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The novel concept of non-orthogonal multiple access (NOMA) aided joint radar and multicast-unicast communication (Rad-MU-Com) is investigated. Employing the same spectrum resource, a multi-input-multi-output (MIMO) dual-functional radar-communication (DFRC) base station detects the radar-centric users (R-user), while transmitting mixed multicast-unicast messages both to the R-user and to the communication-centric user (C-user). In particular, the multicast information is intended for both the R- and C-users, whereas the unicast information is only intended for the C-user. More explicitly, NOMA is employed to facilitate this double spectrum sharing, where the multicast and unicast signals are superimposed in the power domain and the superimposed communication signals are also exploited as radar probing waveforms. First, a beamformer-based NOMA-aided joint Rad-MU-Com framework is proposed for the system having a single R-user and a single C-user. Based on this framework, the unicast rate maximization problem is formulated by optimizing the beamformers employed, while satisfying the rate requirement of multicast and the predefined accuracy of the radar beam pattern. The resultant non-convex optimization problem is solved by a penalty-based iterative algorithm to find a high-quality near-optimal solution. Next, the system is extended to the scenario of multiple pairs of R- and C-users, where a cluster-based NOMA-aided joint Rad-MU-Com framework is proposed. A joint beamformer design and power allocation optimization problem is formulated for the maximization of the sum of the unicast rate at each C-user, subject to the constraints on both the minimum multicast rate for each R&C pair and on accuracy of the radar beam pattern for detecting multiple R-users. The resultant joint optimization problem is efficiently solved by another penalty-based iterative algorithm developed., 14 pages, 12 figures, this work is accepeted for the publication in IEEE Journal on Selected Areas in Communications

BACKGROUND Autosomal dominant tubulointerstitial kidney disease (ADTKD) is a progressive chronic disease that is inherited in an autosomal dominant fashion. Symptoms include hyperuricemia, gout, interstitial nephritis, renal cysts, and progressive renal damage that can lead to end-stage renal disease. Mutations in the uromodulin gene (UMOD) characterize the ADTKD-UMOD clinical subtype of this disease. To date, > 100 UMOD mutations have been identified. Early diagnosis of ADTKD-UMOD is important to treat the disease, slow down disease progression, and facilitate the identification of potentially affected family members. CASE SUMMARY We report a 40-year-old man harboring a novel heterozygous missense mutation in UMOD (c.554G>T; p. Arg185Leu). The patient had hyperuricemia, gout, and chronic kidney disease. The same mutation was detected in his daughter, aunt and cousin. CONCLUSION A single nucleotide substitution in exon 3 of UMOD was responsible for the heterozygous missense mutation (c.554G>T, p.Arg185Leu).

In this paper, an indoor robot navigation system is investigated, where an intelligent reflecting surface (IRS) is employed to enhance the connectivity between the access point (AP) and robotic users. Both single-user and multiple-user scenarios are considered. In the single-user scenario, one mobile robotic user (MRU) communicates with the AP. In the multiple-user scenario, the AP serves one MRU and one static robotic user (SRU) employing either non-orthogonal multiple access (NOMA) or orthogonal multiple access (OMA) transmission. The considered system is optimized for minimization of the travelling time/distance of the MRU from a given starting point to a predefined final location, while satisfying constraints on the communication quality of the robotic users. To this end, a radio map based approach is proposed to exploit location-dependent channel propagation knowledge. For the single-user scenario, a channel power gain map is constructed, which characterizes the spatial distribution of the maximum expected effective channel power gain of the MRU for the optimal IRS phase shifts. Based on the obtained channel power gain map, the communication-aware robot path planing problem is solved by exploiting graph theory. For the multiple-user scenario, a communication rate map is constructed, which characterizes the spatial distribution of the maximum expected rate of the MRU for the optimal power allocation at the AP and the optimal IRS phase shifts subject to a minimum rate requirement for the SRU. The joint optimization problem is efficiently solved by invoking bisection search and successive convex approximation methods. Then, a graph theory based solution for the robot path planning problem is derived by exploiting the obtained communication rate map. Our numerical results verify the effectiveness of the proposed designs., 32 pages,13 figures. a conference version can be found in arXiv:2009.11519

In this article, a novel class of fountain codes with feedback, called relative-entropy-based fountain (REF) codes, is proposed. The transmitter of REF codes adapts the degrees of encoded symbols to make the degree distribution at the receiver close to the robust soliton distribution, where the distance between two distributions is measured by relative entropy. The proposed REF codes are shown to achieve excellent intermediate performance over binary erasure channels (BECs), and binary-input additive white Gaussian noise channels (BI-AWGNCs) for both unicast, and multicast scenarios. For multicast, a non-uniform input symbol selection scheme is proposed to enhance the performance of REF codes. Furthermore, since the feedback is imprecise under noisy channels, the concept “belief” is introduced to improve the reliability of REF codes. Theoretical analysis is performed for the proposed REF codes, with an upper bound, and an approximate lower bound of the intermediate performance of REF codes over BECs derived. Both theoretical analysis, and simulations show that the proposed REF codes outperform the state-of-the-art fountain codes with feedback, in terms of the intermediate performance, with low overhead.

Person reidentification (ReID) refers to the task of verifying the identity of a pedestrian observed from nonoverlapping views in a surveillance camera network. It has recently been validated that reranking can achieve remarkable performance improvements in person ReID systems. However, current reranking approaches either require feedback from users or suffer from burdensome computational costs. In this paper, we propose to exploit a density-adaptive smooth kernel technique to achieve efficient and effective reranking. Specifically, we adopt a smooth kernel function to formulate the neighbor relationships among data samples with a density-adaptive parameter. Based on this new formulation, we present two simple yet effective reranking methods, termed \emph{inverse} density-adaptive kernel based reranking (inv-DAKR) and \emph{bidirectional} density-adaptive kernel based reranking (bi-DAKR), in which the local density information in the vicinity of each gallery sample is elegantly exploited. Moreover, we extend the proposed inv-DAKR and bi-DAKR methods to incorporate the available extra probe samples and demonstrate that when and why these extra probe samples are able to improve the local neighborhood and thus further refine the ranking results. Extensive experiments are conducted on six benchmark datasets, including: PRID450s, VIPeR, CUHK03, GRID, Market-1501 and Mars. The experimental results demonstrate that our proposals are effective and efficient., 39 pages, 18 figures and 12 tables. This paper is an extended version of our preliminary work on ICPR 2018

Person reidentification (ReID) is a challenging task of finding a target pedestrian in a gallery set collected from multiple nonoverlapping camera views. Recently, state-of-the-art ReID performance has been achieved via an end-to-end trainable deep neural network framework, which integrates convolution feature extraction, similarity learning and reranking into a joint optimization framework. In such a framework, the similarity is learned via an embedding network, the reranking is conducted with a random walk, and the whole framework is optimized with a cross-entropy-based verification loss. Unfortunately, the embedding net is difficult to train well because their two-dimensional outputs mutually interfere each other when using the conventional random walk. In addition, the supervision information has not been fully exploited during the training phase due to the binary nature of the verification loss. In this paper, we propose a novel approach, called group-shuffling dual random walks with label smoothing (GSDRWLS), in which random walks are performed separately on two channels—one for positive verification and one for negative verification—and the binary verification labels are properly modified with an adaptive label smoothing technique before feeding into the verification loss in order to train the overall network effectively and to avoid the overfitting problem. Extensive experiments conducted on three large benchmark datasets, including CUHK03, Market-1501 and DukeMTMC, confirm the superior performance of our proposal.

With the advantages of low signaling overhead and latency, grant-free random access (GFRA) becomes a promising technology for supporting massive machine-type communications (mMTC), but poses new challenges for active user detection (AUD) and channel estimation (CE), whose performance mainly depends on the preamble detection. In this paper, we design the composite preamble based on differential phase rotations by aggregating orthogonal Zadoff-Chu (ZC) sequences and multiple root ZC sequences with differential phase rotations to reduce the probability of preamble collisions, thereby improving the performance of AUD and CE. In particular, differential phase rotations extend the preamble set size so that users colliding in orthogonal sequences can be distinguished by phase rotations. In addition, it also reduces non-orthogonal interference and thus reduces CE errors. The preamble detection algorithm and CE scheme are proposed, along with the theoretical analysis of AUD and CE performance to verify the effectiveness of the designed preamble. In addition, the proposed preamble is extended to combine phase rotations with cyclic shifts to further enlarge the preamble set size with low non-orthogonality. Simulation results show that the proposed composite preamble outperforms existing preambles in terms of the probability of detection and CE accuracy.

Compressive sampling matching pursuit (CoSaMP) is an efficient reconstruction algorithm for sparse signal. When the signal is block sparse, i.e., the non-zero elements are presented in clusters, some block sparse reconstruction algorithms have been proposed accordingly. In this paper, we present a new block algorithm based on CoSaMP, called block compressive sampling matching pursuit (BlockCoSaMP). Compared with CoSaMP algorithm, the proposed algorithm shows improved performance when sparse signal is presented in block form. Restricted isometry property (RIP) of measurement matrix is an effective tool for analyzing the performance of the CS algorithm, and Block restricted isometry property (Block RIP) is the extension of traditional RIP. Based on the Block RIP, we derive the sufficient condition to guarantee the convergence of Block-CoSaMP algorithm. In addition, the number of required iterations is obtained. Finally, simulation experiments show that with the increase of block length, the performance of Block-CoSaMP algorithm approaches to that of block subspace pursuit (Block-SP) algorithm. When the block length and sparsity are small, the performance of Block-CoSaMP algorithm is better than that of the CoSaMP, l2/l1 norm and block orthogonal matching pursuit (BOMP) algorithms. Especially, when compared with CoSaMP and l2/l1 norm algorithms, the proposed algorithm exhibits more obvious performance gain.

Intelligent reflecting surface (IRS) enhanced multi-unmanned aerial vehicle (UAV) non-orthogonal multiple access (NOMA) networks are investigated. A new transmission framework is proposed, where multiple UAV-mounted base stations employ NOMA to serve multiple groups of ground users with the aid of an IRS. The three-dimensional (3D) placement and transmit power of UAVs, the reflection matrix of the IRS, and the NOMA decoding orders among users are jointly optimized for maximization of the sum rate of considered networks. To tackle the formulated mixed-integer non-convex optimization problem with coupled variables, a block coordinate descent (BCD)-based iterative algorithm is developed. Specifically, the original problem is decomposed into three subproblems, which are alternatingly solved by exploiting the penalty method and the successive convex approximation technique. The proposed BCD-based algorithm is demonstrated to be able to obtain a stationary point of the original problem with polynomial time complexity. Numerical results show that: 1) the proposed NOMA-IRS scheme for multi-UAV networks achieves a higher sum rate compared to the benchmark schemes, i.e., orthogonal multiple access (OMA)-IRS and NOMA without IRS; 2) the use of IRS is capable of providing performance gain for multi-UAV networks by both enhancing channel qualities of UAVs to their served users and mitigating the inter-UAV interference; and 3) optimizing the UAV placement can make the sum rate gain brought by NOMA more distinct due to the flexible decoding order design., 31 pages, 6 figures

The novel concept of simultaneously transmitting and reflecting (STAR) reconfigurable intelligent surfaces (RISs) is investigated, where the incident wireless signal is divided into transmitted and reflected signals passing into both sides of the space surrounding the surface, thus facilitating a full-space manipulation of signal propagation. Based on the introduced basic signal model of `STAR', three practical operating protocols for STAR-RISs are proposed, namely energy splitting (ES), mode switching (MS), and time switching (TS). Moreover, a STAR-RIS aided downlink communication system is considered for both unicast and multicast transmission, where a multi-antenna base station (BS) sends information to two users, i.e., one on each side of the STAR-RIS. A power consumption minimization problem for the joint optimization of the active beamforming at the BS and the passive transmission and reflection beamforming at the STAR-RIS is formulated for each of the proposed operating protocols, subject to communication rate constraints of the users. For ES, the resulting highly-coupled non-convex optimization problem is solved by an iterative algorithm, which exploits the penalty method and successive convex approximation. Then, the proposed penalty-based iterative algorithm is extended to solve the mixed-integer non-convex optimization problem for MS. For TS, the optimization problem is decomposed into two subproblems, which can be consecutively solved using state-of-the-art algorithms and convex optimization techniques. Finally, our numerical results reveal that: 1) the TS and ES operating protocols are generally preferable for unicast and multicast transmission, respectively; and 2) the required power consumption for both scenarios is significantly reduced by employing the proposed STAR-RIS instead of conventional reflecting/transmiting-only RISs., Comment: 31 pages, 8 figures, this work is accepted for the publication in IEEE Transactions on Wireless Communications

Rationale: Polycystic liver disease (PLD) is an autosomal-dominant disorder that is commonly associated with autosomal-dominant polycystic kidney disease (PKD) but rarely complicated with polycystic lung. Here, we report the first case of severe obstructive jaundice caused by multiple liver cysts in a patient with PLD complicated by PKD and polycystic lung. Patient concerns: A 72-year-old man with a history of PLD complicated with polycystic kidney presented with progressive jaundice, hematuria, poor appetite, nausea, and weight loss since 3 months. Diagnosis: PLD complicated with PKD and polycystic lung was identified using computed tomography, and obstructive jaundice was identified using magnetic resonance imaging and magnetic resonance cholangiopancreatography. Interventions: The patient could not undergo surgery, and was therefore treated with combined bilirubin adsorption and continuous veno-venous hemofiltration. Outcomes: The patient's symptoms and laboratory findings improved after bilirubin adsorption and continuous veno-venous hemofiltration. Unfortunately, the patient was unable to continue the treatment due to financial reasons, and died of shock most likely due to cyst rupture. Lessons: Imaging examination of the lungs is necessary for patients with PLD. Although infrequent, jaundice can occur in these patients and cause severe hyperbilirubinemia. When surgery is contraindicated, blood purification may serve as an alternative treatment for patients with PLD-related obstructive jaundice.

The marriage of wireless big data and machine learning techniques revolutionizes wireless systems by introducing data-driven philosophy. However, the ever exploding data volume and model complexity will limit centralized solutions to learn and respond within a reasonable time. Therefore, scalability becomes a critical issue to be solved. In this article, we aim to provide a systematic discussion of the building blocks of scalable data-driven wireless networks. On one hand, we discuss the forward-looking architecture and computing framework of scalable data-driven systems from a global perspective. On the other hand, we discuss relevant learning algorithms and model training strategies performed at each individual node from a local perspective. We also highlight several promising research directions in the context of scalable data-driven wireless communications to inspire future research.

The fundamental intelligent reflecting surface (IRS) deployment problem is investigated for IRS-assisted networks, where one IRS is arranged to be deployed in a specific region for assisting the communication between an access point (AP) and multiple users. Specifically, three multiple access schemes are considered, namely non-orthogonal multiple access (NOMA), frequency division multiple access (FDMA), and time division multiple access (TDMA). The weighted sum rate maximization problem for joint optimization of the deployment location and the reflection coefficients of the IRS as well as the power allocation at the AP is formulated. The non-convex optimization problems obtained for NOMA and FDMA are solved by employing monotonic optimization and semidefinite relaxation to find a performance upper bound. The problem obtained for TDMA is optimally solved by leveraging the time-selective nature of the IRS. Furthermore, for all three multiple access schemes, low-complexity suboptimal algorithms are developed by exploiting alternating optimization and successive convex approximation techniques, where a local region optimization method is applied for optimizing the IRS deployment location. Numerical results are provided to show that: 1) near-optimal performance can be achieved by the proposed suboptimal algorithms; 2) asymmetric and symmetric IRS deployment strategies are preferable for NOMA and FDMA/TDMA, respectively; 3) the performance gain achieved with IRS can be significantly improved by optimizing the deployment location., Comment: 32 pages, 8 figures. in IEEE Transactions on Wireless Communications

To more efficiently realize the coded multiple-input multiple-output (MIMO) transmission and improve the system performance, in this paper, we propose a framework combining the polar coding and the MIMO technique, namely the polar-coded MIMO (PC-MIMO) systems. The MIMO transmission will be recognized from the novel perspective of channel polarization. Combining the polar coding and modulation, the MIMO transmission channel is decomposed into a series of bit-polarized channels under the three-stage channel transform, that means the antenna $\rightarrow$ modulation $\rightarrow$ bit partition. Based on this generalized channel polarization transform, the PC-MIMO systems allow a joint optimization of polar coding, signal modulation, and MIMO transmission. Specifically, for the first-stage transform, we propose two schemes, namely sequential antenna partition (SAP) and parallel antenna partition (PAP). For the SAP-based PC-MIMO, by using the successive detection of the MIMO signal, the antenna data streams are sequentially decomposed and polarized. In addition, a joint multistage detection and decoding scheme is proposed to combine the multilevel coding method in the second- and third-stage transforms to fully utilize the multiplexing gain and the coding gain. In contrast, the PAP-based PC-MIMO exploits the channel polarization of parallel MIMO detection and concatenates the bit-interleaved coded modulation structure in the second- and third-stage transforms to reduce the processing latency. The simulation results over the MIMO channel with the Rayleigh fast fading show that the proposed PC-MIMO schemes can well outperform the turbo-coded MIMO schemes utilized in many mainstream communication systems.

Non-orthogonal multiple access (NOMA) is one of the key techniques to address the high spectral efficiency and massive connectivity requirements for the fifth generation (5G) wireless system. To efficiently realize NOMA, we propose a joint design framework combining the polar coding and the NOMA transmission, which deeply mines the generalized polarization effect among the users. In this polar coded NOMA (PC-NOMA) framework, the original NOMA channel is decomposed into multiple bit polarized channels by using a three-stage channel transform, that is, user$\to$signal$\to$bit partitions. Specifically, for the first-stage channel transform, we design two schemes, namely sequential user partition (SUP) and parallel user partition (PUP). For the SUP, a joint successive cancellation detecting and decoding scheme is developed, and a search algorithm is proposed to schedule the NOMA detecting order which improves the system performance by enhanced polarization among the user synthesized channels. The "worst-goes-first" idea is employed in the scheduling strategy, and its theoretic performance is analyzed by using the polarization principle. For the PUP, a corresponding parallel detecting scheme is exploited to reduce the latency. The block error ratio performances over the additive white Gaussian noise channel and the Rayleigh fading channel indicate that the proposed PC-NOMA obviously outperforms the state-of-the-art turbo coded NOMA scheme due to the advantages of joint design between the polar coding and NOMA., Comment: First version

In this paper, a novel wireless energy harvesting cooperative jammer (EH-CJ)-aided transmission scheme, which can enhance the security for cooperative cognitive radio networks (CCRNs), is proposed. The EH-CJ first harvests energy from ambient radio frequency (RF) signals transmitted from the primary transmitter (PT). This energy is then used to transmit jamming noise (JN), so that the performance of eavesdroppers' receptions is degraded, and also to deliver additional power to the secondary energy receiver (ER). In this way, the proposed scheme can enhance the security and extend the lifetime for CCRNs. For this system, we formulate an optimization problem for maximizing the secrecy rate of the secondary system under transmit power constraints at the secondary transmitter as well as the EH-CJ, minimum harvested energy at the secondary ER and information rate at the primary receiver (PR). Since this optimization problem is not convex, we propose a two-stage optimization algorithm based upon the Charnes-Cooper transformation and rank-one relaxation to solve it. Computer simulation results have demonstrated that the proposed EH-CJ-aided scheme achieves better secrecy rate performance, as compared to an isotropic JN scheme as well as compared to an equivalent scheme which does not use the aid of EH-CJ.

This paper studies a relay-assisted wireless-powered communication network (WPCN), where a hybrid relay node (HRN) broadcasts radio-frequency energy to energy-harvesting users and forwards these users’ data to a base station in amplify-and-forward (AF) or decode-and-forward (DF) fashions. The joint time duration and power allocation optimization problems for both AF and DF relay-assisted WPCNs are investigated to maximize the energy efficiency of HRN, which are challenging to solve due to the non-convexity and strong coupling of variables. For the DF relay-assisted WPCN, we propose an optimal time duration and power allocation algorithm by decomposing the original problem into three subproblems, i.e., time duration allocation subproblem for users’ information transfer, time duration allocation subproblem for HRN’s energy and information transfer, and HRN’s power allocation subproblem. For the AF rely-assisted WPCN, we propose an iterative algorithm to optimize the transmit power and energy transfer time of HRN, by proving the quasi-concavity of the objective function where the optimal time duration allocation has been derived. The simulation results verify that the proposed algorithms can render significant energy efficiency gains, e.g., the performance gains of the proposed algorithms are about 300% and 150% compared with the existing schemes for the AF relay-assisted WPCN and DF relay-assisted WPCN, respectively, when the noise power is relatively large.

In this paper, a three-dimensional polar code (3D-PC) scheme is proposed to improve the error floor performance of parallel concatenated systematic polar code (PCSPC). The proposed 3D-PC is constructed by serially concatenating the PCSPC with a rate-1 third dimension, where only a fraction λ of parity bits of PCSPC are extracted to participate in the subsequent encoding. It takes full advantage of the characteristics of parallel concatenation and serial concatenation. In addition, the convergence behavior of 3D-PC is analyzed by the extrinsic information transfer (EXIT) chart. The convergence loss between PCSPC λ=0 and different λ provides the reference for choosing the value of λ for 3D-PC. Finally, the simulation results confirm that the proposed 3D-PC scheme lowers the error floor.

In this paper, an ordered successive interference cancellation algorithm is proposed to realize efficient multi-user detection in interleave-division multiple access. In the proposed algorithm, the multi-user detection is implemented in the frequency domain and the bit detection results are obtained by de-spreading the chip detection results. Afterwards, the bit detection results are spread and interleaved to obtain regeneration soft chip results. The regeneration soft chip results are combined with the priori information provided by the channel decoder to obtain soft decision feedback symbols. In the proposed algorithm, the soft decision feedback symbols of detected users are exploited to cancel their interference to the subsequent un-detected users. In addition, the user detection order is optimized according to the signal-to-interference-plus-noise-ratio. The simulation results show that the proposed algorithm can achieve better performance in both single-input-single-output and multiple-input-multiple-output scenario.

In this letter, the authors propose block normalised iterative hard thresholding (BNIHT) algorithm for the recovery of block sparse signal, in which the non-zero elements are presented in clusters. Based on block restricted isometry property, the sufficient conditions to guarantee the convergence of BNIHT are derived. In addition, the number of required iterations is obtained. The simulation experiment shows that BNIHT algorithm is superior to the block IHT (BIHT) algorithm when the step size satisfies μ < 1 .

Sparse code multiple access (SCMA) is one of the nonorthogonal multiple access techniques for the 5G system. SCMA can provide different levels of overloading to meet the diverse traffic connectivity requirements. However, its relatively high computational complexity of multiuser detection is still a significant concern for practical implementation, even when the sparse structure has already been employed. In this paper, a design framework for an improved SCMA multiuser detector is proposed based on the message passing algorithm (MPA). As the primary SCMA detector, two aspects of MPA are simplified and optimized. First, we introduce a lookup table (LUT) scheme to reduce the computational complexity of the ${\max }^*$ operation in the MPA. In contrast to the extensive Jacobian approximation, the proposed LUT method can guarantee the stable convergence of the MPA for SCMA. Second, a series of novel scheduling schemes are proposed to speed up the convergence. A single scheduling MPA (SS-MPA) method is given to enhance the convergence performance of MPA, where the soft messages in the function nodes and variable nodes are serially calculated and synchronously updated. To further improve the throughput of the MPA, a multiple scheduling MPA (MS-MPA) is proposed. In this method, multiple detectors are used to calculate in parallel and update the node messages by different orders. Since the scheduling strategies of message update are optimized, both the SS-MPA and MS-MPA can converge more quickly than the conventional MPA. Theoretical analyses and simulation results regarding the error performance and convergence properties of the above schemes are included.

In this paper, a novel cognitive cooperation model is investigated, in which one multiantenna secondary user (SU) cooperates with two primary users (PUs), reaching a three-party agreement on how to jointly share the authorized spectrum bands, and meanwhile, energy harvesting (EH) and spatial multiplexing (SM) are introduced to make full use of the specious spectrum and energy resources. We call it the EH-SM-enabled two-plus-one cognitive cooperation and focus on the design of the optimal forwarding vector for PUs, optimal beamforming vector for the SU, and optimal power splitting factor for EH, with the objective to maximize SU's achievable transmission rate subject to both minimum transmission rate requirements for two PUs and maximum power consumption constraint for the SU. The formulated problem is nonconvex and difficult to solve directly. For an effective solution, the original rate maximization problem is transformed into the contrapositive power minimization problem, and a low-complexity optimal algorithm is proposed by jointly employing the bisection and golden section methods. Simulation results show that compared with the existing one-plus-one scheme and non-EH-enabled scheme, the proposed scheme greatly improves the SU's achievable transmission rate and, meanwhile, provides more reliable data transmission for PUs.

The block orthogonal matching pursuit (BOMP) algorithm is an efficient method in compressed sensing (CS) for the reconstruction of block-sparse signals, whose non-zero entries occur in clusters. However, due to the non-ideal factors in practice, there exits perturbation in the CS system, which may cause significant performance degradation during reconstruction. In this Letter, a perturbed BOMP algorithm is presented to deal with this problem, which extends BOMP algorithm to the perturbation case. The proposed algorithm performs controlled perturbation on each selected block of support vectors to decrease the orthogonal residual at each iteration. Moreover, the condition of successful reconstruction is derived. Simulation results demonstrate the effectiveness and robustness of the proposed algorithm.

Previous research on orthogonal matching pursuit (OMP) algorithm mainly focuses on the recovery performance of a sparse signal x given an acquired model y = F x + n . A general perturbation model y = ( F + E ) x + n in addition to the above acquired model exists, where E is the measurement perturbation. For this general perturbation model, the new restricted isometry constant of OMP algorithm is shown.

This paper investigates a downlink multiple-input single-output intelligent reflecting surface (IRS) aided non-orthogonal multiple access (NOMA) system, where a base station (BS) serves multiple users with the aid of IRSs. Our goal is to maximize the sum rate of all users by jointly optimizing the active beamforming at the BS and the passive beamforming at the IRS, subject to successive interference cancellation decoding rate conditions and IRS reflecting elements constraints. In term of the characteristics of reflection amplitudes and phase shifts, we consider ideal and non-ideal IRS assumptions. To tackle the formulated non-convex problems, we propose efficient algorithms by invoking alternating optimization, which design the active beamforming and passive beamforming alternately. For the ideal IRS scenario, the two subproblems are solved by invoking the successive convex approximation technique. For the non-ideal IRS scenario, constant modulus IRS elements are further divided into continuous phase shifts and discrete phase shifts. To tackle the passive beamforming problem with continuous phase shifts, a novel algorithm is developed by utilizing the sequential rank-one constraint relaxation approach, which is guaranteed to find a locally optimal rank-one solution. Then, a quantization-based scheme is proposed for discrete phase shifts. Finally, numerical results illustrate that: i) the system sum rate can be significantly improved by deploying the IRS with the proposed algorithms; ii) 3-bit phase shifters are capable of achieving almost the same performance as the ideal IRS; iii) the proposed IRS-aided NOMA systems achieve higher system sum rate than the IRS-aided orthogonal multiple access system., 32 pages, 10 figures

This paper investigates ground-aerial uplink non-orthogonal multiple access (NOMA) cellular networks. A rotary-wing unmanned aerial vehicle (UAV) user and multiple ground users (GUEs) are served by ground base stations (GBSs) by utilizing the uplink NOMA protocol. The UAV is dispatched to upload specific information bits to each target GBSs. Specifically, our goal is to minimize the UAV mission completion time by jointly optimizing the UAV trajectory and UAV-GBS association order while taking into account the UAV's interference to non-associated GBSs. The formulated problem is a mixed integer non-convex problem and involves infinite variables. To tackle this problem, we efficiently check the feasibility of the formulated problem by utilizing graph theory and topology theory. Next, we prove that the optimal UAV trajectory needs to satisfy the \emph{fly-hover-fly} structure. With this insight, we first design an efficient solution with predefined hovering locations by leveraging graph theory techniques. Furthermore, we propose an iterative UAV trajectory design by applying successive convex approximation (SCA) technique, which is guaranteed to coverage to a locally optimal solution. We demonstrate that the two proposed designs exhibit polynomial time complexity. Finally, numerical results show that: 1) the SCA based design outperforms the fly-hover-fly based design; 2) the UAV mission completion time is significantly minimized with proposed NOMA schemes compared with the orthogonal multiple access (OMA) scheme; 3) the increase of GUEs' quality of service (QoS) requirements will increase the UAV mission completion time.

The cloud radio access network (C-RAN) is a promising paradigm to meet the stringent requirements of the fifth generation (5G) wireless systems. Meanwhile, the wireless traffic prediction is a key enabler for C-RANs to improve both the spectrum efficiency and energy efficiency through load-aware network managements. This paper proposes a scalable Gaussian process (GP) framework as a promising solution to achieve large-scale wireless traffic prediction in a cost-efficient manner. Our contribution is three-fold. First, to the best of our knowledge, this paper is the first to empower GP regression with the alternating direction method of multipliers (ADMM) for parallel hyper-parameter optimization in the training phase, where such a scalable training framework well balances the local estimation in baseband units (BBUs) and information consensus among BBUs in a principled way for large-scale executions. Second, in the prediction phase, we fuse local predictions obtained from the BBUs via a cross-validation-based optimal strategy, which demonstrates itself to be reliable and robust for general regression tasks. Moreover, such a cross-validation-based optimal fusion strategy is built upon a well acknowledged probabilistic model to retain the valuable closed-form GP inference properties. Third, we propose a C-RAN-based scalable wireless prediction architecture, where the prediction accuracy and the time consumption can be balanced by tuning the number of the BBUs according to the real-time system demands. The experimental results show that our proposed scalable GP model can outperform the state-of-the-art approaches considerably, in terms of wireless traffic prediction performance.

Vascular calcification is a main cause of increased cardiovascular morbidity and mortality in chronic kidney disease (CKD) patients. This study aimed to investigate the role of the bone morphogenetic protein (BMP) signaling pathway in the early development of vascular calcification in CKD. A CKD vascular calcification rat model was established by providing rats with a 1.8% high-phosphorus diet and an intragastric administration of 2.5% adenine suspension. The kidney and aortic pathologies were analyzed. Blood biochemical indicators, serum BMP-2 and BMP-4 levels, and aortic calcium content were determined. The expression levels of BMP-2, BMP-4, bone morphogenetic protein receptor-IA (BMPR-IA), and matrix Gla protein (MGP) in aorta were examined by quantitative real-time polymerase chain reaction and immunohistochemistry. Compared with the normal control (Nor) rats, the CKD rats exhibited a significantly decreased body weight and an increased kidney weight as well as abnormal renal function and calcium-phosphorus metabolism. Aortic von Kossa and Alizarin red staining showed massive granular deposition and formation of calcified nodules in aorta at 8 weeks. The aortic calcium content was significantly increased, which was positively correlated with the serum BMP-2 (r=0.929; P<0.01) and serum BMP-4 (r=0.702; P<0.01) levels in CKD rats. The rat aortic BMP-2 mRNA level in the CKD rats was persistently increased, and the BMP-4 mRNA level was prominently increased at the 4th week, declining thereafter. Strong staining of BMP-2, BMP-4, BMPR-IA, and MGP proteins was observed in the tunica media of the aorta from the 4th week after model induction. In conclusion, activation of the BMP signaling pathway is involved in the early development of vascular calcification in CKD. Therefore, elevated serum BMP-2 and BMP-4 levels may serve as serum markers for CKD vascular calcification.

An efficient codebook optimization algorithm is proposed to maximize mutual information in sparse code multiple access (SCMA). At first, SCMA signal model is given according to superposition modulation structure, in which the channel matrix is column-extended. The superposition model can well describe the relationship between the codebook matrix and received signal. Based on the above model, an iterative codebook optimization algorithm is proposed to maximize mutual information between discrete input and continuous output. This algorithm can efficiently adapt to multiuser channels with arbitrary channel coefficients. The simulation results show that the proposed algorithm has good performance in both AWGN and non-AWGN channels. In addition, message passing algorithm (MPA) works well with the codebook optimized according to the proposed algorithm.

Polar codes represent an emerging class of error-correcting codes with power to approach the capacity of a discrete memoryless channel. This overview article aims to illustrate its principle, generation and decoding techniques. Unlike the traditional capacity-approaching coding strategy that tries to make codes as random as possible, the polar codes follow a different philosophy, also originated by Shannon, by creating a jointly typical set. Channel polarization, a concept central to polar codes, is intuitively elaborated by a Matthew effect in the digital world, followed by a detailed overview of construction methods for polar encoding. The butterfly structure of polar codes introduces correlation among source bits, justifying the use of the SC algorithm for efficient decoding. The SC decoding technique is investigated from the conceptual and practical viewpoints. State-of-the-art decoding algorithms, such as the BP and some generalized SC decoding, are also explained in a broad framework. Simulation results show that the performance of polar codes concatenated with CRC codes can outperform that of turbo or LDPC codes. Some promising research directions in practical scenarios are also discussed in the end.

Recent work in the area of compressed sensing mainly focuses on the perfect recovery of the entire support for sparse signals. However, partial support recovery, where a part of the signal support is correctly recovered, may be adequate in many practical scenarios. In this study, in the high-dimensional and noisy setting, the authors develop the probability of partial support recovery of the optimal maximum-likelihood (ML) algorithm. When a large part of the support is available, the asymptotic mean-square-error (MSE) of the reconstructed signal is further developed. The simulation results characterise the asymptotic performance of the ML algorithm for partial support recovery, and show that there exists a signal-to-noise ratio (SNR) threshold, beyond which the increase of SNR cannot bring any obvious MSE gain.

A hybrid automatic repeat request (HARQ) scheme based on a novel class of rate-compatible polar (\mbox{RCP}) codes are proposed. The RCP codes are constructed by performing punctures and repetitions on the conventional polar codes. Simulation results over binary-input additive white Gaussian noise channels (BAWGNCs) show that, using a low-complexity successive cancellation (SC) decoder, the proposed HARQ scheme performs as well as the existing schemes based on turbo codes and low-density parity-check (LDPC) codes. The proposed transmission scheme is only about 1.0-1.5dB away from the channel capacity with the information block length of 1024 bits., Comment: Submitted to IEEE Communications Letters

Channel polarisation results are extended to the case of communications over parallel channels, where the channel state information is known to both the encoder and decoder. Given a set of parallel binary-input discrete memoryless channels (B-DMCs), by performing the channel polarising transformation over independent copies of these component channels, we obtain a second set of synthesised binary-input channels. Similar to the single-channel case, we prove that as the size of the transformation goes infinity, some of the resulting channels tend to completely noised, and the others tend to noise-free, where the fraction of the latter approaches the average symmetric capacity of the underlying component channels. For finite-length polar coding over parallel channels, performance is found to be relied heavily on the specific channelmapping scheme. To avoid exhaustive searching, an empirically good scheme that is called equal-capacity partition channel mapping is proposed and numerical results show that the proposed scheme significantly outperforms random mapping. Further, utilising the above results, a polar coding method for arbitrary code length is proposed, which has potential applications in practical systems.

In this paper, polar codes are first applied in non-orthogonal multiple access (NOMA) and the channel polarization idea is extended to NOMA, which is a major multiple access technique in 5G systems. The polar coded NOMA (PC-NOMA) scheme is proposed, whereby the NOMA channel is decomposed into a series of binary-input channels under a two-stage channel polarization transform. In the first stage, the NOMA channel is divided into a group of user synthesized channels by using the multi-level coding structure. In the second stage, based on the structure of bit-interleaved code modulation, user synthesized channels are further decomposed into binary polarized channels. Then, a joint successive cancellation decoding scheme is given to construct the multiuser receiver of PC-NOMA. Finally, a low complexity search algorithm is proposed to schedule the NOMA decoding order which improves the error performance by enhanced polarization among user synthesized channels. The block error ratio performances over additive white Gaussian noise channels indicate that the proposed PC-NOMA obviously outperforms the turbo coded NOMA scheme due to the advantages of the two-stage polarization.

The recent increasing interest in cognitive radio networks has motivated the study and development of new approaches capable of coping with the intrinsic challenges of this kind of network, such as dynamic spectrum availability, distributed and heterogeneous network architectures, and soaring complexity. The bio-inspired approaches, with appealing characteristics such as autonomy, adaptation and collective intelligence of collaborative individuals, have been extensively studied. This paper presents a comprehensive survey of bio-inspired approaches for cognitive radio networks, emphasizing their domains of application. Specifically, ant colony optimization and particle warm optimization are further investigated with examples and numerical simulation.

The authors consider a cognitive radio network in which a set of cognitive users make opportunistic spectrum access to one primary channel by time-division multiplexing technologies. Multiple Input Multiple Output techniques (MIMO) are similarly considered to enhance the stable throughput for cognitive links while they should guarantee co-channel interference constraints to the primary link. Here, two different cases are considered: one is that cognitive radio network is distributed; the other is centrally-controlled that cognitive radio network has a cognitive base station. In the first case, how to choose one fixed cognitive user and power control for each transmission antenna at the cognitive base station are considered to maximize the cognitive link’s stable throughput. In the second case, a scheme to choose a group of cognitive users and a Zero-Forcing method to pre-white co-channel interference to the primary user, are also proposed in order to maximize cognitive base station’s sum-rate. The algorithm can be employed to realize opportunistic spectrum transmission over the wireless fading channels.