem is decomposed into two subproblems, which are subsequently solved in succession through a combination of gradient projection method and convex relaxations. The benefits of the proposed waveform are illustrated through numerical simulations.

In this paper, a new framework for designing the radar transmit waveform is established through shaping the radar Ambiguity Function (AF). Specifically, the AF of the phase coded waveforms are analyzed and it is shown that a continuous/discrete phase sequence with the desired AF can be obtained by solving an optimization problem promoting equality between the AF of the transmit sequence and the desired AF. An iterative algorithm based on Coordinate Descent (CD) method is introduced to deal with the resulting non-convex optimization problem. Numerical results illustrate the proposed algorithm make it possible to design sequences with remarkably high tolerance towards Doppler frequency shifts, which is of interest to the future generations of automotive radar sensors.

The paper investigates the applicability of a conventional communications signal for sensing and detection in vehicular scenario. This Joint Radar-Communications (JRC) system caters to an automotive setting characterized by a multi-target environment including clutter. In this contribution, an automotive radar employing phase modulated communication signal is considered wherein the detection of other vehicles is enabled in presence of clutter. Particularly, it is shown when the clutter is dominant in the environment, bit error rate (BER) of communications will suffer. Further, the receiver processing includes demodulation of the communication symbols at communicating vehicle in addition to the extraction of the radar parameters − range and Doppler shifts of the targets − at the JRC-equipped vehicle. The study investigates the impact of various parameters in determining the performance of the JRC set-up.

This paper presents designing precoding technique at the gateway of a multibeam mobile satellite systems, enabling full frequency reuse pattern among the beams. Such a system brings in two critical challenges to overcome. The inter-beam interference makes applying interference mitigation techniques necessary. Further, when the user terminals are mobile the Channel State Information (CSI) becomes time-varying which is another challenge to overcome. Therefore, the gateway has only access to an outdated CSI, which can eventually limit the precoding gains. In this way, employing a proper CSI estimation mechanism at the gateway can improve the performance of the precoding scheme. In this context, the objectives of this paper are two folds. First, we present different CSI feedback mechanisms which aim at preserving a lower CSI variations at the gateway. Then, we develop the corresponding precoding schemes which are adapted with the proposed CSI feedback mechanisms. To keep the complexity of the proposed precoding schemes affordable, we consider a maritime communication scenario so that the signals received by mobile user terminals suffer from a lower pathloss compared to the Land Mobile communication. Finally, we provide several simulations results in order to evaluate the performance of the proposed precoding techniques.

This paper aims to design joint precoding and onboard beamforming at a multiple gateway multibeam satellite system. Full frequency reuse pattern is considered among the beams and each gateway serves a cluster of adjacent beams such that multiple clusters are served through a set of gateways. However, two issues are required to be addressed. First, the interference in both user and feeder links is the bottleneck of the whole system and employing interference mitigation techniques is essential. Second, as the data demand increases, the ground and space segments should employ extensive bandwidth resources in the feeder link accordingly. This entails embedding an extra number of gateways aiming to support a fair balance between the increasing demand and the corresponding required feeder link resources. To tackle these problems, this paper studies the impact of employing a joint multiple gateway architecture and on-board beamforming scheme. It is shown that by properly designing the on-board beamforming scheme, the number of gateways can be kept affordable even if the data demand increases. The proposed beamforming scheme can partially mitigate the interference in the user link. While the user and feeder link channels vary over time, this paper focuses on designing fixed beamforming which is sufficiently robust to the variations in both channels, leading to keep payload complexity low. Moreover, Zero Forcing precoding technique is employed at the gateways to reject the interference in the feeder links as well as it helps the proposed fixed on-board beamforming by partially equalizing the interference in user link.

This paper present On-Board Precoding (OBP) for a multiple gateway multibeam satellite system where full frequency reuse pattern is employed at both user and feeder links. By reducing the Channel State Information (CSI) round-trip delay to half, OBP offers significant benefits in the emerging multiple gateway scenario in terms of lower gateways coordination. However, two critical issues need to be addressed: (a) interference in both user and feeder links is the bottleneck of the whole system and employing interference mitigation techniques is essential, (b) clear push towards non-adaptive (fixed) payload implementation, leading to low computationally complex satellite architectures. In order to fulfill requirements (a) and (b), this paper studies the impact of employing a fixed OBP technique at the payload which is sufficiently robust to the variations in both user and feeder link channels. In addition to (a) and (b), the provided simulation results depict the performance gain obtained by our proposed OBP with respect to the conventional interference mitigation techniques in multiple gateway multibeam systems.

This paper aims to design joint on-ground precoding and on-board beamforming of a multiple gateway multibeam satellite system in a hybrid space-ground mode where full frequency reuse pattern is considered among the beams. In such an architecture, each gateway serves a cluster of adjacent beams such that the adjacent clusters are served through a set of gateways that are located at different geographical areas. However, such a system brings in two challenges to overcome. First, the inter-beam interference is the bottleneck of the whole system and applying interference mitigation techniques becomes necessary. Second, as the data demand increases, the ground and space segments should employ extensive bandwidth resources in the feeder link accordingly. This entails embedding an extra number of gateways aiming to support a fair balance between the increasing demand and the corresponding required feeder link resources. To solve these problems, this study investigates the impact of employing a joint multiple gateway architecture and onboard beamforming scheme. It is shown that by properly designing the on-board beamforming scheme, the number of gateways can be kept affordable even if the data demand increases. Moreover, Zero Forcing (ZF) precoding technique is considered to cope with the inter-beam interference where each gateway constructs a part of block ZF precoding matrix. The conceived designs are evaluated with a close-to-real beam pattern and the latest broadband communication standard for satellite communications.

In this paper, we present a centralized method for real-time rainfall estimation using carrier-to-noise power ratio ($C/N$) measurements from broadband satellite communication networks. The$C/N$data of both forward link and return link are collected by the gateway station from the user terminals in the broadband satellite communication network and stored in a database. The$C/N$for such Ka-band scenarios is impaired mainly by the rainfall. Using signal processing and machine learning techniques, we develop an algorithm for real-time rainfall estimation. Extracting relevant features from$C/N$, we use artificial neural network in order to distinguish the rain events from dry events. We then determine the signal attenuation corresponding to the rain events and examine an empirical relationship between rainfall rate and signal attenuation. Experimental results are promising and prove the high potential of satellite communication links for real environment monitoring, particularly rainfall estimation.

This paper deals with the problem of precoding, scheduling, and link adaptation in next generation mobile interactive multibeam satellite systems. In contrast to the fixed satellite services, when the user terminals move across the coverage area, additional challenges appear. Due to the time varying channel, the gateway has only access to a delayed version of the channel state information (CSI), which can eventually limit the overall system performance. However, in contrast to general multiuser multiple-input multiple-output terrestrial systems, the CSI degradation in multibeam mobile applications has a very limited impact for typical fading channel and system assumptions. Under realistic conditions, the numerical results show that precoding can offer an attractive gain in the system throughput compared with the conservative frequency reuse allocations.

This paper investigates the implications of employing multiple transmissions as a diversity mechanism to counter the turbulence induced fading in satellite systems with optical feeder link and RF user links. Such a technique necessitates optimal estimation of the transmitted signal using the different streams on-board the satellite. Unlike traditional combining techniques like Maximal Ratio Combining (MRC) which assume signal independent noise, the aforementioned estimation is made interesting by the signal dependent noise characteristic of the optical processing in such systems. This paper investigates various estimators in the presence of signal dependent noise and assesses them based on performance and complexity. Towards this, the paper devises a signal model to illustrate the effect of various signal dependent and independent noise sources and derives estimators based on (a) averaging, (b) Minimum Mean Square Error (MMSE), and (c) Maximum Likelihood (ML). The bias and the Mean Squared Error (MSE) of these estimators are analyzed and the impact of signal dependent noise characterized. Based on these results and complexity considerations, the paper presents recommendations for the receiver to be used in future generation of satellite systems with optical feeder links.

The paper studies the problem of waveform design for a joint Radar-Communications (RadComms) system in an automotive setting characterized by a multitarget environment. The envisaged joint waveform allows exploitation of existing infrastructure to support additional functionalities. In this contribution, an automotive radar employing Phase Modulated Continuous Waveform (PMCW) is considered wherein, the transmission of communications bits is additionally facilitated. Particularly, the RadComms system is enabled by the transmission of Differential Phase Shift Keying (DPSK) communications symbols, each symbol modulated by the PMCW sequence. Further, the receiver processing includes demodulation of the communication symbols in addition to the extraction of the radar parameters — range, Angles of Arrival (AoA) and Doppler returns of the targets. In particular, FFT and subspace-based methods are proposed for estimating the radar parameters which are subsequently used in the demodulation of the communication symbols using diversity combining techniques. The performed study demonstrates the impact of the joint set-up on the two systems. It also highlights the effect of system parameters on the performance of receiver algorithms and the trade-off between the FFT and sub-spaced based processing.

In this paper, we present an optimization framework for designing precoding (a.k.a. beamforming) signals that are instrumental in achieving a fair user performance through the networks. The precoding design problem in such scenarios can typically be formulated as a non-convex max-min fractional quadratic program. Using a penalized version of the original design problem, we derive a simplified quadratic reformulation of the problem in terms of the signal (to be designed). Each iteration of the proposed design framework consists of a combination of power method-like iterations and the Gram-Schmidt process, and as a result, enjoys a low computational cost. Moreover, the suggested approach can handle various types of signal constraints such as total-power, per-antenna power, unimodularity, or discrete-phase requirements - an advantage which is not shared by other existing approaches in the literature.

In this paper, we investigate the performance of constellations optimized for transmissions in dual-polar mobile satellite applications. These four-dimensional constellations (inphase and quadrature per polarization) are designed for joint transmission over the two polarizations. Such constellations enhance the reliability of the system by providing certain redundancy into their design. Their performance is compared with transmission of independent 2D constellations over each polarization. As performance metrics, the pragmatic achievable mutual information and the bit error rate have been considered. The gains serve to indicate the need to further investigate 4D constellation design and its application in dual-polar MIMO systems.

Precoding for multibeam satellites with aggressive frequency reuse has attracted interest of late towards enhancing system capacity. Most of the works on precoding mitigate the linear co-channel interference between the beams caused by frequency reuse. However, the high power amplifier (HPA), an integral part of the satellite payload, is inherently non linear. Non-linear amplification combined with the linear co-channel interference introduces non-linear co-channel distortions at the receiver. Further, signals with very high peak to average power ratios (PAPR), typical of spectrally efficient modulations, are sensitive to the non-linear characteristic of the HPA and necessitate large back-off to have manageable distortion levels. In this work, a novel architecture comprising multistream Crest Factor Reduction (signal pre-clipping) and Signal Predistortion (SPD) in cascade, is devised to counter the non-linearities and co-channel interference in multibeam satellite systems. An iterative algorithm to optimize the parameters of the signal clipping and predistortion is devised taking recourse to analytical derivations. The proposed joint estimation paradigm is shown to compare favorably with state-of-art and provides a framework to combine predistortion and precoding.

The demand for wider bandwidths has motivated the need for wireless systems to migrate to higher frequency bands. In line with this trend is an envisaged deployment of Ka-band (or mmWave) cellular infrastructure. Further, to improve the spectral efficiency, developing full-duplex radio transceivers is gaining momentum. In view of this move, the paper proposes the possibility of reusing the satellite feeder uplink band in the full-duplex small cells. The motivation for such a reuse is two-fold :(a) there is virtually no interference from the small cells to the incumbent in-orbit satellite receiver, and (b) directive feeder antennas, with possibly additional isolation and processing causing negligible interference to the small cells. The presented interference analysis clearly supports the proposed coexistence.

In this work, we study the design of a precoder on the user downlink of a multibeam satellite channel. The variations in channel due to phase noise introduced by on-board oscillators and the long round trip delay result in outdated channel information at the transmitter. The phase uncertainty is modelled and a robust design framework is formulated based on availability and power constraints. The optimization problem is cast into the convex paradigm after approximations and the benefits of the resulting precoder are highlighted.

Efficient power amplification is inherently a non linear operation that introduces unwanted interference in the amplified signal. Strong inter-symbol interference is generated when the amplifier non linearity is combined with channel memory effects. Further, signals with very high peak to average power ratio, typical of multiple carrier systems, are even more sensitive to the non linearities resulting in severe distortion effects. Signal pre-clipping (crest factor reduction) and predistortion are conventional countermeasure techniques to reduce the generated non linear distortion and improve power and spectral efficiency. In this work, novel optimization methods for predistortion and pre-clipping are analytically derived for a general non-linear communication channel with memory. A combined architecture in which crest factor reduction is followed by signal predistortion is proposed and the parameters are estimated resorting to iterative algorithms based on least squares method. Performance evaluation of the estimation techniques shows the effectiveness of the derived algorithms and significant gain compared to previously known methods.

A digital predistortion (DPD) scheme is presented for non‐linear distortion mitigation in multi‐carrier satellite communication channels. The proposed DPD has a multiple‐input multiple‐output architecture similar to data DPD schemes. However, it enhances the mitigation performance of data DPDs using a multi‐rate processing algorithm to achieve spectrum broadening of non‐linear operators. Compared to single carrier (single‐input single‐output) signal (waveform) DPD schemes, the proposed DPD has lower digital processing rate reducing the required hardware cost of the predistorter. The proposed DPD outperforms, in total degradation, both data and signal DPD schemes. Further, it performs closest to a channel bound described by an ideally mitigated channel with limited maximum output power.

Design of high bandwidth and reliable feeder links are central toward provisioning new services on the user link of a multibeam satellite communication system. Toward this, utilization of the Q/V band and an exploitation of multiple gateways (GWs) as a transmit diversity measure for overcoming severe propagation effects are being considered. In this context, this contribution deals with the design of a feeder link comprising$N+P$GWs ($N$active and$P$redundant GWs). Toward provisioning the desired availability, a novel switching scheme is analyzed and practical aspects such as prediction-based switching and switching rate are discussed. Unlike most relevant works, a dynamic rain attenuation model is used to analytically derive average outage probability in the fundamental$1+1$GW case. Building on this result, an analysis for the$N+P$scenario leading to a quantification of the end-to-end performance is provided. This analysis aids system sizing by illustrating the interplay between the number of active and redundant GWs on the chosen metrics: average outage and average switching rate.

Joint on-board amplification of multiple carriers reduces hardware and weight, enabling cost-efficient satellite architectures. However, on-board power amplification is inherently a non-linear operation and the distortion effects drastically increase when the high-power amplifier (HPA) is operated in multiple-carrier mode due to the generated inter-modulation products. In order to achieve the desired on-board power efficiency and to reduce the non-linear distortion, specific countermeasures need to be put in place. Emerging multi-gateway satellite systems where, in the most general case, each carrier is uplinked independently from a different gateway, would gain further flexibility by adopting a multicarrier architecture. While advanced predistortion techniques are already available for the single-gateway scenario, no solution has been proposed for the multi-gateway multicarrier scenario. In this work, we propose novel distributed predistortion techniques to improve spectral and power efficiency in multi-gateway non-linear satellite channels. Further, we analyze the multiple-carrier predistortion parameter estimation error with respect to noise sensitivity, proposing a robust version of the indirect learning method. Distributed processing becomes sensitive to synchronization amongst the actors, and we present an evaluation of the sensitivity of proposed techniques to imperfections.

Multibeam satellite systems offer flexibility that aims at efficiently reusing the available spectrum. To fully exploit the flexibility advantages, the payload resources—transmit power and bandwidth—must be efficiently allocated among multiple beams. This paper investigates the resource optimization problem in multibeam satellites. The NP-hardness and inapproximability of the problem are demonstrated motivating the use of metaheuristics. A systematic approach accomplishing the best traffic match is carried out. The additional requirement of minimizing the total power consumption is then considered, giving rise to a multi-objective optimization approach. The solutions to thea prioriaccomplished traffic matching optimization are used to enhance the efficiency of the multi-objective metaheuristic method proposed and, consequently, of the multibeam satellite system. The optimized performance is represented by the Pareto front, which provides trade-off points between total power consumption and rate achieved. This allows the decomposition of the problem into independent color-based sub-problems rendering the proposed two-stage optimization framework suitable for dimensioning the next generation multispot satellite systems.

The market trends towards the use of smaller dish antennas for TV satellite receivers, as well as the growing density of broadcasting satellites in orbit require the application of robust adjacent satellite interference (ASI) cancellation algorithms at the receivers. The wider beamwidth of a small size dish and the growing number of satellites in orbit impose an overloaded scenario, i.e., a scenario where the number of transmitting satellites exceeds the number of receiving antennas. For such a scenario, we present a two stage receiver to enhance signal detection from the satellite of interest, i.e., the satellite that the dish is pointing to, while reducing interference from neighboring satellites. Towards this objective, we propose an enhanced List-based Group-wise Search Detection (LGSD) receiver architecture that takes into account the spatially correlated additive noise and uses the signal-to-interference-plus-noise ratio (SINR) maximization criterion to improve detection performance. Simulations show that the proposed receiver structure enhances the performance of satellite systems in the presence of ASI when compared to existing methods.

Current satellite communication architectures can be potentially improved by applying multicarrier power amplification. Joint on-board amplification of multiple carriers reduces hardware and weight, enabling cost-efficient satellite architectures. However, on board power amplification is inherently a non-linear operation, whose distortion effects drastically increase when the high power amplifier (HPA) is operated in multiple carrier mode due to the generated inter-modulation products. In order to achieve the desired on-board power efficiency and to reduce the non-linear distortion, specific countermeasures need to be put in place. In this work we provide a novel data predistortion technique for multiple carriers satellite channel based on look up table (LUT) for high throughput applications. In contrast to known single carrier LUT techniques, the entries of the table are obtained by pursuing an analytical approach exploiting channel model and implementing recursive least squares techniques (RLS). A novel method to reduce the number of LUT entries based on LUT partitioning is proposed. Considerable performance gains over standard polynomial based predistortion are realized by the proposed technique.

With the growing application of high order modulation techniques, the mitigation of the non-linear distortions introduced by the power amplification, has become a major issue in telecommunication. More sophisticated techniques to counteract the strong generated interferences need to be investigated in order to achieve the desired power and spectral efficiency. This work proposes a novel approach for the definition of a transmitter technique (predistortion) that outperforms the standard methods with respect to both performance and efficiency.

Exploiting transmit diversity amid a high number of multiple gateways (GW) is a new research challenge in Q/V band satellite communication providing data rates of hundreds of Gbit/s. In this paper, we propose a practical switching strategy in a scenario with N+P GWs (N active and P redundant GWs) towards achieving GW transmit diversity. Differently from other works, the treatment in this paper is analytical and explores two key factors: outage performance and switching rate in detail. Further, the interplay between the number of redundant and active GWs on the availability is illustrated highlighting the contribution of the work towards system sizing.

We consider the problem of detecting a sparse random signal from the compressive measurements without reconstructing the signal. Using a subspace model for the sparse signal where the signal parameters are drawn according to Gaussian law, we obtain the detector based on Neyman-Pearson criterion and analytically determine its operating characteristics when the signal covariance is known. These results are extended to situations where the covariance cannot be estimated. The results can be used to determine the number of measurements needed for a particular detector performance and also illustrate the presence of an optimal support for a given number of measurements.

An increasing number of hybrid mobile systems comprising a satellite and a terrestrial component are becoming standardized and realized. The next generation of these systems will employ higher dimensions adopting multiple-input multiple-output orthogonal frequency division multiplexing (MIMO-OFDM) techniques. In this work, we build upon recent studies of dual polarization MIMO for each component and propose the use of full-rate full-diversity (FRFD) codes adopting a space-frequency paradigm. We also propose a scheme taking advantage of the separation between the subcarriers to enhance the coding gain. By critically assessing the different options for the 4 transmit, 2 receive hybrid scenario taking into account system and channel particularities, we demonstrate that the proposed scheme is a solution for enhancing the performance of next generation hybrid mobile satellite systems.

Spectrum sharing between two independent, co-existing transmit-receive pairs (TRPs) is formulated as a non-cooperative game with the TRPs as players, their individual link rates as payoffs and power allocation over the utilized spectral bands as the strategy. A Nash Equilibrium (NE) corresponds to the outcome of such a game and TRPs iteratively use the water-filling algorithm according to an agreed order for achieving the NE. Dynamics of this distributed algorithm is studied to determine the conditions for convergence and characterize the resulting NE. A sufficient condition on global convergence is derived and is shown to be tighter than existing ones. Further, a novel characterization of the globally achievable NE based on necessary conditions is presented. Some of these results are also extended to multiple NE scenarios where local convergence is exhibited.

This paper discusses the performance of Golden codes in a hybrid satellite/terrestrial system framework employing dual polarized MIMO-OFDM transmissions. Although the use of Golden codes in satellite and terrestrial scenarios has been studied independently, a realistic performance assessment must involve both components taking into account the relative delay between their reception. In fact, this work exploits the relative delay to create a multipath scenario and further improve the coding gain of the Golden codes, which is otherwise fixed. This is made possible by utilizing the Golden code in a Space-Frequency coding framework instead of the traditional Space-Time paradigm. The separation between the subcarriers constituting a Golden code is shown to be central to the coding gain enhancement and an algorithm to choose this separation is provided.

We consider the interaction of several transmit-receive pairs coexisting in the same area and communicating using the same portion of the spectrum. Using a game theoretic framework, each pair is regarded as a player whose payoff function is the individual link rate and power is allocated using the iterative water-filling algorithm. We find properties of the resulting Nash equilibria and derive conditions for when various operating points are achievable. The analysis presented herein extends previous work by characterizing the set of stable solutions for a multi-user system. Also, we show how the game can be modified to obtain better operating points in terms of sum rate compared to the iterative water-filling algorithm. The increase in performance corresponding to one such modification is evaluated and compared to the iterative water-filling algorithm by numerical simulations.

A scenario consisting of two transmitter-receiver pairs coexisting in the same area and communicating using the same portion of the spectrum is considered. Decentralized coding strategies are employed at the transmitter side and no cooperation is assumed between the two systems. We investigate the structure of Nash equilibria corresponding to simultaneous water-filling solutions and propose a scheme that improves overall performance of systems with weak mutual interference. The resulting scheme provides a spectrum sharing rule in the form of a modified utility function. The conditions for optimality are presented and a numerical example illustrates the performance compared to a scheme employing the link rate as utility function.

In this paper, we address the issue of perturbed information at the transmitter in a multiple-input multiple-output (MIMO) system. Two schemes, correlation matrix feedback and channel information feedback, are proposed to convey information necessary for achieving capacity to the transmitter. This information is perturbed due to various impairments. A perturbation analysis is carried out to study the variations in mutual information for each of the proposed schemes. For ergodic channels, this analysis is used to design a MIMO system with a limited rate feedback. Using a codebook-based approach, vector quantizers are designed to minimize the loss in ergodic capacity for each of the proposed schemes, and their performance is studied. A non-codebook method, parameter-based feedback, is also presented, and the underlying rate-performance tradeoff is highlighted

Three reduced complexity equalization schemes for Zero-padded OFDM systems are described. These schemes guarantee Zero-Forcing (ZF) equalization irrespective of the channel nulls. Two of these schemes implement the minimum-norm ZF equalizer efficiently using QR decomposition. In the third scheme, the channel zeros are grouped as being inside or outside or on the unit circle. These groups are then equalized sequentially in a manner so as to tackle excess noise amplification. The three schemes are compared for their computational complexity and Bit Error Rate (BER) performance. It is shown that the attractive scheme depends on the system specifications. The BERComputations trade off occurring in the choice of the right algorithm is also highlighted.