Low Earth Orbit (LEO) satellite communication is crucial for developing sixth-generation (6G) networks. The integration of massive multiple-input multiple-output (MIMO) technology is being actively researched to enhance the performance of LEO satellite communication systems. However, the limited power resources of LEO satellites pose significant challenges to improving energy efficiency (EE) under power-constrained conditions. Typical optimization-based methods often lack real-time adaptability and computational efficiency. This paper proposes innovative solutions to address the challenges of precoding in massive MIMO LEO satellite communications. Specifically, we introduce a deep unfolding of the Dinkelbach algorithm and the weighted minimum mean square error (WMMSE) approach to achieve enhanced EE. This transformation of iterative optimization procedures into a graph neural network (GNN) leads to faster convergence and improved computational efficiency. Furthermore, we apply the Taylor expansion method to approximate matrix inversion within the GNN framework. Numerical experiments demonstrate the superiority of our proposed method in terms of complexity and robustness, achieving significant improvements over other state-of-the-art methods.

Low Earth Orbit (LEO) satellite communication is a critical component in the development of sixth generation (6G) networks. The integration of massive multiple-input multipleoutput (MIMO) technology is being actively explored to enhance the performance of LEO satellite communications. However, the limited power of LEO satellites poses a significant challenge in improving communication energy efficiency (EE) under constrained power conditions. Artificial intelligence (AI) methods are increasingly recognized as promising solutions for optimizing energy consumption while enhancing system performance, thus enabling more efficient and sustainable communications. This paper proposes approaches to address the challenges associated with precoding in massive MIMO LEO satellite communications. First, we introduce an end-to-end graph neural network (GNN) framework that effectively reduces the computational complexity of traditional precoding methods. Next, we introduce a deep unfolding of the Dinkelbach algorithm and the weighted minimum mean square error (WMMSE) approach to achieve enhanced EE, transforming iterative optimization processes into a structured neural network, thereby improving convergence speed and computational efficiency. Furthermore, we incorporate the Taylor expansion method to approximate matrix inversion within the GNN, enhancing both the interpretability and performance of the proposed method. Numerical experiments demonstrate the validity of our proposed method in terms of complexity and robustness, achieving significant improvements over state-of-the-art methods.

Integrated communications and localization (ICAL) will play an important part in future sixth generation (6G) networks for the realization of Internet of Everything (IoE) to support both global communications and seamless localization. Massive multiple-input multiple-output (MIMO) low earth orbit (LEO) satellite systems have great potential in providing wide coverage with enhanced gains, and thus are strong candidates for realizing ubiquitous ICAL. In this paper, we develop a wideband massive MIMO LEO satellite system to simultaneously support wireless communications and localization operations in the downlink. In particular, we first characterize the signal propagation properties and derive a localization performance bound. Based on these analyses, we focus on the hybrid analog/digital precoding design to achieve high communication capability and localization precision. Numerical results demonstrate that the proposed ICAL scheme supports both the wireless communication and localization operations for typical system setups.

In this paper, a radio-frequency (RF) domain symbol level precoding technique is developed for reconfigurable intelligent surface (RIS)-assisted downlink multiuser multiple-input single-output (MU-MISO) systems. We study a system with a base station (BS) employing an analog architecture formed by a phase shifting network which serves a number of single antenna users with the help of a RIS. Such an architecture facilitates significant reductions in power consumption and hardware complexity. The objective of this paper is to jointly derive the optimal RF precoder, RIS reflection matrix and receive processing coefficients, subject to constraints on the BS analog architecture, the total transmit power, and the structure of the RIS reflection matrix. To that end, a difficult nonconvex optimization problem is formulated and solved. An efficient algorithmic solution is developed for the considered problem. Numerical results show that the derived solution offers significant energy efficiency gains when compared to non-RIS-assisted approaches.

The future sixth generation (6G) networks will feature great importance on the integration of communications and localization, to realize the Internet of Everything (IoE). In this paper, we investigate the hybrid precoding design for the integrated communications and localization (ICAL) in the massive multiple-input multiple-output (MIMO) low Earth orbit (LEO) systems. In particular, we first derive an upper bound of the communication spectral efficiency (SE) and the squared position error bound (SPEB) of localization. Then, we formulate a multi-objective optimization problem to simultaneously operate communications and localization. Simulation results demonstrate the satisfactory performance of the proposed massive MIMO LEO ICAL system for typical setups.

This paper considers a multiple-input multiple-output (MIMO) joint radar-communication (JRC) transmission with hybrid precoding and low resolution digital to analog converters (DACs). An energy efficient radio frequency (RF) chain and DAC bit selection approach is presented for a sub-arrayed hybrid MIMO JRC system. We introduce a weighting formulation to represent the combined radar-communications information rate. The presented selection mechanism is incorporated with fractional programming to solve an energy efficiency maximization problem for JRC which selects the optimal number of RF chains and DAC bit resolution. Subsequently, a weighted minimization problem to compute the precoding matrices is formulated, which is solved using an alternating minimization approach. The numerical results show the effectiveness of the proposed method in terms of high energy efficiency whilst maintaining good rate and desirable radar beampattern performance.

The space-air-ground-sea integrated network (SAGSIN) plays an important role in offering global coverage. To improve the efficient utilization of spectral and hardware resources in the SAGSIN, integrated sensing and communications (ISAC) has drawn extensive attention. Most existing ISAC works focus on terrestrial networks and cannot be straightforwardly applied in satellite systems due to the significantly different electromagnetic wave propagation properties. In this work, we investigate the application of ISAC in massive multiple-input multiple-output (MIMO) low earth orbit (LEO) satellite systems. We first characterize the statistical wave propagation properties by considering beam squint effects. Based on this analysis, we propose a beam squint-aware ISAC technique for hybrid analog/digital massive MIMO LEO satellite systems exploiting statistical channel state information. Simulation results demonstrate that the proposed scheme can operate both the wireless communications and the target sensing simultaneously with satisfactory performance, and the beam-squint effects can be efficiently mitigated with the proposed method in typical LEO satellite systems.

This paper investigates the downlink (DL) transmit design for massive multiple-input multiple-output (MIMO) low-earth-orbit (LEO) satellite communication systems, where only the slow-varying statistical channel state information is exploited at the transmitter. The channel model for the DL massive MIMO LEO satellite system is established, in which both the satellite and the user terminals (UTs) are equipped with uniform planar arrays. Observing the rank-one property of the channel matrices, we show that the single-stream precoding for each UT is the optimal choice that maximizes the ergodic sum rate. This favorable result simplifies the complicated design of transmit covariance matrices into that of precoding vectors without any loss of optimality. Then, an efficient algorithm is devised to compute the precoding vectors. Furthermore, we formulate an approximate transmit design based on the upper bound on the ergodic sum rate, for which the optimality of single-stream precoding still holds. We show that, in this case, the design of precoding vectors can be simplified into that of scalar variables, for which an effective algorithm is developed. In addition, a low-complexity learning framework is proposed for optimizing the scalar variables. Simulation results demonstrate that the proposed approaches can achieve significant performance gains over the existing schemes.

The massive multiple-input multiple-output (MIMO) transmission technology has recently attracted much attention in the non-geostationary, e.g., low earth orbit (LEO) satellite communication (SATCOM) systems since it can significantly improve the energy efficiency (EE) and spectral efficiency. In this work, we develop a hybrid analog/digital precoding technique in the massive MIMO LEO SATCOM downlink, which reduces the onboard hardware complexity and power consumption. In the proposed scheme, the analog precoder is implemented via a more practical twin-resolution phase shifting (TRPS) network to make a meticulous tradeoff between the power consumption and array gain. In addition, we consider and study the impact of the distortion effect of the nonlinear power amplifiers (NPAs) in the system design. By jointly considering all the above factors, we propose an efficient algorithmic approach for the TRPS-based hybrid precoding problem with NPAs. Numerical results show the EE gains considering the nonlinear distortion and the performance superiority of the proposed TRPS-based hybrid precoding scheme over the baselines.

Massive multiple-input multiple-output (MIMO) is promising for low earth orbit (LEO) satellite communications due to the potential in enhancing the spectral efficiency. However, the conventional fully digital precoding architectures might lead to high implementation complexity and energy consumption. In this paper, hybrid analog/digital precoding solutions are developed for the downlink operation in LEO massive MIMO satellite communications, by exploiting the slow-varying statistical channel state information (CSI) at the transmitter. First, we formulate the hybrid precoder design as an energy efficiency (EE) maximization problem by considering both the continuous and discrete phase shift networks for implementing the analog precoder. The cases of both the fully and the partially connected architectures are considered. Since the EE optimization problem is nonconvex, it is in general difficult to solve. To make the EE maximization problem tractable, we apply a closed-form tight upper bound to approximate the ergodic rate. Then, we develop an efficient algorithm to obtain the fully digital precoders. Based on which, we further develop two different efficient algorithmic solutions to compute the hybrid precoders for the fully and the partially connected architectures, respectively. Simulation results show that the proposed approaches achieve significant EE performance gains over the existing baselines, especially when the discrete phase shift network is employed for analog precoding.

Massive multiple-input multiple-output (MIMO) technology has attracted much attention in low earth orbit (LEO) downlink satellite communication (SATCOM) systems recently since the energy efficiency (EE) and spectral efficiency can be significantly improved. In order to reduce the power consumption for the massive MIMO LEO SATCOM systems, we focus on the hybrid analog/digital architecture in this work. Considering the limited resolution of the phase shifters in practical MIMO SATCOM systems, a twin-resolution phase shifting (TRPS) network is proposed to make a meticulous tradeoff between the power consumption and array gains. In addition, we examine the impact of the distortion, introduced by the power amplifiers (PAs) to the system design, by considering nonlinear PA models. Moreover, we propose an efficient algorithm for TRPS-based hybrid precoding with nonlinear PAs. Numerical results show the EE gains considering nonlinear distortion and the performance superiority of the proposed hybrid architecture compared with the baselines.

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.

We investigate the downlink (DL) transmit strategy for massive multiple-input multiple-output (MIMO) low-earth-orbit (LEO) satellite communication (SATCOM) systems, in which only the slow-varying statistical channel state information is known at the transmitter side. First, we derive the massive MIMO LEO satellite channel model, when the uniform planar arrays are deployed at both the satellite and user terminals (UTs). Building on the rank-one property of the satellite channel matrices, we show that transmitting a single data stream to each UT is optimal in the sense that the ergodic sum rate is maximized. This result is of great importance for massive MIMO LEO SATCOM systems, since the sophisticated design of transmit covariance matrices is turned into that of precoding vectors, without loss of optimality. Furthermore, we develop an algorithm to compute the precoding vectors. Simulation results show the significant performance gains of the proposed approaches over the existing schemes.

In this work, we consider an eavesdropping scenario in wireless multi-user (MU) multiple-input single-output (MISO) systems with channel coding in the presence of a multi-antenna eavesdropper (Eve). In this setting, we exploit machine learning (ML) tools to design a hard decoding scheme by using precoded pilot symbols as training data. Within this, we propose an ML framework for a multi-antenna hard decoder that allows an Eve to decode the transmitted message with decent accuracy. We show that MU-MISO systems are vulnerable to such an attack when conventional block-level precoding is used. To counteract this attack, we propose a novel symbol-level precoding scheme that increases the bit-error rate at Eve by obstructing the learning process. Simulation results validate both the ML-based attack as well as the countermeasure, and show that the gain in security is achieved without affecting the performance at the intended users.

Herein, a precoding scheme is developed for orthogonal frequency division multiplexing (OFDM) transmission in multiple-input multiple-output (MIMO) systems that use one-bit digital-to-analog converters (DACs) and analog-to-digital converters (ADCs) at the transmitter and receiver, respectively, as a means to reduce the power consumption. Two different one-bit architectures are presented. In the first, a single user MIMO system is considered where the DACs and ADCs of the transmitter and the receiver are assumed to be one-bit and in the second, a network of analog phase shifters is added at the receiver as an additional analog-only processing step with the view to mitigate some of the effects of coarse quantization. The precoding design problem is formulated and then split into two NP-hard optimization problems, which are solved by an algorithmic solution based on the Cyclic Coordinate Descent (CCD) framework. The design of the analog post-coding matrix for the second architecture is decoupled from the precoding design and is solved by an algorithm based on the alternating direction method of multipliers (ADMM). Numerical results show that the proposed precoding scheme successfully mitigates the effects of coarse quantization and the proposed systems achieve a performance close to that of systems equipped with full resolution DACs/ADCs.

Constant Envelope (CE) precoding is an efficient technique for systems based on massive antenna arrays since the constant amplitude of the transmit signal facilitates the use of power efficient non-linear transmitter circuitry, such as power amplifiers (PAs). On the other hand, Orthogonal frequency-division multiplexing (OFDM) is a well-known multicarrier transmission scheme which is used to mitigate the effects of multipath propagation, but usually leads to high peak-to-average-power ratio (PAPR). Herein, the problem of CE MIMO-OFDM precoding for transmission over frequency selective channels is tackled. First, a novel efficient formulation is proposed, where the precoding problem is formulated as an unconstrained nonlinear least-squares problem. Next, using the new formulation the problem is solved using the Gauss-Newton algorithm. Numerical results show that the proposed solution is more efficient than the current state of the art techniques both from the aspect of computational complexity and the overall system performance.

In this paper, Constant-Envelope Precoding techniques are presented for satellite-based communication systems. In the developed transmission technique the signals of the antennas are designed to be of constant amplitude, improving the robustness of the latter to the nonlinear distortions on satellite systems, introduced by the employed on-board Traveling-Wave-Tube-Amplifiers. We consider the forward link of a multi-beam broadband satellite system where the aim is to design the signals at the gateway such that the desired symbols are transmitted to the intended user terminals and the transmitted signals from the satellite terminal are of constant amplitude. At first, the gateway signals are designed given that a fixed on-board beamformer is applied to the satellite terminal. Then, the case of an adaptive on-board beamformer is considered which is designed jointly with the gateway signals. The design of the gateway signals and the adaptive on-board beamformer, in the second case, requires solving difficult nonconvex problems. Efficient algorithmic solutions are developed based on the saddle point method. The effectiveness of the proposed approaches is verified via numerical results.

Herein, we consider constant envelope precoding in a multiple-input multiple-output orthogonal frequency division multiplexing system (CE MIMO-OFDM) for frequency selective channels. In CE precoding the signals for each transmit antenna are designed to have constant amplitude regardless of the channel realization and the information symbols that must be conveyed to the users. This facilitates the use of power-efficient components, such as phase shifters (PS) and nonlinear power amplifiers, which are key for the feasibility of large-scale antenna array systems because of their low cost and power consumption. The CE precoding problem is firstly formulated as a least-squares problem with a unit modulus constraint and solved using an algorithm based on coordinate descent. The large number of optimization variables in the case of the MIMO-OFDM system motivates the search for a more computationally efficient solution. To tackle this, we reformulate the CE precoding design into an unconstrained nonlinear least-squares problem, which is solved efficiently using the Gauss-Newton algorithm. Simulation results underline the efficiency of the proposed solutions and show that they outperform state of the art techniques.

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.

Low resolution analog-to-digital converters (ADCs) can be employed to improve the energy efficiency (EE) of a wireless receiver since the power consumption of each ADC is exponentially related to its sampling resolution and the hardware complexity. In this paper, we aim to jointly optimize the sampling resolution, i.e., the number of ADC bits, and analog/digital hybrid combiner matrices which provides highly energy efficient solutions for millimeter wave multiple-input multiple-output systems. A novel decomposition of the hybrid combiner to three parts is introduced: the analog combiner matrix, the bit resolution matrix and the baseband combiner matrix. The unknown matrices are computed as the solution to a matrix factorization problem where the optimal, fully digital combiner is approximated by the product of these matrices. An efficient solution based on the alternating direction method of multipliers is proposed to solve this problem. The simulation results show that the proposed solution achieves high EE performance when compared with existing benchmark techniques that use fixed ADC resolutions.

Most physical-layer security (PLS) works employ information theoretic metrics for performance analysis. In this paper, however, we investigate PLS from a signal processing point of view, where we rely on bit-error rate (BER) at the eavesdropper (Eve) as a metric for information leakage. Recently, symbol-level precoding (SLP) has been shown to enhance PLS in the presence of an Eve. In this work, nonetheless, we introduce a machine learning (ML) based attack to which even SLP schemes can be vulnerable. Namely, this attack manifests when an Eve utilizes ML in order to learn the precoding pattern when precoded pilots are sent. With this ability, an Eve can decode data with favorable accuracy. As a countermeasure to this attack, we propose a novel security enhanced precoding technique. The proposed countermeasure yields high BER at the Eve, which makes symbol detection practically infeasible for the latter, thus providing physical-layer security between the base station (BS) and the users. In the numerical results, we validate both the attack and the countermeasure, and show that this gain in security can be achieved at the expense of only a small additional power consumption at the transmitter.

The huge volume of data that are available today requires data-selective processing approaches that avoid the costs in computational complexity via appropriately treating the non-innovative data. In this paper, extensions of the well-known adaptive filtering LMS-Newton and LMS-Quasi-Newton Algorithms are developed that enable data selection while also addressing the censorship of outliers that emerge due to high measurement errors. The proposed solutions allow the prescription of how often the acquired data are expected to be incorporated into the learning process based on some a priori information regarding the environment. Simulation results on both synthetic and real-world data verify the effectiveness of the proposed algorithms that may achieve significant reductions in computational costs without sacrificing estimation accuracy due to the selection of the data.

In this paper, we consider three transmitter designs for symbol-level-precoding (SLP), a technique that mitigates multiuser interference (MUI) in multiuser systems by designing the transmitted signals using the channel state information and the information-bearing symbols. The considered systems tackle the high hardware complexity and power consumption of existing SLP techniques by reducing or completely eliminating fully digital radio frequency (RF) chains. The first proposed architecture referred to as, Antenna Selection SLP, minimizes the MUI by activating a subset of the available antennas and thus, reducing the number of required RF chains to the number of active antennas. In the other two architectures, which we refer to as RF domain SLP, the processing happens entirely in the RF domain, thus eliminating the need for multiple fully digital RF chains altogether. Instead, the analog phase shifters directly modulate the signals on the transmit antennas. The precoding design for all the considered cases is formulated as a constrained least squares problem and efficient algorithmic solutions are developed via the Coordinate Descent method. Simulations provide insights into the power efficiency of the proposed schemes and the improvements over the fully digital counterparts.

In this letter, we consider a network of single-antenna sensors that aim at the estimation of an unknown deterministic parameter. The sensors collect the observations and forward them to a fusion center via applying a phase-only beamforming weight. The derivation of the optimal beamforming weights requires the solution of a unit-modulus quadratic program (UQP), which in the relevant literature is solved via the semidefinite relaxation (SDR) technique or via a variation of the analytic constant modulus algorithm. The former achieves better performance, though it exhibits high computational cost that increases drastically with the number of sensors. The latter requires much less complexity, though it achieves worse performance. To that end, we propose an efficient algorithm for the solution of UQPs based on the alternating direction method of multipliers. The new approach achieves almost identical performance to that of the SDR-based approach while exhibiting significantly reduced computational complexity. The convergence of the proposed algorithm to a Karush–Kuhn–Tucker point is theoretically studied, and its effectiveness is verified via numerical results.

In this paper, the problem of sub-channel selection for carrier aggregation (CA) systems is examined. CA enables the achievement of high data rate links via simultaneous transmissions over multiple component carriers. A CA system usually occupies only a limited number of sub-channels M of these components due to limitations on the maximum permitted number of sub-channels per system. From an information theoretic point of view, a CA system should detect and employ the M-best sub-channels out of the N available ones. To that end, such a system probes a subset of sub-channels during each coherence time via pilot transmission. Then, for the best M sub-channels, one-bit feedback information is transmitted in order to prohibit the transmission through sub-channels with gain below a threshold. The aim is to derive tractable forms via employing the extreme value theory for the sum rate (lower bound on ergodic capacity) achieved by the system under Rayleigh fading and then, to optimize jointly the training length and power, the number of the probed sub-channels (probing bandwidth size) and the feedback threshold such that the sum rate is maximized by considering the sub-channel estimation error. The accuracy of the theoretical analysis is verified by numerical results.

Interference is identified as a critical issue for satellite communication (SATCOM) systems and services. There is a growing concern in the satellite industry to manage and mitigate interference efficiently. While there are efficient techniques to monitor strong interference in SATCOM, weak interference is not so easily detected because of its low interference to signal and noise ratio (ISNR). To address this issue, this paper proposes and develops a technique which takes place on-board the satellite by decoding the desired signal, removing it from the total received signal and applying an Energy Detector (ED) in the remaining signal for the detection of interference. Different from the existing literature, this paper considers imperfect signal cancellation, examining how the decoding errors affect the sensing performance, derives the expressions for the probability of false alarm and provides a set of simulations results, verifying the efficiency of the technique.

Hybrid Analog-Digital transceivers are employed with the view to reduce the hardware complexity and the energy consumption in millimeter wave/large antenna array systems by reducing the number of their Radio Frequency (RF) chains. However, the analog processing network requires power for its operation and it further introduces power losses, dependent on the number of the transceiver antennas and RF chains, that have to be compensated. Thus, the reduction in the power consumption is usually much less than it is expected and given that the hybrid solutions present in general inferior spectral efficiency than a fully digital one, it is possible for the former to be less energy efficient than the latter in several cases. Existing approaches propose hybrid solutions that maximize the spectral efficiency of the system without providing any insight on their actual energy requirements/efficiency. To that end, in this paper, a novel algorithmic framework is developed based on which energy efficient hybrid transceiver designs are developed and their performance is examined with respect to the employed number of RF chains. Solutions are proposed for fully and partially connected hybrid architectures. Numerical results provide insight on when a hybrid transceiver is the most energy efficient solution or not.

Hybrid analog-digital transceivers are employed with the view to reduce the hardware complexity and the energy consumption in millimeter wave/large antenna array systems by reducing the number of their radio frequency (RF) chains. However, the analog processing network requires power for its operation and it further introduces power losses, dependent on the number of the transceiver antennas and RF chains that have to be compensated. Thus, the reduction in the power consumption is usually much less than it is expected and given that the hybrid solutions present in general inferior spectral efficiency than a fully digital one, it is possible for the former to be less energy efficient than the latter in several cases. Existing approaches propose hybrid solutions that maximize the spectral efficiency of the system without providing any insight on their energy requirements/efficiency. To that end, in this paper, a novel algorithmic framework is developed based on which energy efficient hybrid transceiver designs are derived and their performance is examined with respect to the number of RF chains and antennas. Solutions are proposed for fully and partially connected hybrid architectures and for both single- and multi-carrier systems under the orthogonal frequency division multiplexing modulation. Simulations and theoretical results provide insight on the cases, where a hybrid transceiver is the most energy efficient solution or not.

The extension of long term evolution (LTE) networks in unlicensed spectrum areas under the licensed assisted access concept aims at achieving higher transmission rates via the aggregation of the aforementioned bands along with the licensed ones within the 3G Partnership Project framework. A prospect carrier aggregation (CA) scheme should handle efficiently the coexistence of the LTE systems that compete for the same unlicensed spectrum areas along with their incumbent users (i.e., Wi-Fi). In this paper, a novel CA scheme is proposed for licensed/unlicensed MIMO LTE systems that allocates optimally the resources (power and resource blocks) of an evolved Node B to user equipments. Furthermore, the proposed approach handles the coexistence matters within the unlicensed bands with an efficient decentralized way. The new scheme involves the solution to a mixed integer nonlinear programming problem and thus, an optimal low complexity method is proposed based on the Lagrange dual decomposition. Furthermore, the proposed technique is extended to the imperfect channel state information (CSI) case. To that end, a novel listen-before-talk scheme is developed via which the required unlicensed bands CSI are estimated in a blind manner. The performance of all of the proposed techniques is verified via indicative simulations.

In conventional cognitive radio systems, the secondary user employs a “listen-before-talk” paradigm, where it senses if the primary user is active or idle, before it decides to access the licensed spectrum. However, this method faces challenges, with the most important one being the reduction of the secondary user’s throughput, as no data transmission takes place during the sensing period. In this context, the idea of simultaneous spectrum sensing and data transmission is proposed. This paper studies a system model where this concept is obtained through the collaboration of the secondary transmitter with the secondary receiver. First, the secondary receiver decodes the signal from the secondary transmitter, removes it from the total received signal, and then carries out spectrum sensing in the remaining signal in order to determine the presence/absence of the primary user. Different from the existing literature, this paper considers the imperfect signal cancellation, evaluating how the decoding errors affect the sensing reliability, and derives the analytical expressions for the probability of false alarm. Finally, numerical results are presented illustrating the accuracy of the proposed analysis.

In this paper a novel low complexity scheme that achieves also reduced feedback overhead is studied for reporting the channel state information in Carrier aggregation MIMO systems. Such systems usually involve a large number of carriers and users, so the calculation and reporting of the required RI/PMI/CQI indexes could result in high computational complexity and feedback overhead. Given that the user scheduling and rate is based on these reported values, efficient computation and feedback schemes are crucial for achieving performances that can accommodate the wireless systems requirements. To that end, at first we propose a novel scheme for the RI/PMI/CQI calculation that exploits the adjacent channels correlation in order to reduce the complexity. Then, a novel distributed scheduling scheme is proposed based on a stable matching approach that requires reduced feedback information. The performance of the proposed approach is evaluated by indicative simulations.

In this paper a novel Carrier Aggregation (CA) scheme is proposed for downlink MIMO LTE-A Systems. The proposed approach achieves increased transmission rates by establishing the communication links via both licensed and unlicensed bands without generating or experiencing interference to/from the users of the latter bands. To that end, a rate optimization problem is defined and solved subject to the previous zero interference constraints, a total power constraint and a maximum number of aggregated bands constraint. It turns out that the previous problem is a Mixed Integer Non Linear Programming (MINLP) one that requires an exhaustive search procedure in order to be solved. To tackle this, an optimal low complexity method is proposed based on the Lagrange dual decomposition. The performance of the original (MINLP) and the low-complexity proposed techniques is verified via indicative simulations.

Eigenvalue-based spectrum sensing (EBSS) techniques may operate in a totally blind manner while they offer remarkably improved performance for specific types of signals compared with energy-based methods. In the literature so far, only batch and centralized cooperative EBSS techniques have been considered, which, however, suffer from limitations that render them impractical in several cases. Thus, the aim of this paper is to develop practical cooperative adaptive versions of typical EBSS techniques that could be applied in a completely decentralized manner. To this end, at first, novel adaptive EBSS techniques are developed for the maximum eigenvalue detector, the maximum-minimum eigenvalue detector, and the generalized likelihood ratio test scheme, respectively, for a single-user (no cooperation) case. Then, a novel distributed subspace tracking method is proposed, which enables the cooperating nodes to track the joint subspace of their received signals. Based on this method, cooperative decentralized versions of the adaptive EBSS techniques are subsequently developed that overcome the limitations of the existing batch centralized approaches. The performance of the proposed methods is verified via indicative simulations.

In this paper adaptive Eigenvalue-Based Spectrum Sensing (EBSS) techniques are proposed for multi-antenna cognitive receivers. In cases where fading channels are involved, and hence real-time processing is required, the adaptive techniques offer lower complexity and exhibit improved performance as compared to the corresponding batch EBSS techniques. At first, novel adaptive EBSS techniques are developed for the Maximum Eigenvalue Detector (MED), the Maximum-Minimum Eigenvalue Detector (MMED), and the Generalized Likelihood Ratio Test (GLRT) schemes, respectively, based on well-studied subspace tracking methods. Moreover, close approximations for the distribution functions of the adaptive test statistics of the MED, MMED and GLRT schemes are derived in order to compute the decision thresholds for a given probability of false alarm. The performance of the adaptive EBSS methods is verified via indicative simulations.

In this paper a new Opportunistic Interference Alignment (OIA) technique is presented that can be applied in a blind manner to Cognitive Radio systems. The proposed technique aims at relaxing the strong assumptions made in existing approaches where it is assumed that the Secondary User (SU) has knowledge of Channel State Information (CSI) concerning the Primary User's (PU) transmissions, although this may not be possible, in general. First, the OIA technique of [1] is extended so that the required CSI can be blindly estimated from the second order statistics of the received symbols. More specifically, the Secondary Receiver post-coding matrix is re-designed in order to null-out completely the interference generated by the PU's transmissions, in contrast to the original work where an interference reduction approach was adopted. Then, a novel estimation mechanism is proposed that enables the SU to apply the new OIA technique in a blind manner. The performance of the proposed technique is evaluated both theoretically as well as via simulations, under perfect and imperfect CSI.

In the present paper, we propose two novel algorithms which enable the relay cooperation for the distributed computation of the beamforming weights in a blind and adaptive manner, without the need to forward the data to a fusion center. In the first scheme, the beamforming vector is computed through minimization of the total transmit power subject to a receiver quality-of-service constraint (QoS). In the second scheme, the beamforming weights are obtained through maximization of the receiver signal-to-noise-ratio (SNR) subject to a total transmit power constraint. The proposed approaches distribute the computational overhead equally among the relay nodes and achieve close performance to the one of the optimal beamforming solutions. Note, that the aforementioned optimal solutions are derived assuming perfect channel state information at the relays' side. In order to verify the performance of the proposed approaches, indicative simulations were carried out for static and time-varying channels.

In this paper novel subspace-based blind schemes are proposed and applied to the sparse channel identification problem. Moreover, adaptive sparse subspace tracking methods are proposed so as to provide efficient real-time implementations. The new algorithms exploit the subspace sparsity either via employing ℓ1-norm relaxation or through greedy-based optimization. The derived schemes have been tested in a Zero-Prefix Orthogonal Frequency Division Multiplexing (ZP-OFDM) system and it turns out that, compared to state-of-art existing schemes, they offer improved performance in terms of convergence rate and steady-state error.

While Half-Duplex cooperative systems provide an alternative means to achieve diversity gain, they suffer from multiplexing gain loss due to the necessary two-phase transmission protocols that are employed. In this paper two new transmission techniques are presented for systems with multi-antenna relay nodes employing the Decode-and-Forward (DF) protocol which achieve improved multiplexing and diversity gains. The first technique extends to the multi-antenna relays case our previous work for single-antenna relay nodes [5]-[6] and is based on the channel's matrix singular value decomposition (SVD). The second technique exploits the multiple-antenna relays so as to achieve Full-Duplex communication by permitting the latter ones to transmit and receive simultaneously. The transmissions' orthogonality is achieved via a novel distributed interference nulling mechanism. The proposed schemes' performance is theoretically studied and the results are verified through typical simulations.

A new technique for the uplink transmission of a cooperative system consisting of single-antenna source and relay nodes and a multi-antenna destination node is presented. The proposed technique is based on the singular value decomposition of the channel matrix and, for the same rate, it exhibits higher diversity gain as compared to the existing ones. Theoretical analysis of the technique is carried out when the Decode-and-Forward protocol is employed. The analysis reveals the way the decoding errors occurring at the relays degrade the performance of the system. Furthermore, the proposed technique is extended to a multi-user environment so as to exploit multi-user diversity. A novel criterion is suggested for selecting the best transmitting user at each time slot. The performance of the system in the multi-user case is theoretically studied and the number of users needed so as the system to achieve its maximum performance is computed. The derived theoretical results are verified via typical simulations.