Inter-satellite communication (ISC) is a critical component of low Earth orbit (LEO) satellite communication systems, enabling seamless data exchange between satellites within the same or across different orbital planes. This paper investigates the impact of position and attitude perturbations caused by gravitational forces, theJ2effect, aerodynamic torques, and Earth’s magnetic field between inter-orbital LEO satellite pairs on ISC channels. It reveals that such perturbations significantly degrade the LEO ISC link performance at the terahertz (THz) band. To address this challenge, a novel statistical symbol-level power and modulation adaptation (SSL-PMA) algorithm is proposed to mitigate the effects of the perturbations, enhance spectral efficiency, and reduce transmission power consumption under varying perturbation levels. Simulation results demonstrate that the proposed algorithm achieves substantial spectral efficiency gains over conventional adaptive and non-adaptive schemes, maintaining bit error rate (BER) performance comparable to or better than that of fixed modulation schemes across different perturbation levels. Notably, transmission power savings of up to 40% are achieved at inter-plane angles of 90◦ and 150◦, with slightly lower savings at 30◦. These findings highlight the potential of the proposed SSL-PMA algorithm for efficient and adaptive transmission in future THz-band inter-satellite communication systems.

This article introduces a novel Line-of-Sight (LoS) multiple-input-multiple-output (MIMO) communication architecture leveraging nontraditional orbital angular momentum (OAM) beams. Challenging the conventional paradigm of hollow-emitting OAM beams, this study presents an innovative OAM generator that produces directional OAM beams without central energy voids, aligning their radiation patterns with those of conventional planar wave horn antennas. Within the main lobe of radiation patterns, the phase variation characteristics inherent to OAM beams are ingeniously maintained, linking different OAM modes to the linear wavefront variation gradients, thereby reducing channel correlation in LoS scenarios and significantly augmenting the channel capacity of LoS-MIMO frameworks. Empirical validations conducted through a meticulously designed LoS-MIMO experimental platform reveal significant improvements in channel correlation coefficients, communication stability, and bit error rate (BER) compared to systems utilizing traditional planar wave antennas. The experiment results underscore the potential of the novel OAM-based system to improve current LoS-MIMO communication protocols, and offer both academic and engineering guidance for the construction of practical communication infrastructures. Beyond its immediate contributions, this article underscores a pivotal shift in the field of communications, pointing out that traditional communication algorithms have primarily focused on baseband signal processing while often overlooking the electromagnetic (EM) characteristics of the physical world. This research highlights that, in addition to radiation patterns, the wavefront phase variations of traditional antennas represent a new degree of freedom that can be exploited. Consequently, future communication algorithms designed around reconfigurable EM wavefront properties hold the promise of ushering wireless communications into a new era.

Continuous phase frequency shift keying (CPFSK) has been widely used in wireless communications, as its continuous phase and constant envelop lead to high bandwidth and power efficiency. In non-cooperative communication systems, the CPFSK parameters such as modulation index, pulse shape and pulse length are unknown to the receiver and need to be estimated. This is a challenging task especially when the transmission is bursty. In this paper, long short term memory (LSTM) neural networks are proposed to tackle this problem. Specifically, three LSTM networks with identical structure but trained separately are used to estimate three important CPFSK parameters (modulation index h, pulse length$L$and pulse shape$q$(t)) in parallel. Simulation results show that by suitably preprocessing the input data before the LSTM network, the proposed blind estimation scheme is able achieve high estimation accuracy of more than 95% in most cases.

Inter-satellite communication (ISC) is an essential part of LEO satellite communication systems, allowing satellites to communicate with each other in the same or different orbital planes. This paper presents a prediction model to estimate the distance and Doppler shift between two LEO satellites in different orbital planes. Using this prediction model, a channel model for rapidly changing inter-orbital ISC channels is established. Moreover, a novel dynamic symbol-level power and modulation pre-adaptation (SL-PMPA) algorithm is proposed to achieve power-efficient high throughput transmission under the rapidly changing ISC channel. Simulation results demonstrate that compared to conventional transmission modulation schemes, the proposed dynamic SL-PMPA algorithm not only significantly enhances the throughput under the same BER threshold, but also effectively reduces the transmission power.

In this paper, the problem of blind equalization of high-order quadrature amplitude modulation (QAM) signals is tackled by using a batch equalizer based on support vector regression (SVR). Anew set of error functions weighted by neighborhood symbol decisions and augmented by generalized power factors p and q, are proposed to be used as the penalty terms in SVR, and the optimal values of p and q are determined. In addition, we propose a method to remove the high online computational complexity incurred by the inclusion of neighborhood terms in the new error function. Simulation results show that with about the same complexity, the optimized SVR-NA-SBD-(p, q) attain much lower residual inter-symbol-interference and higher probability of convergence than the best known SVR-MMA, and it needs only about 1400 symbols to achieve a BER of 10-4 for 256QAM in a multipath channel. In contrast, the conventional SVR-MMA needs more than 4000 symbols to achieve such BER.

Algebraic codes such as BCH code are receiving renewed interest as their short block lengths and low/no error floors make them attractive for ultra-reliable low-latency communications (URLLC) in 5G wireless networks. This article aims at enhancing the traditional adaptive belief propagation (ABP) decoding, which is a soft-in-soft-out (SISO) decoding for high-density parity-check (HDPC) algebraic codes, such as Reed-Solomon (RS) codes, Bose-Chaudhuri-Hocquenghem (BCH) codes, and product codes. The key idea of traditional ABP is to sparsify certain columns of the parity-check matrix corresponding to the least reliable bits with small log-likelihood-ratio (LLR) values. This sparsification strategy may not be optimal when some bits have large LLR magnitudes but wrong signs. Motivated by this observation, we propose a Perturbed ABP (P-ABP) to incorporate a small number of unstable bits with large LLRs into the sparsification operation of the parity-check matrix. In addition, we propose to apply partial layered scheduling or hybrid dynamic scheduling to further enhance the performance of P-ABP. Simulation results show that our proposed decoding algorithms lead to improved error correction performances and faster convergence rates than the prior-art ABP variants.

In this paper, we consider pilot-aided channel estimation and equalization for single-carrier and single-carrier frequency division multiple-access (SC-FDMA) transmission over doubly-selective channels (DSC). To reduce the channel estimation (CE) parameters, the DSC is modelled using a complex-exponential basis expansion model (CX-BEM) with non-uniform BEM frequencies. We optimize the CX-BEM basis functions using CE error minimization as the objective. As a result, the channel modelling error is greatly reduced. Next, we propose a BEM-based per-survivor processing (PSP) technique and combine it with a decision-directed channel estimator to obtain a channel-tracking equalizer at the receiver. The resultant BEM-PSP receiver significantly improves the channel estimation mean square error (MSE) and the bit error rates (BER) performance in fast fading multi-path channel, thanks to the channel tracking capability embedded within its Viterbi equalizer. Finally, we employ cross-frame channel interpolation, and power distribution between the data and pilot symbols to further improve the system performance at high fading rates. Extensive simulation results show that our BEM-PSP receiver outperforms many existing methods and approaches close to an ideal receiver with perfectly known CSI under various fading scenarios.

In this paper, the reconstruction of the feedback polynomial as well as the initial state of a linear feedback shift register (LFSR) in a synchronous scrambler placed after a channel encoder is studied. The study is first based on the assumption that the channel is noiseless and then extended to the noisy channel condition. The dual words, which are orthogonal to the codewords generated by the channel encoder, are used in the reconstruction algorithm. The number of bits required by the new algorithm is compared with another recently proposed algorithm and results show that the number of bits required to do the reconstruction can be significantly reduced.

It is well known that in order to build linear scramblers and stream ciphers that are robust against correlation-based reconstruction, primitive polynomials which do not have sparse multiples of moderate degrees must be used. In this paper, the existence and density of such ‘good primitive polynomials’ are studied. Two theoretical lower bounds on the degree d of the primitive polynomial are derived. When d is larger than the first lower bound, there exists at least one primitive polynomial of degree d which does not have any sparse multiple of moderate degree and when d is larger than the second lower bound, it is almost guaranteed that a randomly chosen primitive polynomial of degree d does not have any sparse multiples of moderate degree. To make the lower bound tight, the distribution of the minimum degrees of sparse multiples of primitive polynomials is investigated in this paper. From comparison, it can be seen that the lower bounds obtained in this paper are much better than the previous results reported in the literature.

In this paper, the problem of reconstruction of the feedback polynomial in a linear scrambler is studied. Our work contains two parts. In the first part, schemes to improve the performance of an existing reconstruction algorithm are proposed. Simulation results show that both the detection capability and speed of the existing algorithm are significantly improved by using our proposed schemes. In the second part, the reconstruction of linear scramblers in the presence of channel noise is investigated. We consider flipped bits due to noise as well as insertion of bits in the scrambled bit sequence. For both cases, factors which affect the performance of the reconstruction algorithm are discussed.

The existing algorithm for reconstruction of the feedback polynomial in a linear scrambler relies on the assumption that the input sequence is produced by a biased memoryless source and the source bias is known and used for reconstruction. In this paper, the problem of reconstruction of the feedback polynomial in a linear scrambler without knowledge of the source bias is studied. An algorithm is proposed to reconstruct the scrambler without knowledge of the source bias and factors which affect the performance of this algorithm are discussed. A scheme which reduces the number of bits used in the reconstruction without affecting the detection capability of the reconstruction algorithm is also proposed in this paper.

A simplified parallel step-by-step decoding algorithm is proposed for decoding Reed-Solomon (RS) codes. It uses new method to calculate the determinants of the temporarily changed syndrome matrices, based on the property of these matrices determined in this paper. By using the proposed method, the calculations of the determinants of the temporarily changed syndrome matrices become much simpler and thus the computational complexity of the step-by-step decoding algorithm is significantly reduced.