Power domain Non-orthogonal multiple access (PDNOMA) has been widely considered to achieve the efficient short packet transmission (SPT). The most significant characteristic of SPT is that the finite blocklength coding is adopted, yielding a capacity backoff referred to as channel dispersion. However, the existing PD-NOMA schemes were widely investigated with the finite blocklength i.i.d. coding, which results in a large channel dispersion that dramatically decreases the capacity. To tackle this problem, we aim for investigating the PD-NOMA with the finite blocklength shell coding, which provides a smaller dispersion compared with the finite blocklength i.i.d. coding. On this basis, considering the demands of green communications, we conduct resource allocation to minimize the power consumption for the PD-NOMA scheme. Finally, simulation results verify the effectiveness of the proposed methods and compare the performance of the schemes with different coding strategies.

The convolutional neural network (CNN) based radio frequency fingerprint identification (RFFI), as an emerging device authentication technique, can identify wireless devices from their emitted radio-frequency (RF) transmissions. However, the variation of wireless channel may significantly impact accuracies of CNN-based RFFI systems, for instance, a CNN trained on signals collected on one day may fail to classify signals collected on other days. In this paper, we explore the time generalization of CNN-based RFFI systems by analyzing an open dataset that contains 6 WiFi devices operating on 4 days. Firstly, through visual analytics, it is found that equalizing signals can reduce the effect of the wireless channel variation, which is beneficial for the CNN to extract discriminative features and improve the classification accuracy. Secondly, convolutional autoencoder (CAE) based pre-training scheme is designed to obtain better generalization ability. Finally, as the input of the CNN, three signal representations are investigated in time, frequency, and time-frequency domains, namely in-phase and quadrature (IQ) samples, discrete Fourier transform (DFT) results and spectrogram, respectively. Experimental results show that the IQ-based CNN can reach the best performance, and the classification accuracy exceeds 95% for WiFi devices operating on different days.

A user equipment (UE)-assisted hybrid coherent and noncoherent transmission scheme is designed for cell-free massive multiple-input–multiple-output (MIMO), operating in the presence of phase offsets caused by both imperfect radio frequency (RF) chains and asynchronous reception. First, considering the effects of both factors, we derive closed-form spectral efficiency (SE) expressions for hybrid transmission under conjugate beamforming and zero-forcing (ZF) precoders. Based on these expressions, we introduce the concept of superposition gain to clarify the rationality of hybrid transmission. To leverage its advantages, we propose an access point (AP) grouping algorithm and its enhanced version, which groups the serving APs based on downlink (DL) equivalent channel at the UE side. Since the DL equivalent channel incorporates information on both RF and delay phase offsets, hybrid transmission based on this algorithm can simultaneously address both. Additionally, to implement this UE-assisted hybrid transmission with low overhead, we propose a signaling interaction strategy that extends traditional processes by introducing ZF-based DL beamforming pilot transmission for obtaining the DL equivalent channel, along with indication method for reporting the grouping results. Moreover, to further improve performance, a sequential convex approximation power allocation algorithm is proposed for hybrid transmission. Finally, simulations show that UE-assisted hybrid transmission achieves nearly fivefold improvement in 95%-likely SE over coherent transmission in the presence of both RF and delay phase offsets.

Multisource localization (MSL) for overlapped signals has attracted much attention, and the existing methods rely on the combination of multitype measurements, which puts higher requirements on the receiver. Besides, these methods can not avoid the problem of measurement-source association. In view of this, on the basis of broadband signal time-frequency spectrogram detection (TFSD), we propose an MSL scheme for overlapped signals based on index ambiguity elimination, which can avoid the above-mentioned measurement-source association problem by extracting the pure part of each signal component. Specifically, we first conduct the time-frequency transformation of the received signal, and analyze the overlapping types of the time-frequency blocks (TFBs) in two aspects: 1) inter-TFB, i.e., the overlapping types between the TFBs and 2) intra-TFB, i.e., the overlapping types between signal components contained in the TFB. On this basis, the TFB nonoverlapping part extraction algorithm is designed to eliminate the overlap between TFBs. Afterward, the signal segmentation algorithm based on the signal characteristic change mechanism is designed to obtain the pure part of each signal component, that is, eliminating the index ambiguity of each signal component contained in the extracted nonoverlapping TFB. Finally, the angle-of-arrival (AOA) information of multiple receivers for a certain signal can be obtained through the AOA estimation method, as well as the location of source device corresponding to the signal can be estimated by the triangulation method. Simulation and experiment results verify the effectiveness of the designed scheme.

In light of the demands of green communications and latency constraints in 5G, this paper investigates the resource allocation to minimize the power consumption for 2-user uplink non-orthogonal multiple access (NOMA) schemes with finite blocklength codes. In contrast to the existing research, we consider the achievable bound developed with the finite blocklength Gaussian shell codes (FBGSC) as it is greater than the widely used i.i.d. Gaussian achievable bound. To sufficiently explore the potential of NOMA, three typical 2-user uplink NOMA schemes are investigated in this paper, in terms of the classical power domain NOMA, rate splitting multiple access (RSMA) and NOMA with joint decoding (NJD). Simulation results indicate that with FBGSC, NJD always outperforms the other two schemes. In addition, it is found that, with FBGSC, RSMA outperforms NOMA only if the channel quality difference is small. To the best of our knowledge, we are the first to investigate uplink NOMA and RSMA with the shell bound developed by Scarlett (2017), which is the state of the art for their corresponding channels in the finite blocklength regime. Besides, we are also the first to investigate the power/energy-efficient transmission for all these three NOMA schemes with the shell codes.

Time-frequency localization (TFL) based intelligent wideband spectrum sensing is capable of achieving precise dynamic spectrum management. Recent studies demonstrate that object detectors can achieve excellent TFL performance in simple electromagnetic scenarios when trained with massive and labeled datasets. However, in real-world concurrent cognitive scenarios that allow users to reuse the same frequency band under a certain interference constraint, the phenomenon of signal overlapping in the time-frequency domain will seriously degrade the performance of object detector. To the best of our knowledge, no comprehensive analysis has been conducted to assess the impact of overlapping in TFL. To fill this research gap, we analyze the impact of overlapping and identify three challenges: variety of overlapping, hard to label, and feature destruction. To enhance the robustness of the detector, we first adopt a self-supervised learning (SSL) framework based on a masked autoencoder. This framework aims to pre-train a backbone with excellent feature extraction ability using unlabeled dataset to overcome variety of overlapping and labeling difficulties. Subsequently, we develop a transformer based robust TFL (TRTFL) detector. This detector is designed to leverage both time-frequency correlation and fine-grained features, effectively addressing issues related to feature destruction. Finally, simulation results demonstrate the superiority of the proposed method and the effectiveness of SSL framework and TRTFL. Compared to existing detectors, the TRTFL achieves superior feature extraction, yielding a mean average precision (mAP) of 90.70% in overlapping signal scenarios. Moreover, the TRTFL with SSL can achieve an mAP of up to 95.08% outperforming the state-of-the-art.

With a great potential of improving the service fairness and quality for user equipments (UEs), cell-free massive multiple-input multiple-output (mMIMO) has been regarded as an emerging candidate for 6G network architectures. Under ideal assumptions, the coherent joint transmission (CJT) serving mode has been considered as an optimal option for cell-free mMIMO systems, since it can achieve coherent cooperation gain among the access points. However, when considering the limited fronthaul constraint in practice, the non-coherent joint transmission (NCJT) serving mode is likely to outperform CJT, since the former requires much lower fronthaul resources. In other words, the performance excellence and worseness of single serving mode (CJT or NCJT) depends on the fronthaul capacity, and any single transmission mode cannot perfectly adapt the capacity limited fronthaul. To explore the performance potential of the cell-free mMIMO system with limited fronthauls by harnessing the merits of CJT and NCJT, we propose a CJT-NCJT hybrid serving mode framework, in which UEs are allocated to operate on CJT or NCJT serving mode. To improve the sum-rate of the system with low complexity, we first propose a probability-based random serving mode allocation scheme. With a given serving mode, a successive convex approximation-based power allocation algorithm is proposed to maximize the system’s sum-rate. Simulation results demonstrate the superiority of the proposed scheme.

The convolutional neural network (CNN) is effective to classify radio frequency (RF) signals of unmanned aerial vehicles (UAVs), although the variation of UAV operating channels can degrade the performance of the CNN. As the CNN is data hungry, an intuitive solution is to capture UAV signals of all channels to train the CNN. However, the signal collection is time-consuming and expensive. Hence, this paper proposes a data augmentation scheme based on the frequency characteristics of UAV signals, which approximately simulates UAVs operating on different channels. With this scheme in the training pipeline, we use signals of a single UAV channel to train CNNs that can classify UAVs operating on arbitrary channels. Extensive indoor and outdoor experiments are conducted, and the collected signals are also released as part of the technical contributions of our work. The experimental results show that the proposed data augmentation scheme can improve the classification accuracy of CNNs by 60%.

Cell-Free massive multiple-input multiple-output (MIMO), which provides high spectral efficiency (SE) through the coherent joint transmission, has drawn much academic research interest. However, the implementation of coherent joint transmission is restricted by the phase offset caused by hardware defect and asynchronous reception. In this paper, by combining the advantages of the high performance provided by coherent joint transmission and the phase offset robustness provided by non-coherent joint transmission, we propose a hybrid transmission scheme based on access point (AP) grouping, which ensures decent coherent joint transmission by minimizing the phase offset between collaborative APs within the group, and avoids large phase offset between groups by executing non-coherent joint transmission. Moreover, to facilitate simulation verification of the performance of the proposed scheme under various preprocessing methods, we derive a generalized version of SE expression of the hybrid transmission scheme using successive interference cancellation. Furthermore, simulations are provided to verify the effectiveness of our proposed scheme.

Time-frequency localization based intelligent wideband spectrum sensing is essential for achieving precise dynamic spectrum access and management. Currently, object detection based detectors can achieve excellent time-frequency localization performance in a simple electromagnetic environment. However, the overlapping of signals in the time-frequency domain can corrupt signal features and degrade detector performance. In this paper, we propose a transformer based robust time-frequency localization (TRTFL) detector that fully extracts the features of the time-frequency domain correlation to improve its robustness in solving the aforementioned problem. Furthermore, to exploit the convolution operation for mining fine-grained features, we embed a convolutional layer with a small kernel in the transformer block. Finally, simulation results validate the advantages of TRTFL compared to existing detectors and demonstrate its robustness for overlapping signals in spectrogram.

Compared with the traditional spectrum sensing methods that can only detect the presence or absence of signals, deep learning-based time-frequency localization (TFL) methods can obtain two-dimensional time-frequency information (TFI). However, for time-frequency domain overlapped signals, TFL methods can only obtain the contour TFI of the whole time-frequency block (TF-Block), but cannot obtain the TFI and corresponding relationship of each component signal contained in the TF-Block, which is named index ambiguity here. To use spectrum resources more efficiently, it is need to mine the usage of time-frequency resources in multidimensional space as much as possible, such as the time-frequency resources occupation and direction-of-arrival (DoA) of each component signal, which can help the users to avoid interference in spectrum reuse. In this paper, a processing framework is designed to eliminate the index ambiguity. Based on the result of TFL, rank features are extracted using the sliding window method to characterize the signal property, and then a signal segmentation algorithm is designed to obtain the concrete composition of overlapped signals. Finally, the DoA of each component signal is obtained based on the signal segmentation information. Simulation results demonstrate that the proposed method can efficiently and accurately extract multidimensional information to eliminate the index ambiguity of overlapped signals.

The advancements in unmanned aerial vehicle (UAV) technology have brought immense convenience to society. However, unauthorized UAVs pose a serious threat to personal privacy, public safety, and aviation security. Therefore, accurate UAV detection and classification are crucial. Moreover, with the increased popularity of UAVs, the likelihood of multiple UAVs appearing in the same area simultaneously has also dramatically increased. Recent studies demonstrate that object detectors, such as FasterRCNN and YOLO, can be used to detect and classify multiple UAVs based on spectrograms. To our best knowledge, the object detectors are directly used to classify UAV without considering the characteristics of the UAV signal spectrogram, which results in a decrease in recognition performance. In this article, we analyze the characteristics of the UAV signal spectrogram in detail and conclude two problems, i.e., prior anchor mismatch and cross-domain detection, hindering the implementation of object detector for UAV recognition. To solve prior anchor mismatch, we propose an anchor-free detector based on keypoint and design a novel keypoints matching algorithm to improve recognition performance. To solve cross-domain detection, we propose an adversarial learning-based data adaptation method, which can generate domain-independent and domain-aligned features. Finally, the experiments adopt practical spectrogram and synthetic spectrogram to verify the superiority of the proposed anchor-free detector and the effectiveness of the proposed data adaptation method.

With the increasing security concerns of the satellite network due to the broadcasting nature and the inherent openness of satellite-terrestrial communications, the satellite spectrum and terrestrial node resource trading based cooperation in cognitive satellite terrestrial networks (CSTNs) has gained a lot attention. However, the existing literature has not well considered the fairness issue in resource trading, which may cause cooperation failure between the satellite and terrestrial networks when their own benefits are impaired. To tackle this issue, in this paper we propose a two-layer hierarchical game framework for a multi-terrestrial base stations (BSs) CSTN scenario to guarantee the fairness of resource trading between the satellite and terrestrial networks and thus achieve a win-win situation for both networks. Specifically, a coalition formation game is adopted to study the cooperative behaviors among the terrestrial BSs. Herein, we propose a distributed merge-and-split based coalition formation algorithm to determine the coalition structure, of which the stability, convergence, and complexity are theoretically investigated. Moreover, a Stackelberg game is introduced to model the competition between the satellite and terrestrial BSs, where the satellite acts as the leader and the terrestrial BSs act as the followers. The Stackelberg equilibrium (SE) for the Stackelberg game is derived based on the backward induction method. We then design a distributed algorithm to obtain the coalition structure and SE for the proposed two-layer hierarchical game framework. Finally, simulations are presented to validate our theoretical results.

By transmitting task-related information only, semantic communications yield significant performance gains over conventional communications. However, the lack of mature semantic theory about semantic information quantification and performance evaluation makes it challenging to perform resource allocation for semantic communications, especially when multiple tasks coexist in the network. To cope with this challenge, we propose a quality-of-experience (QoE) based semantic-aware resource allocation method for multi-task networks in this paper. First, semantic entropy is defined to quantify the semantic information for different tasks, and the relationship between semantic entropy and Shannon entropy is analyzed. Then, we develop a novel QoE model to formulate the semantic-aware resource allocation in terms of semantic compression, channel assignment, and transmit power. The compatibility of the formulated problem with conventional communications is further demonstrated. To solve this problem, we decouple it into two subproblems and solved them by a developed deep Q-network (DQN) based method and a proposed low-complexity matching algorithm, respectively. Finally, simulation results validate the effectiveness and superiority of the proposed method, as well as its compatibility with conventional communications.

Among the realm of ultra-reliable and low-latency communication (URLLC), a challenging problem is how to realize an efficient transmission meeting both latency and reliability requirements. As a promising technique to tackle the above problem, cooperative non-orthogonal multiple access (C-NOMA) has gained much attention in recent years. When the relay (R) is close to the source, the superior of C-NOMA has been verified if the cooperation is implemented in the first phase. However, the case where R is close to the destination, which also frequently appears in the cooperative transmission, has not been well investigated for URLLC yet. To fill up this gap, we propose a packet re-management based C-NOMA transmission scheme in which the cooperation is implemented in the second phase. Further, considering the importance of power consumption, we aim for jointly optimizing blocklength, power, re-managed packet size, and block error rate to minimize the power consumption subject to URLLC requirements and the maximum power constraint. To tackle the above non-convex and implicit problem, we first propose an algorithm to obtain a sub-optimal solution. Then, an extended algorithm is proposed to further approach the optimal solution. Simulation results verify the effectiveness of the proposed scheme and algorithms.

Recently, the secure transmission in cognitive satellite terrestrial network (CSTN) has gained much attention, where the interference from the terrestrial network is utilized to enhance the security of the satellite network, provided that these two networks share the spectrum. In the existing literature, the satellite and terrestrial networks are assumed to be naturally willing to cooperate with each other to improve the performance of the entire CSTN system. However, these two networks generally belong to different authorities in practice and will not cooperate if their own benefits are impaired. From this perspective, we propose a Stackelberg game based secure transmission strategy for the CSTN to motivate cooperation, where the satellite network acts as the leader and the terrestrial network acts as the follower. Specifically, we model the utility function of the satellite network as the secrecy rate assisted by the terrestrial network. Moreover, the utility function of the terrestrial network is modeled as its obtained transmission rate discounted by the cost of total transmission energy. On this basis, we adopt the backward induction method to determine the Stackelberg equilibrium, from which both the utilities of the satellite and terrestrial networks are maximized. Finally, simulation results are presented to validate our theoretical results.

Over the past few years, commercial drones have grown in popularity. However, the pervasive use of drones may pose a range of secure risks to sensitive areas such as airports and military bases. Hence, drone detection and identification are critical and necessary for governments and security agencies. This paper proposes a radio frequency identification (RFI) system for drones based on spectrogram and convolutional neural network (CNN). Specifically, spectrogram is used to represent fine-grained time-frequency characteristics of drone signals. Then CNN is designed to infer drone types by identifying their spectrograms. In practice, drones have different operating channels, and any one of them can be selected for signal transmission. It means that the carrier frequencies of their signals are unknown, which may result in misclassifications. To address this problem, we collect drone signals from all potential frequency bands, and demonstrate that carrier frequency offset (CFO) compensation can significantly improve the system performance. Experimental evaluation is performed in real wireless environments involving 6 drones and a Universal Software Radio Peripheral (USRP) X310 platform. Moreover, the proposed spectrogram-based CNN can reach the best performance compared with the IQ-based and FFT-based CNNs. The classification accuracy is beyond 98% for drones operating on arbitrary channels.

With the aim of accomplishing intelligence tasks, semantic communications transmit task-related information only, yielding significant performance gains over conventional communications. To guarantee user requirements for different tasks, we study the semantic-aware resource allocation in a multi-cell multi-task network in this paper. Specifically, an approximate measure of semantic entropy is first developed to quantify the semantic information for different tasks, based on which a novel quality-of-experience (QoE) model is proposed. We formulate the QoE-aware resource allocation in terms of the number of transmitted semantic symbols, channel assignment, and power allocation. To solve this problem, we first decouple it into two independent subproblems. The first one is to optimize the number of transmitted semantic symbols with given channel assignment and power allocation, which is solved by the exhaustive search method. The second one is the channel assignment and power allocation subproblem, which is modeled as a many-to-one matching game and solved by the proposed low-complexity matching algorithm. Simulation results demonstrate the effectiveness and superiority of the proposed method on the overall QoE.

To realize the efficient transmission under ultra-reliable and low-latency communication (URLLC), cooperative non-orthogonal multiple access (C-NOMA), in which the two-phase cooperative transmission is considered and non-orthogonal multiple access (NOMA) is implemented during the cooperation, has gained much attention in recent years. When the cooperation is implemented in the first phase of relaying, the C-NOMA scheme has superb performance only when the relay (R) is close to the source, since NOMA gain comes from the channel quality difference. To expand the versatility of C-NOMA in the case where R is close to the destination (D), we propose a packet re-management-based C-NOMA transmission scheme, in which the cooperation is implemented in the second phase of relaying. Further, considering the importance of power consumption in 5G, we also propose an efficient algorithm to minimize the power consumption, subject to URLLC requirements. Specifically, to tackle the above non-convex and implicit problem, we first provide a tight upper bound approximation of channel dispersion to make the problem explicit. Then, on the basis of block coordinate descent and successive upper bound approximation frameworks, a recursion method is proposed to solve the joint optimization problem. Simulation results verify the effectiveness of the proposed scheme in the case where R is close to D.

With the vigorous development of vehicular communications in 5G and beyond networks, cognitive satellite-terrestrial networks are expected to support multitudinous services and applications in future intelligent transportation systems and mobile Internet. To this aim, we introduce a cognitive satellite-vehicular network (CSVN) in this paper, where the secondary vehicular communications are featured with mobility. To realize friendly coexistence between satellite and vehicular networks as well as efficient resource utilization, we investigate the tradeoff between energy efficiency (EE) and spectrum efficiency (SE) and analyze the associated power allocation in the CSVN. Specifically, by introducing a preference factor which reflects the priority level of EE/SE, we firstly propose a unified EE-SE tradeoff metric to adapt to dynamic vehicular environments. Based on the formulated EE-SE tradeoff metric, we derive a power allocation strategy under the interference power constraints imposed by primary satellite communications. Finally, simulation results are provided to show the effects of preference factor, interference constraints, and vehicle velocity on the EE-SE tradeoff performance.

With the ever increasing spectrum demand of broadband multimedia services, cognitive satellite terrestrial networks have emerged as a promising paradigm for future space information networks. To provide services with diverse delay quality-of-service (QoS) requirements in an energy-limited system, in this paper, we investigate energy efficient power allocation for cognitive satellite terrestrial networks. Employing statistical delay-QoS metric, power allocation schemes are formulated as optimization problems to maximize effective energy efficiency of secondary satellite communications while satisfying interference constraints imposed by primary terrestrial communications. Specifically, allowing for the availability of instantaneous channel state information (CSI) of the secondary transmitter-primary receiver link, optimal transmit powers are derived for both the cases of statistical and instantaneous interference constraints. Moreover, to provide a theoretical insight on the performance of the considered network, we derive closed-form expressions for the outage probability based on the obtained optimal transmit powers. The simulation results demonstrate the validity of the theoretical results and show the impacts of the delay exponent, interference constraint, and aggregate interference from terrestrial networks on the performance of satellite networks.

Allowing frequency reuse between satellite and terrestrial networks, the integrated satellite-terrestrial network can spatially optimize the usage of scarce spectrum resource and is thus becoming one of the most promising infrastructures for future multimedia services. Taking the requirements of both efficiency and reliability in satellite communications into account, we propose an adaptive transmission scheme for the integrated network in this paper, where the satellite can communicate with the destination user either in direct mode or in cooperative mode. Specifically, we first investigate the symbol error rate (SER) performance of two transmission modes with co-channel interference under composite multipath/shadowing fading. Taking the derived SERs as constraints, we formulate the adaptive transmission scheme as an optimization problem with the objective of maximizing energy efficiency (EE) and discuss the trade-off among EE, spectral efficiency (SE), and SER. Furthermore, economic efficiency is also analyzed as a complementary performance measure to SE and EE. Simulation results show that the proposed scheme can increase the attainable EE of satellite communications, which indicates that we should choose the transmission mode adaptively according to different interfering scenarios and shadowing degrees, rather than adopting cooperative transmission aggressively.

With a great potential to support multitudinous services and applications, mobile device-to-device (D2D) communications are conceived as a candidate paradigm for the future intelligent transportation systems and mobile Internet. To optimize the performance of underlaying mobile D2D communication systems with mutual interference caused by resource reuse, we propose two scenario-related power allocation schemes and investigate the energy efficiency (EE) and spectral efficiency (SE) trade-off. A 3-D vehicle-to-vehicle channel model is adopted to characterize propagation characteristics in realistic vehicular environments. We observe that a small degradation in EE around its peak value can significantly increase the SE for high vehicular traffic density (VTD) scenarios, while a marginal degradation in SE results in a considerable gain in EE for low VTD scenarios. Therefore, we maximize the SE subject to EE requirement in high VTD scenarios and maximize EE subject to SE requirement in low VTD scenarios. Moreover, to provide comprehensive understanding and further facilitate the practicality of EE-SE trade-off, economic efficiency (ECE) is employed as a general evaluation criterion to assess the efficacy of tradeoff. Finally, extensive simulations are provided to reveal the tradeoff quantitatively and demonstrate the viability that ECE can serve as a general metric for EE–SE trade–off in vehicular environments under different communication conditions.

In this paper, we consider a cognitive satellite-terrestrial network where the satellite communication operates in the microwave frequency bands allocated to terrestrial networks in an underlay mode. Taking the statistical delay quality-of-service (QoS) requirements into account, we investigate the effective capacity of the satellite network while satisfying interference-power limitations imposed by terrestrial networks. Specifically, the primary terrestrial transmitters that would result in aggregate interference at the satellite receiver are modeled as points of a Poisson point process (PPP). By characterizing the aggregate interference as a gamma distribution, we obtain a closed-form expression for the effective capacity of the secondary satellite network. Finally, simulation results are provided to not only demonstrate the validity of the theoretical results, but also show the effects of system parameters such as delay exponent of satellite communications, interference-power limitations of terrestrial networks, and intensity of terrestrial transmitters on the performance of the satellite network.

Given the fact that the relay contributes to higher data rate or more reliable transmission at the expense of extra power consumption, we investigate the performance of relay aided Device-to-Device (D2D) communications from the perspective of energy efficiency (EE). Firstly, a closed-form expression for the EE of the considered network over Nakagami-m fading channels is derived. Then, taking into account the effects of practical modulation and coding schemes, we propose a channel quality indicator (CQI) based power control approach to maximize the EE of relay aided D2D communications while guaranteeing the interference limitation. The proposed scheme could avoid unnecessary power increment and is applicable in real time resource allocation. Simulation results demonstrate the validity of the theoretical analysis and illustrate that the CQI based scheme can improve EE.

In this paper, we investigate the performance of a distributed space-time coding based hybrid satellite- terrestrial cooperative network (DSTC-HSTCN), where both the satelliterelay and satellite-destination links undergo the Shadowed-Rician fading, whereas the relay-destination link follows the Rayleigh fading. In particular, we address the problem in a downlink distributed Alamouti coded satellite system with a single fixed terrestrial relay. By assuming that amplify-and-forward (AF) protocol is adopted at the terrestrial relay, we first derive the analytical expressions for the joint probability density function (PDF) and moment generating function (MGF) of the signal to noise ratio (SNR) for the cooperatively encoding phase. Then, based on the Meijer-G functions, we present an approximated yet accurate method to evaluate the outage probability and symbol error rate (SER) of the considered networks. Finally, Simulation results are provided to demonstrate the validity of the theoretical analysis.