Satellite-enabled Internet of Things (IoT) services such as maritime sensing, aviation tracking, emergency telemetry, and wide-area monitoring require timely network-state awareness to support access control, load balancing, and resource scheduling. In Low Earth orbit (LEO) constellations, conventional resource-state reporting via ground stations is limited by short and intermittent contact windows, while large-scale LEO-to-LEO relaying is constrained by inter-satellite capacity and multi-hop latency. We investigate a LEO-medium Earth orbit (MEO) architecture where MEO satellites act as persistent aggregation nodes that collect resource-state updates from many IoT-serving LEO satellites over cross-orbit links. Due to spatially non-uniform IoT traffic and heterogeneous service rhythms, LEO satellites exhibit different resource-evolution time scales, making fixed-period sensing either waste signaling for slowly varying satellites or produce stale information for rapidly varying satellites. This creates a coupled trade-off between reporting delay and Age of Information (AoI), under limited LEO–MEO sensing capacity. To address this issue, we propose a rhythmic resource-state sensing framework that adapts each LEO satellite’s reporting cadence using a dynamic frame structure and a deep reinforcement learning policy trained by proximal policy optimization to minimize average reporting delay subject to AoI and sensing constraints. Simulations across constellation scales and traffic patterns show that the proposed approach reduces the average reporting delay by 22.5% on average and up to 25.5% compared with a heuristic baseline, while reducing the composite delay–freshness cost by 16.7% on average and up to 25.7%.

Satellites operate in harsh space environments for extended periods, making fault diagnosis a critical strategy for extending their lifespan. The federated learning fault diagnosis method based on telemetry data is a novel approach in the field of satellite fault diagnosis. This method can both address the data privacy issues across different TT&C centers and improve the poor fault diagnosis performance caused by the low quality of the training data set of a single TT&C center. However, challenges arise in the deployment and application of federated learning methods due to "Communication-limited" and "Extreme Heterogeneity" at some TT&C centers. To solve such a problem, this paper proposes a streaming federated learning framework for satellite fault diagnosis-FedSAT, with efficient communication and privacy protection. Under this framework, the TT&C center with communication constraints only needs to participate in a single round of federated learning for the system to obtain a well-performing and stable model. Simulations verify the effectiveness of the proposed framework.

The rapid expansion of satellite mega-constellations has highlighted the potential of satellite edge computing as a promising solution for mobile multimedia communications. While reinforcement learning has been explored in satellite communication systems, significant challenges remain, including high latency and limited resources. This study addresses these challenges by focusing on the joint optimization of communication, computing, and caching resources in satellite edge computing to support mobile multimedia applications. A mixed-integer nonlinear programming (MINLP) problem is formulated with the objective of minimizing the total delay experienced by mobile users, subject to multidimensional resource capacity constraints, which are NP-hard and computationally intractable to solve in polynomial time. To address this complexity, we propose a multi-agent federated reinforcement learning (MAFRL) approach as an efficient solution. In this framework, each satellite operates as an autonomous learning agent equipped with an actor-critic network structure. The proposed MAFRL method demonstrates superior performance, achieving lower delays compared to all baseline approaches. It effectively optimizes delay-sensitive mobile multimedia communications by minimizing total delay and improving task-offloading ratios. To the best of the authors’ knowledge, this study is the first to introduce an MAFRL-based approach for resource allocation in satellite edge computing, marking a significant contribution to the field.

Extreme disasters will cause significant damage to the conventional communication system of the distribution network, thereby affecting the emergency repair and reconstruction of the distribution network. This paper considers transmitting the distribution network data through satellites to ensure the normal data transmission of the distribution network during disasters. In view of the limited transmission rate between the distribution network equipment and the satellite network, this paper focuses on the resource allocation problem, aiming to increase the amount of data transmitted of the distribution network. Specifically, this paper first models the resource allocation problem as an accesschannel joint allocation problem. Then, this paper decomposes the optimization problem into two separate issues: access optimization and channel optimization, which are iteratively solved to derive the resource allocation scheme. Finally, this paper takes a typical satellite system combined with a typical province of China to conduct simulations. The results show that our algorithm can effectively improves the total weighted transmission data.

Due to the harsh space environment, the Satellite Power System (SPS) may be affected greatly. As the heart of satellite, fault in SPS can lead to mission failure and huge economic losses. Therefore, it is necessary to design Fault Diagnosis (FD) algorithm to find and solve the fault as soon as possible. However, since the configuration of different satellites varies widely, it is unrealistic to design a universal FD algorithm based on traditional model-based methods. Data-driven FD is a possible way, for which it can learn and extract features from data without precise knowledge of system. This paper proposes a novel data-driven FD approach for SPS using Gramian angular field (GAF) algorithm and Deep Learning (DL) technique. Specifically, GAF encodes multiple Time Series (TS) related to fault into 2-D images while retains their original information. Then we construct a dataset and use Convolutional Neural Network (CNN) based DL models to capture normal and fault features. Furthermore, Deep Transfer Convolutional Neural Network (DTCNN) is used to improve the accuracy, stability and convergence time of DL models. The accuracy and F1-score of GAF-DTCNN are 97.8% and 0.944 for a geostationary satellite, which illustrates the potential and feasibility of our proposed FD scheme for SPS.

With the flourishing of multiple Low Earth Orbit (LEO) satellite constellations in wireless communication networks, traditional satellite Tracking, Telemetry, and Command (TT&C) systems encounter difficulties in resolving the TT&C resource scheduling problem. However, Networked TT&C systems can significantly expand the TT&C resource scheduling space by applying Inter-Satellite Links (ISLs). To establish a feasible optimization strategy for Networked TT&C systems and evaluate system performance under different conditions, we optimized the Networked TT&C system by designing a networked TT&C resource scheduling algorithm from an overall perspective. The optimization problem of the Networked TT&C system was decomposed into two primary components: the Satellite to Ground Links (SGLs) resource allocation problem and the ISLs topology generation problem. These were solved by a greedy algorithm and the Improved Breadth-First-Search (IBFS) algorithm, respectively. Simulation results demonstrate that the performance of the designed Networked TT&C system is significantly better than the upper bound performance of groundbased TT&C systems under appropriate conditions.

Numerous mega low-Earth orbit (LEO) satellite constellation plans have recently become a significant part of the future satellite communication era. Since the existing ground-based and geostationary Earth orbit (GEO)-based telemetry system is unsuitable for monitoring the working status of mega LEO constellations, the networked satellite telemetry system is used to achieve the full time, low delay telemetry in this article, which is a significant scenario of satellite Internet of things. In order to satisfy the data transmission requirements of extensive satellites, this article formulates the channel allocation problem, which aims at maximizing the total transmitted data value by allocating multiple medium-Earth orbit (MEO) beams in multiple time slots to serve multiple LEO satellites. Considering that the data generation states of LEO satellites are hybrid constant and stochastic, that the MEO satellites could allocate channels more timely than the ground mission center, and that the action space for channel allocation is too large, the multiagent deep-reinforcement-learning-based algorithm is adopted to solve the channel allocation problem. Furthermore, this article designs the connections of the output layer of the deep$Q$network so as to reduce the computation and storage overhead. Finally, the upper bound performance (UBP) of the channel allocation problem is analyzed and numerical simulation is performed to verify the effectiveness of our proposed channel allocation algorithm.

Numerous mega low earth orbit (LEO) satellite constellation plans have recently emerged as an indispensable part of the future satellite communication era. Since the traditional ground-based and geostationary earth orbit (GEO)-based telemetry systems are unsuitable for monitoring the operation status of mega constellations, a networked telemetry system is adopted to achieve full time, low delay telemetry in this paper. In order to satisfy the data transmission requirements of extensive satellites, this paper formulates the channel allocation problem, which aims at maximizing the overall transmitted data value by allocating different medium earth orbit (MEO) beams in different time slots to different LEO satellites. Since the data generation states of LEO satellites are hybrid constant and stochastic, the MEO satellites could allocate channels more timely than the ground mission center, and the action space for channel allocation is too large, the multi-agent deep reinforcement learning based algorithm is consequently adopted to solve the channel allocation problem. This paper verifies the effectiveness of our proposed channel allocation algorithm by numerical simulation.

Due to limited link resources and increasing demand for data transmission, receivers in satellite communications are required to achieve carrier synchronization, demodulation, and decoding operations at low signal-to-noise ratio (SNR) conditions, thus making full use of the link budget. In this context, code-aided (CA) carrier synchronization is a promising solution, synchronizing low-SNR signals without additional pilot overhead. However, traditional CA carrier synchronization suffers from a narrow operating range and, in practice, performs poorly in tracking dynamic signals with large Doppler frequency offsets. To solve such a problem, we presents a low-complexity algorithm to improve the operating range of CA carrier synchronization. This algorithm tolerates large carrier offsets through coarse correction based on a confidence metric function (CMF). Additionally, a candidate list mechanism is introduced to reduce the computational overhead. The operating range of the algorithm can be adjusted by varying its parameters to sustain carrier recovery under different dynamic scenarios. Simulations verify the effectiveness of the proposed algorithm.

Recently, numerous mega LEO satellite constellation plans have emerged as an indispensable part supporting the future 6G satellite communications. Since the traditional telemetry systems are inappropriate for monitoring the operation status of all satellites, a networked telemetry system is adopted to achieve full time, low delay telemetry for mega LEO satellite constellations, which is a significant scenario of satellite Internet of Things. Furthermore, this article formulates a channel allocation problem to maximize the overall transmitted data amount. Adopting the dual decomposition method, this problem can be decomposed and transformed into a dual problem and multiple path scheduling subproblems for each LEO satellite. This article develops an optimal dynamic programming algorithm to solve the path scheduling subproblems and an iterative algorithm to solve the channel allocation problem. Numerical simulations show that the proposed channel allocation algorithm improves the transmitted data amount and approximates the upper bound performance.

In recent years, various mega-constellation plans have been issued and the number of planned satellites has been increasing. However, the boundary of satellite number that existing tracking, telemetry and command (TT&C) resources can support has never been analyzed. This paper discusses TT&C capacity of mega-constellations for the first time. Under the requirement of instantaneity and reliability of TT&C system, the boundary of constellation satellites number that can be supported based on the inter-satellite link is explored. We first establish the satellite TT&C model based on N*D/D/1 queuing model. Then we analyze the relationship between satellites number, waiting delay and package loss probability under normal, compressed and abnormal telemetry transmission scenarios. Finally, the results of theoretical analysis, numerical and Monte Carlo simulation are given. Results show that the networked TT&C system with inter-satellite link will greatly increase the number of satellites that can be supported.

In the upcoming 6G communication era, the satellite Internet based on mega constellations will become an indispensable extension of the terrestrial communication network. However, it is difficult for the traditional ground-based and space-based telemetry systems to satisfy the requirements on monitoring the mega constellation. This paper designs the networked telemetry system, where data is transmitted through the inter-satellite-links (ISL) of the low earth orbit (LEO) and medium earth orbit (MEO) satellites. Furthermore, the resource allocation problem for the networked telemetry system is decomposed using the block coordinate descent method into the access scheduling and the subchannel-power coordinate allocation subproblems , which are iteratively solved to optimize the resource allocation scheme. Finally, the simulation results shows that the proposed resource allocation algorithm effectively increases the transmitted data amount of the system.

For the Telemetry, Tracking, and Command (TT&C) system of hypersonic aircraft, high-speed relative movement and long-distance between aircraft and TT&C station cause serious Doppler effects and low signal-to-noise ratio (SNR). Therefore, the fast and accurate acquisition is a core issue for the TT&C signals recovery. This paper proposes a novel algorithm to alleviate the computational burden caused by high dynamics, based on the idea of reducing order and narrowing search range. Additionally, to improve the acquisition sensitivity of the weak signals, a superposition design, which enhances the peak values at the edges of the frequency resolution, is introduced to address the scalloping loss problem. Also, numerical results demonstrate that the proposed algorithm attains greater acquisition probability and better acquisition sensitivity compared to existing acquisition algorithms.

Due to the limited frequency spectrum resources, it is challenging to satisfy the Telemetry, Tracking, and Command (TT&C) requirements of mega-constellations in the near future. An intuitive idea is to compress the telemetry data with high redundancy, but outliers in telemetry data will significantly deteriorate the compression performance. Currently, outlier detection algorithms require a combination of expert experience and physical models, which have limitations in space environments, especially where unexpected failures undergo. To tackle these problems, this paper proposes an outlier detection method for telemetry data based on Kernel based Extreme Learning Machine (KELM). Additionally, to improve outlier detection accuracy, the Improved Sparrow Search Algorithm (ISSA), a novel swarm intelligence optimization technique, is adopted to optimize the algorithm parameters jointly. Take the actual telemetry data of the smart communication satellite of Tsinghua University as a case study to verify the superiority of the proposed algorithm. Also, the simulation results demonstrate that the proposed algorithm can efficiently improve the TT&C capacity of mega-constellations, even without additional ground TT&C stations and Tracking and Data RelaySatellites.

Abstract With the rapid development of NGSO satellite constellation, multi‐antenna gateway station has become a trend to increase the capacity of communication system. Since the wide use of Ku/Ka bands as well as the dynamic characteristics of NGSO networks, the co‐frequency interference between constellations is inevitable, and the interference limits become important constraints to the system capacity of multi‐antenna gateway stations. In this paper, the allocation of radio resources, including power and beam, under the interference constraints is formulated as an optimization problem. However, traditional optimization methods may not be suitable for NGSO satellite coexistence scenarios due to the fast movements of NGSO satellites. In order to address this problem, the optimization problem is transformed into two subproblems, and a Jensen's Inequality‐based Fast Iteration (JI‐FI) algorithm is designed to solve both of them iteratively. Simulation results show that the interference can be effectively reduced to the value below the threshold and beam and power resources can be allocated simultaneously by adopting the proposed algorithm. Compared with the traditional methods, the proposed scheme can significantly improve the system capacity by more than 25%, and it can greatly reduce the computational complexity.

Network slicing is one the key features of software-defined networks (SDNs) and can be used in next-generation communication networks. Admission control of network slices is the basis of providing the heterogeneous quality-of-service performance guarantee and maximizing the optimization objectives of the network operator. Various admission control mechanisms have been proposed in the literature, including those based on traffic forecasting. However, recurrent neural network-based probabilistic forecasting models have not been given thorough consideration for slice admission control. In this study, the network slicing scheme design problem is formulated mathematically, with an equivalent formulation of the constrained bandwidth-sharing scheme. Then, a DeepAR-based slice admission control mechanism is proposed for sequential decision making for network slice requests in SDN, with the support of the SDN controller. An improved variant is further proposed with a closed-loop parameter update mechanism. The experiments based on real-world historical traffic data validate the effectiveness of the proposed mechanisms, with metrics, including revenue, resource reservation and utilization ratios, and service admission ratio.

In the upcoming 6G communication era, the satellite Internet based on mega constellations will become an indispensable extension of the terrestrial communication network. However, it is difficult for the traditional ground-based and geostationary earth orbit (GEO)-based telemetry systems to satisfy the requirements on monitoring the mega constellation. This paper designs the networked telemetry system, where data is transmitted through the inter-satellite-links (ISL) of the low earth orbit (LEO) and medium earth orbit (MEO) satellites. Furthermore, the resource allocation problem for the networked telemetry system is decomposed using the block coordinate descent method into the access scheduling and the subchannel-power coordinate allocation subproblems, which are iteratively solved to optimize the resource allocation scheme. Finally, the simulation results shows that the proposed resource allocation algorithm effectively increases the transmitted data amount of the system and approximates the upper bound performance.

As an alternative to traditional meteorological methods, rain attenuation in satellite-to-Earth microwave communication signals has been used for rainfall reconstruction in recent years. In this article, the existing 2-D rain field reconstruction problem is extended to a 3-D scenario by leveraging the low Earth orbit satellite system. A compressive sensing approach is further proposed to solve the 3-D rain field reconstruction problem. The Starlink system is used as a reference, and two synthetic rain events near the Great Barrier Reef in Australia, which are generated from the weather research and forecasting model, are used to evaluate the reconstruction performance. Simulation results show that the compressive sensing approach performs better than both the traditional least squares and the least absolute shrinkage and selection operator approaches.

As an important development trend of satellite communication networks in the future, Ka-band satellite communication has the characteristics of wide bandwidth, high speed, excellent anti-interference performance and small equipment. However, there is a disadvantage that Ka-band signals are easily affected by rain attenuation, so it is necessary to reasonably select ground stations to avoid this attenuation. This paper first introduces the general function and site selection principle of ground stations and analyzes the problems of these ground stations. Then, this paper presents the site selection method of Ka-band ground stations from the two aspects of the site selection process and average accessible rate calculation, considering rain attenuation. Finally, the actual average transmission rates of some possible ground stations in China are calculated under the constraints of some parameters given by a Ka-band MEO satellite constellation.

Abstract Multipath interference commonly exists in wireless communication, navigation and radar systems, which may cause severe signal fading and bit error rate (BER) performance degradation. Therefore the detection of multipath interference is urgently needed. This paper proposes a high order statistics (HOS) based multipath interference detection method, since the HOS of the received signal show distinct difference when multipath interference exists. Moreover, the generalized theoretical values of the moments and cumulants, which release the demand for prior knowledge of timing information and symbol period, are deduced in this paper. It makes the proposed HOS features based detection method robust in blind environment. Computer simulations are performed to verify the proposed method.

Human perception sensitivity to video distortion is vital for visual quality assessment (VQA). Different from the perception mechanism of image distortion that has been thoroughly studied, the perception of video distortion is inevitably influenced by motion of dynamic content due to the characteristics of the human visual system (HVS). In this paper, electroencephalography (EEG) is used as a novel psychophysiological method to study the human perception sensitivity to quantification-aroused video distortion under various content motion conditions. For this purpose, we conduct experiments to record the EEG signals of the subjects when they are watching distorted videos. According to the feature analysis of EEG data, the P300 component aroused by human perception of video quality change is selected as the indicator of human perception of distortion. By the means of classification based on linear discriminant analysis (LDA), it is found that the separability of the P300 component, which is measured by the area under curve (AUC) of the receiver operating characteristic (ROC), is positively correlated with the perceptibility of distortion. The correlation provides a valid psychophysiological method, which is exempt from being influenced by subjective bias due to human high-level cognitive activities, for evaluating distortion perceptibility. In addition, the regression analysis results demonstrate a sigmoid-typed quantitative relation between the perceptibility of distortion and separability of the P300 component. Based on such relation, the perceptibility thresholds of distortion corresponding to various content motion speeds are calibrated by EEG signals and it is found that the content motion speed has a significant impact on distortion perceptibility.

Human perception threshold of video distortion is vital for visual quality assessment. Traditionally, calibrating the perception threshold of distortion relies on subjective test, which may suffer from strategy and bias of the human. In this paper, electroencephalography (EEG) is used as a novel psychophysiological method to evaluate the human perception of quantification-aroused video distortion. By the means of classification based on linear discriminant analysis (LDA), the separability of event-related potentials (ERPs) aroused by human perception to video quality change is measured by the area under curve (AUC) of the receiver operating characteristic (ROC). Relating the EEG signals to behavior data, the sigmoid-typed relationship between the perceptibility of distortion and separability of the P300 component is discovered. Based on this relationship, the perceptibility of distortion can be evaluated by EEG signals alone, which provides a potential neurally informed method for the calibration of perception threshold of video distortion.

In this study, the authors propose a novel intra‐data centre network (DCN) architecture based on cascaded micro‐electro‐mechanical system switches for dynamic DCN connectivity provisioning. Through combining the single‐carrier frequency‐division‐multiplexed with the proposed architecture, this structure can achieve unprecedented flexibility in dealing with both mice and elephant flow in the DCN. Multiple‐input multiple‐output switching is experimentally demonstrated through intra‐ and inter‐data centre switching scenarios. A numerical investigation is also performed to evaluate the performance of the fixed‐grid transceivers, flex‐grid transceivers and mixed‐fixed/flex grid transceivers in the same switching scenario. The results show that the flex‐grid transceivers significantly reduced the service latency and blocking probability compared to the fixed‐grid transceivers. The proposed architecture would be better applied in the field of all‐optical DCNs.

The single‐channel blind separation performance of the mixed signals with parameters estimation error in paired carrier multiple access (PCMA) satellite communication systems is analysed in this study. Current literatures have theoretically analysed the blind separation performance using the symbol detection error probability of PCMA signals. However, the parameters of PCMA signals are assumed to be accurate during their derivations, which are not practical in blind separation scenario. In this study, the parameters ' estimation error is considered during the derivation of the blind separation performance of PCMA signals. Thus, the derived performance is more general. The simulation separation result is obtained by per‐survivor processing method. It is in accordance with the derived performance.

Noise uncertainty, which is unavoidable in practice, can severely limit the detection performance of cooperative spectrum sensing. Derived from the conventional hard combination scheme, the improved one-out-of-N rule is presently the most effective algorithm to reduce the influence of noise uncertainty. However, in this algorithm the abandonment of local test statistic when it falls in the uncertainty region may lose some useful information. In this paper, first we analyze the optimality of the improved one-out-of-N rule and indicate that there should be an optimal number of cooperative users that minimizes the total error rate. Then, a new weighted hard combination scheme which makes full use of the uncertainty region is further proposed. The optimizations of weight coefficient and user number in this new scheme are also investigated. Numerical results show that the minimal total error rate and its corresponding user number of this scheme are both lower than those of the improved one-out-of-N rule.

Serially coded and interleaved continuous phase modulation (C2PM) with iterative demodulation and decoding is investigated for burst communication. The optimization and simplified decoding for C2PM system with short frame are discussed, including simplified demodulator and matched interleaver. Simulation results show that for coded GMSK system under AWGN, the performance is better than individual convolutional code. Especially when BER is 1×10−5, it performs better than Turbo code and LDPC code with the same frame length, and there is no error floor occurring. Complexity analysis shows that the coded GMSK system has lower implementation complexity in comparison with Turbo and LDPC code. Moreover, efficient hardware implementation based on Xilinx Vertex-4 FPGA platform is presented. The practical performances prove that this system is suitable for burst communication.

In deep space communications, the received signals are always highly dynamic and very weak, which makes it quite challenging to achieve reliable data reception. To tackle these problems, a robust algorithm which joins tracking and code‐aided synchronisation is proposed in this study. The frequency lock loop‐assisted phase lock loop is used as the tracking loop, which integrates the characteristics of both outstanding dynamic performance and precise measurement. The code‐aided synchronisation is adopted as the demodulation and decoding loop, which utilises the relationship among the coded symbols to assist signal demodulation under the low signal‐to‐noise ratio condition. It is worth pointing out that the power tradeoff between the main carrier and subcarrier signals which operate in the two loops mentioned above is also optimised to minimise the total transmitting power. Simulation results showed that the proposed scheme with code rate 1/5 low‐density parity‐check codes could almost eliminate the effect of these factors, with the excellent performance, which closely approximates the ideal decoder in additive white Gaussian noise channel.

In this paper, a 4-transmit-antenna space time block coding (STBC) scheme with 1-bit differential feedback is proposed. This scheme gives full transmit diversity and spatial coding rate over time-selective fading channels by rotating the signal constellations for certain angles determined by the differential feedback information. By utilizing differential feedback, our method can provide much better channel orthogonality and track the variation of the channel more accurately and timely under time-selective fading circumstances, comparing to recent works presenting other quantized feedback schemes. Simulation results show that the 1-bit differential feedback scheme achieves near-optimum performance and outperforms existing algorithms without adding more overhead or complexity.

A new scheme of the extended irregular repeat- accumulate (eIRA) coded tamed frequency modulation (TFM) for deep space communications is introduced in this paper. It is mainly based on the optimal concatenation of the eIRA code and the continuous phase encoder (CPE) decomposed from TFM. In order to obtain good performance of the eIRA coded TFM, the main principle of our scheme is to jointly optimize the concatenation by eliminating the short loops in the Tanner graph of the eIRA code and the coding part of TFM and to jointly decode between them. Then our scheme without interleaving and the contrast scheme with interleaving are simulated in an additive white Gaussian noise (AWGN) channel which is typical in deep space communications. Simulation results have shown that our scheme can obtain lower complexity and less decoding delay (due to the reduction of interleaving) at the cost of just 0.1-0.15 dB performance loss when compared to the scheme with interleaving given bit-error-ratio (BER) of 10 5. Therefore, our scheme can be used to implement the coded TFM system for deep space communications with good performance and low complexity.

An improved design of receiving filter for non- ideally shaped modulated signals which provides better performance than existing schemes is proposed. By concerning both Inter-Symbol-Interference (ISI) and the degree of waveform mismatch between the transmitted signal and impulse response of the receiving filter, the exact expression of the Signal-to-Noise Ratio (SNR) loss that represents the performance degradation is derived, and the performance is compared with that of the ideally shaped signals' demodulation. The existing schemes such as root-raised-cosine (RRC) receiving filters don't perform well for non-ideally shaped situations, since large degrees of both ISI and waveform mismatch exist. To improve the performance and avoid the complexity of applying equalizer in receivers, the proposed scheme designs the impulse response of the receiving filter by optimizing the SNR loss to the minimum value, and it uses simulated annealing as the optimization algorithm. It is shown that the new method can achieve better performance than existing schemes, especially when the modulation order or the SNR is relatively high. Finally the conclusion is validated by simulation results.

Signal-to-noise ratio (SNR) estimation is an important issue in many communication systems. This paper proposed a new non-data-aided (NDA) SNR estimator for BPSK and QPSK modulated signals. The estimation is based on pseudo-error probability (PEP), which can be estimated without additional cost for sampled systems. The performance of the proposed estimator is examined numerically in terms of its bias and normalized mean square error. The Cramer-Rao lower bound (CRLB) for PEP-based SNR estimation is also derived. Comparing with the existing SNR estimation schemes such as maximum likelihood (ML) estimators, the proposed algorithm can achieve similar performance with much less computational cost.