The combination of multiple-input multiple-output (MIMO) technology and sparse code multiple access (SCMA) can significantly enhance the spectral efficiency of future wireless communication networks. However, the receiver design for downlink MIMO-SCMA systems faces challenges in developing multi-user detection (MUD) schemes that achieve both low latency and low bit error rate (BER). The separated detection scheme in the MIMO-SCMA system involves performing MIMO detection first to obtain estimated signals, followed by SCMA decoding. We propose an enhanced separated detection scheme based on lightweight graph neural networks (GNNs). In this scheme, we raise the concept of coordinate point relay and full-category training, which allow for the substitution of the conventional message passing algorithm (MPA) in SCMA decoding with image classification techniques based on deep learning (DL). The features of the images used for training encompass crucial information such as the amplitude and phase of estimated signals, as well as channel characteristics they have encountered. Furthermore, various types of images demonstrate distinct directional trends, contributing additional features that enhance the precision of classification by GNNs. Simulation results demonstrate that the enhanced separated detection scheme outperforms existing separated and joint detection schemes in terms of computational complexity, while having a better BER performance than the joint detection schemes at high Eb/N0 (energy per bit to noise power spectral density ratio) values.

The Common Media Application Format (CMAF) is a standard for adaptive bitrate live streaming. The CMAF adapts chunk encoding and enables low-latency live streaming. However, conventional bandwidth estimation for adaptive bitrate streaming underestimates bandwidth because download time is affected not only by network bandwidth but also by the idle times between chunks in the same segment. Inaccurate bandwidth estimation decreases the quality of experience of the streaming client. In this paper, we propose a chunk-grouping method to estimate the available bandwidth for adaptive bitrate live streaming. In the proposed method, by delaying HTTP request transmission and bandwidth estimation using grouped chunks, the client estimates the available bandwidth accurately due to there being no idle times in the grouped chunks. In addition, we extend the proposed method to dynamically change the number of grouping chunks according to buffer length during downloading of the previous segment. We evaluate the proposed methods under various network conditions in order to confirm the effectiveness of the proposed methods.

This paper describes a novel low-latency 4K 60 fps HEVC (high efficiency video coding)/H.265 multi-channel encoding system with content-aware bitrate control for live streaming. Adaptive bitrate (ABR) streaming techniques, such as MPEG-DASH (dynamic adaptive streaming over HTTP) and HLS (HTTP live streaming), spread widely on Internet video streaming. Live content has increased with the expansion of streaming services, which has led to demands for traffic reduction and low latency. To reduce network traffic, we propose content-aware dynamic and seamless bitrate control that supports multi-channel real-time encoding for ABR, including 4K 60 fps video. Our method further supports chunked packaging transfer to provide low-latency streaming. We adopt a hybrid architecture consisting of hardware and software processing. The system consists of multiple 4K HEVC encoder LSIs that each LSI can encode 4K 60 fps or up to high-definition (HD) ×4 videos efficiently with the proposed bitrate control method. The software takes the packaging process according to the various streaming protocol. Experimental results indicate that our method reduces encoding bitrates obtained with constant bitrate encoding by as much as 56.7%, and the streaming latency over MPEG-DASH is 1.77 seconds.

Sparse code division multiple access (SCMA) is a non-orthogonal multiple access (NOMA) technology that can improve frequency band utilization and allow many users to share quite a few resource elements (REs). This paper uses the modulation of lattice theory to develop a systematic construction procedure for the design of SCMA codebooks under Gaussian channel environments that can achieve near-optimal designs, especially for cases that consider large-scale SCMA parameters. However, under the condition of large-scale SCMA parameters, the mother constellation (MC) points will overlap, which can be solved by the method of the partial dimensions transformation (PDT). More importantly, we consider the upper bounded error probability of the signal transmission in the AWGN channels, and design a codeword allocation method to reduce the inter symbol interference (ISI) on the same RE. Simulation results show that under different codebook sizes and different overload rates, using two different message passing algorithms (MPA) to verify, the codebook proposed in this paper has a bit error rate (BER) significantly better than the reference codebooks, moreover the convergence time does not exceed that of the reference codebooks.

Platform-mounted small antennas increase dielectric loss and conductive loss and decrease the radiation efficiency. This paper proposes a novel antenna design method to improve radiation efficiency for platform-mounted small antennas by characteristic mode analysis. The proposed method uses mapping of modal weighting coefficient (MWC) and infinitesimal dipole and evaluate the metal casing with 100mm × 55mm × 23mm as a platform excited by an inverted-F antenna. The simulation and measurement results show that the radiation efficiency of 5% is improved with the whole system from 2.5% of the single antenna.

Sparse code multiple access (SCMA) based on the message passing algorithm (MPA) for multiuser detection is a competitive non-orthogonal multiple access technique for fifth-generation wireless communication networks Among the existing multiuser detection schemes for uplink (UP) SCMA systems, the serial MPA (S-MPA) scheme, where messages are updated sequentially, generally converges faster than the conventional MPA (C-MPA) scheme, where all messages are updated in a parallel manner. In this paper, the optimization of message scheduling in the S-MPA scheme is proposed. Firstly, some statistical results for the probability density function (PDF) of the received signal are obtained at various signal-to-noise ratios (SNR) by using the Monte Carlo method. Then, based on the non-orthogonal property of SCMA, the data mapping relationship between resource nodes and user nodes is comprehensively analyzed. A partial codeword transmission of S-MPA (PCTS-MPA) with threshold decision scheme of PDF is proposed and verified. Simulations show that the proposed PCTS-MPA not only reduces the complexity of MPA without changing the bit error ratio (BER), but also has a faster convergence than S-MPA, especially at high SNR values.

In industrial control processes, control performance influences the quality of products and utilization efficiency of energy; hence, the controller is necessarily designed according to user-desired control performance. Ideal control performance requires fast response for transient state and maintaining user-specified control performance for steady state. Hence, an algorithm to tune controller parameters to match the requirements for transient state and steady state is proposed. Considering the partial learning ability of the cerebellar model articulation controller (CMAC) neural network, it is utilized as a “tuner” of controller parameters in this study, since then the controller parameters can be tuned in both transient and steady states. Moreover, the fictitious reference iterative tuning (FRIT) algorithm is combined with CMAC in order to avoid problems, which may be caused by system modeling error and by using only a set of closed-loop data, the desired controller can be calculated in an off-line manner. In addition, the controller selected is a proportional-integral-derivative (PID) controller. Finally, the effectiveness of the proposed method is numerically verified by using some simulation and experimental examples.

This paper proposes a novel blind adaptive array scheme with subcarrier transmission power assignment (STPA) for spectrum superposing in cognitive radio networks. The Eigenvector Beamspace Adaptive Array (EBAA) is known to be one of the blind adaptive array algorithms that can suppress inter-system interference without any channel state information (CSI). However, EBAA has difficulty in suppressing interference signals whose Signal to Interference power Ratio (SIR) values at the receiver are around 0dB. With the proposed scheme, the ST intentionally provides a level difference between subcarriers. At the receiver side, the 1st eigenvector of EBAA is applied to the received signals of the subcarrier assigned higher power and the 2nd eigenvector is applied to those assigned lower power. In order to improve interference suppression performance, we incorporate Beamspace Constant Modulus Algorithm (BSCMA) into EBAA (E-BSCMA). Additionally, STPA is effective in reducing the interference experienced by the primary system. Computer simulation results show that the proposed scheme can suppress interference signals received with SIR values of around 0dB while improving operational SIR for the primary system. It can enhance the co-existing region of 2 systems that share a spectrum.

Beamforming with sparse constraint has shown significant performance improvement. In this letter, a least squares constant modulus blind adaptive beamforming with sparse constraint is proposed. Simulation results indicate that the proposed approach exhibits better performance than the well-known least squares constant modulus algorithm (LSCMA).

This paper proposes an adaptive antenna using a combination of on-off and CMA algorithms. With the proposed technique, the on-off algorithm is first employed to search for a desired signal direction in which maximum received power is achieved. Then, interference is suppressed by performing CMA. Simulations are conducted according to the potential application of the proposed adaptive antenna. The simulation results show the SINR improvement implying that the proposed adaptive antenna can be applied to microwave RFID systems in order to resolve reader collision. Furthermore, the proposed adaptive antenna is implemented and then experimented. The experimental results verify that the proposed adaptive antenna can reduce interference resulting in the collision problem.

This paper proposes a novel blind multiuser detection scheme using CMA (Constant Modulus Algorithm) adaptive array. In the proposed scheme, the received signal is processed in two steps. In the primary step, only one user is captured by the CMA adaptive array, and at the same time, the other users' directions of arrival (DOA) are estimated. In the secondary step, initial weight vectors are set based on the estimated DOAs, and it processes with CMAs again to capture the other users in parallel. Thus, all the users are detected exactly and recovered separately. The Least-squares CMA is applied as an optimization algorithm to improve the performance of the proposed scheme, and the performances using the proposed scheme with linear arrays and circular arrays are discussed in detail. Simulation results are presented to verify the performance of the proposed scheme.

In this paper, a novel CMA (constant modulus algorithm) algorithm employing fast convolution in the DFT (discrete Fourier transform) domain is proposed. We propose a non-linear adaptation algorithm that minimizes CMA cost function in the DFT domain. The proposed algorithm is completely new one as compared to the recently introduced similar DFT domain CMA algorithm in that, the original CMA cost function has not been changed to develop DFT domain algorithm, resulting improved convergence properties. Using the proposed approach, we can reduce the number of multiplications to O(Nlog2 N), whereas the conventional CMA has the computation order of O(N2). Simulation results show that the proposed algorithm provides a comparable performance to the conventional CMA.

Blind beamforming plays an important role in multiple-input multiple-output (MIMO) Systems, radar, cognitive radio, and system identification. In this paper, we propose a new algorithm for multiple blind beamforming algorithm based on the least square constant modulus algorithm (LSCMA). The new method consists of the following three parts: (a) beamforming of one signal with LSCMA. (b) direction-of-arrival (DOA) estimation of the remaining signals by rooting the weight vector polynomial. (c) beamforming of the remaining signals with linear constraints minimum variance (LCMV) method. After the convergence of LSCMA, one signal is captured and the arrival angles of the remaining signals can be obtained by rooting the weight vector polynomial. Therefore, beamforming can be quickly established for the remaining signals using LCMV method. Simultaneously the DOA of the signals can also be obtained. Simulation results show the performance of the presented method.

The ESIGN signature scheme was initially proposed in 1985. Since then, several variants have been proposed, but only a few have been formally supported using the methodology of provable security. In addition, these schemes are different from the ESIGN-PSS signature scheme submitted to ISO/IEC-14888-2 for standardization. It is believed that ESIGN-PSS is secure against the chosen-message attack, however, there has not yet been any report verifying this belief. This paper presents the security proofs of ESIGN-PSS and a variant of this scheme, denoted ESIGN-PSS-R, which is a signature scheme comprising the ESIGN signature mechanism and the PSS-R mechanism.

In this Letter, we propose a new adaptive step-size widely linear constant modulus algorithm (CMA) in DS-CDMA systems especially for time-varying interference environments. The widely linear estimation enables CMA to produce better output signal to interference plus noise ratio (SINR) and the adaptive step-size tackles the time-varying interference environment effectively. The simulations confirm that the proposed algorithm shows better performance in a DS-CDMA system employing a BPSK modulation than other algorithms without use of widely linear processing.

A new receiver structure that combines the constant modulus algorithm (CMA) and the Kalman filter (KF) is investigated to exploit the advantages of both algorithms; simple implementation of blind algorithms, and excellent tracking ability, respectively. The proposed scheme achieves faster convergence and adaptability to the channel variation, which is verified through comparative simulations in doubly-selective (time- and frequency-selective) fading channels.

In this letter we verify that a blind adaptive algorithm operating at a low intermediate frequency (Low-IF) can be applied to a system where carrier phase synchronization has not been achieved. We consider a quadrature amplitude shift keyed (QPSK) signal as the transmitted signal, and assume that the orthogonal low intermediate sinusoidal frequency used to generate the transmitted signal is well known. The proposed algorithm combines two algorithms: Namely, the least mean square (LMS) algorithm which has a cost function with unique minimum, and the constant modulus algorithm (CMA), which was first proposed by Godard. By doing this and operating the equalizer at a rate greater than the symbol rate, we take advantage of the variable amplitude of the sub-carriers and the fast convergence of LMS algorithm, so as to achieve a faster convergence speed. When the computer simulation results of the proposed algorithm are compared with the constant modulus algorithm (CMA) and the modified CMA (MCMA), we observed that the proposed algorithm exhibited a faster convergence speed.

A novel design of Cerebellar Model Articular Controller (CMAC) is presented in this article. The controller is designed by means of a content addressable memory (CAM) to replace a hash-coding function, which is adopted by generic CMACs to tackle memory space problem how a large space maps into a small one. With a different address mapping method from hash-coding methods, each memory location of the proposed architecture includes two tuples: One is the conceptual address stored in a CAM, and another is the weight associated with the conceptual address stored in a SRAM. The CAM, with capability of fast comparison, is used to determine if any of CAM's content is identical to current conceptual address in parallel. If no match occurs, an associated mask function is triggered to expand searching range, which is centered by current conceptual address with a radius defined by the number of maskable bits. If a location in the CAM carries the similar address, the weight (in SRAM) related to this matching location would be shared and updated by both the current conceptual address and the conceptual address in this location. Therefore, the control noises caused by hash-coding methods can be attenuated significantly in either the training or the recall phases in the proposed architecture. Furthermore, if there is no match in current search, after the mask function is executed, the new conceptual address with an initial weight value would be stored in a CAM cell sequentially indexed by an incremental pointer. Instead of storing the information by scattering it over the memory, the proposed architecture sequentially stores the information by the index of this pointer to increase the memory utilization. Simulation results, (1) one input variable and two input variables cases of function approximations, (2) a truck backer-upper control, demonstrate the plausible performance of the proposed CMAC architecture. The architecture and the design criteria for the proposed controller are also discussed.

Adaptive arrays have been recognized as an attractive mean for overcoming multipath fading and interference rejection in the field of mobile communications. In, an adaptive array applicable to single-user spread spectrum (SS) systems has been proposed. In this system, any a priori information concerning incoming signal, even the spreading code and synchronization, is not necessary while it achieves equalizing, beamforming and despreading of a received signal, simultaneously. In this paper, we propose a multistage blind adaptive array antenna based on the above-mentioned adaptive algorithm in order to realize blind signal processing that is applicable to multi-user SS systems. Behavior and performance of the proposed multistage system are examined through computer simulations.

In this paper, we propose a pipelined architecture for an equalizer based on the Multilevel Modified Constant Modulus Algorithm (MMCMA). We also provide the correction factor that mathematically converts the proposed pipelined adaptive equalizer into an equivalent non-pipelined conventional MMCMA based equalizer. The proposed method of pipelining uses modules with 6 filter coefficients, resulting in an overall latency of a single sampling period, along the main transmission line. The basic concept of the proposed architecture is to implement the Finite Impulse Response (FIR) filter and the algorithm portion of the adaptive equalizer, such that the critical path of the whole circuit has a maximum of three complex multipliers and three adders.