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Nonorthogonal multiple access (NOMA) has been identified as a promising technology in millimeter-wave (mmWave) multiple-input–multiple-output (MIMO) communication networks for Internet-of-Things (IoT) applications, which has the advantages of both massive connectivity and high spectral efficiency. However, few researchers have considered the probability of introducing NOMA to a positioning system. In this article, a novel mmWave MIMO-NOMA-based positioning system is proposed, which is capable of meeting the requirements of IoT applications. We establish a NOMA-based positioning model from the perspective of the system level, along with the design of a transmission strategy. To characterize the positioning performance, the position error bound (PEB) is selected as evaluation criteria and theoretical expressions of the PEB are provided. Simulations of comparing localization performance between NOMA and conventional orthogonal multiple access (OMA) are conducted by using the theoretical analysis. The numerical results show that the application of NOMA to localization is a viable way to reduce the PEB compared to OMA. This article further shows under what circumstances can NOMA outperform OMA in terms of localization performance and the corresponding parameter settings.

In this paper, one of the constituent codes, Rate-1 node, is used to simplify Successive-Cancellation List (SCL) decoding of non-binary polar codes for reducing the decoding complexity. First, we derive the Logarithmic Likelihood Ratio based (LLR-based) path metric of non-binary polar codes in SCL decoding. Then we propose that the path metric only depends on the LLR value at the top of Rate-1 node tree, which avoids traversing a complete decoding tree in non-binary SCL decoding. Finally, we design a novel reliability metric, which is used to select the unreliable symbols from the LLRs at the top of Rate1 node tree. By the proposed metric, we select the unreliable symbols to generate the candidate paths, which avoids splitting paths for all symbols of Rate-1 node in the conventional SCL decoding. Simulation results show that the proposed non-binary SCL decoding reduces significantly the computation and time complexity without the performance loss.

This paper investigates the application of non-orthogonal multiple access (NOMA) to satellite communication network over Shadowed-Rician fading channels. The impact of imperfect successive interference cancellation (ipSIC) on NOMA-based satellite network is taken into consideration from the perspective of practical scenarios. We first derive new exact expressions of outage probability for the p-th terrestrial user and provide the corresponding asymptotic analysis results. The diversity order of zero and p are achieved by the p-th terrestrial user with ipSIC and perfect successive interference cancellation (pSIC), respectively. Finally, the presented simulation results show that: 1) On the condition of pSIC, the outage behaviors of NOMA-based satellite network are superior to that of orthogonal multiple access; 2) With the value of residual interference increasing, the outage performance of terrestrial users with ipSIC is becoming worse seriously; and 3) Infrequent light shadowing of Shadowed-Rician fading brings the better outage probability compared to frequent heavy and average shadowing., Comment: 5 pages, 3 figures

In cloud computing, an important concern is to allocate the available resources of service nodes to the requested tasks on demand and to make the objective function optimum, i.e., maximizing resource utilization, payoffs and available bandwidth. This paper proposes a hierarchical multi-agent optimization (HMAO) algorithm in order to maximize the resource utilization and make the bandwidth cost minimum for cloud computing. The proposed HMAO algorithm is a combination of the genetic algorithm (GA) and the multi-agent optimization (MAO) algorithm. With maximizing the resource utilization, an improved GA is implemented to find a set of service nodes that are used to deploy the requested tasks. A decentralized-based MAO algorithm is presented to minimize the bandwidth cost. We study the effect of key parameters of the HMAO algorithm by the Taguchi method and evaluate the performance results. When compared with genetic algorithm (GA) and fast elitist non-dominated sorting genetic (NSGA-II) algorithm, the simulation results demonstrate that the HMAO algorithm is more effective than the existing solutions to solve the problem of resource allocation with a large number of the requested tasks. Furthermore, we provide the performance comparison of the HMAO algorithm with the first-fit greedy approach in on-line resource allocation.

Depth images extracted by light-coding-based depth cameras are widely used to reconstruct three-dimensional scenes in recent years. However, the retrieved depth accuracy greatly influences the reconstruction quality. Here, the authors present an appropriate depth extraction method based on subpixel matching to improve the depth accuracy. The proposed method utilises nearest neighbour interpolation to the projector's image plane to obtain depth values at subpixel accuracy, thereby better preserving the important depth information without changing any inner structure of the depth camera. Experimental results show that the proposed method improves the depth images both on image quality and depth accuracy.

Depth estimation has widely demands in autopilot and scene reconstruction. Although depth estimation has been greatly improved by deep learning, there is still some room for improvement. Usually, stereo-matching-based depth estimation matches low-resolution features and then up-samples depth map to full-resolution. Such methods suffer from low accuracy because of information loss in low-resolution features. To solve this problem, a depth estimation method via multi-resolution gradual-refining stereo matching is proposed. As with classic methods, this method first extracts pyramid features by convolution network and estimates low-resolution depth maps. Then, as an innovation, stereo-matching at each pyramid feature is successively executed using the low-resolution maps as initial depth, which limits search range of stereo-matching for accuracy and efficiency. Results of stereo-matching are used as residuals to refine the depth maps. Experimental results demonstrate that accuracy of depth estimation by the proposed method is significantly improved, but computational complexity does not much increase compared with classic methods.

Radio frequency (RF) measurement technology provides a relative navigation solution that can be of great importance to have significant potential for application to the satellite groups. With the development of the RF relative measurement sensors, it is found that the antenna configuration of the sensors affects the precision of relative position and attitude measurement. This study proposes improvements to the precision of the sensors by the virtue of the optimal antenna configuration. Furthermore, the concept of dilution of precision (DOP) is extended to the RF relative measurement sensors, and a new dilution called IDOP is proposed as a benchmark to determine the precision of relative position and attitude measurement in engineering applications of the space mission. In order to select the optimal antenna configuration in real time in a scene where the intersatellite position and attitude change dynamically, this study presents an optimal antenna configuration selection strategy and models the antenna configuration selection as a combinatorial optimization problem. Furthermore, the genetic algorithm (GA) with two encoding mechanisms is proposed to solve this problem. Finally, numerical results are presented to verify the robustness.

As a class of bit-flipping (BF) decoder, the noisy gradient descent bit flipping (NGDBF) algorithm outperforms the original GDBF and other BF algorithms. To increase the reliability of the inversion function, we propose a modified NGDBF algorithm by introducing an adjustment factor on the syndrome. Moreover, for the multi-bit flipping, we present an adaptive inversion threshold which depends on the numbers of negative bipolar syndromes. The simulation results show that our proposed algorithm outperforms the NGDBF algorithm for both single-bit and multi-bit flipping schemes.

This paper tackles the problem of channel estimation and decoding in environments that exhibit time-varying block-memoryless impulsive noise. In order to perform the estimation in continuous value space and the decoding in discrete value space jointly, a novel message passing framework is proposed that combines the sampling-importance resampling (SIR) estimator and the LDPC sum-product decoder in an iterative manner. The resampling process within the framework is further improved based on the asymptotic performance analysis for mismatched decoding. The simulation results prove the validity of the enhanced SIR estimator in the proposed method and demonstrate our method can achieve better performance than the LDPC decoder with quantile estimator.

This letter presents a graphics processing unit (GPU)-based non-binary low density parity check multi-codeword decoder with both kernel execution and data transfer optimizations. A novel multi-codeword data structure and its corresponding parallelism are proposed to boost the compute unified device architecture kernel execution. Moreover, practical methods of hiding the data transfer latency are presented to improve data transfer efficiency. Experimental results demonstrate that the throughput speedups achieved by the proposed decoder range from $3.12 \times$ to $185 \times$ over various Galois fields compared with the existing works on GPU.

In this paper, a novel low-complexity method for channel-dependent construction of polar codes is designed by exploiting the partial order (PO) relation to eliminate redundant computations. The proposed method selects the information set with a threshold, thus avoiding the reliability computation of unnecessary synthetic channels and the cost of sorting. We first introduce two key iterative strategies based on the PO relation of the synthetic channels. Then, we analyze the iteration process of the proposed method through a binary tree and a linear table structure and further derive the theoretical complexity of the proposed method. We validate the theoretical analysis through simulation results. Finally, considering the practical application, we propose a suboptimal simplified version of the proposed method to significantly reduce the complexity. Compared with the sorting method, the proposed simplified method needs only at most 10% of the comparison numbers in the process of the information set selection with negligible performance loss under the cyclic redundancy check-aided successive cancellation list decoding.

The grid-connected inverter with virtual synchronous generator (VSG) control technology can improve the friendliness of a distributed power supply to the power grid. However, its low-voltage ride-through (LVRT) capability is insufficient, which results in difficulties in limiting the current and provide reactive power support. A new LVRT control strategy based on the smooth switching is proposed in this paper. In this strategy, the voltage source mode of VSG is transformed into current source mode to limit the output current and provide reactive power support through the proportional resonance current control algorithm under grid fault. Furthermore, the feedback tracking synchronization strategy of the phase angle is employed to realize the smooth switching between two modes. When the grid fault recovers, it can directly switch back to grid-connected operation mode through a delay module without an additional algorithm. The simulation results verify the correctness and feasibility of the proposed control strategy.

Structured light systems have become an effective tool for reconstructing three-dimensional models of objects due to the advent of low-price, high-speed depth cameras such as Kinect. However, this kind of active depth sensor extracts low-quality depth maps because of its inaccurate image matching process. This study proposes a depth extraction method based on image rectification for accurate image matching. Due to the sizes of the projected patterns and the captured images are usually different, a virtual camera is defined, through which the rectified images are generated to match the images in the real camera at pixel level. Experiments on simulated and hardware platforms demonstrate that the proposed method achieves efficient rectification and obtains better-quality depth maps.

In this paper, a Doppler scaling fast Fourier transform (Doppler-FFT) algorithm for filter bank multi-carrier (FBMC) is proposed, which can efficiently eliminate the impact of the Doppler scaling in satellite communications. By introducing a Doppler scaling factor into the butterfly structure of the fast Fourier transform (FFT) algorithm, the proposed algorithm eliminates the differences between the Doppler shifts of the received subcarriers, and maintains the same order of computational complexity compared to that of the traditional FFT. In the process of using the new method, the Doppler scaling should be estimated by calculating the orbital data in advance. Thus, the inter-symbol interference (ISI) and the inter-carrier interference (ICI) can be completely eliminated, and the signal to interference and noise ratio (SINR) will not be affected. Simulation results also show that the proposed algorithm can achieve a 0.4 dB performance gain compared to the frequency domain equalization (FDE) algorithm in satellite communications.

Scrambling is a way of enhancing physical layer security presented in many papers. However, few of these papers have mentioned the scrambling effect on the polar coding secrecy. In this letter, we introduce a new scrambling module combined with a polar coding system, in which pre-assigned key is unnecessary. The presented scrambling module combines linear and nonlinear structure. The presented linear structure increases the branch number in a low-complexity way to enhance algorithm diffusion power, and the nonlinear part can resist the differential attack and reduces the mutual information between its input and output. With the proposed module, the bit channels, which are good for Bob and Eve, can also be applied to transmit secret message, making transmission rate of the secret massage configurable. Simulation results show that our method can guarantee the secrecy transmission between Alice and Bob.

Using multiple depth cameras can extend the field of view, which is beneficial to three-dimensional (3D) reconstruction. However, multiple active depth cameras would interfere with each other, which degrades the quality of depth images significantly. In this paper, we first present a depth extraction method based on sampling of the space at camera viewpoints to address the interference problem. Compared with the previous plane-sweeping method based on structured-light stereo and multiview stereo, depth images with less depth errors and better shape of objects are generated. Then, we analyze factors affecting depth accuracy for light-coding-based depth cameras and demonstrate the proposed depth extraction method also improves the accuracy of depth images by utilizing multiple projected patterns. Based on the analysis, subpixel-based matching (SPM) is finally applied to further improve the depth accuracy obtained by the depth extraction method. In the image matching process of our method, nearest neighbor interpolation is utilized to each projector's image plane to increase their resolution, which leads to the improved depth accuracy at subpixel level and avoids the drawbacks of changing parameters of light-coding-based depth cameras. Experimental results under simulated and real-world examples validate our analysis and show that the proposed method not only mitigates the impacts of interference effectively but also further improves the accuracy of depth images by using SPM.

In this letter, an iterative analysis method based on an extrinsic information transfer (EXIT) chart and quantized density evolution is developed for low-density parity-check (LDPC) codes over channels with symmetric alpha-stable ( $\text{S}\alpha \text{S}$ ) impulsive noise. Based on the proposed scheme, a method to optimize ensembles of LDPC codes under different levels of impulsiveness is presented and it is shown that the information rates of our optimized code ensembles can achieve 95.4% and 94.7% of the channel capacity for $\alpha =1.8$ and $\alpha =1$ , respectively. Furthermore, experimental results, including EXIT curves, thresholds, and bit-error rate performance of optimized code ensembles, are obtained to verify the effectiveness of our analysis.

Polar codes attract a lot of attention due to their provably capacity-achieving property with low encoding and decoding complexity. Frozen bits, the special structure of polar codes, are helpful for signal-to-noise ratio (SNR) estimation. A novel SNR estimation algorithm based on frozen bits is proposed in this letter. In the proposed algorithm, the mapping from SNR to frozen bit error rate is theoretically analyzed when code length is infinite and approximately calculated when code length is finite. Then, SNR can be estimated according to the mapping. Simulation results show that the proposed algorithm has better estimation performance with a relatively lower complexity.

Polar codes, as the first channel code that can provably achieve the channel capacity, have received increasing attention these years. However, in practical application, the decoding of polar codes still has many aspects that need improvement. One of the key bottlenecks of polar codes decoding is the high latency of SC (Successive Cancellation) -based decoding algorithms. As one of the solutions to this problem, many SCL (Successive Cancellation List) decoding algorithms with the multi-bit decision are proposed. Despite of the reduction of decoding latency, the complexity spent for computation and sort of path candidates of these algorithms has significantly increased in contrast with the conventional SCL algorithm. In this paper, we propose a novel SCL decoding algorithm with multi-bit decision for polar codes, named as Flexible and Simplified Multi-bit Successive Cancellation List (FSMSCL) decoding. The proposed algorithm further reduces the latency compared to other existing multi-bit decoding algorithms. On the other hand, we provide different path-splitting schemes for different code blocks to control the complexity of computation and sort of path metrics. In the analysis, the experiment results show that the proposed algorithm has similar FER performance compared to the conventional SCL algorithm but its decoding latency outperforms other peer decoding algorithms with multi-bit decision.

Emerging applications in the radio access network (RAN), such as proactive caching and the massive machine type communications, will generate traffic with a Large Volume of data but can tolerate long Transmission Delay (LVTD). Together with a rapid growth of overall traffic demand, traffic with LVTD features brings lots of challenges to the backhaul in RANs. In this paper, we study how to schedule traffic in multi-hop backhaul networks with the help of unmanned aerial vehicles (UAVs), In the proposed system, the UAVs are employed to establish broadband connections with the ground terminals through free-space optical links and serve as data collectors between terrestrial access points so as to alleviate the communication burden on the terrestrial network. Through numerical simulations, it is demonstrated that the novel network scheduling scheme combined with dynamic UAV path planning can provide significant performance gain.

Mobile terminal positioning has recently been recognized as an indispensable functionality for the upcoming Fifth Generation (5G) wireless networks. However, in harsh environments like urban and indoors, multipath caused by signal reflection or diffraction still severely affects the performance of 5G positioning. Aiming to reduce the positioning errors caused by multipath, we propose a novel multipath mitigation method called Two-Stage Multipath Estimating Delay Lock Loop (TS-MEDLL) by appropriately modifying the structure of conventional MEDLL. The experimental results demonstrate that in 5G positioning, the proposed TS-MEDLL outperforms the existing methods and has a 60% improvement in the medium delay multipath mitigation performance compared with the conventional MEDLL.

Information, signal amplitude and phase change drastically in broadband multimedia communications and broadcasting. Channel coding is a key technology to ensure reliable information transmission in broadband mobile multimedia communication system. Polar codes are the first channel codes that can be proved to achieve channel capacity. In this paper, we propose a fast simplified multi-bit successive cancellation list (Fast-SMSCL) decoding method for polar codes. In our proposed Fast-SMSCL decoding, we take fully use of consecutive information bits and achieve prominent latency reduction of 3% to 58% compared to the existing simplified multi-bit successive cancellation list decoding. In addition, we also propose an architecture of Fast-SMSCL decoder, especially path memory unit and partial sum network. Finally, we implement the decoder on FPGA which achieves high throughput of 125Mbps.

This paper proposes a non-orthogonal multiple access system based on rateless codes to improve anti-jamming performance. Firstly, each user’s data is encoded by rateless codes. Then all the data is superimposed to form some composite signals, which are broadcast to every user. The power factors of each user and the superimposed signals are adjusted by the transmitter according to feedback provided by the users. This paper also analyzes the packet loss ratio, the frame error rate (FER) and the decoding time. The simulation results show that the proposed scheme not only reduces the FER, but also improves the system throughput.

In this letter, we propose a segmented CRC-aided spinal code (SCA-spinal code) where an encoding structure is designed based on a novel sliding window decoding algorithm. The proposed algorithm reduces the decoding computation significantly by terminating the decoding process earlier when the segment decoding results fail to pass the corresponding CRC check. The decoding results are checked once a segment decoding is complete. Furthermore, we propose a transmission strategy after the decoding process fails to save the transmitting symbols and therefore improve the spectral efficiency. The numerical results show that our proposed SCA-spinal code reduces decoding complexity and improves spectral efficiency.

A high precision method for position and attitude determination is presented. One of the difficulties in pose determination is the lack of reliable visual features. As a result, we exploit a novel convolutional neural network for stereo matching, so as to solve the problem of image feature extraction. The network is based on a siamese network and incorporates dense block into each branch. There is occlusion problem in the close-range phase of measuring object. In order to solve the occlusion, a multi-view system is established. The measurement results meet the accuracy requirements of pose determination and verify the feasibility of the method.

In this paper, we proposed a block neural network (BlockNN) algorithm for polar code. We equally divide the 2n bit polar code into many small sub-blocks according to the encoding rules of polar code, then put these sub-blocks into the neural network of the same structure for processing. This decoding algorithm is non-iterative and inherently enables a high level of parallelization, while showing a competitive BER(bit error arte) performance. On the aspect of hardware implementation, this decoding structure of the neural network can be multiplexed and the computational complexity does not increase with the code length, only related to the size of the block.

Device positioning has generally been recognized as an important service of LTE Advanced pro and the upcoming fifth generation (5G) mobile communication. The Observed Time Difference of Arrival (OTDoA) technique that utilizes cellular signals for positioning is considered as a promising candidate technology capable of meeting users’ growing demands for high-precision and ubiquitous device positioning. The application of ultra-dense network (UDN) in 5G systems brings new opportunities and challenges to device positioning. However, existing researches mainly focus on the communication characteristics of UDN, while its positioning characteristics have not been analyzed thoroughly. In this study, a software simulation platform of OTDoA positioning in 5G UDN is first established based on the current 5G standards. Then, the performance of OTDoA technique in UDN and its influencing factors are comprehensively analyzed by heat map method over our simulation platform. The simulation results demonstrate that in 5G UDN, the unsatisfactory geometric distribution quality of base stations used for positioning and the non-line-of-sight (NLoS) propagation of cellular signals are the main factors that cause the performance degradation of OTDoA positioning. Moreover, based on the above analysis, we also provide some meaningful guidance for the development or improvement of OTDoA positioning technique used in 5G UDN.

Belief Propagation (BP) decoding provides soft outputs and features high-level parallelism. In this paper, we propose an optimized software BP decoder for polar codes on graphics processing units (GPUs). A full-parallel decoding architecture for codes with length n ≤ 2048 is presented to simultaneously update n/2 processing elements (PEs) within each stage and achieve high on-chip memory utilization by using 8-bit quantization. And, for codes with length n > 2048, a partial-parallel decoding architecture is proposed to partly update PEs of each stage in parallel and coalesced global memory accesses are performed. Experimental results show that, with incorporation of the G-matrix based early termination criterion, more than 1 Gbps throughput for codes n ≤ 1024 can be achieved on NVIDIA TITAN Xp at 5 dB while the decoding latency is less than 1 ms. Compared with the state-of-the-art works, the proposed decoder achieves throughput speedups from 2.59× to 131× and provides good tradeoff between error performance and throughput.

In this paper, we consider the problem of estimating the number of unrecovered active users in a coded random access systems over a collision channel with both packet erasures and slot erasures. Each user transmits multiple packet replicas in random slots of the frame and successive interference cancellation (SIC) is exploited at the receiver. Each packet replica is transmitted over an erasure channel. Moreover, the whole signal received in a slot may be erased, making the slot content useless. The developed algorithm can track over SIC iterations the probability distribution of the sought number of users, given all information that it is possible to gather through SIC iterations. The algorithm is validated by numerical simulation, showing that it can considerably reduce the mean square error of the estimation process.

Due to having a better performance of bit error rate (BER), systematic polar codes have been potentially applied in digital data transmission. In the systematic polar coding, source bits are transmitted transparently. In this paper, we propose a scheme of novel partial systematic polar coding in which the encoded codeword is only composed of partial source bits with respect to the encoded word of systematic polar codes. To effectively reduce the resource consumption of the systematic encoder/decoder under all-zero frozen bits, the partial systematic polar codes are introduced subsequently. Then the simulation results in terms of core \( F = \left[ {\begin{array}{*{20}l} 1 \hfill & 0 \hfill \\ 1 \hfill & 1 \hfill \\ \end{array} } \right] \) are provided to demonstrate the aforementioned analysis with negligible difference of BER performance.

Channel asymmetry 1 and channel correlation are frequently encountered in wireless communication systems. Orthogonal transmission schemes are usually inefficient in dealing with these problems. In this paper, in order to boost the throughput performance for multiple-input multiple-output broadcast communications in the presence of channel asymmetry and/or channel correlation, we study optimization algorithms for multi-user superposition coding beamforming (SCBF). Starting with solving the minimum power optimization problem for the two-user case, we derive the optimal solution structure of the problem and two types of dedicated algorithms that could efficiently find the optimal solutions with all parameter setups. Extensions are then made to the same problem with the signals of more than two users multiplexed in the power domain as well as to the rate region computation problem. Finally, to adapt our algorithms to more general cases, novel hybrid precoding schemes are proposed, where certain user grouping strategy is used to combine zero-forcing beamforming and SCBF. Numerical simulations are provided to show that with our algorithms, a considerable performance gain is achieved by SCBF comparedto the other orthogonal transmission methods.

Theoretically, conventional decoders for polar codes can be entirely replaced by neural network (NN) with enough size and enough training, which called NN decoder. But the exponentially increasing training complexity becomes unacceptable when information length increases, which means only decoders for short codes can be trained practically. However, a successive cancellation (SC) decoder for long polar codes can be divided into several SC decoders for short codes, which can be replaced by several short codes NN decoders, then the whole decoder becomes our NN aided SC (NNSC) decoder. Besides, we defined Universal Set of NN, which can be combined into NNSC decoders for any long polar codes. In this paper, the main purpose of constructing NNSC decoder is increasing decoding efficiency of polar codes by taking advantage of NN, and in the meantime ensuring an acceptable performance compared to conventional decoding algorithms.

This paper presents a grid-connected dual stator-winding induction generator (DWIG) wind power system suitable for wide wind speed ranges. The parallel connection via a unidirectional diode between dc buses of both stator-winding sides is employed in this DWIG system, which can output a high dc voltage over wide wind speed ranges. Grid-connected inverters (GCIs) do not require booster converters; hence, the efficiency of wind energy utilization increases, and the hardware topology and control strategy of GCIs are simplified. In view of the particularities of the parallel topology and the adopted generator control strategy, we propose a novel excitation–capacitor optimization solution to reduce the volume and weight of the static excitation controller. When this excitation–capacitor optimization is carried out, the maximum power tracking problem is also considered. All the problems are resolved with the combined control of the DWIG and GCI. Experimental results on the platform of a 37 kW/600 V prototype show that the proposed DWIG wind power system can output a constant dc voltage over wide rotor speed ranges for grid-connected operations and that the proposed excitation optimization scheme is effective.

This letter proposes a novel nonlinear constellation mapping (NLCM) scheme for the rate compatible modulation (RCM) approach in low-to-moderate signal to noise ratios (SNRs). In NLCM, each RCM coded symbol is mapped to a complex-valued amplitude phase shift keying (APSK) constellation point. Moreover, we provide a novel method of designing code degree and weight set for a wide SNR range. The proposed NLCM is developed to increase the throughput and decrease the peak to average power ratio (PAPR) at the same time. Both analysis and simulation results show that the NLCM scheme outperforms the original RCM in terms of throughput and PAPR.

To improve the performance of bit-interleaved polar-coded modulation (BIPCM), a joint successive cancellation (SC) decoding algorithm for BIPCM is proposed. In the proposed algorithm, the correlation between coded bits, which are mapped into the same symbol is considered. Based on the recursive structure of the proposed algorithm, the demapping and deinterleaving units are combined with SC decoder. Thus, the information bits are directly decoded by the likelihood ratios (LRs) of the received symbols instead of the LRs of coded bits. Simulation results show that the performance of existing BIPCM schemes can be improved 0.75-1.25 dB with the proposed algorithm.

In this letter, we present a graphics processing unit (GPU)-based LDPC convolutional code (LDPC-CC) pipeline decoder with optimized parallelism. The proposed decoder exploits different granularities of decoding parallelism for both the compute unified device architecture (CUDA) kernel execution stage and the data transfer stage. Moreover, the parameter selection criteria for decoder implementation are designed to avoid exhaustive search of all the combinations of parameters. The experiments are carried out on Nvidia GTX460 and GTX580 platforms. The results demonstrate the proposed decoder achieves about 3 times speedup compared to the existing GPU-based work.

With the rapid increase in the number of wireless sensor terminals in smart grids, backscattering has become a very promising green technology. By means of backscattering, wireless sensors can either reflect energy signals in the environment to exchange information with each other or capture the energy signals to recharge their batteries. However, the changing environment around wireless sensors, limited radio frequency and various service priorities in uplink communications bring great challenges in allocation resources. In this paper, we put forward a backscatter communication model based on business priority and cognitive network. In order to achieve optimal throughput of system, an asynchronous advantage actor-critic (A3C) algorithm is designed to tackle the problem of uplink resource allocation. The experimental results indicate that the presented scheme can significantly enhance overall system performance and ensure the business requirements of high-priority users.

In this paper, we present an ARM based decoder for Low Density Parity Check (LDPC) codes. To maximize the efficiency of the parallel execution and fully utilize the ARM processors’ capacity, instruction-level parallelism optimizations are applied. Compression storage and efficient data prefetching optimizing methods are used to enhance the memory cache efficiency. The experiments are carried out on NVIDIA Jetson K1 platform. Our decoder can achieve 1.85 times speed up compared with the related work. And the decoder has better energy efficiency comparing to the decoders on x86-CPU and GPU.

In this paper, we propose two novel min-sum (MS) decoding algorithms based on deep learning for polar codes, an offset min-sum (OMS) and a scaling offset min-sum (SOMS) algorithm. The parameters of both algorithms are different from iteration to iteration, and are obtained by training over a deep neural network. Our simulation results show that the OMS algorithm has roughly the same error performance as a previously proposed multiple scaling min-sum (MSMS) algorithm, and that the SOMS algorithm performs better than all existing BP-based algorithms. Since the OMS algorithm requires only an addition as opposed to a multiplication in the MSMS algorithm, the OMS algorithm is more suitable for hardware implementation. The two proposed decoding algorithms provide a tradeoff between complexity and error performance.

List size determines the ability of successive-cancellation list (SCL) decoding algorithm assisted by the cyclic redundancy check (CRC) for polar codes. To decode a received codeword correctly with exactly matching a list size, a just right list size is required to be estimated. In this paper, based on the reliability degree of the codeword decoded by the successive-cancellation (SC) decoding, a list estimation method has been proposed for a poor case of channel environments, where the log-likelihood ratio (LLR) from SC decoding output is utilized to estimate a list size. The list estimation aids the polar code decoding and achieves admirable results in performance.

In order to make full use of the transmission capability of the LEO satellite channel, this paper proposes a variable rate adaptive transmission scheme. We adopt rateless spinal codes to achieve a fine-grained bit rate and adopt the truncated incremental redundancy hybrid automatic repeat request (IR- HARQ) protocol to shorten the transmission delay of a certain message. A variable rate allocation (VRA) algorithm is proposed which uses the probability distribution of the number of symbols needed for successful decoding (the decoding CDF) of a packet and a dynamic programming strategy to calculate the optimal bit rate in every transmission attempt. Simulation results show that our proposed adaptive transmission scheme can increase average throughput by about 5.5% and 8.6% compared with the digital video broadcasting second generation (DVB-S2) standard when maximum transmission times are $K=2$ and $K=3$ respectively.

This paper addresses the problem of detecting the number of unrecovered active users in a coded random access system with successive interference cancellation (SIC) at the receiver. The considered transmission protocol is contention resolution diversity slotted Aloha, where each user generates and transmits in random slots multiple packet replicas. A maximum a posteriori detector, capable to track the a posteriori probability mass function of the number of not yet recovered active users over SIC iterations, is developed for the case of availability of a priori information on the active users' distribution. Numerical results are provided, illustrating the detection algorithm capability to achieve low mean square error values taking advantage of new observations gathered throughout the SIC process.

Gaussian approximation (GA) is widely used for constructing polar codes. However, due to the complex integration required in exact GA (EGA) algorithm, the computational complexity will increase exponentially with the polarization levels. Though the conventional approximate GA (AGA) succeeds in reducing the computational complexity, it results in a disastrous loss in the performance of frame error ratio (FER) when the code length goes long. In this paper, we discover some estimable features in the derivatives of GA algorithm. These features reveal the possibility and feasibility to simplify GA by utilizing curve estimation. Therefore, we then design new piecewise functions to approximate the derivatives and conclude some criteria for GA simplification. Guided by new derivative functions and proposed criteria, we design a new GA algorithm, named simplified GA (SGA). Numeric and simulation results show that SGA reduces the computational complexity and at the same time, guarantees remarkable performance with a long code length.

In this paper, a Graphics Processing Unit (GPU) implementation of a Successive-Cancellation List (SCL) decoder for polar codes is proposed. Based on the compute unified device architecture (CUDA), the strategy to parallelize the decoding procedure is designed to reduce the latency. Moreover, the optimal design of data structures for several kinds of intermediate information is presented. In order to reduce the global memory accesses in list pruning, The GPU-adapted lazy-copy strategy is elaborated. By means of the parallel computing capabilities of GPUs, the proposed decoder achieves high throughput of 41Mbps on NVIDIA GTX 980 and 65Mbps on TITAN X while decoding the code with length of 1024 bits and 32 lists.

This paper investigates the impact of relay selection (RS) on the performance of cooperative non-orthogonal multiple access (NOMA), where relays are capable of working in either full- duplex (FD) or half-duplex (HD) mode. The locations of relays considered in the networks are modeled using stochastic geometry. The single-stage relay selection (SRS) scheme is proposed. In order to characterize the performance of SRS scheme, new closed-form expressions for both exact and asymptotic outage probabilities are derived. Based on the analytical results, the diversity orders achieved by the SRS scheme for FD/HD cooperative NOMA are obtained. It is confirmed that the FD-based SRS schemes obtain a zero diversity order, while the HD-based RS is capable of achieving a diversity order of $K$. Simulation results show that the outage performance of FD-based RS scheme outperforms HD-based RS scheme in the low signal-to-noise radio (SNR) region rather than in the high SNR region.

Carrier tracking is laid great emphasis and is the difficulty of signal processing in deep space communication system. For the autonomous radio receiving system in deep space, the tracking of the received signal is automatic when the signal to noise ratio (SNR) is unknown. If the frequency-locked loop (FLL) or the phase-locked loop (PLL) with fixed loop bandwidth, or Kalman filter with fixed noise variance is adopted, the accretion of estimation error and filter divergence may be caused. Therefore, the Kalman filter algorithm with adaptive capability is adopted to suppress filter divergence. Through analyzing the inadequacies of Sage–Husa adaptive filtering algorithm, this paper introduces a weighted adaptive filtering algorithm for autonomous radio. The introduced algorithm may resolve the defect of Sage–Husa adaptive filtering algorithm that the noise covariance matrix is negative definite in filtering process. In addition, the upper diagonal (UD) factorization and innovation adaptive control are used to reduce model estimation errors, suppress filter divergence and improve filtering accuracy. The simulation results indicate that compared with the Sage–Husa adaptive filtering algorithm, this algorithm has better capability to adapt to the loop, convergence performance and tracking accuracy, which contributes to the effective and accurate carrier tracking in low SNR environment, showing a better application prospect.