In this paper, we investigate an uplink multibeam low-Earth orbit (LEO) satellite Internet of Things (Sat-IoT) system employing rate-splitting multiple access (RSMA) under imperfect channel state information (CSI) in the finite blocklength (FBL) regime, and formulate an optimization problem to maximize the effective throughput by jointly optimizing beamforming, transmit power allocation, and rate-splitting factors. The problem is highly non-convex due to the nonlinear FBL achievable rate expressions. To obtain a tractable solution, we propose a regime-based SINR-threshold substitution that converts the FBL minimum-rate constraints into equivalent SINR constraints, and incorporate CSI imperfections into the SINR constraints in a tractable manner. An alternating DC–SCA–SDR algorithm is then proposed, where bilinear couplings are addressed via first-order Taylor expansions and a rank-promotion penalty is introduced to facilitate beamformer extraction. Simulation results demonstrate that the proposed design achieves higher effective throughput than other schemes in the FBL regime under imperfect CSI.

This paper considers a Low-Earth-Orbit (LEO) satellite communication system integrated with 5G New Radio, which performs conventionally initial access beam management through multiple measurements and reports. However, such management scheme may lead to outdated measurement results and large terminal access delay, in the presence of significant propagation losses, high dynamics, and limited number of antennas on LEO satellites. Addressing these issues, we propose an initial access beam management framework based on the dynamic coordination of signaling and service beams. This framework includes the optimization of the beam alignment process and the analysis of the coordination mechanism of signaling and service beams with a queueing model. Specifically, this queueing model is further used to assist in modeling an optimization problem with respect to the number and direction of signaling and service beams, to achieve the dynamic balance between coverage capability and service efficiency. Furthermore, a Lyapunov based initial access beam management (LYP-IABM) algorithm is specially designed to find the optimized solution of the modeled problem in a low complexity and online manner. The mathematical derivation is also provided to prove the correctness of the designed algorithm. Simulation results show that, the proposed online LYP-IABM algorithm not only outperforms the traditional offline algorithms, but also demonstrates the superiority comparing with the online benchmarking algorithms, in terms of reducing the time complexity by more than 90%.

In this paper, we consider a communication internet of things network where multiple low-Earth-orbit (LEO) satellites provide services to massive terminals under grant free-random access (GF-RA) mode. Due to the high mobility of LEO satellites and the uncoordinated access characteristics of GF-RA, acquiring instantaneous channel state information (CSI) becomes particularly challenging and the spatial configuration among satellites varies rapidly, which all severely restricts the implementation of multi-beam jointly processing (MBJP) in uplink. To overcome these challenges, we establish a unified multi-satellite cooperative MBJP (MSC-MBJP) framework, through leveraging the regional statistical CSI (RsCSI) and the specially designed satellite selection scheme. Specifically, we firstly construct a set consisting of the specially selected satellites in real time, to meet the requirements of maximizing the resources-utilization efficiency, reducing handover frequency among satellites as well as upgrading the performance of MBJP. Then, we propose the distributed MSC-MBJP and centralized MSC-MBJP schemes. Different from the distributed scheme, the centralized scheme relies on collecting the data from all cooperative satellites. Subsequently, considering the intrinsic relationship between MBJP and user scheduling, we jointly optimize the beamforming vectors and the link matrix to address the optimization problems associated with the proposed two schemes. Extensive simulations demonstrate that the proposed centralized MSC-MBJP scheme outperform the conventional single-satellite MBJP scheme. Moreover, the proposed distributed MSC-MBJP scheme can achieve the comparable user capacity when the transmit power of terminals is 35 dBmW. Especially, the proposed distributed MSC-MBJP scheme exists the superiority of the access success rate for terminals located near the area boundary, in terms of nearby 27% increment.

We explore a satellite internet of things (SIoT) system, wherein multiple IoT users are allowed to access the SIoT in a grant-free manner. Such a manner may incur the severe co-frequency interference (CFI), and decease the user capacity in terms of the number IoT users successfully accessing the SIoT. To this end, we first design two beam preprocessing oriented user grouping methods, called user distribution based user grouping (UDUG) and user position based user grouping (UPUG), respectively. With respect to the UDUG, we propose the regional statistical channel state information based multi-beam processing (RsCSI-MBP). For the UPUG, we conceive the user statistical channel state information with position error based MBP (UsCSIPe-MBP). Both the RsCSI-MBP and the UsCSIPe-MBP schemes can mitigate inter-beam CFI relying on MBP, wherein the optimized beamforming vectors are obtained by using generalized Rayleigh quotient. Furthermore, the user capacity, preamble collision probability and packet loss probability are analyzed for the RsCSI-MBP and UsCSIPe-MBP schemes. Numerical results demonstrate that the UsCSIPe-MBP scheme outperforms the RsCSI-MBP and the conventional seven-color frequency-reuse multibeam schemes in terms of a higher user capacity and a lower packet loss probability, even considering the satellite mobility, phase and amplitude inconsistence of the antenna array used.

In this paper, we explore a satellite based spectrum-sensing system, where the spectrum -sensing data are used to assist spectrum sharing. However, these data may be outdated due to the long propagation delay of the satellite links. Such outdated data may incur wrong spectrum-sharing decisions, resulting in co-frequency interference. To avoid this negative effect caused by the long delay, we propose a joint long short-term memory and autoregressive moving average (LSTM-ARMA) aided spectrum-prediction scheme, where a LSTM-ARMA model is constructed and trained relying on a specially designed loss function, which can decrease prediction error by combining the LSTM and the ARMA. Furthermore, using the historical spectrum-sensing data, the well-trained LSTM-ARMA is used to predict the future spectrum occupancy in advance. The prediction performance of the proposed LSTM-ARMA is evaluated relying on an actually measured dataset captured from the Tiantong-1 satellite. Performance evaluations show that the proposed LSTM-ARMA scheme outperforms the conventional LSTM, the ARMA and the convolutional neural network and bidirectional long short-term memory schemes in terms of a lower mean absolute error (MAE). Moreover, the proposed LSTM-ARMA can simultaneously predict the spectrum situation of multiple transponders, whilst maintaining a low MAE.

In this letter, we investigate a satellite based spectrum-sensing system in the presence of the dynamically varying noise floor, which may degrade the performance of the traditional energy detection method. To tackle the varying noise floor, we propose a blind noise estimation aided energy detection (BNE-ED) algorithm relying on two stages. Specifically, in the first stage, the noise floor is estimated with the aid of the bivariate empirical mode decomposition (BEMD) in a real-time way, through exploring the relationships between the real part and the imaginary part of each mode of the BEMD. Moreover, the spectrum-sensing signals are detected relying on the estimated noise floor in the second stage. Furthermore, an optimal noise-estimation time to signal-detection time ratio is also analyzed in a spectrum-sensing time slot. Simulation results show that the proposed BNE-ED may achieve superior performance of both noise estimation and spectrum sensing.

Satellite based spectrum sensing is studied for a system consisting of multiple satellites and a gateway (GW), where these satellites perform spectrum sensing and send mass spectrum-sensing data to the GW. To address the challenges of mass spectrum-sensing data and limited transmission capacity of the links from spectrum-sensing satellites to the GW, we propose a method called joint anomalous data repairing and deep convolutional neural network based spectrum reconstruction (ADRD-SR), which can reconstruct the original spectrum-sensing data from the incomplete data. Specifically, the GW preprocesses the incomplete data using the anomalous data repairing algorithm. A deep convolutional neural network is constructed and well trained, then it is activated to reconstruct the preprocessed spectrum data. Additionally, to sustain good reconstruction performance by tracing the dynamical spectrum-sensing data, we design a real-time evaluation oriented spectrum reconstruction framework, through seeking the events when the mean absolute error (MAE) becomes larger than a predefined threshold. Furthermore, the ADRD-SR method can reduce the MAE by more than 68% over the conventional reconstruction methods. Moreover, the reconstructed spectrum data can be used to assist spectrum sensing, and the corresponding probability of correct detection is only degraded by 5% even when 75% of the data is discarded.

In this study, the authors explore a wireless network consisting of multiple smart terminals relying on multi‐mode in the presence of an eavesdropper, where the eavesdropper may be deployed to wiretap the wireless communications deliberately. In general, a smart terminal can be switched between the licensed modes (e.g. LTE) and the unlicensed modes (e.g. WiFi) according to user's demands (e.g. costs). The designed tradeoff is that the demands can be met by switching, but the security of wireless transmissions may be deteriorated, as the wireless communications can be overheard by the eavesdropper. The authors propose a transmission mode aware smart terminal scheduling (TM‐STS) scheme to improve the security of wireless transmissions of a smart terminal. Moreover, they analyse the security‐reliability tradeoff (SRT) of the TM‐STS scheme. They also present the SRT of the conventional round‐robin aided smart terminal scheduling (Rr‐STS) scheme. Numerical results show that the TM‐STS scheme outperforms the Rr‐STS scheme in terms of its SRT, and reveal that increasing the number of smart terminals not only can upgrade the SRT of the TM‐STS scheme, but also can be a major influence on the probability of mode switching between a licensed mode and an unlicensed mode of the TM‐STS scheme.

The Internet of Things (IoT) is a burgeoning paradigm that changes our lives greatly. In many cases, IoT devices are located in remote areas, which can't be served by terrestrial access networks. As a consequence, Low Earth Orbit (LEO) satellite communication system may play an important evolution of 5G, and becomes of paramount importance for those scenes relying on its disadvantages. Additionally, the first integration of terrestrial communication and satellite communication in 5G also provides more technical support for the 5G system. Based on the analysis of the special application scenarios and traffic distribution characteristics of LEO satellite based IoT, this paper describes a simulation method for the traffic of LEO satellite based IoT. The simulation result shows that the distribution of business in LEO satellite system has great suddenness and variability both in time and space. Since the non-uniformity is not conducive to the stability of the system, it is necessary to guide the construction of LEO satellite based IoTs according to the distribution characteristics and laws of the business.

In recent years, the Internet of Things (IoT) technology has shown a good momentum of development and has been widely used in various fields of society. Satellite IoT, as an important complement and extension of the ground IoT, provides services in areas where oceanic and desert areas cannot build terrestrial IoT base stations. However, due to limited spectrum resources, spectrum sharing between terrestrial IoT and satellite IoT is likely to be required, which will inevitably lead to uplink and downlink co-channel interference between systems. This paper analyzes various interference scenarios that may occur on the uplink and downlink of terrestrial IoT and satellite IoT. Then, we establishes a global dynamic model of interference volume, and obtains the dynamic distribution of global interference of low-orbit satellite IoT through simulation. Finally, the paper uses the low-orbit satellite IoT global interference scenarios, interference distribution model and interference analysis model to obtain the dynamic analysis of the global uplink and downlink interference of the low-orbit satellite IoT, and uses the interference analysis results to give suggestions for the deployment of Low-orbit Satellite IoT.

This paper investigates the physical-layer security (PLS) of wireless transmissions with the aid of multiple decode-and-forward one-way relays in the presence of both eavesdropping attacks and channel estimation errors (CEEs). To protect wireless transmission with CEE, two opportunistic relaying schemes are conceived to upgrade the PLS of the wireless communications with the aid of relays, namely, the CEE-oriented pure relay selection (CEE-PRS) and CEE-oriented jammer-aided relay selection (CEE-JRS), respectively. Moreover, the security-reliability tradeoff (SRT) is designed due to that increasing the transmit power may enhance the reliability, but the security may be sacrificed concurrently, where the security and reliability are characterized by the intercept probability (IP) and outage probability (OP), respectively. We then analyze the IP and OP of the CEE-PRS and CEE-JRS schemes. Furthermore, both the CEE-oriented direct transmission (CEE-DT) and the perfect channel estimation-oriented direct transmission (PCE-DT) schemes are also analyzed for comparison purposes. It is shown that although the SRT of the wireless communications can indeed be degraded by the eavesdropping attacks in low CEE regions, the proposed CEE-PRS and CEE-JRS schemes are capable of significant improving the SRT performance of wireless communications, demonstrating the advantage of the proposed CEE-PRS and CEE-JRS schemes against eavesdropping.

We study the physical-layer security of a cognitive radio system in the face of multiple eavesdroppers (EDs), which is composed of a secondary base station (SBS), multiple secondary users (SUs) as well as a pair of primary transmitter (PT) and primary receiver (PR), where the SUs first harvest energy from their received radio frequency signals transmitted by the PT and then communicate with the SBS relying on opportunistic scheduling. We consider two specific user scheduling schemes, namely, the channel-aware user scheduling (CaUS) and the energy-aware user scheduling (EaUS). In the CaUS scheme, an SU having the best instantaneous SU-SBS link (spanning from SUs to SBS) will be activated to communicate with the SBS. By contrast, the EaUS scheme takes into account both the amount of energy harvested from the PT and the instantaneous quality of the SU-SBS link. We analyze the security-reliability tradeoff (SRT) of both the CaUS and EaUS schemes in terms of their intercept versus outage probability. We also provide the SRT analysis of traditional round-robin user scheduling (RrUS) used as a benchmarker of the CaUS and EaUS schemes. We demonstrate that the EaUS scheme achieves the best outage and secrecy performance in the high main-to-eavesdropper ratio (MER) region, but a worse secrecy performance than the CaUS method in the low-MER region. Moreover, from a security versus reliability perspective, the CaUS outperforms both the EaUS and the RrUS in the low-MER region. Surprisingly, this also implies that although the user scheduling criterion of EaUS exploits the knowledge of both the amount of harvested power and instantaneous channel state information (CSI), it exhibits a degraded physical-layer security in the low-MER region due to the fact that the increased harvested energy is beneficial not only for the legitimate SBS receiver but also for the EDs.

In this paper, we investigate the physical-layer security for a wireless two-hop relay network in the presence of channel estimation errors when estimating the channel state information (CSI) of the main links (from the source via relays to the destination) and wiretap links (from the source and relays to the eavesdropper). The eavesdropper can not only overhear the confidential signals transmitted by the relays, but also tap the secret messages transmitted by the source. We present an channel estimation error oriented relay selection (CEEoRS) scheme to improve the security-reliability tradeoff (SRT). We also analyze the intercept probability and outage probability of the CEEoRS scheme, where the intercept probability and outage probability can quantify the security and reliability, respectively. For comparison purposes, the traditional direct transmission with channel estimation errors (TDTwCEE) is also analyzed. It is shown that the proposed CEEoRS scheme outperforms the TDTwCEE scheme in terms of its SRT. More specifically, the proposed CEEoRS scheme is capable of significantly improving the SRT performance of wireless communications through increasing the number of relays.

In this paper, we consider a cooperative relay network consisting of a source, a destination, and multiple decode-and-forward (DF) relays in the presence of multiple eavesdroppers, which intend to tap confidential messages transmitted by both the source and the relays. We propose a so-called secrecy maximization oriented relay selection (SMORS) scheme to improve the physical-layer security of wireless communications. In the SMORS scheme, a relay with the maximal secrecy rate is selected among all the DF relays to forward the source signal. We analyze the intercept probability of the proposed SMORS scheme as well as the traditional max-min relay selection scheme. Numerical results show that the proposed SMORS scheme outperforms the conventional max-min relay selection scheme in terms of the intercept probability. Additionally, it is shown that with an increasing number of eavesdroppers, the intercept performance of wireless communications degrades, which can be well addressed using the proposed SMORS scheme through increasing the number of relays. By contrast, increasing the number of relays has little impact on the intercept probability for the conventional max-min relay selection scheme, especially when the number of relays is sufficiently high (e.g., exceeding 20 relays).

In this study, the authors investigate the physical‐layer security in a cognitive amplify‐and‐forward relay network consisting of a secondary transmitter (ST) and a secondary destination (SD) with the aid of multiple secondary relays (SRs) in the face of multiple eavesdroppers. In cognitive radio networks, increasing transmit power may not always be beneficial in terms of improving the channel capacity of cognitive transmissions, which would not only cause an extra interference to primary user, but also enhance the possibility of successfully intercepting the cognitive transmissions at an eavesdropper because an improved signal strength is received in this case. The authors propose two relay selection schemes to improve the physical‐layer security of cognitive transmissions against eavesdropping attacks, which are referred to as the global and partial channel state information based relay selection, denoted by GCSIbRS and PCSIbRS, respectively. The authors analyse the intercept probability of the proposed GCSIbRS and PCSIbRS, as well as the traditional round‐robin and all‐relay transmission schemes. It is shown that the proposed GCSIbRS and PCSIbRS schemes both outperform the conventional round‐robin and all‐relay schemes in terms of their intercept probability performance.