In the last years, it has been shown that nonlinear (NL) orthogonal frequency division multiplexing (OFDM) can outperform linear OFDM because the nonlinear distortion has useful information about the transmitted signals. However, only a maximum likelihood (ML) receiver can exploit this information. Despite its potential, the optimal ML receiver for NL OFDM is highly complex, and its performance is difficult to simulate. Moreover, although some theoretical performance bounds exist for OFDM transmissions involving many subcarriers and high signal-to-noise ratio (SNR), the behavior of NL OFDM under more practical SNR conditions remains insufficiently explored. This paper focuses on optimal detection methods for NL OFDM systems. We present a comprehensive analysis of how the distortion received on different subcarriers contributes to the signal of a specific subcarrier and derive a performance bound for the ML detection of NL OFDM that is applicable across a broad range of SNR values. Furthermore, we propose a practical iterative decision-directed receiver that achieves significant performance improvements over linear OFDM in both uncoded and coded setups.11This work supported by Fundação para a Ciência e Tecnologia and Instituto de Telecomunicações under the project UIDB/50008/2020, DOI:10.54499/UIDB/50008/2020, and Universidade Lusófona.

This work proposes a novel approach to reinforce localization security in wireless networks in the presence of malicious nodes that are able to manipulate (spoof) radio measurements. It substitutes the original measurement model by another one containing an auxiliary variance dilation parameter that disguises corrupted radio links into ones with large noise variances. This allows for relaxing the non-convex maximum likelihood estimator (MLE) into a semidefinite programming (SDP) problem by applying convex-concave programming (CCP) procedure. The proposed SDP solution simultaneously outputs target location and attacker detection estimates, eliminating the need for further application of sophisticated detectors. Numerical results corroborate excellent performance of the proposed method in terms of localization accuracy and show that its detection rates are highly competitive with the state of the art.

This paper proposes a deep joint source-channel coding (DJSCC) to minimize the age of information (AoI) for image transmission. A new content-based AoI metric called age of misclassified information (AoMI) is introduced to estimate the freshness of the information in an image classification system. AoMI is a critical metric in timely information delivery, measuring the age of the most recently received and correctly classified image at the receiver. The proposed system leverages a deep neural network at the transmitter to map image pixels directly to channel input symbols, eliminating the need for separate source and channel coding. At the receiver, the channel output is processed to perform image classification. To analyze the AoMI performance of the system, a stochastic hybrid systems (SHS) approach is employed. Closed-form expressions for the average AoMI (AAoMI) are derived, providing insights into the impact of system parameters on the AoMI. Simulation results demonstrate the effectiveness of the proposed DJSCC-based system in achieving lower AoMI compared to traditional separate source and channel coding schemes. The findings highlight the potential of deep learning techniques to maintain the freshness of the information in wireless communication systems. This work paves the way for the design of wireless communication systems that prioritize the freshness of delivered information – this is crucial in applications such as real-time monitoring, surveillance, and control systems.

In this paper, we investigate performance of a random access scheme that exploits binary-tree algorithm (BTA) with the free access. We assume a scenario where the receiver is capable to perform both multi-packet reception (MPR) and successive interference cancellation (SIC), where for the purpose of the latter only the last received and undecoded signal can be stored. We distinguish between two variants of the algorithm, where in the first the SIC can be triggered by a decoding event but also executed blindly among yet undecoded slots, while in the second the receiver can only execute the SIC after the decoding event. We analytically derive the maximum stable throughput (MST) of the scheme assuming Poisson arrivals. The evaluation shows that the scheme is able of achieving a favorable performance in comparison to the scenarios when only either MPR or SIC with single-slot memory is used, making it a suitable candidate for an access solution in applications that are characterized with a massive number of users and sporadic traffic arrivals. We also compare the performance of the scheme with the best performing BTA scheme that also exploits K-MPR and SIC and does not have memory limitations, showing that the relative difference in the MST’s of the two schemes diminishes with K.

Simultaneous wireless information and power transfer (SWIPT) enabled wireless cooperative communication system is an emerging technology for future wireless communication applications. Furthermore, the Internet of Things (IoT) serves a diverse range of purposes, some of which are mission-critical and require constantly evolving real-time data. Thus, the information received at the destination must be updated timely manner to ensure its freshness. A performance metric named age of information (AoI) has been introduced to measure the freshness of received information. This study estimates the AoI of a SWIPT assisted decode and forward two-way relay assisted status update system in which two sources attempt to exchange status updates as quickly as possible to the destination. The relay system employs short packet communication to adhere to the latency and reliability requirements of the wireless communication system. We study the average Age of Information (AAoI) at the destination in the proposed relay network and derive approximations for the weighted sum AAoI under two different types of transmission scheduling policies at the relay: transmit without waiting (TWW) and wait until charged (WUC). Furthermore, the effects of transmission power, packet size, the distance between relay and sources and block-length on the weighted sum AAoI of the proposed SWIPT assisted short packet relay network are extensively investigated. The performance differences of the considered transmission policies are compared and insights are provided. Numerical simulations using the Monte Carlo method have been employed to validate derived analytical expressions.

This paper investigates rate-splitting multiple access (RSMA) networks with multiusers assisted by aerial intelligent reflecting surfaces (AIRS). To improve the sum-rate of the system, the UAV’s trajectory and phase-shift vectors are optimized, in which the mobility scenarios with static and dynamic users are explored. In particular, long short-term memory (LSTM)-based frameworks for predicting the UAV’s trajectory and the phase-shift of the reflecting elements of AIRS are proposed. For more insight, a third model is created by combining information from the static and dynamic scenarios. Furthermore, to improve the transmit beamforming at the BS, an algorithm based on alternating optimization (AO) under the assumptions of imperfect successive interference cancelation (SIC) is presented. Training progress and testing results are provided to demonstrate the efficiency of the proposed models. In addition, numerical simulations are presented to verify the performance gains in terms of sum-rate. The simulation results show that the UAV performs better in trajectory prediction and phase-shift when different investigated scenarios are not combined.

This paper introduces an adaptive technique for grouping variable nodes in decoding non-binary LDPC codes that involve symbol flipping. The technique considers both the individual symbol reliability and majority-based voting when grouping variable nodes in each iteration. Groups are formed based on the cumulative density of the variable nodes, with the least reliable variable nodes given the highest priority. The decoding algorithm then proceeds to decode subsequent groups in order of decreasing priority. The results of numerical analysis demonstrate that this approach strikes a balance between computational complexity and bit error rate performance, making it suitable for various applications, such as data storage and mobile networks.

In this paper, we investigate the design and analysis of a novel hybrid optical wireless communication (OWC)/radio frequency (RF) solution suitable for massive machine-type communications (mMTC). We consider a two-tier network architecture where a massive collection of indoor OWC-based small cells, each consisting of low-cost Internet of Things (IoT) devices and an OWC access point, are connected to the network infrastructure via an outdoor low-power wide-area network (LP WAN). Both indoor OWC and outdoor LP WAN tiers of the mMTC system deploy the Slotted ALOHA (SA) random access protocol. For the proposed hybrid OWC/RF IoT system, we are interested in the error probability in the finite block-length regime, i.e., the probability that a short block-length data packet originating from an OWC-based IoT device is delivered at its nearest LP WAN base station. Based on the derived error probability expression, we present numerical results that indicate important insights into the design of an SA-based hybrid OWC/RF IoT system.

Multicarrier-based frequency splitting simultaneous wireless information and power transmission (FS-SWIPT) signals are very prone to nonlinear effects due to the combination of high-power energy harvesting (EH) subcarriers with low-power data subcarriers. In this article, we study the impact of nonlinear effects in FS-SWIPT signals, as well as ways to minimize these effects on the data transmission performance. We start by analytically characterizing the impact of nonlinear devices on the signals, showing that the nonlinear effects on data subcarriers are exacerbated by a factor close to the ratio between the power spectral densities of EH and data terms. This suggests that conventional receivers, that treat nonlinear distortion as an additional noise term, can have very poor performance. Then, we present an iterative receiver that iteratively estimates and cancels nonlinear distortion, and is able to have a performance close to the linear case. Finally, we study the optimum performance of FS-SWIPT signals with strong nonlinear distortion levels, showing that, by using optimum receivers, the nonlinear distortion term can be used to improve the performance of FS-SWIPT, even outperforming the linear case.

In this paper a new Actor-Critic algorithm is proposed for distributed off-policy multi-agent reinforcement learning. It is composed of the Emphatic Temporal Difference ETD${\left(\lambda \right)}$algorithm (at the Critic stage) and a complementary distributed consensus-based algorithm using the exact gradients of a given criterion function (at the Actor stage). It is demonstrated that the algorithm converges weakly to the invariant set of an ordinary differential equation (ODE) characterizing the whole algorithm. Simulation results are presented as an illustration of high efficiency of the proposed algorithm.

In this paper, a new network model for visible light communication (VLC) is proposed using delta-orthogonal multiple access (D-OMA) scheme and coordinated multi-point (CoMP) transmission. CoMP enables the coordination among the group of access points (APs) such that each user receives the signals from many access points and thereby achieves the diversity gain. Therefore, all the users experience good signal quality including the users in the boundary of the visible region. D-OMA allows the partial overlapping among the sub-bands of the non-orthogonal multiple access (NOMA) clusters to improve the massive access under allowable interference levels. A Closed form expression for the bit error rate (BER) of the proposed VLC network is derived. Also, optimal power allocation at the VLC transmitter is applied using Karush-Kuhn Tucker (KKT) conditions to maximize the user data rate. Numerical results demonstrate that the proposed CoMP-based D-OMA VLC network outperforms the existing NOMA scheme.

Assuming a cooperative non-orthogonal multiple access (NOMA) system with rate fairness in a scenario with multiple users and arbitrary relays, this paper investigates adaptive power allocation (PA), relay selection (RS), and user pairing (UP) policies. Specifically, two adaptive PA optimization problems are formulated, one at the base station (BS) and another at the selected relays. Closed-form expressions for the power allocation factors are derived as well as an algorithm that provides the optimal solution at the BS. In order to show the superiority of the proposed study, our results are compared with other benchmark schemes in terms of outage probability, Jain's fairness index, and average sum rate.

Being able to accurately locate wireless devices, while guaranteeing high-level of security against spoofing attacks, benefits all participants in the localization chain (e.g., end users, network operators, and location service providers). On the one hand, most of existing localization systems are designed for innocuous environments, where no malicious adversaries are present. This makes them highly susceptible to security threats coming from interferers, attacks, or even unintentional errors (malfunctions), and thus, practically futile in hostile settings. On the other hand, existing secure localization solutions make certain (favorable) assumptions regarding the network topology (e.g., that the target device lies within a convex hull formed by reference points), which restrict their applicability. Therefore, this work addresses the problem of target localization in randomly deployed wireless networks in the presence of malicious attackers, whose goal is to manipulate (spoof) the estimation process and disable accurate localization. We propose a low-complex solution based on clustering and weighted central mass to detect attackers, using only the bare minimum of reference points, after which we solve the localization problem by a bisection procedure. The proposed method is studied from both localization accuracy and success in attacker detection point of views, where closed-form expressions for upper and lower bounds on the probability of attacker detection are derived. Its performance is validated through computer simulations, which corroborate the effectiveness of the proposed scheme, outperforming the state-of-the-art methods.

Simultaneous wireless information and power transmission (SWIPT) is one of the popular technique used in radio frequency energy harvesting (EH). Frequency splitting based SWIPT (FS-SWIPT) is a novel SWIPT technique that can be adopted with frequency domain multiple access signal transmission protocol for the near field EH application in biomedical sensors. In this paper, we implement FS-SWIPT in orthogonal frequency division multiplexed (OFDM) signal, the OFDM signal has separate sub carriers for the data symbols and the energy carrying symbols. At the receiver, the energy and information symbols are based on their sub carriers by using FS-SWIPT technique. The amplification of high energy OFDM signal at transmitter by using a high power amplifier are susceptible to non linear distortion (NLD), which degrades the bit error rate (BER) performance. We analyze the impact of NLD on the BER performance of FS-SWIPT and validate the analytical results by using simulations.

This letter proposes a novel robust mitigation technique to address the problem of target localization in adverse non-line-of-sight (NLOS) environments. The proposed scheme is based on combined received signal strength and time of arrival measurements. Influence of NLOS biases is mitigated by treating them as nuisance parameters through a robust approach. Due to a high degree of difficulty of the considered problem, it is converted into a generalized trust region sub-problem by applying certain approximations, and solved efficiently by merely a bisection procedure. Numerical results corroborate the effectiveness of the proposed approach, rendering it the most accurate one in all considered scenarios.

This letter addresses the problem of simultaneous localization of multiple targets in three-dimensional cooperative wireless sensor networks. To this end, integrated received signal strength and angle of arrival measurements are employed. By exploiting the convenient nature of spherical representation of the considered problem, the measurement models are linearized and a sub-optimal estimator is formulated. Unlike the maximum likelihood estimator, which is highly non-convex and difficult to tackle directly, the derived estimator is quadratic and has a closed-form solution. Its computational complexity is linear in the number of connections and its accuracy surpasses the accuracy of existing ones in all considered scenarios.

Portfolio selection problem that deals with the optimal allocation of capital is a well-known hard optimization problem in the domains of economics and finance. Basic version of the problem is multi-objective since it deals with maximization of return with simultaneous minimization of risk. Additional real world constraints, including cardinality, make the problem even harder. Many techniques and heuristics have been applied to this intractable optimization problem, however swarm intelligence algorithms have been implemented only few times for this task, even though they are known to be very successful for that class of problems. In this paper, we hybridized artificial bee colony algorithm with elements inspired by genetic algorithms to obtain better balance between intensification and diversification, especially during late stages, and applied the proposed improved algorithm to the cardinality constrained mean-variance version of the portfolio selection problem. Experimental results on standard benchmark datasets from five stock indexes and comparative analysis with other cutting edge algorithms have shown that our proposed algorithm achieved better results considering all relevant metrics i.e. mean Euclidean distance between standard efficiency frontier and heuristic efficiency frontier from sets of Pareto optimal portfolios obtained by tested algorithms, mean return error and variance of return error.

In this paper we present bare bones fireworks algorithm implemented and adjusted for solving radio frequency identification (RFID) network planning problem. Bare bones fireworks algorithm is new and simplified version of the fireworks metaheuristic. This approach for the RFID network planning problem was not implemented before according to the literature survey. RFID network planning problem is a well known hard optimization problem and it poses one of the most fundamental challenges in the process of deployment of the RFID network. We tested bare bones fireworks algorithm on one problem model found in the literature and performed comparative analysis with approaches tested on the same problem formulation. We also performed additional set of experiments where the number of readers is considered as the algorithm's parameter. Results obtained from empirical tests prove the robustness and efficiency of the bare bones fireworks metaheuristic for tackling the RFID network planning problem and categorize this new version of the fireworks algorithm as state-of-the-art method for dealing with NP-hard tasks.

Wireless sensor networks are widely used and consequently represent an important research field. The objective of the node localization problem, that belongs to the group of NP-hard tasks, is to find geographical coordinates of each sensor node with unknown position that are randomly deployed in the monitoring area. Such hard optimization problems are successfully solved by the swarm intelligence algorithms. This paper presents hybridized recent swarm intelligence moth search algorithm adapted for solving localization problem in wireless sensor networks. The application of the moth search algorithm for node localization problem was not found in the literature survey. In the experimental section of this paper we show comparative analysis between the original and hybridized moth search algorithm, as well as with other state-of-the-art algorithms that were tested on the same problem instances of node localization problem. According to experimental results, both, basic moth search and hybridized moth search algorithms are promising approaches for dealing with this kind of problem.

In this work, target localization problem in adverse indoor environments is addressed, where most (if not all) links are non-line-of-sight (NLOS). Localization accuracy in such environments is highly affected by multipath, which makes the problem very challenging. Hence, in order to enhance the localization accuracy, received signal strength (RSS) and time of arrival (TOA) integrated measurements, are considered here. Nevertheless, the derived joint maximum likelihood (ML) problem is highly non-convex and has no closed-form solution; thus, some approximations are required to solve it. We show that, for small noise power, the ML estimator can be tightly approximated by another (non-convex in general) one, given in a form of a generalized trust region sub-problem (GTRS). Hence, exact solution of the derived estimator can be readily obtained by merely a bisection procedure. The proposed algorithm is compared with the state-of-the-art (SOA) RSS/TOA algorithms, as well as its RSS-only and TOA-only complements. Our simulations validate the effectiveness of the proposed approach, outperforming the SOA algorithms in all considered scenarios, and show the benefit of the measurement fusion.

Vehicles need to locate other vehicles and network infrastructure elements on unmanned autonomous vehicle (UAV) systems. Human passengers also need to locate and be located by the vehicles, preferentially using a portable device, such as a smartphone. This paper analyses the accuracy of several localization algorithms in the remote location of entities running WiFi access points, using measurements collected in moving vehicles using a new application developed by us. The algorithms analysed include closed form estimators and one based on second order cone programming (SOCP) relaxation, which exhibits the best accuracy and is capable of estimating the path loss exponent and the transmission power. Although, due its lower complexity, the Levenberg-Marquardt algorithm was better suited for the stand-alone Android prototype application. The results show that real-time accurate positioning of static/slow moving remote entities is possible, even though the accuracy degrades when the measuring vehicle's speed increases.

This work addresses the range-based target localization problem in adverse non-line-of-sight (NLOS) environments. We start by deriving the maximum likelihood (ML) estimator from the measurement model, since it is asymptotically efficient. However, this estimator is highly non-convex and difficult to solve directly. Hence, we convert the localization problem into a generalized trust region sub-problem (GTRS) framework. Although still non-convex in general, the derived estimator is strictly decreasing over a readily obtained interval, and thus, can be solved exactly by a bisection procedure. In huge contrast to existing algorithms, which either require the knowledge about the magnitude of the NLOS bias or to a priori distinguish between line-of-sight (LOS) and NLOS links, the new one does not require such prerequisites. Also, the computational complexity of the proposed algorithm is linear in the number of reference nodes, unlike the majority of existing ones. Our simulation results show that the new algorithm possesses a steady NLOS bias mitigation capacity and that it represents an excellent alternative in the sense of the trade off between accuracy and complexity. To be more specific, it not only matches the performance of existing methods (majority of which significantly more computationally complex) but outperforms them in general. Moreover, the performance of the proposed algorithm is validated through real-indoor experimental data.

This letter addresses the problem of target localization based on two-way time of arrival (TW-ToA) measurements with clock imperfections. In addition to the target location, the turn-around times and clock skews are considered unknown. Since an optimal estimator for this problem cannot be tackled directly, we approximate it by a suboptimal, robust one, formulated as a generalized trust region subproblem. Even though nonconvex in general, exact solution of the derived estimator can be obtained by just a bisection procedure. Simulation results validate the effectiveness of the proposed technique, matching the performance of the state of the art with significantly lower computational complexity.

This work addresses the target tracking problem that makes use of combined measurements, namely received signal strength (RSS) and angle of arrival (AoA). By linearizing the measurement models and incorporating the prior knowledge obtained from target state transition model, we show that the application of the Kalman filter (KF) to the considered tracking problem is straightforward. Then, an extension of the linearization approach to the case where the target transmit power is not known is introduced and applied to the measurement model to obtain an estimate of the transmit power. By taking advantage of this estimated value, we show that the proposed KF algorithm can easily be generalized to the case of unknown transmit power. Our simulation results confirm the efficacy of the proposed algorithms in comparison with the existing one, as well as the robustness of the proposed approach to not knowing the transmit power. Finally, the supremacy of using the Bayesian approach in comparison with the classical one which disregards the prior knowledge information is also validated through computer simulations.

Orthogonal frequency division multiplexing (OFDM) schemes have high envelope fluctuations and peak- to-average power ratio (PAPR), making them very prone to nonlinear distortion effects, which can affect significantly the performance when conventional receivers are employed. However, it was recently shown that strong nonlinear distortion effects on OFDM signals do not necessarily lead to performance degradation. In fact, nonlinear OFDM schemes can outperform linear ones when optimum maximum likelihood (ML) receivers are employed. In this paper, we considered OFDM schemes with strong nonlinear distortion effects and we proposed a low- complexity detection scheme able to approach the optimum ML performance. Our technique is based on the fireworks algorithm (FWA) and allows excellent trade-offs between performance and complexity.

In this study, the authors consider a peak‐to‐average power ratio (PAPR) reduction for orthogonal frequency‐division multiplexing systems based on the decomposition of the set of subcarriers in subsets of subcarriers, denoted resource blocks, each one weighted by a different complex factor. They present a new iterative sphere‐geodesic descent method for obtaining these weighting factors so as to minimise the PAPR of the transmitted signals. This method, which they term geodesic descent method, efficiently makes use of the Riemannian structure of the power constraint. The authors’ performance results show that the proposed technique provides good trade‐off between the PAPR reduction and the bit error rate performance, for both uncoded and coded scenarios.

We address the received signal strength (RSS) based target localization problem in large-scale cooperative wireless sensor networks (WSNs). Using the noisy RSS measurements, we formulate the localization problem based on the maximum likelihood (ML) criterion. Although ML-based solutions have asymptotically optimal performance, the derived localization problem is non-convex. To overcome this difficulty, we propose a convex relaxation leading to second-order cone programming (SOCP) estimator, which can be solved efficiently by interior-point algorithms. Moreover, we investigate the case where target nodes limit the number of cooperating nodes by selecting only those neighbors with the highest RSS. This simple procedure can reduce the energy consumption of an algorithm in both communication and computation phase. Our simulation results show that the proposed approach outperforms significantly the existing ones in terms of the estimation accuracy and convergence. Furthermore, the new approach does not suffer significant performance degradation when the number of cooperating nodes is reduced.

We consider 3-D target positioning in noncooperative wireless sensor network (WSN) by employing received signal strength (RSS) and angle-of-arrival (AoA) measurements. Based on the least squares (LS) criterion, we derive a novel objective function for solving the hybrid localization problem. Despite the fact that the resulting optimization problem is non-convex, we show that it can be approximated into a convex problem by applying second-order cone programming (SOCP) relaxation. Moreover, we show that the objective function can be written in the form that belongs to the generalized trust region subproblems (GTRS), which can be solved exactly. Our numerical results exhibit remarkable performance of the new approaches, reducing the estimation error for more than 5 m and 3 m, in comparison to the state-of-the-art approach.

It was shown recently that we can decompose multilevel constellations as the sum of constant-envelope components which can be amplified and transmitted by separate antennas, allowing power-efficient transmitters, together with directivity at the constellation level without changing on the radiation pattern associated to the set of antennas. However, errors in the direction estimates can lead to substantial performance performance degradation since the constellations seen at the receiver can be substantially distorted. In this paper we present an improved receiver that is designed taking into account constellation distortion effects inherent to errors in direction estimates. It is shown that these "smart" receivers, optimized taking into account the apparent constellation at the receiver side can substantially outperform conventional receivers that assume that assume undistorted constellations.

Random access approaches in Low Earth Orbit (LEO) satellite networks are usually incompatible with the Quality of Service (QoS) requirements for multimedia traffic, especially when hand-held terminals must operate with a very low signal-to-noise ratio. This paper proposes the Satellite Random Network Diversity Multiple Access (SR-NDMA) protocol that handles multimedia traffic under this context through the combination of a random and scheduled access scheme. The protocol uses a multi-packet receiver, for Single Carrier with Frequency Domain Equalization (SC-FDE) in the uplink, that gradually reduces the packet error rate with additional transmissions. The paper proposes analytical performance models for the throughput, delay and energy efficiency - as long as terminals have finite queues. System parameters are defined to enhance the energy efficiency while satisfying the QoS requirements for limited queue and bit-rate constraints. Results show that the proposed system is energy efficient and provides enough QoS to support multimedia services such as video telephony.

Multilevel modulations can ensure high spectral efficiency but at cost of energy efficiency and envelope fluctuations that can compromise the amplification efficiency at the transmitter. Due to strict power and bandwidth constrains it is also desirable to adopt energy efficient amplification combined with powerful equalizer to reduce interference impact. In this paper we propose a method for amplification of multilevel constellations compatible with grossly nonlinear amplifiers based on the constellation's decomposition as a sum of BPSK (Bi-Phase Shift Keying) sub-constellations. Our approach relies on an analytical characterization of the mapping rule were the constellation symbols are written as a linear function of the transmitted bits. This analytical method is then employed to design iterative receivers such as IB-DFE (Iterative Block Decision Feedback Equalization), that can cope with higher sensitivity to ISI effects (InterSymbol Interference) of the resulting high order constellations. It is also defined a pragmatic method for designing IB-DFE receivers that can be employed with any constellation. It is also shown that the resulting method for designing an SC-DFE receiver (Single Carrier-Decision Feedback Equalization) does not require a significant increase in system complexity and can be used for the computation of the receiver parameters for any constellation.

In this paper, a novel algorithm for transmit beamforming to single cochannel multicast group is presented. The problem of minimizing the total power transmitted by the antenna array subject to interference constraints at the primary receivers and quality-of-service (QoS) constraints at the secondary receivers is addressed. It is shown that this problem, which is nonconvex NP-hard, can be approximated by a convex second-order cone programming (SOCP) problem. Then, an iterative algorithm in which the SOCP approximation is successively refined is proposed. Simulation results show the superior performance of the proposed approach in terms of the total beamforming power, feasibility and computational complexity as compared to the existing ones1.

A novel algorithm for transmit beamforming to single cochannel multicast group is presented in this paper. We consider the max-min fairness (MMF) based beamforming problem where the maximization of the smallest receiver signal-to-noise ratio (SNR) over the secondary users subject to constraints on the transmit power and interference caused to the primary users. It is shown that this problem, which is nonconvex NP-hard, can be approximated by a convex second-order cone programming (SOCP) problem. Then, an iterative algorithm which successively improves the SOCP approximation is presented. Simulation results show the superior performance of the proposed approach, together with a reduced computational complexity, as compared to the state-of-the-art approach.

Software radio signals with several channels have high dynamic range, making them very prone to nonlinear distortion effects, namely those inherent to an efficient power amplification. In this paper we present analytical approach evaluation for evaluating the impact of nonlinear devices in software radio signals. As an application, we will consider the nonlinear amplification of software radio signals where we have a high number of channels with substantially different powers.

The optimal beamforming problems for cognitive multicast transmission are quadratic nonconvex optimization problems. The standard approach is to convert the problems into the form of semi-definite programming (SDP) with the aid of rank relaxation and later employ randomization techniques for solution search. However, in many cases, this approach brings solutions that are far from the optimal ones. We consider the problem of minimizing the total power transmitted by the antenna array subject to quality-of-service (QoS) at the secondary receivers and interference constraints at the primary receivers. It is shown that this problem, which is known to be nonconvex NP-hard, can be approximated by a convex second-order cone programming (SOCP) problem. Then, an iterative algorithm in which the SOCP approximation is successively improved is presented. Simulation results demonstrate the superior performance of the proposed approach in terms of total transmitted power and feasibility, together with a reduced computational complexity, as compared to the existing ones, for both the perfect and imperfect channel state information (CSI) cases. It is further shown that the proposed approach can be used to address the max-min fairness (MMF) based beamforming problem.

This paper addresses the energy-based localization problem in wireless sensor networks. The maximum likelihood (ML) location estimation problem is a difficult optimization problem due to the non-convexity of the objective function, and finding an exact solution is difficult. In this work, an approximate solution to the ML localization is presented, by relaxing the minimization problem into semidefinite programming form. Simulation results show that the proposed algorithm outperforms the existing solutions.

We investigate the non-coherent single-user MIMO channel in the low signal-to-noise (SNR) regime from two viewpoints: capacity and probability of error analysis. The novelty in both viewpoints is that an arbitrary correlation structure is allowed for the Gaussian observation noise. First, we look at the capacity of the spatially correlated Rayleigh fading channel. We investigate the impact of channel and noise correlation on the mutual information for the on-off and Gaussian signaling schemes. Our results establish that, in the low SNR regime, mutual information is maximized when the transmit antennas are fully correlated (the same holds for the receive array). Then, we consider the deterministic channel setup and perform a pairwise error probability (PEP) analysis for the GLRT receiver. This leads to a codebook design criterion on which we base the construction of new space-time constellations. Their performance is assessed by computer simulations and, as a byproduct, we show that our codebooks are also of interest for Bayesian receivers which decode constellations with non-uniform priors.

We address the problem of space-time codebook design for non-coherent communications in multiple-antenna wireless systems. The channel matrix is assumed deterministic (no stochastic model assumed) and unknown at both the receiver and the transmitter. In contrast with other approaches, the Gaussian observation noise has an arbitrary correlation structure, known by the transmitter and the receiver. To handle the unknown deterministic space-time channel, a GLRT receiver is implemented. We propose a new methodology for space-time codebook design under this non-coherent setup. This optimizes the probability of error of the receiver's detector in the high SNR regime, thus solving a high-dimensional nonlinear non-smooth optimization problem in a two-step approach: (i) firstly, a convex SDP relaxation yields a rough estimate of the optimal codebook; (ii) this is then refined through a geodesic descent optimization algorithm that exploits the Riemannian geometry imposed by the power constraints on the space-time codewords. Computer simulations demonstrate that, for the specific case of spatio-temporal white observation noise, our codebooks are marginally better than those provided by state-of-art known solutions. However, the most relevant conclusion is that, for correlated noise environments, our method provides codes that significantly outperform other known codes