Space-air-ground integrated networks (SAGIN) combine the wide-area coverage of satellites with airborne platforms acting as relays, enabling efficient data delivery for large-scale Internet of Things (IoT) sensing applications. To further enhance transmission efficiency, this paper incorporates airborne onboard processing to reduce communication workloads and proposes an intelligent beam hopping strategy tailored to spatially uneven traffic demands. Specifically, we design a multi-agent deep reinforcement learning (MADRL)-based beam hopping framework, where satellite agents coordinate beam scheduling while considering spatial service heterogeneity and the diverse computational capacities of airborne platforms. To reduce the complexity introduced by individual task requirements, we integrate a summarized statistical profile of the computation tasks into the agent’s observation space, including average and maximum computation efficiencies across tasks. Simulation results demonstrate that the proposed scheme significantly accelerates convergence and reduces the data backlog by up to 80%, especially under scenarios with considerable heterogeneity in service demands and airborne platform computational capabilities.

Non-orthogonal multiple access (NOMA) with successive detection receivers, e.g., successive interference cancellation (SIC), is a potential technology for satellite multi-user communication due to its lower complexity. However, within a spot beam, multi-user interference (MUI) is complicated by channel-induced time-frequency offset, while the path loss differences that the receiver relies on for MUI suppression almost disappear. To overcome the above obstacle, this paper exploits the delay-Doppler (D-D) domain circular shifting property under time-frequency offsets and proposes a D-D domain spectral shaping multiple access (SSMA) technique. By analyzing the influence of D-D domain spectrum on channel capacity, we identify that an enlarged inter-user power gap can be derived at the receiver by constructing a non-uniform D-D domain spectrum. Inspired by this, a D-D domain multi-branch structure-based shaping framework is proposed to flexibly construct the user-consistent power envelope. Meanwhile, two additional signal designs are introduced to ensure that the D-D domain information density and constellation fit to the constructed power envelope. First, by adjusting the transmission rate of the signal on each branch, we optimize the information density with a non-uniform pattern. Second, by introducing a branch-wise phase rotation and deploying an iterative variational approximation method, the shape of the composite constellation is reconstructed. In addition, we also design a branch-bundling-based successive detection receiver using an alternating direction method of multipliers. This receiver can flexibly combine detectable branch signals while maintaining the complexity close to the traditional SIC receiver. Analysis and simulation results reveal that the proposed D-D domain SSMA has a higher achievable rate and can provide$1\sim 4.5$dB bit error rate performance gain compared to the typical D-D domain NOMA.

Many headache patients often report cognitive disturbances, but tactile cognitive data are limited. Applying computing-aided strategies to reveal the association between migraine without aura (MOA) or tension-type headache (TTH) and tactile cognition is one of the research highlights. The aim of this study was to investigate whether MOA or TTH patients had a decline in tactile discrimination by utilizing a tactile angle discrimination tester. A cross-sectional study was performed between 1 January 2021, and 1 January 2022. A total of 301 participants were enrolled, with 107 in control, 90 in MOA, and 104 in TTH groups. A tactile cognition tester was used to objectively examine tactile discrimination in all participants. Tactile angle discrimination thresholds were measured to compare tactile cognitive functions among three groups. There were no statistically significant differences in their demographic characteristics. Compared to the normal control group, the MOA and TTH groups exhibited significantly higher tactile angle discrimination thresholds (showing decline in tactile discrimination), whereas no significant differences were found between the MOA and TTH groups. Differences in tactile angle discrimination thresholds were observed between young (≤ 44 years old) and middle-aged/elderly (≥ 45 years old) participants in the normal control group but not in the MOA and TTH groups. Moreover, the tactile deficits shown in the MOA or TTH groups were evident only in young participants. This study first demonstrated that patients with MOA or TTH, especially those patients younger than 44 years old, had decreased tactile angle discrimination ability, suggesting decline of tactile cognition.

Downloading a large amount of data from a low Earth orbit satellite to a ground station can be challenging due to the limited contact window, dynamic channel quality, solar energy supply, and thermal management without an atmosphere. Considering such dynamics, this paper proposes a joint design of in-orbit computation and communication for download time minimization. We combine the non-convex thermal constraints and energy constraints into unified energy budget constraints with upper bound approximation, and computational efficiency is achieved by decomposing the resulting large-scale problem into a non-convex communication sub-problem, a convex computation sub-problem solvable with interior point method and a master problem that optimizes the energy budget allocation between computation and communication. The communication sub-problem is solved with a generalized-benders-decomposition-based algorithm that decouples downlink scheduling and power allocation based on a closed-form solution of optimal dual variables in the power allocation primal problem. And the master problem is solved with ternary search by proving the minimal download time is quasi-convex with respect to the energy budget allocation between computation and communication. Simulation results demonstrate that the proposed solution effectively reduces the download time, especially under strict energy constraints and severe channel variations.

The reliability of low Earth orbit (LEO) mega constellation networks (MCNs) under large-scale geographical failure of satellites remains unrevealed. In this paper, we propose an algorithm to assess the connectivity, average latency and hop count by considering topology changes resulting from geographical failure, under different topology management. Numerical simulations are conducted based on the traffics source from end users distributed among the 100 most populous cities. The results show that the MCNs are generally reliable against geographical failure, as a geographical failure with a radius of 3000 km can at most disconnect 8% of the end users, while increasing the average hop count and latency of the remaining users by less than 10%. Also, topology reconfiguration after failure have a greater impact on the hop count than latency.

Wireless power transfer (WPT) via resonant laser beam from a spatially distributed laser resonator not only benefits the instinct safety but also has important potential of adaptive operation without positioning and aiming when retroreflectors are used to enable the alignment-free operation of the laser. Despite the intense theoretical modeling and experimental efforts with different laser schemes, the adaptive resonant beam WPT across large dynamic range, including both working distance and Field of View (FoV), has never been demonstrated, due to the absence of true alignment-free laser. In this work, we summarized the requirements on the dynamic range and discussed the optimization criteria of dynamic range and laser safety, including the telescope in the laser resonator, the resonator stability, coupled-resonator scheme, and influences of the optical aberrations. The analysis indicated that loss induced by optical aberrations is the main issue limits the dynamic range. In the experiment, alignment-free laser with large dynamic range for WPT application was demonstrated with improved resonator design and optical design to compensate the aberrations. Efficient multiwatt optical output was obtained across a working distance of 1–5 m, a receiver FoV of ±30°, and a transmitter FoV of 4.6°; and over 1.3-W electrical output was obtained via an InGaAs photovoltaic cell, with dc–dc WPT efficiency being 4%. Our work proved the feasibility of large-dynamic-range alignment-free laser with distributed resonator, which paves the way for practical resonant beam charging and communication applications based on it for the Internet of Things devices.

Industrial Internet of Things (IIoT) brings opportunities for innovation and upgrading to industrial production, but meanwhile, it poses unprecedented challenges on the physical layer of wireless communications. In this article, we propose a novel frequency hopping transmission scheme for IIoT, which owns much high efficiency and strong anti-jamming ability. This new scheme is built based on the combination of coherent fast frequency hopping (CFFH) and cyclic code shift key (CCSK), named phase cyclic shift-keying-based CFFH (PCSK-CFFH). For the proposed PCSK-CFFH system, the shift of orthogonal base sequence is adopted to modulate the phases among interhop, instead of transmitting each hop with the same phase as conventional CFFH. This scheme is able to further exploit the extra dimensions of CFFH signals and enables the CFFH system to transmit additional information at no extra cost on either bandwidth or power. Moreover, the bit error rate performance of the proposed PCSK-CFFH system is analyzed theoretically. To improve its ability against severe narrow-band jamming, the selection combining is introduced in our scheme. Furthermore, the anti-jamming performance of the proposed PCSK-CFFH system is also demonstrated. Finally, the simulation results are shown to demonstrate that: by introducing the interhop dimension to CFFH, PCSK-CFFH improves the power efficiency and spectrum efficiency by multiple times and simultaneously inherits the excellent anti-jamming performance of the conventional CFFH system. It can be concluded that the proposed PCSK-CFFH scheme has a great potential to be applied to low-power and highly reliable IIoT communications.

Decision making could be an extremely complicated process. Especially, in situations in which the diversity of backgrounds and experience of decision makers (DMs) may require various forms to model their opinions. DMs’ rational choice even may be distorted by their judgements due to limitations of human cognitive competence. Generally, the interactions between criteria, the bipolar scale with negative values, and linguistic-valued aspirations on criteria are also needed to be addressed. In addition, several types of input information may be stochastic or uncertain, such as criteria evaluations, capacities, and preference coefficients. Our aim is to address all previous issues simultaneously by proposing a novel model that incorporates Choquet integral, bicapacity, regret theory, and stochastic multiobjective acceptability analysis (SMAA), i.e., SMAA–bicapacity–Choquet–regret for heterogeneous linguistic multiple criteria decision-making (MCDM) problems with interactive criteria with bipolar scale and 2-tuple aspirations. The heterogeneous linguistic information can offer a highly flexible way to express DMs’ preference; a novel 2-tuple aspirations-based utility within regret can better reflect the subjective perceptions of DMs in MCDM problems with 2-tuple aspirations on criteria; bicapacity can weigh the criteria and their coalitions, and Choquet integral concerning bicapacity can aggregate interactive criteria with bipolar scale; and finally, an application to evaluate medical waste disposal technologies is given, and a comparative analysis is performed to verify the applicability and effectiveness of the proposed model.

Satellite Internet of Things (IoT) is a promising technical trend to tackle the ubiquitous connection requirements for 5G and beyond. In satellite IoT, the conventional random access scheme faces huge interaction overhead caused by the complicated signaling interactions and the large propagation delay of the satellite-ground link. To tackle this problem, we propose a simplified random access scheme. By utilizing the collision tolerance of non-orthogonal multiple access (NOMA) technology, random access and data transmission are integrated to reduce the overall interaction delay. At the transmitter side, the transmitted symbols are spread by the non-orthogonal sequence and the preamble corresponding to the non-orthogonal sequence. At the receiver side, the received signal is iteratively processed based on the minimum mean square error (MMSE) and serial interference cancellation (SIC). The simulation results show that the proposed scheme can reduce the transmission delay without losing the success rate of random access compared with the conventional scheme that only relies on preamble detection.

Multiple-attribute group decision making (MAGDM) under linguistic environment is an important part of modern decision sciences, and information aggregation operator plays an import role in solving MAGDM problems. In this paper, an approach for solving MAGDM problem with q-rung orthopair fuzzy 2-tuple linguistic information is developed. First, the q-rung orthopair fuzzy 2-tuple linguistic weighted averaging (q-ROFTLWA) operator and the q-rung orthopair fuzzy 2-tuple linguistic weighted geometric (q-ROFTLWG) operator are presented. Furthermore, the q-rung orthopair fuzzy 2-tuple linguistic Muirhead mean (q-ROFTLMM) operator and the q-rung orthopair fuzzy 2-tuple linguistic dual Muirhead mean (q-ROFTLDMM) operator are proposed on the basis of Muirhead mean (MM) operator and dual Muirhead mean (DMM) operator. Then, an approach is developed to deal with MAGDM problem under q-rung orthopair fuzzy 2-tuple linguistic environment based on the proposed operators. Finally, a numerical example for selecting desirable emergency alternative(s) in the process of designing emergency preplan is given to illustrate the application of the developed method and demonstrate its effectiveness.

The rapid booming of future smart city applications and Internet of things (IoT) has raised higher demands on the next-generation radio access technologies with respect to connection density, spectral efficiency (SE), transmission accuracy, and detection latency. Recently, faster-than-Nyquist (FTN) and nonorthogonal multiple access (NOMA) have been regarded as promising technologies to achieve higher SE and massive connections, respectively. In this paper, we aim to exploit the joint benefits of FTN and NOMA by superimposing multiple FTN-based transmission signals on the same physical recourses. Considering the complicated intra- and interuser interferences introduced by the proposed transmission scheme, the conventional detection methods suffer from high computational complexity. To this end, we develop a novel sliding-window detection method by incorporating the state-of-the-art deep learning (DL) technology. The data-driven offline training is first applied to derive a near-optimal receiver for FTN-based NOMA, which is deployed online to achieve high detection accuracy as well as low latency. Monte Carlo simulation results validate that the proposed detector achieves higher detection accuracy than minimum mean squared error-frequency domain equalization (MMSE-FDE) and can even approach the performance of the maximum likelihood-based receiver with greatly reduced computational complexity, which is suitable for IoT applications in smart city with low latency and high reliability requirements.

Massive connectivity is one of the most challenging issues for Internet of Things (IoT) to achieve the quality of service provisions required by the numerous IoT devices. Non-orthogonal multiple access (NOMA) technology, where multiple users multiplex on the same radio resources, is a promising candidate for next generation wireless networks (the 5th Generation, 5G) and has been expected to meet the requirements of high spectral efficiency and massive connections of 5G mobile communication systems. However, conventional downlink NOMA simply superimposes several single-user constellations, which does not consider the interactions between multiple data streams. This paper proposes a novel deep learning-aided downlink NOMA scheme by parameterizing the bit-to-symbol mapping and multi-user detection with deep neural networks (DNN). The network is trained in an end-to-end fashion with synthetic data, and then the trained bit-to-symbol mapping is extracted to derive the multi-user constellation for downlink NOMA. Simulation results demonstrate that, with the proposed constellations, our scheme achieves significantly lower symbol error rate than conventional downlink NOMA.

Wireless sensor network (WSN) is a key enabling technology for Internet of Things (IoT), where the sensed data reported by the distributed sensors are transmitted to a core node for intelligent computation and decision. However, the isolation between wireless communication and computing leads to a waste of radio resources, since not all sensed data are required for making a precise enough decision. Hence, we propose a wireless neural network (WNN) to integrate the neural computing and wireless communication by exploiting the superposition characteristics of radio channels as well as the reciprocity between deep artificial neural network and multi-tier WSN. The learning ability of WNN is further enhanced by introducing multi-carrier transmission where the transmit gain of each sub-carrier can be freely trained to increase the number of adjustable network parameters. Experiments on some datasets demonstrate that, similar decision accuracy can be achieved compared with the conventional isolated method, while the radio resource consumption can be greatly reduced due to superposition transmissions.

Q-rung orthopair fuzzy sets (q-ROFSs), initially proposed by Yager, are a new way to reflect uncertain information. The existing intuitionistic fuzzy sets (IFSs) and Pythagorean fuzzy sets are special cases of the q-ROFSs. However, due to insufficiency in available information, it is difficult for decision makers to exactly express the membership and nonmembership degrees by crisp numbers, and interval membership degree and interval nonmembership degree are good choices. In this paper, we propose the concept of interval-valued q-rung orthopair fuzzy set (IVq-ROFS) based on the ideas of q-ROFSs and some operational laws of q-rung orthopair fuzzy numbers (q-ROFNs). Then, some interval-valued q-rung orthopair weighted averaging operators are presented based on the given operational laws of q-ROFNs. Further, based on these operators, we develop a novel approach to solve multiple-attribute decision making (MADM) problems under interval-valued q-rung orthopair fuzzy environment. Finally, a numerical example is provided to illustrate the application of the proposed method, and the sensitivity analysis is further carried out for the parameters.

In this paper, a novel approach is developed to deal with multiple-attribute group decision-making (MAGDM) problem under q-rung orthopair fuzzy environment. Firstly, some operators have been proposed to aggregate q-rung orthopair fuzzy information, such as the q-rung orthopair fuzzy generalized power averaging (q-ROFGPA) operator, the q-rung orthopair fuzzy generalized power weighted averaging (q-ROFGPWA) operator, the q-rung orthopair fuzzy generalized power geometric (q-ROFGPG) operator, and the q-rung orthopair fuzzy generalized power weighted geometric (q-ROFGPWG) operator. In addition, some desirable properties and special cases of these operators are discussed. Second, a novel approach is developed to solve MAGDM problem under the q-rung orthopair fuzzy environment based on the proposed q-ROFGPWA and q-ROFGPWG operators. Finally, a practical example is given to illustrate the application of the proposed method, and further the sensitivity analysis and comparative analysis are carried out.

Tactile Internet of Things (IoT) requires ultraresponsive and ultrareliable connections for massive IoT devices. As a promising enabler of tactile IoT, grant-free nonorthogonal multiple access (NOMA) exploits the joint benefit of grant-free access and nonorthogonal transmissions to achieve low latency massive access. However, it suffers from the reduced reliability caused by random interference. Hence, we formulate a variational optimization problem to improve the reliability of grant-free NOMA. Due to the intractability of this problem, we resort to deep learning by parameterizing the intractable variational function with a specially designed deep neural network, which incorporates random user activation and symbol spreading. The network is trained according to a novel multiloss function where a confidence penalty based on the user activation probability is considered. The spreading signatures are automatically generated while training, which matches the highly automatic applications in tactile IoT. The significant reliability gain of our scheme is validated by simulations.

Enabling nonorthogonal multiple access (NOMA) in device-to-device (D2D) communications under the millimeter wave (mmWave) multiple-input multiple-output (MIMO) cellular network is of critical importance for 5G wireless systems to support low latency, high reliability, and high throughput radio access. In this paper, the closed-form expressions for the outage probability and the ergodic capacity in downlink MIMO-NOMA mmWave cellular network with D2D communications are considered, which indicates that NOMA outperforms TDMA. The influencing factors of performance, such as transmission power and antenna number, are also analyzed. It is found that higher transmission power and more antennas in the base station can decrease the outage probability and enhance the ergodic capacity of NOMA.

Owing to the superior performance in spectral efficiency, connectivity, and flexibility, nonorthogonal multiple access (NOMA) is recognized as the promising access protocol and is now undergoing the standardization process in 5G. Specifically, dozens of NOMA schemes have been proposed and discussed as the candidate multiple access technologies for the future radio access networks. This paper aims to make a comprehensive overview about the promising NOMA schemes. First of all, we analyze the state‐of‐the‐art NOMA schemes by comparing the operations applied at the transmitter. Typical multiuser detection algorithms corresponding to these NOMA schemes are then introduced. Next, we focus on grant‐free NOMA, which incorporates the NOMA techniques with uplink uncoordinated access and is expected to address the massive connectivity requirement of 5G. We present the motivation of applying grant‐free NOMA, as well as the typical grant‐free NOMA schemes and the detection techniques. In addition, this paper discusses the implementation issues of NOMA for practical deployment. Finally, we envision the future research challenges deduced from the recently proposed NOMA technologies.

Grant‐free transmission, which simplifies the signaling procedure via uplink instant transmission, has been recognized as a promising multiple access protocol to address the massive connectivity and low latency requirements for future machine type communications. The major drawback of grant‐free transmission is that the contaminations among uncoordinated transmissions can reduce the data throughput and deteriorate the outage performance. In this paper, we propose a rate‐adaptive multiple access (RAMA) scheme to tackle the collision problems caused by the grant‐free transmission. Different from the conventional grant‐free (conv‐GF) scheme which transmits a single signal layer, RAMA transmits the signals with a multilayered structure, where different layers exhibit unequal protection property. At the receiver, the intra‐ and interuser successive interference cancellation (SIC) receiving algorithm is employed to detect multiple data streams. In RAMA, the users can achieve rate adaptation without the prior knowledge of the channel conditions, since the layers with high protection property can be successfully recovered when the interference is severe, while other layers can take advantage of the channel when the interference is less significant. Besides, RAMA also facilitates the SIC receiving since the multiple layers in the transmission signals can provide more opportunities for interference cancellation. To evaluate the system performance, we analyze the exact expressions of the throughout and the outage probability of both conv‐GF and RAMA. Finally, theoretical analysis and simulation results validate that the proposed RAMA scheme can simultaneously achieve higher average throughput and lower outage performance than conv‐GF. Meanwhile, RAMA shows its robustness with large user activation probability, where the collisions among users are severe.

Depth image super-resolution reconstruction has gained significant popularity due to its practicability. However, conventional depth image super-resolution reconstruction methods access high frequency information either from a high-resolution depth image database or from a high-resolution color image of the same scene, which is limited in specific applications. In this paper, a novel joint nonlocal sparse representation model is proposed, which is able to capture the interdependency of low-resolution depth and intensity information. As a relative new and not well addressed problem, we reconstruct a high-resolution depth image from a single low-resolution depth image with a low-resolution color image as reference. Experiment results demonstrate that the proposed method outperforms many current state-of-the-art depth map super-resolution approaches on both visual effects and objective image quality.

Massive Machine Type Communication (mMTC) is one key usage scenario in future 5G. To fulfill the massive connection requirement in mMTC, and reduce the signaling overhead, a novel grant free Random Non-Orthogonal Multiple Access (RNOMA) scheme is proposed in this paper, inspired by the concept of conventional NOMA. In RNOMA, one physical resource area is reserved and divided into orthogonal resource units (RU), where each user is allowed to persuade-randomly transmit the same packet on each RU according to optimized probability. At the receiver, inter- and intra-RU Successive Interference Cancelation (SIC) is applied for multi-user receiving. A frame structure is also designed to enable RNOMA, where signaling overhead on informing each UE with assigned resources is reduced. Simulation results show, the proposed method is robust and is able to achieve significantly better performance than novel random access method, i.e. irregular repetition slotted aloha, where its access degree distribution is optimized for binary erasure channel.

Survival information potential (SIP) is defined by the survival distribution function instead of the probability density function (PDF) of a random variable. SIP can be used as a risk function equipped with learning error compensation ability while this SIP based risk function does not involve the estimation of PDF. This is desirable for a robust learning application in view of the error compensation ability. The learning error compensation scheme provided by SIP requires rank information of learning errors. The accuracy of error compensation desires a large number of input data but is computationally expensive. It is shown that the error compensation can be approximated by an error-related distribution. Based on this approximation, a dictionary based error compensation scheme is proposed to obtain a fixed-budget recursive online learning method. This proposed method is compared with several well-known online learning methods including least-mean-square method, least absolute deviation method, affine projection algorithm, recursive least-mean-square method, and sliding window based SIP method. Simulation results validate the outstanding smooth and consistent convergence performance of the proposed method particularly in α-stable-noise environments.

A medium-sized Massively Open Online Course (MOOC) was hosted by Peking University in 2013. Altogether 192 Chinese students came to Peking University and learned face-to-face, another 311 Chinese students participated online. This study targeted one research question: Were there any significant differences between the learning experiences and outcomes of onsite and online students? Although onsite students had lower attrition and higher completion rates than their online peers, no significant difference was detected between the average assignment scores of the onsite and online participants who had completed all the assignments. Learners also responded to a survey asking for their learning experiences. There were no significant differences between the online and onsite students' ratings of technology quality and usability of course management system, instructional content, and the design of learning assessment. Findings from this first empirical study on a Chinese MOOC will inform researchers and practitioners interested in introducing MOOCs to Chinese students.

Hyperspectral remote sensing is a technique based on the spectroscopy, which contains abundant spectral information besides the spatial information of the images, and overcomes the limitations of the wide-band remote sensing detection. When classifying hyperspectral and multispectral images with the existing algorithms, we use only the spectral information more often. This paper presents an one-class classification techniques, which is based spatial-contextual term, this study modifies the decision function and constraints of support vector data description. Experimental results show that the proposed method achieves good classification performance on hyperspectral image.

Joint Timing and Channel Estimation (JTCE) for bandlimited long-code-aided Multi-Carrier Direct-Sequence Code Division Multiple Access (MC-DS-CDMA) systems is investigated. We establish the optimal multiuser timing and channel estimates for the uplink MC-DS-CDMA receiver by minimising a weighted least squares cost function with respect to K independent parameters, where K is the number of active users. A guided random search procedure known as Repeated Weighted Boosting Search (RWBS) is invoked for numerically solving this challenging multivariate optimisation problem, and thereby for producing near-optimal timing and channel estimates. The Cramer-Rao Lower Bound (CRLB) for the JTCE problem of interest is derived to benchmark the performance of the proposed RWBS based estimator. Quantitatively, for the scenario of K=10 users, E_b/N_0≥3 dB where E_b is the energy per bit and N_0 the single-sided noise power spectral density, and for a near-far ratio of 10 dB, the RWBS based estimator using an observation window of 20 symbols is shown to approach the CRLB at a complexity 10 orders of magnitude lower in comparison to its full maximum likelihood search based counterpart. The proposed algorithm does not require the transmission of known pilots, yet it is capable of handling time-variant channel states.

The code timing acquisition problem of bandlimited long-code based DS-CDMA systems operating in doubly-selective time-varying multipath channels is considered. We extend the unstructured schemes proposed by Buzzi to long-code spreading, which first estimates the Symbol Weighted Composite Channel Impulse Response (SW-CCIR) vectors and then extracts the timing informations. We also propose novel structured acquisition schemes, which directly estimate the multipath propagation delays by exploiting the a priori knowledge of the chip waveform. Both the structured and unstructured schemes are blind, since they require no other a priori information but the aperiodic spreading code of the desired user. Our numerical results demonstrate that the structured acquisition significantly outperforms both its unstructured counterpart and the classic correlator-based acquisition at a reasonable complexity increase.

We present a robust threshold setting strategy with modest computational complexity for frequency domain interference excision in direct sequence spread-spectrum (DSSS) commu-nication systems. The proposed strategy calculates the threshold by multiplying the minimum value of the averaged squared magnitude with a predefined scaling factor. An analytical framework for choosing the scaling factor is developed based on the principle of constant false-alarm rate (CFAR). Numerical results indicate that the new strategy outperforms existing ones in a wide range of partial-band jamming scenarios.

One of the main implementation disadvantages of a multicarrier communication system is the possibly high peak to average power ratio of the transmitted signals which cause the requirement of highly cost linear amplifiers with large dynamic range. One proposed solution is given by Haiming Wang [1] which is based on the algorithm of Hopfield neural network (HNN). Also, in our previous work [2],we demonstrated the solution based on the immune clonal selection algorithm (ICSA) which has a better performance than [1]. However, a important disadvantage of the ICSA is the need of high number of iteration. In this paper, we will show a hybrid solution which adopts both the concept of ICSA and HNN. According to the simulation results, this solution maintained the good performance of PAPR reduction, meanwhile, the number of iteration is significantly reduced.