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This paper proposes a novel bit loading algorithm to multicarrier non-orthogonal multiple access (NOMA) systems to maximize the system total throughput while satisfying the users’ individual quality of service (QoS) constraints. Although bit loading is generally a non-deterministic polynomial-time (NP)-hard problem, even for orthogonal multiple access (OMA), the mutual interference between the users, and the dependence of power coefficients and modulation orders are additional challenges that add substantial complexity to the optimization problem. Therefore, in this paper, we propose an efficient bit loading algorithm for multicarrier NOMA systems and compare the complexity and throughput with a corresponding OMA system. The obtained results show that for the case of NOMA, bit loading provides an additional degree of freedom that allows non-uniform spectrum sharing among the users. In particular scenarios, the system may experience hybrid modes of operation, where the system switches between NOMA and OMA. The obtained numerical results show that NOMA can outperform OMA by 100% in terms of spectral efficiency, for a two-user scenario. The complexity of the loading process for NOMA is noticeably higher than OMA, which is due to the high complexity associated with the bit error rate (BER) computation in the case of NOMA. [ABSTRACT FROM AUTHOR]

This paper proposes an efficient algorithm for robust sensor placement with the purpose of recovering a source signal from noisy measurements. To model uncertainty on the spatially-variant sensors gain and on the spatially correlated noise, we assume that both are realizations of Gaussian processes. Since the signal to noise ratio (SNR) is also uncertain in this context, to achieve a robust signal extraction, we propose a new placement criterion based on the maximization of the probability that the SNR exceeds a given threshold. This criterion can be easily evaluated using the Gaussian process assumption. Moreover, to reduce the computational complexity of the joint maximization of the criterion with respect to all sensor positions, we suggest a sequential maximization approach, where the sensor positions are chosen one at a time. Finally, we present numerical results showing the superior robustness of the proposed approach when compared to standard sensor placement criteria aimed at interpolating the spatial gain and to a recently proposed criterion aimed at maximizing the average SNR. [ABSTRACT FROM AUTHOR]

Power control is becoming increasingly essential for the fifth-generation (5G) and beyond systems. An example use-case, among others, is the unmanned-aerial-vehicle (UAV) communications where the nearly line-of-sight (LoS) radio channels may result in very low signal-to-interference-plus-noise ratios (SINRs). The authors in (Chiang et al., 2007) proposed to efficiently and reliably solve this kind of non-convex problem via a series of geometrical programmings (GPs) using condensation approximation. However, it is only applicable for a small-scale network with several communication pairs and practically infeasible with more (e.g., tens of) nodes to be jointly optimized. We therefore in this paper aim to provide new insights into this problem. By properly introducing auxiliary variables, the problem is transformed to an equivalent form which is simpler and more intuitive for condensation. A novel condensation method with linear complexity is also proposed based on the form. The enhancements make the GP-based power control feasible for both small- and especially large-scale networks that are common in 5G and beyond. The algorithm is verified via simulations. A preliminary case study of uplink UAV communications also shows the potential of the algorithm. [ABSTRACT FROM AUTHOR]

This paper proposes a flexible pilot assignment method to jointly optimize the uplink and downlink data transmission in multi-cell Massive multiple input multiple output (MIMO) systems with correlated Rayleigh fading channels. By utilizing a closed-form expression of the ergodic spectral efficiency (SE) achieved with maximum ratio processing, we formulate an optimization problem for maximizing the minimum weighted sum of the uplink and downlink SEs subject to the transmit powers and pilot assignment sets. This combinatiorial optimization problem is solved by two sequential algorithms: a heuristic pilot assignment is first proposed to obtain a good pilot reuse set and the data power control is then implemented. Numerical results manifest that the proposed algorithm converges fast to a better minimum sum SE per user than the algorithms in previous works. [ABSTRACT FROM AUTHOR]

The energy detection technology is recommended in the licensed assisted access (LAA) scheme by 3GPP because of its simplicity and low cost. However, due to its inherent limitation, there may exist imperfect channel detection, which can lead to the decrease of the channel utilization efficiency and the deterioration of fairness. The imperfect detection can generally be represented by the detection probability and false alarm probability, which depend on detection time, signal to noise ration (SNR), sampling rate and energy threshold. However, among the parameters, only the energy threshold can be dominated by LAA small base stations (SBSs) in the LAA scheme. Therefore, the energy threshold should be dynamically adjusted in the changeable channel environment such that the detection accuracy can improved as high as possible. Consider the fact that the optimization theory cannot be used to optimize the energy threshold since the expressions of performance indexes about the energy threshold are extremely complex, a Q-learning based energy threshold optimization algorithm (QLET) is thus proposed in the paper, where LAA SBSs act as the agent, the energy threshold is defined as the agent action, the different combinations of fairness and throughput are defined as the agent states, and the fairness and the reward function are also redefined. In order to ensure the smooth implementation of the proposed QLET algorithm, the information exchange mechanism, where the sending-confirmation mechanism and 1- persistent CSMA are used, is also proposed. Based on the proposed QL framework, the agent can learn the optimal energy threshold by repeatedly interacting with the environment, which enables the coexistence system to obtain the best coexistence performance. A large number of simulation results show that the proposed QLET is superior to the traditional fixed energy threshold scheme (FET) in terms of the fairness, WiFi collision probability and transmission delay, and that QLET is almost the same as FET in term of throughput. [ABSTRACT FROM AUTHOR]

Line spectral estimation is a classical signal processing problem that aims to estimate the line spectra from their signal which is contaminated by deterministic or random noise. Despite a large body of research on this subject, the theoretical understanding of this problem is still elusive. In this paper, we introduce and quantitatively characterize the two resolution limits for the line spectral estimation problem under deterministic noise: one is the minimum separation distance between the line spectra that is required for exact detection of their number, and the other is the minimum separation distance between the line spectra that is required for a stable recovery of their supports. The quantitative results imply a phase transition phenomenon in each of the two recovery problems, and also the subtle difference between the two. We further propose a sweeping singular-value-thresholding algorithm for the number detection problem and conduct numerical experiments. The numerical results confirm the phase transition phenomenon in the number detection problem. [ABSTRACT FROM AUTHOR]

Alternating direction method of multipliers (ADMM) is an efficient implementation of linear programming (LP) decoding for low-density parity-check (LDPC) codes. By adding penalty terms to the objective function of the LP decoding model, ADMM variable node (VN) penalized decoding can suppress the non-integral solutions and improve the frame error rate (FER) performance in the low signal-to-noise ratio (SNR) region. In this paper, we propose a novel ADMM check node (CN) penalized decoding algorithm. Codeword solutions which satisfy all parity-check equations will have smaller penalty values than non-codeword solutions, including the non-integral solutions. We discuss the required properties of CN-penalty functions, propose a few functions that satisfy those properties, and study their performance/complexity trade-offs. We also investigate the convergence properties of the proposed algorithm and prove that its performance is independent of the transmitted codeword. Using Monte Carlo simulations and instanton analysis, we then demonstrate that the proposed CN-penalized decoder outperforms ADMM VN penalized decoders in both waterfall and error floor regions. This comes at the expense of some increase in the decoding complexity. [ABSTRACT FROM AUTHOR]

This paper investigates the design and performance of delayed bit-interleaved coded modulation (DBICM) with low-density parity-check (LDPC) codes. For Gray labeled square M-ary quadrature amplitude modulation (QAM) constellations, we investigate the optimal delay scheme with the largest spectrum efficiency of DBICM for a fixed maximum number of delayed time slots and a given signal-to-noise ratio. When analyzing the capacity of DBICM, we find two important properties: the capacity improvement due to delayed coded bits being mapped to the real and imaginary parts of the transmitted symbols are independent of each other; a pair of delay schemes with delayed coded bits having identical bit-channel capacity lead to equivalent DBICM capacity. Using these two properties, we efficiently optimize the delay scheme for any uniform Gray-QAM systems. Furthermore, these two properties enable efficient LDPC code designs regarding unequal error protection via bit-channel type classifications. Moreover, we use protograph-based extrinsic information transfer charts to jointly optimize degree distributions and channel assignments of LDPC codes and propose a constrained progressive edge growth like algorithm to jointly construct LDPC codes and bit-interleavers for DBICM, taking distinctive bit-channel’s capacity into account. Simulation results demonstrate that the designed LDPC coded DBICM systems significantly outperform LDPC coded BICM systems. [ABSTRACT FROM AUTHOR]

Fog radio access network (Fog-RAN), which pushes caching and computing capabilities to the network edge, is capable of efficiently delivering content to users by using carefully designed caching placement and content replacement algorithms. In this paper, the transmission scheme design and coding parameter optimization will be considered for coded caching in Fog-RAN, where the reliability of content delivery, i.e., content outage probability, is used as the performance metric. The problem will be formulated as a complicated multi-objective probabilistic combinatorial optimization. A novel maximum $b$ -matching approach will then be proposed to obtain the Pareto optimal solution with fairness constraint. Based on the fast message passing approach, a distributed algorithm with a low memory usage of $O(M+N)$ is also proposed, where $M$ is the number of users and $N$ is the number of fog access points (Fog-APs). Although it is usually very difficult to derive the closed-form formulas for the optimal solution, the approximation formulas of the content outage probability will also be obtained as a function of coding parameters. The asymptotic optimal coding parameters can then be obtained by defining and deriving the outage exponent region and diversity-multiplexing region. Simulation results will illustrate the accuracy of the theoretical derivations, and verify the outage performance of the proposed approach. Therefore, this paper not only proposes a practical distributed Fog-AP selection algorithm for coded caching but also provides a systematic way to evaluate and optimize the performance of Fog-RANs. [ABSTRACT FROM AUTHOR]

In this paper, an adaptive nonlinear differential limiter (ANDL) is proposed to efficiently alleviate the impact of impulsive noise (IN) in a communication system. Unlike existing nonlinear methods, the ANDL is implemented in the analog domain where the broader acquisition bandwidth makes outliers more detectable and consequently it is easier to remove them. While the proposed ANDL behaves like a linear filter when there is no outlier, it exhibits intermittent nonlinearity in response to IN. Therefore, the structure of the matched filter in the receiver is modified to compensate the filtering effect of the ANDL in the linear regime. In this paper, we quantify the performance of the ANDL by deriving a closed-form analytical bound for the average signal-to-noise ratio at the output of the filter. The calculation is based on the idea that the ANDL can be perceived as a time-variant linear filter whose bandwidth is modified based on the intensity of the IN. In addition, by linearizing the filter time parameter variations, we treat the ANDL as a set of linear filters where the exact operating filter at a given time depends upon the magnitude of the outliers. The theoretical average bit error rate is validated through simulations and the performance gains relative to classical methods such as blanking and clipping are quantified. [ABSTRACT FROM AUTHOR]

In this paper, a novel demapper for 2-D non-uniform constellations (2D-NUCs) is proposed, exploiting the characteristics of these constellations. It represents the combination of two underlying demapping techniques targeting the ATSC 3.0 compliant OFDM transceiver. On the one hand, for low code rates, we define a metric to perform condensed demapping. On the other hand, for high code rates, adaptive sub-region demapping is proposed. In this paper, a combination of both demapping methods is designed showing comparable performance to the classical ML demapper. The gap does not exceed 0.1 dB for all code rates of the ATSC 3.0 standard. Higher complexity reduction, from 79.2% to 95.4%, than state of art 2D-NUC demappers is obtained for 2D-256NUCs. These results are validated for ideal and non-ideal channel state information over additive white noise Gaussian and Rayleigh independently and identically distributed channels. Results are extended to 2D-1kNUCs and 2D-4kNUCs showing demapping complexity reduction from 96% to 99.7% with a negligible impact on performance. [ABSTRACT FROM AUTHOR]

This paper presents a novel autocorrelation-based algorithm that uses statistical knowledge to detect orthogonal frequency division multiplexing signals in channels where frequency offset is present. The algorithm may be viewed as a significant improvement over other types of autocorrelation algorithms that appear in literature that lead to false alarm due to the hardware impairment of frequency offset. The algorithm works by making an unbiased estimate of the square of an autocorrelation coefficient and from that deduces an appropriate probability density function for the phase angle of the complex test statistic and thereby palliating the effect of phase distortion introduced by the frequency offset. It is shown that the algorithm presented in this paper can be implemented on a testbed, as well as overcome simulations that have been specifically designed to have worst case frequency offset phase distortion conditions. [ABSTRACT FROM AUTHOR]

In multi-user downlink small cell networks, cooperative resource allocation (RA) within a small cell cluster is a key technique to enhance network capacity. However, capacity-maximizing RA in frequency-selective fading channels requires global channel state information (CSI) of users within a small cell cluster, which makes it infeasible in practical networks with limited direct link capacity. To circumvent this global CSI assumption, most of the existing studies on RA have been based on several CSI assumptions such as local CSI and local CSI at the transmitters (CSIT). Nevertheless, cost functions with local CSI or local CSIT in the literature rely on heuristic formulations, because the sum-rate cannot be computed if without global CSI. In this paper, we propose a deep reinforcement learning-based RA algorithm to maximize the sum-rate for any given limited information on instantaneous CSI or sum-rate at the previous period. The proposed scheme is not restricted to certain CSI assumptions, but attempts to find the best RA for any given information such as quantized local CSI and quantized local CSIT; thus, it is applicable to any given direct link capacity. The proposed algorithm is self-adaptive in time-varying channels, since it is not divided into training and test phases. We modify the target neural network (TNN) scheme to enhance the sum-rate and the convergence speed. Numerical simulations confirm that: i) the proposed algorithm outperforms the conventional algorithms even under the same CSI assumption such as local CSI and local CSIT; ii) a flexible trade-off between the amount of CSI and the sum-rate is realizable in practical systems. [ABSTRACT FROM AUTHOR]

The direct position determination (DPD) algorithms have been shown to outperform conventional two-step methods in localization accuracy under poor observation conditions (e.g., low signal-to-noise ratios or short signal observation durations). In this paper, we consider the distributed DPD of a static source in large-scale sensor networks. Different from the centralized DPD (CDPD) algorithms where all the raw signal samples are transmitted globally to a fusion center to solve for the source position, in our method the signal samples are only exchanged locally between neighboring sensors, and the source position is estimated using the proposed DPD algorithm distributively. The problem-specific cost function, which is different from the cost function of CDPD, is first derived. To obtain the cost function at each sensor, we propose to transmit the low-dimensional sufficient statistics across the sensor networks instead of the original high-dimensional raw signal samples, and to reconstruct the cost function via simple interpolations. Simplification of cost function evaluation is also developed to further reduce complexity. Finally, source position is estimated at each sensor efficiently via the importance sampling algorithm. Simulation results demonstrate the superior performance of the proposed method over existing adaptive distributed DPD as well as the two-step algorithms under poor observation conditions, and its computational efficiency is significantly improved compared with the CDPD. [ABSTRACT FROM AUTHOR]

Combining intelligent reflecting surface (IRS) and non-orthogonal multiple access (NOMA) is an effective solution to enhance communication coverage and energy efficiency. In this paper, we focus on an IRS-assisted NOMA network and propose an energy-efficient algorithm to yield a good tradeoff between the sum-rate maximization and total power consumption minimization. We aim to maximize the system energy efficiency by jointly optimizing the transmit beamforming at the BS and the reflecting beamforming at the IRS. Specifically, the transmit beamforming and the phases of the low-cost passive elements on the IRS are alternatively optimized until the convergence. Simulation results demonstrate that the proposed algorithm in IRS-NOMA can yield superior performance compared with the conventional OMA-IRS and NOMA with a random phase IRS. [ABSTRACT FROM AUTHOR]

A fusion of inertial navigation system and global positioning system (GPS) has been proposed to implement a robust navigation system for a transporter. In several cases, the GPS signals are not reliable to localize a transporter. In order to overcome this difficulty, a fault detection and isolation (FDI) algorithm has been used to detect and isolate the contaminated satellite signals. When the FDI algorithm is used alone, and the number of reliable satellite signals becomes less than four owing to GPS signal errors, the receiver cannot estimate the position precisely. In this paper, a modified FDI algorithm has been proposed and applied for the navigation of a transporter on a flat yard in order to avoid losing its positioning while it passes through a structured area. In the modified FDI algorithm, signal-to-noise ratio, measurement of quality indicator, and Doppler shift data are incorporated. In order to verify the effectiveness of the proposed algorithm, the navigation experiments have been conducted on a campus using a mobile robot to simulate the transporter on a shipbuilding yard and the results are demonstrated in this paper. [ABSTRACT FROM AUTHOR]

The distributed power control algorithms available in this paper assume perfect estimation of interference power at the receivers and often, the availability of perfect local channel state information (CSI). However, perfect estimation of interference power or acquisition of channel information is impractical. In this paper, we consider a generic class of distributed power control algorithms and analyze the resulting performance loss when faced with errors in CSI and/or interference level estimation. We approximate such power control algorithms with linearized state space dynamics wherein the errors in the estimation of received interference or the CSI, can be modeled as additive observation noise. Based on the proposed model, we derive lower bounds on the norm of the errors in the transmit power vector and receive signal-to-interference-plus-noise ratio (SINR) vector. Our analysis suggests that when the target SINR vector approaches the Pareto-optimal bound, the errors in the transmit vectors could grow without bound. As a consequence, to maintain a performance loss within an acceptable range, either much longer estimation times or lower target SINRs are required. Our numerical results confirm the validity of our analysis and the validity of the proposed bounds. [ABSTRACT FROM PUBLISHER]

This paper studies robust energy optimization for the cloud radio access network (C-RAN). The objective of this paper is to jointly minimize network power consumption through optimizing the base station (BS) mode, multi-user (MU)-BS association, and beamforming vectors given imperfect channel state information (CSI). To solve this non-trivial problem, we first transform the problem to a semi-definite programming (SDP) one using the S-lemma with the aid of the semi-definite relaxation technique, and then propose a SDP-based group sparse beamforming approach to solve it iteratively. Since the computational complexity of solving SDP problems is intractable, we propose to translate the uncertainty in the CSI to the uncertainty in its covariance matrix, and then recast the original problem as a mixed-integer second-order cone programming problem. We further propose a two-stage rank selection framework to determine the BS mode and MU-BS association separately and successively. Simulation results demonstrate the convergence of our proposed algorithms, and validate the effectiveness of the proposed algorithms in minimizing the network power consumption of the C-RAN. [ABSTRACT FROM PUBLISHER]

Terahertz (THz) band communication is envisioned as a key technology to satisfy the increasing demand for ultrabroadband wireless systems, thanks to its ultrabroad bandwidth. Tailored for the unique properties of pulse-based communications in the THz band, two timing acquisition algorithms are proposed and analyzed thoroughly in this paper. First, a low-sampling-rate (LSR) synchronization algorithm is proposed, by extending the theory of sampling signals with finite rate of innovation in the communication context and exploiting the properties of the annihilating filter. The simulation results show that the timing accuracy at an order of ten picoseconds is achievable. In particular, the LSR algorithm has high performance with uniform sampling at $1/20$ of the Nyquist rate when the signal-to-noise ratio (SNR) is high (i.e., greater than 18 dB). Complementary to this, a maximum likelihood (ML) approach for timing acquisition is developed, which searches for the timing offsets by adopting a two-step acquisition procedure based on the ML criterion. The simulation results show that the ML-based algorithm is well suitable in the low SNR case with a half-reduced search space. For further evaluation, the error performance and the resulting bit-error-rate sensitivity to the timing errors in the LSR and the ML algorithms are both analytically and numerically studied. This paper provides very different and promising angles to efficiently and reliably solve the timing acquisition problem for pulse-based THz band wireless systems. [ABSTRACT FROM PUBLISHER]

In this paper, we propose an optimal random beamforming technique in mmWave MIMO-NOMA transmission systems. In our model, we consider a NOMA system with a user pairing scheme that works based on the distance between the cellular base station (BS) and the active mobile users. The aim of this is to reduce the system overall overhead for massive connectivity and achieve the desired Quality of Service (QoS). We present an optimization problem with the aims to find the optimal random beamforming coefficients by minimizing the outage probability. We analyze the convexity of the outage probability for three user pairing schemes and prove that the proposed optimal random beamforming is convex in high signal-to-noise ratio (SNR). We utilize the gradient-based algorithm namely the gradient descent algorithm to find optimal random beamforming coefficients. Our simulation results demonstrate that the proposed transmission schemes reduce the outage probability and improve network performance compared to previous works. [ABSTRACT FROM AUTHOR]

Channel gain based path selection has been recently proposed to simplify the network design of a cluster-based cross-layer multihop vehicular communications. In vehicular network systems, end-to-end packet delay increases with the growing number of vehicles associated in that path. In delay-sensitive application, the information exchange must be successfully carried out very quickly to ensure the quality of infotainment services. In the case of excessive packet delay, some important information either are needed to be enqueued before reaching the destination or are lost. The packet loss makes it impractical to deploy the network in practice. In this paper, a new delay-sensitive buffer-aided relay selection is proposed using the channel-based greedy scheduling algorithm (CGSA) to overcome the limitations imposed by processing delay. The proposed scheme significantly minimizes average delay by selecting a set of alternative paths based on individual node buffer status. This scheme has less packet delay than existing Max-link scheme with very close system performances, making it an ideal candidate for delay-sensitive applications. Finally, both analytical and simulation results are provided to demonstrate the performance improvement of the proposed algorithm compared with the existing relaying schemes. [ABSTRACT FROM AUTHOR]

This paper proposes the integration of non-orthogonal multiple access (NOMA) and broadcasting with buffer-aided (BA) full-duplex (FD) relaying to improve the spectral efficiency of a single-source two-user network. Furthermore, opportunistic relay selection is employed, activating one or two FD relays to support the end-to-end communication. The network performance is improved through buffers, allowing to switch to half-duplex (HD) transmission/reception when FD operation is not feasible. A hybrid BA FD/HD NOMA relay selection algorithm is proposed, herein called $\mathop {\mathrm{BAHyNOMA}}$ , orchestrating the three aforementioned techniques, towards a simple yet efficient and robust relay selection. $\mathop {\mathrm{BAHyNOMA}}$ is evaluated in terms of outage probability, average sum-rate and average delay, and it is observed that the average sum-rate can be doubled, while maintaining low latency. [ABSTRACT FROM AUTHOR]

Linear regression without correspondences is the problem of performing a linear regression fit to a dataset for which the correspondences between the independent samples and the observations are unknown. Such a problem naturally arises in diverse domains such as computer vision, data mining, communications and biology. In its simplest form, it is tantamount to solving a linear system of equations, for which the entries of the right hand side vector have been permuted. This type of data corruption renders the linear regression task considerably harder, even in the absence of other corruptions, such as noise, outliers or missing entries. Existing methods are either applicable only to noiseless data or they are very sensitive to initialization or they work only for partially shuffled data. In this paper we address these issues via an algebraic geometric approach, which uses symmetric polynomials to extract permutation-invariant constraints that the parameters $\xi ^{*} \in \mathbb {R} ^{\text {n}}$ of the linear regression model must satisfy. This naturally leads to a polynomial system of n equations in n unknowns, which contains $\xi ^{*}$ in its root locus. Using the machinery of algebraic geometry we prove that as long as the independent samples are generic, this polynomial system is always consistent with at most n! complex roots, regardless of any type of corruption inflicted on the observations. The algorithmic implication of this fact is that one can always solve this polynomial system and use its most suitable root as initialization to the Expectation Maximization algorithm. To the best of our knowledge, the resulting method is the first working solution for small values of n able to handle thousands of fully shuffled noisy observations in milliseconds. [ABSTRACT FROM AUTHOR]

In the heterogeneous networks (HetNets), the downlink-uplink decoupled (DUDE) user association is a very promising technology that can employ different association strategies for uplink and downlink. The DUDE mode has great potential for improving system capacity and frequency efficiency. However, interference between the DUDE user equipment (DUDE UE), macro user equipment (MUE) and small user equipment (SUE) still needs to be considered and properly tackled. Therefore, this paper presents an optimization scheme of frequency allocation and power control to improve the communication quality of users. Firstly, the multiple region frequency allocation (MRFA) scheme is proposed for non-uniform user distribution to alleviate interference among users and allocate spectrum resources equally to dense users. This scheme is modelled as a sub-region partition problem with maximum sum rate, which can disperse densely distributed users to different sub-bands for transmission. Secondly, the convergent power control (CPC) algorithm is considered to improve the transmission performance of each user. It can converge the transmit power to the optimal transmit power for reducing interference among co-channel users when there is no disturbance in the network. The CPC algorithm can also make the signal-to-interference-plus-noise-ratio (SINR) of each active user always greater than the target threshold until the end of communication by dynamically adjusting the transmit power of users. Simulation results show that the joint scheme of MRFA and CPC achieves a higher system throughput and better user performance than existing frequency allocation schemes or power control schemes. [ABSTRACT FROM AUTHOR]

A fundamental question for edge detection in noisy images is how faint can an edge be and still be detected. In this paper we offer a formalism to study this question and subsequently introduce computationally efficient multiscale edge detection algorithms designed to detect faint edges in noisy images. In our formalism we view edge detection as a search in a discrete, though potentially large, set of feasible curves. First, we derive approximate expressions for the detection threshold as a function of curve length and the complexity of the search space. We then present two edge detection algorithms, one for straight edges, and the second for curved ones. Both algorithms efficiently search for edges in a large set of candidates by hierarchically constructing difference filters that match the curves traced by the sought edges. We demonstrate the utility of our algorithms in both simulations and applications involving challenging real images. Finally, based on these principles, we develop an algorithm for fiber detection and enhancement. We exemplify its utility to reveal and enhance nerve axons in light microscopy images. [ABSTRACT FROM AUTHOR]

For decoding low-density parity-check (LDPC) codes, the attenuated min-sum algorithm (AMSA) and the offset min-sum algorithm (OMSA) can outperform the conventional min-sum algorithm (MSA) at low signal-to-noise-ratios (SNRs), i.e., in the “waterfall region” of the bit error rate curve. This paper demonstrates that, for quantized decoders, MSA actually outperforms AMSA and OMSA in the “error floor” region, and that all three algorithms suffer from a relatively high error floor. This motivates the introduction of a modified MSA that is designed to outperform MSA, AMSA, and OMSA across all SNRs. The new algorithm is based on the assumption that trapping sets are the major cause of the error floor for quantized LDPC decoders. A performance estimation tool based on trapping sets is used to verify the effectiveness of the new algorithm and also to guide parameter selection. We also show that the implementation complexity of the new algorithm is only slightly higher than that of AMSA or OMSA. Finally, the simulated performance of the new algorithm, using several classes of LDPC codes (including spatially coupled LDPC codes), is shown to outperform MSA, AMSA, and OMSA across all SNRs. [ABSTRACT FROM AUTHOR]

Layered transmission is studied in this paper to accommodate various device capabilities and channel conditions. The problem of efficiently broadcasting a common set of layer-encoded data from a single base station to multiple users over quasi-static fading channels is examined. Considering the maximum tolerable delay, a block-level utility is defined in terms of average probability of successful reception of each layer. Based on non-orthogonal multiple access scheme, we propose a complete algorithm to jointly optimize power allocation and transmission rates. By utilizing the idea of opportunistic scheduling, we first derive the success probability of one single layer in homogeneous scenarios, then extend the results to heterogeneous scenarios where users are subject to different channel statistics. The multi-layer optimization is realized by iteratively repeating the single-layer operations. Starting from the base layer, we first choose an optimal subset of users as the corresponding target group, then apply the single-layer allocation process until no power resources are left. Computer simulation and discussion confirm that our method maximizes the block-level utility at the same time balances the tradeoff between multiuser diversity gain and multicast gain. [ABSTRACT FROM AUTHOR]

The key to successful detection of a localized bearing fault lies in extracting fault-induced impulse responses. Unfortunately, these responses are often contaminated by background noise. The popular K-singular value decomposition (K-SVD) denoising algorithm can be used to extract impulse responses from noise, but it may obtain weak impulse responses against reliable fault detection. An SOSO boosting technique is proposed to improve the performance of the K-SVD denoising algorithm. Given an initial denoised signal, this paper proposes iteratively repeating the following SOSO boosting procedure: 1) Strengthen the underlying signal by adding the previous denoised signal to the noisy input signal; 2) Operate the K-SVD denoising algorithm on the strengthened signal; 3) Subtract the previous denoised signal from the restored signal-strengthened outcome; and 4) Operate the modified K-SVD denoising algorithm again on the resulting signal after the subtraction. The last step, i.e., the secondary denoising step, plays a crucial role in preventing the enhancement of the residual noise that originates from the first denoised result. The results of numerical and experimental studies show that the SOSO-based K-SVD denoising algorithm not only enhances weak impulse responses significantly but also reduces potential residual noise effectively compared to the SOS-based and the original K-SVD denoising algorithms. [ABSTRACT FROM AUTHOR]

The line-of-sight (LoS) dominant air-ground channels have posed critical interference issues in cellular-connected unmanned aerial vehicle (UAV) communications. In this paper, we propose a new base station (BS) cooperative beamforming (CB) technique for the cellular downlink to mitigate the strong interference caused by the co-channel terrestrial transmissions to the UAV. Besides the conventional CB by cooperatively transmitting the UAV’s message, the serving BSs of the UAV exploit a novel CB-based interference transmission scheme to effectively suppress the terrestrial interference to the UAV. Specifically, the co-channel terrestrial users’ messages are shared with the UAV’s serving BSs and transmitted via CB so as to cancel their resultant interference at the UAV’s receiver. To optimally balance between the CB gains for UAV signal enhancement and terrestrial interference cancellation, we formulate a new problem to maximize the UAV’s receive signal-to-interference-plus-noise ratio (SINR) by jointly optimizing the power allocations at all of its serving BSs for transmitting the UAV’s and co-channel terrestrial users’ messages. First, we derive the closed-form optimal solution to this problem in the special case of one serving BS for the UAV and draw useful insights. Then, we propose an algorithm to solve the problem optimally in the general case. As the optimal solution requires centralized implementation with exorbitant message/channel information exchanges among the BSs, we further propose a distributed algorithm that is amenable to practical implementation, based on a new divide-and-conquer approach, whereby each co-channel BS divides its perceived interference to the UAV into multiple portions, each to be canceled by a different serving BS of the UAV with its best effort. Numerical results show that the proposed centralized and distributed CB schemes with interference transmission and cancellation (ITC) can both significantly improve the UAV’s downlink performance as compared to the conventional CB without applying ITC. [ABSTRACT FROM AUTHOR]

This paper studies the energy-efficient power allocation problem for physical-layer security in multi-user (MU) distributed massive multiple-input multiple-output (MIMO) systems. A new metric called global average secrecy energy efficiency (GASEE) is proposed to measure the MU secrecy energy efficiency (SEE) with a single eavesdropper (Eve). We first derive closed-form expressions for the signal to interference-plus-noise ratios (SINRs) of legitimate users and the Eve with pilot contamination. Under a power consumption model that incorporates transmit power, backhaul power, remote antenna unit (RAU) circuit and signal processing power, and with transmit power constraints as well as SINR constraints for both users and the Eve, the GASEE maximization problem is formulated as a joint optimization of power allocation, RAU clustering, RAU selection and artificial noise (AN) selection. The formulated problem is a mixed integer nonlinear program (MINLP), which is solved by a double-loop procedure. In the outer loop, the denominator of objective is approximated as a linear function. In the inner loop, an efficient algorithm is proposed to find a near-optimal solution to the approximated problem by solving a sequence of sub-problems. Simulation results demonstrate that the proposed algorithm converges fast and achieves a higher GASEE than some heuristics. [ABSTRACT FROM AUTHOR]

In this paper, we propose a new beam allocation strategy aiming to maximize the average successful tracking probability (ASTP) of a single user time-varying millimeter-wave MIMO system. In contrast to most existing works that employ one transmitting-receiving (Tx-Rx) beam pair once only, we investigate a more general framework, where the Tx-Rx beam pairs are allowed to be used repeatedly in each beam training period to improve the received signal powers in specific directions. In the case of orthogonal Tx-Rx beam pairs, a power-based estimator is employed to track the time-varying AoA and AoD of the channel, and the resulting training beam pair sequence design problem is formulated as an integer nonlinear programming (I-NLP) problem. By dividing the feasible region into a set of subregions, the formulated I-NLP is decomposed into a series of concave sub I-NLPs, which can be solved by recursively invoking a nonlinear branch-and-bound algorithm. To reduce the computational cost, we relax the integer constraints of each sub I-NLP and obtain a low-complexity solution via solving the Karush-Kuhn-Tucker conditions of their relaxed problems. For the case when the Tx-Rx beam pairs are overlapped in the angular space, we estimate the updated AoA and AoD via an orthogonal matching pursuit (OMP) algorithm. Moreover, since no explicit expression for the ASTP exists for the OMP-based estimator, we derive a closed-form lower bound of the ASTP, based on which a favorable beam pair allocation strategy can be obtained. Numerical results demonstrate the superiority of the proposed beam allocation strategy over existing benchmarks. [ABSTRACT FROM AUTHOR]

In this paper, a power allocation space-time coding scheme based on the self-coding property of residual loop interference (RLI) for two-way full-duplex (FD) amplify-and-forward (AF) relaying networks is proposed. The relay and direct link cooperate to forward the received signals to destination node, forming the self-coded distributed space-time code, in which the relay performs self-coding using the RLI after implementing some self-interference cancellation techniques. Then, an iterative power allocation algorithm is presented. With the proposed power allocation algorithm, both amplifying factor and transmit powers of terminal nodes are jointly optimized to maximize the smaller signal-to-interference-plus-noise ratio (SINR) of terminal nodes under a total power constraint when estimated instantaneous channel state information is available. We theoretically demonstrate the benefits of the proposed power allocation algorithm compared to equal power allocation. Simulation results show that the proposed scheme achieves significant improvement in performance compared with existing schemes for FD relaying systems and half-duplex relaying systems. [ABSTRACT FROM AUTHOR]

In this paper, we investigate the joint signal to interference plus noise ratio (SINR) thresholds optimization and resource allocation to maximize the sum-rate of Device-to-Device (D2D) communications while still retaining the rate requirements for active cellular users (CUs), when the inactive CUs are used as opportunistic relays under three operational modes: without using network-coding (NNC), using traditional high-layer network-coding (HNC), and using physical-layer network-coding (PNC). Under Rayleigh fading, we show that, given the selections of relays, this sum-rate maximization in no-relay scheme, NNC, HNC, and PNC opportunistic relay schemes can be formulated as a mixed integer non-linear programming (MINLP), which is NP-hard in general. To find the solution to the MINLP, we propose a two-step approach to solve the problem: 1) for each possible pairing of a D2D pair and a CU, we derive the optimal SINR thresholds to obtain the maximum transmission rate of the D2D pair while satisfying the rate requirement of the CU; 2) based on the maximum transmission rates of D2D pairs for each possible pairing in the first step, we develop a bipartite-matching method to find the optimal pairing CUs for D2D pairs. Finally, according to the solution to the MINLP, we propose an iterative relay selection algorithm to find out the relays that can further improve the sum-rate of D2D communications. Extensive simulation results demonstrate that, compared with the scenario without relaying, the NNC, HNC, and PNC opportunistic relay schemes achieve a maximum performance enhancement of 106%, 138%, and 168%, respectively. [ABSTRACT FROM AUTHOR]

The paper derives the stability bound of the initial mean-square deviation of an adaptive filtering algorithm based on minimizing the 2Lth moment of the estimation error, with L being an integer greater than 1. The analysis is done for a time-invariant plant with even input probability density function. Dependence of the stability bound on the algorithm step-size, type of the noise distribution, signal-to-noise ratio (SNR), and L is studied. It is shown that the stability bound is decreasing in the step-size. The stability bound decreases as the tail of the noise probability density function becomes lighter for the same steady-state misadjustment. This result is surprising since, for example, it is expected that the stability of the least mean 2Lth algorithm for binary noise distribution is better than that for uniform distribution. The stability bound is proportional to the reciprocal of the SNR, another surprising result. Finally, the stability bound is decreasing in L, which implies a limitation on the ability to improve the performance of the adaptive filter by increasing the order of the cost function. Theoretical results are supported by simulations. [ABSTRACT FROM AUTHOR]

This paper considers a method for improving physical layer security of wireless networks with full-duplex radio. In particular, fast algorithms are developed to compute power allocations in subcarriers, subject to power and rate constraints, to maximize the secrecy capacity of a three-node network consisting of a source, a full-duplex destination, and an eavesdropper. A residual level of radio self-interference channel is considered. The optimal power allocation at the destination is found to be significant especially when its power budget is high. Also studied in this paper are a network with multiple full-duplex destinations and another network with multiple sources. Using the algorithms developed in this paper, we are able to show that a multiuser strategy that optimizes the power distributions among the users (in terms of either the sources or the destinations) can yield a substantial gain of secrecy capacity over a single-user strategy. [ABSTRACT FROM PUBLISHER]

In this paper, we propose a compressive random access (CRA) scheme using multiple resource blocks (RBs) to support massive connections for machine type communications. The proposed CRA scheme is scalable. As a result, if the number of devices increases, more RBs can be added to support them. Thanks to multiple RBs, we can employ fast retrial between RBs for re-transmissions of collided packets, which can result in short access delay. For stable CRA with fast retrial, we derive conditions (with a rate control scheme), and analyze the steady-state performance to find the throughput and delay. Through analysis and simulation results, we can see that the proposed scheme can perform better than conventional multichannel ALOHA and enjoy a tradeoff between the performance and complexity in terms of the number of RBs. [ABSTRACT FROM AUTHOR]

In this paper, we propose two groups of recursive codes, namely the Hadamard recursive carrier interferometry (HRCI) codes and diagonal recursive carrier interferometry (DRCI) codes, to simultaneously reduce the peak-to-average power ratio (PAPR) and suppress the phase noises and residual carrier frequency offset (RCFO) for high-speed orthogonal frequency division multiplexing (OFDM) systems. We exploit the recursion property of the Hadamard and diagonal matrices to constitute the proposed HRCI code and DRCI code using the carrier interferometry (CI) code, and present the system model in details to spread OFDM symbols in the frequency domain. Then, we prove that both HRCI and DRCI generation matrices keep their invertibility during the recursions. As a direct result of the new code design, the phase intervals are enlarged and the signals are shifted in the time domain, thus both the PAPR and the phase noises are reduced, and the RCFO can be mitigated. Moreover, the enlarged phase intervals enable the transceiver blocks including the channel estimator, which estimate the phase noise based on the received signals, to improve the performance. Furthermore, we present the expressions for the HRCI and DRCI embedded transmitted and received signals, and derive the analytical signal-to-interference-plus-noise ratio (SINR) expressions for bit error rate (BER) performance analysis. Then, we analyze the effects of presented codes on the phase differences and provide the computational complexity analysis. Numerical simulations verify the effectiveness of our theoretical analysis of SINR. Additionally, under different parameter settings, which consider the working conditions of practical systems, we demonstrate that the presented robust HRCI and DRCI coded OFDM systems can suppress the PAPR satisfactorily and better BER performances can be achieved by the recursive CI-aided spread OFDM systems with phase noise rejection and RCFO compensation when compared with counterpart systems. [ABSTRACT FROM AUTHOR]

Distribution matching is a fixed-length invertible mapping from a uniformly distributed bit sequence to shaped amplitudes and plays an important role in the probabilistic amplitude shaping framework. With conventional constant-composition distribution matching (CCDM), all output sequences have identical composition. In this paper, we propose multiset-partition distribution matching (MPDM), where the composition is constant over all output sequences. When considering the desired distribution as a multiset, MPDM corresponds to partitioning this multiset into equal-sized subsets. We show that MPDM allows addressing more output sequences and, thus, has a lower rate loss than CCDM in all nontrivial cases. By imposing some constraints on the partitioning, a constructive MPDM algorithm is proposed which comprises two parts. A variable-length prefix of the binary data word determines the composition to be used, and the remainder of the input word is mapped with a conventional CCDM algorithm, such as arithmetic coding, according to the chosen composition. Simulations of 64-ary quadrature amplitude modulation over the additive white Gaussian noise channel demonstrate that the block-length saving of MPDM over CCDM for a fixed gap to capacity is approximately a factor of 2.5–5 at medium to high signal-to-noise ratios. [ABSTRACT FROM AUTHOR]

In this paper, we show that any sequence of infinite lattice constellations which is good for the unconstrained Gaussian channel can be shaped into a capacity-achieving sequence of codes for the power-constrained Gaussian channel under lattice decoding and non-uniform signaling. Unlike previous results in the literature, our scheme holds with no extra condition on the lattices (e.g., quantization-goodness or vanishing flatness factor), thus establishing a direct implication between AWGN-goodness, in the sense of Poltyrev and capacity-achieving codes. Our analysis uses properties of the discrete Gaussian distribution in order to obtain precise bounds on the probability of error and achievable rates. In particular, we obtain a simple characterization of the finite-blocklength behavior of the scheme, showing that it approaches the optimal dispersion coefficient for high signal-to-noise ratio. We further show that for low signal-to-noise ratio, the discrete Gaussian over centered lattice constellations cannot achieve capacity, and thus a shift (or “dither”) is essentially necessary. [ABSTRACT FROM AUTHOR]

In this paper, spatial modulation (SM) is introduced to layered division multiplexing (LDM) systems for enlarging the spectral efficiency (SE) over broadcasting transmission. First, the SM aided LDM (SM-LDM) system is proposed, in which different layered services utilize SM for terrestrial broadcasting transmission with different power levels. Then a SE analysis framework for SM-LDM systems is proposed, which is suitable for the SM-LDM systems with linear combining. Moreover, the closed-form SE lower bound of SM-LDM systems with maximum ratio combining (MRC) is derived, which is based on this framework. Since the theoretical SE analysis of single transmit antenna (TA) LDM systems with MRC and spatial multiplexing (SMX) aided LDM systems with MRC lacks a closed-form expression, the closed-form SE is also derived for these systems. Monte Carlo simulations are provided to verify the tightness of our proposed SE lower bound. Furthermore, it can be shown via simulations that our proposed SM-LDM systems always have a better SE performance than single-TA LDM systems, which can even outperform the SE of SMX aided LDM (SMX-LDM) systems. [ABSTRACT FROM AUTHOR]

Adaptive signal detection algorithms in unknown interference are generally formulated under the assumption that training sets are available for the estimation of the test cell interference characteristics. A significant problem occurs in applications such as radar surveillance when the interference covariance matrices of vectors from the training cells and that of the test cell vector are mismatched. False alarm rates may increase beyond acceptable levels and thus overwhelm a receiver if the interference in the test cell is not adequately cancelled. A number of adaptive detection algorithms with the constant false alarm rate (CFAR) property have been designed assuming the availability of training data that have the same covariance matrix as the interference in the test cell (although the adaptive coherence estimator algorithm requires the two covariance matrices to be related by a positive multiplicative constant that is not required to be known). The detection threshold in these CFAR detectors can be selected without knowledge of the interference-plus-noise covariance matrix. To the best of our knowledge, an analytical characterization of the performance effects of a receiver using a mismatched training set (perhaps inadvertently) in adaptive detection is currently not available. This paper addresses the problem analytically. An exact analysis of the effects of interference covariance matrix mismatch on the performance of the adaptive matched filter test is carried out. Results provide insights to the specific aspects of covariance matrix mismatch that cause the increase in false alarms when compared to the matched training set case. Analytical results are illustrated with an example and verified independently with simulations. These results are useful in developing CFAR algorithms under conditions of mismatched training. [ABSTRACT FROM AUTHOR]

In this paper, we propose a generalized channel decoding scheme for nonbinary physical-layer network coding (CD-NC) in two-way relay channels (TWRCs), where two source nodes A and B exchange their nonbinary symbols via a relay. The two sources use the same nonbinary low-density parity-check (LDPC) channel code over the integer ring $\mathbb {Z}_M$ and $M$ -pulse-amplitude modulation, respectively. The existing channel decoding schemes for nonbinary network coding suffer severe rate loss compared with the cut-set bound of TWRC, especially in the low-to-medium signal-to-noise ratio regime. The proposed CD-NC can decrease the rate loss. Our contributions are as follows: 1) We develop a generalized nonbinary sum product algorithm (G-SPA) for CD-NC according to the principle of virtual encoding of the superimposed symbols. Simulation results show that our CD-NC can achieve significant performance gains over the conventional nonbinary network coding for both additive white Gaussian noise and fading channels; and 2) We exploit two-dimensional fast-Fourier-transform-based belief propagation (2-D-FFT-BP) and extended min-sum (EMS) decoding algorithms to reduce the decoding complexity of G-SPA. Simulation results show that the 2-D-FFT-BP has the same performance as G-SPA, while EMS can greatly reduce the decoding complexity of G-SPA at the cost of slight performance degradation. [ABSTRACT FROM AUTHOR]

To ensure link reliability and signal receiving quality, robust adaptive beamforming (RAB) is vital important in mobile communications. In this paper, we propose a new RAB algorithm based on interference-plus-noise covariance (INC) matrix reconstruction and steering vector (SV) estimation. In this method, the INC matrix is reconstructed by estimating all interferences SVs and powers, as well as the noise power. The interference SVs are estimated by using the Capon spatial spectrum together with robust Capon beamforming principle, subsequently the interference powers are estimated based on the orthogonality between different signal SVs. On the other hand, the desired signal SV is estimated via maximizing the beamformer output power by solving a quadratic convex optimization problem. The proposed algorithm only needs to know in advance the array geometry and angular sector, in which the desired signal lies. Simulation results indicate that the proposed algorithm outperforms the existing RAB techniques in terms of the overall performance in cases of various mismatches. [ABSTRACT FROM AUTHOR]

Ultradense heterogeneous networks (HetNets) are emerging as an inevitable approach to tackle the capacity crunch in cellular networks. However, imbalanced load among small and macrocells and poor resource utilization as a consequence in HetNets remains a long-standing problem. This paper addresses this problem by presenting a solution for maximization of coverage and capacity while minimizing load imbalance among macro and small cells. Most recent studies on the topic focus on either optimization of coverage, capacity or load, or a combination of two of these three intertwined objectives. We formulate the optimization problem as a function of two hard parameters namely antenna tilt and transmit power, and a soft parameter, cell individual offset, that affect the coverage, capacity, and load directly. The resulting solution is a combination of the otherwise conflicting coverage and capacity optimization (CCO) and load balancing (LB) self-organizing network (SON) functions. In the presented joint CCO-LB solution, a conflict free operation of CCO and LB is ensured by designing a novel load aware user association methodology and resolving the effects of shadowing on coverage probability using stochastic approximation. The problem is proven to be nonconvex and is solved using genetic algorithm, sequential quadratic programming, and pattern search algorithms. The proposed CCO-LB solution is compared against two recently proposed CCO and CCO-LB solutions in the literature. Results show that the proposed solution can yield significant gain in terms of throughput, spectral efficiency, and load distribution. [ABSTRACT FROM AUTHOR]

Spectrum sensing in next-generation wireless radio networks is considered a key technology to overcome the problem of spectrum scarcity. Unfortunately, many approaches to spectrum sensing do not work well in low signal-to-noise ratio (SNR) environments. This paper proposes and analyzes a new algorithm named kernelized generalized likelihood ratio test (KGLRT) for spectrum sensing in cognitive radio systems to overcome this problem. Effectively, KGLRT uses a nonlinear kernel to map input data onto a high-dimensional feature space; then, the widely accepted (linear) generalized likelihood ratio test is used for hypothesis testing. This new algorithm gives a gain of 4 dB in SNR over its linear counterpart. A theoretical analysis for this algorithm is given for the first time and is shown analogous to algorithms used in image signal processing. The detection metrics are found to be concentrated random variables; furthermore, the probability distributions of the detection metrics are proved to follow the F -distributions, which agree with the results obtained using the concentration inequality. The analytical thresholds are derived for target false-alarm probabilities. The thresholds are independent of noise power; thus, the proposed algorithm can overcome noise uncertainty issues at very low SNR levels. Simulations validate the theoretical results. [ABSTRACT FROM AUTHOR]

This paper explores the problem of joint mode selection, spectrum management, power control, and interference mitigation for device-to-device (D2D) communication underlaying a Long Term Evolution-Advanced (LTE-A) network. We consider a dynamic mode selection scenario, in which the modes (D2D or cellular) of the devices depend on optimal allocations. To improve the quality of service (QoS) for the users, the optimization objective in a corresponding problem is formulated in terms of buffer size of user equipments (UEs), which is estimated based on buffer status information collected by the UEs. The realizations of a resource allocation approach presented in the paper include its real-time and non-real-time implementations, as well as two modifications applicable to a standard LTE-Direct (LTE-D) network. Performance of the proposed algorithms has been evaluated using the OPNET-based simulations. All algorithms show improved performance in terms of mean packet end-to-end delay when compared to most relevant schemes proposed earlier. [ABSTRACT FROM AUTHOR]

This paper deals with signal reconstruction for multichannel high-resolution wide-swath synthetic aperture radar (HRWS-SAR) systems. One of the commonly used algorithms for processing HRWS-SAR data is Krieger's digital beamforming (DBF) algorithm. This algorithm works quite well for cases with uniform space–time sampling as well as moderately nonuniform sampling. However, for cases with highly nonuniform sampling, Krieger's DBF algorithm in its original form suffers from a severe degradation in the signal-to-noise ratio, and for cases with coinciding sampling, this algorithm even fails to function. In this paper, we will solve these problems such that an improved DBF (IDBF) algorithm is developed. The key of the IDBF algorithm consists of two procedures: The first one implements an operation called ambiguity-index screening, which is based on two novel quantities, termed the equivalent sampling spacing and the effective reconstructed Doppler bandwidth; and the other key procedure implements a Doppler-spectrum weighting based on a newly defined parameter that is termed the maximum-to-minimum ratio of the noise spectrum. Simulation results show that the newly proposed IDBF algorithm shares a pronounced improvement in the imaging performance over Krieger's DBF algorithm. Moreover, simulation results also demonstrate that the IDBF algorithm exhibits a more robust and consistent overall performance than several other existing reconstruction algorithms. [ABSTRACT FROM PUBLISHER]

Space-time adaptive processing (STAP) is a family of algorithms frequently employed in surface moving target indication radar systems to enable detection of moving objects in the presence of fixed (i.e., nonmoving) clutter. In this paper, two new closed-form expressions that quantify the loss associated with the STAP notch centered on clutter are derived in terms of system parameters of interest. Although there are many excellent reports, books, and papers focused on STAP, a simple yet accurate approximation for the STAP notch has not previously appeared. It is also shown that a new, accurate approximation for the important STAP metric known as minimum detectable velocity may be derived from the STAP notch expression. Furthermore, accurate expressions are derived that predict when ?aperture-limited? STAP performance may be obtained. This work provides the first analytical, unifying connection between these STAP metrics. As they are implemented in compact closed-form expressions, the new results are attractive for system design. The significant computational benefits associated with the new results can be very advantageous in trade studies or large simulations in which STAP performance estimates must be computed thousands of times. It is expected that this work will be of great use to both readers with a background in STAP and readers new to this field. For readers with STAP experience, this work provides a new understanding of the drivers of STAP performance and of the interconnections between STAP performance measures. For readers new to the STAP field, this work removes the mystery associated with STAP by providing intuition into how STAP works. With accurate, analytical expressions, system engineers can now implement accurate predictions of STAP performance without the necessity of constructing patch-based analysis tools that estimate STAP performance by computing the notch associated with hundreds to thousands of clutter patches. [ABSTRACT FROM AUTHOR]

This paper presents a novel robust quasi-adaptive beamforming (RQAB) scheme against direction-of-arrival (DOA) mismatch. Unlike existing robust adaptive beamforming (RAB) methods, the proposed approach obtains the ultimate beamformer weight vector in a quiescent manner, nevertheless, it possesses the remarkable ability of interference rejection and desired signal reception. In this method, a two-step procedure is devised to design the quasi-adaptive weight vector. More specifically, the conventional sample matrix inversion (SMI) beamformer is first applied to find out all notch angles outside the region of interest (ROI) where the desired signal comes with a high probability. It is shown that these notch angles contain the DOAs of interferences. Then, a multipoint accurate array response control ( $ {\text {MA}}^2\text {RC}$ ) algorithm is utilized to synthesize a beampattern with the same sidelobe notch levels as the SMI, and nearly constant response over the ROI. Contrary to conventional approaches that are vulnerable to the contamination of the training data by the desired signal, our proposed approach exhibits outstanding performance under this common scenario. Moreover, besides the DOA mismatch, the proposed approach is also insensitive to the SNR, number of snapshots and mismatch angle. Additionally, different from many optimization-based RAB methods, the proposed RQAB approach offers an analytical expression of the beamformer weight vector and, hence, is computationally attractive. Typical simulation examples are provided to demonstrate the superiority of the RQAB scheme. [ABSTRACT FROM AUTHOR]

This paper considers coordinated multicast beamforming in a multicell multigroup multiple-input single-output system. Each base station (BS) serves multiple groups of users by forming a single beam with common information per group. We propose centralized and distributed beamforming algorithms for two different optimization targets. The first objective is to minimize the total transmission power of all the BSs while guaranteeing the user-specific minimum quality-of-service targets. The semidefinite relaxation (SDR) method is used to approximate the nonconvex multicast problem as a semidefinite program (SDP), which is solvable via centralized processing. Subsequently, two alternative distributed methods are proposed. The first approach turns the SDP into a two-level optimization via primal decomposition. At the higher level, intercell interference powers are optimized for fixed beamformers, whereas the lower level locally optimizes the beamformers by minimizing BS-specific transmit powers for the given intercell interference constraints. The second distributed solution is enabled via an alternating direction method of multipliers, where the intercell interference optimization is divided into a local and a global optimization by forcing the equality via consistency constraints. We further propose a centralized and a simple distributed beamforming design for the signal-to-interference-plus-noise ratio (SINR) balancing problem in which the minimum SINR among the users is maximized with given per-BS power constraints. This problem is solved via the bisection method as a series of SDP feasibility problems. The simulation results show the superiority of the proposed coordinated beamforming algorithms over traditional noncoordinated transmission schemes, and illustrate the fast convergence of the distributed methods. [ABSTRACT FROM PUBLISHER]