This paper proposes a unified analytical framework for reconfigurable intelligent surface (RIS)-assisted two-way full-duplex (TW-FD) communication systems in 6G Internet of Things (IoT) scenarios. The proposed framework specifically addresses RISs with N reflective elements, facilitating efficient bidirectional communication. A novel unified moment-based analytical approach is developed, accommodating diverse fading models including Rayleigh, Nakagami-n, Weibull, Nakagami-m, and κ-μ, thereby significantly enhancing the versatility and practicality for complex 6G IoT environments. To comprehensively validate the applicability of our analysis, both independently identically distributed (i.i.d.) and independently non-identically distributed (i.n.i.d.) fading channel scenarios are investigated. By employing the Edgeworth expansion method, we derive analytical expressions for the probability density function (PDF) and cumulative distribution function (CDF) of the end-to-end signal-to-interference-plus-noise ratio (SINR). Additionally, closed-form expressions for probability, average symbol error rate (SER) for various modulation schemes, and end-to-end ergodic rate are provided. Monte Carlo simulations demonstrate the accuracy and robustness of the theoretical models proposed. The results presented in this work not only contribute substantially to the analytical methodologies for RIS-assisted communication but also offer practical guidance for the design and optimization of future 6G IoT systems.

This paper addresses the challenges of image transmission in satellite communication networks, where bandwidth constraints, high interference, and latency issues significantly limit conventional transmission methods. We propose a novel semantic communication framework that adapts to various computational capabilities of receiving terminals in Internet of Everything (IoE). Our approach leverages the Swin Transformer V2 architecture to extract and transmit task-relevant semantic features rather than raw image data, significantly reducing bandwidth requirements while maintaining high reconstruction quality. The proposed system dynamically adjusts its encoding and decoding processes based on receiver computational capacities, enabling efficient image transmission to heterogeneous terminals ranging from high-performance stations to resource-constrained devices. Extensive experiments on various datasets demonstrate that our framework outperforms conventional JPEG+LDPC schemes and state-of-the-art deep learning-based approaches in terms of both PSNR performance and semantic communication utility across various signal-to-noise ratios. The framework shows particular robustness in low-SNR and low-CBR environments, addressing the “efficiency-compatibility” dilemma in resource-constrained satellite communications.

A reconfigurable intelligent surface (RIS)-assisted non-orthogonal multiple access (NOMA) system is investigated, where the transmitter (Alice) is a dual-functional radar-communication (DFRC) base station (BS) that aims to sense the location of a potential warden (Willie), while simultaneously transmitting public and covert signals to the legitimate users, Carol and Bob, respectively. Both cases of known and unknown Willie locations are considered. For the known-location case, assuming perfect channel state information (CSI) at Willie, a covert rate maximization is formulated with the joint optimization of active and passive beamforming, which is solved using successive convex approximation (SCA), penalty method, and semidefinite relaxation (SDR). For the unknown-location case, we propose to estimate Willie’s location via radar sensing and develop a sensing-based imperfect CSI model. In particular, the CSI error uncertainty is bounded by the sensing accuracy, which is characterized by the Cramér-Rao bound (CRB). Subsequently, a robust communication rate maximization problem is formulated under the constraints on quality-of-service (QoS) of Carol, sensing accuracy, and covertness level. The Schur complement and S-procedure are employed to handle the non-convex constraints. Numerical results compare the system performance under the two cases, and demonstrate the significant covert performance superiority of the sensing-based imperfect CSI model and NOMA over the general norm-bounded imperfect CSI model and the orthogonal multiple access scheme. Furthermore, the dual yet contradictory effects of sensing on covert communications are revealed. It is also found that Alice primarily utilizes Carol’s signal for sensing, while allocating almost all of Bob’s signal for communication.

Real-time speech communication over wireless networks remains challenging, as conventional channel protection mechanisms cannot effectively counter packet loss under stringent bandwidth and latency constraints. Semantic communication has emerged as a promising paradigm for enhancing the robustness of speech transmission by means of joint source channel coding (JSCC). However, its cross-layer design hinders practical deployment due to the incompatibility with existing digital communication systems. To address this, we perform JSCC over the network layer to combat packet loss and support real deployment. Inspired by the generative latent modeling, we propose Glaris, a generative latent-prior-based resilient speech semantic communication framework that performs resilient transform coding in the generative latent space. Generative latent priors enable high-quality packet loss concealment (PLC) at the receiver side, well-balancing semantic consistency and reconstruction fidelity. Additionally, an integrated error resilience mechanism is designed to mitigate the error propagation and improve the effectiveness of PLC. Compared with traditional packet-level forward error correction (FEC) strategies, our new method achieves enhanced robustness over dynamic wireless networks while reducing redundancy overhead significantly. Experimental results on the LibriSpeech dataset demonstrate that Glaris consistently outperforms existing error-resilient codecs, achieving JSCC-level robustness while maintaining seamless compatibility with existing systems, and it also strikes a favorable balance between transmission efficiency and error resilience.

Intelligent task-oriented semantic communications (SemComs) have witnessed great progress with the development of deep learning (DL), where multi-task SemComs that perform multiple tasks simultaneously attach great importance due to its high efficiency. However, the study of robust multi-task-oriented semantics transmission is still in early stages. In this paper, we propose a semantic-aware hybrid automatic repeat request (SemHARQ) framework for the robust and efficient transmissions of multi-task semantic features. First, to improve the robustness and effectiveness of semantic coding, a multi-task semantic encoder is proposed. Meanwhile, a feature importance ranking (FIR) method is investigated to ensure the important features delivery under limited channel resources. Then, to accurately detect the possible transmission errors, a novel feature distortion evaluation (FDE) network is designed to identify the distortion level of each feature, based on which an efficient HARQ method is proposed. Specifically, the corrupted features are retransmitted, where the remaining channel resources are used for incremental transmissions. The system performance is evaluated under different channel conditions in multi-task scenarios in the Internet of Vehicles. Extensive experiments show that the proposed framework outperforms state-of-the-art works by more than 20% in rank-1 accuracy for vehicle re-identification, and 10% in vehicle color classification accuracy in the low signal-to-noise ratio regime.

The powerful phase modulation capability of reconfigurable intelligent surfaces (RIS) endows them with the potential to replace future base station (BS) antenna arrays. However, current research on planar transmissive RIS-enabled BSs (PT-RIS-BSs) suffers from angle-sensitive limitations, failing to meet the demands of dynamic low-altitude communications. This paper proposes a spherical transmissive RIS-enabled BS (ST-RIS-BS) architecture, where traditional array antennas in BS are replaced by a combination of an omnidirectional antenna and a ST-RIS, achieving stable gain across all spatial directions. Specifically, considering angle-sensitive gain characteristics, three-dimensional (3D) signal incidence/departure angles, and the effective responsive elements of ST-RIS, we establish a ST-RIS-BS model tailored for dynamic low-altitude communications. Furthermore, through spatial integration methods, we derive the spatial average gain for both PT-RIS and ST-RIS, theoretically demonstrating a performance enhancement of up to 36.6% for the spherical configuration. Additionally, addressing a typical application scenario in the future low-altitude economy where uplink user communications coexist with unmanned aerial vehicle (UAV) data collection, we verify the performance of proposed ST-RIS-BS by the joint optimization of RIS phase shifts, transmit power, user scheduling, and UAV 3D trajectory, which is solved by using block coordinate descent and successive convex approximation techniques. This approach maximizes data collection while guaranteeing uplink user rates. Simulation results validate the effectiveness of the proposed scheme, showing that the ST-RIS solution achieves a 45% increase in data collection compared to the PT-RIS configuration.

As a paradigm shift towards pervasive intelligence, semantic communication (SemCom) has shown great potentials to improve communication efficiency and provide user-centric services by delivering task-oriented semantic meanings. However, the exponential growth in connected devices, data volumes, and communication demands presents significant challenges for practical SemCom design, particularly in resource-constrained wireless networks. In this work, we propose a task-agnostic semantic communication (TASC) framework capable of supporting multimodal data across diverse tasks. To investigate the interplay between communication and intelligent tasks from an information-theoretic perspective, we introduce a distributed multimodal information bottleneck (DMIB) principle, which enables the extraction of minimal sufficient unimodal and multimodal representations by eliminating redundant information while preserving task-relevant semantics. To further reduce the communication overhead, we develop an adaptive semantic feature transmission method under dynamic channel conditions. Then, TASC is trained based on federated meta-learning (FML) to learn a well-initialized model for rapid adaptation and generalization. To gain deep insights, we conduct theoretical analysis and devise resource management to accelerate convergence while minimizing the training latency and energy cost. Moreover, we develop a joint user selection and resource allocation algorithm to address the non-convex problem with theoretical guarantees. Extensive simulation results validate the effectiveness and superiority of the proposed TASC compared to baselines.

Rapid signal fluctuations due to blockage effects cause severe risks of handover failures (HOF) and ping pongs (PPs). By reconfiguring line-of-sight (LoS) Links through passive reflections, intelligent reflecting surface (IRS) has the potential to maintain link stability and resolve this issue. However, existing handover (HO) process analyses have not introduced both blockage effects and IRS reflections, thus fail to explore the potential of IRS in this aspect.This paper analyzes the IRS-aided HO process by tracking the Line-of-Sight (LoS) state of moving users, where effects of LoS state transitions are characterized by modifying the state transition probability of HO process. Specifically, LoS states involve LoS, non-LoS, and IRS-reconfigured LoS, and the state transition probabilities are obtained through exact blockage modeling and IRS reflection analysis, taking into account the correlation of adjacent moments. In addition, Markov Chain-based analytical models are designed for the process of HO, HOF, and PP considering HO parameters (Time-to-Trigger and HO margin), whose transition probabilities are integrated with all LoS states. The results indicate that under severe blocking effects, the trade-off of HO parameters becomes ineffective, with HOF and PP probabilities remaining high. However, after introducing IRS, a viable range of HO parameters emerges.

Semantic communications (SemCom) is considered one of the key technologies for next-generation communications. However, most SemCom systems utilize Deep Learning (DL) based joint source-channel coding (JSCC), which are incompatible with existing digital communication systems. In this paper, we propose a novel digital SemCom system based on variable product quantization (VPQ-SemCom), which harnesses multiple lightweight codebooks to represent images and dynamically optimize bitrates according to the entropy of semantic features to adapt to transmission scenarios with multiple bandwidths and SNRs. Specifically, product quantization (PQ), which can represent semantic features with several lightweight codebooks, is introduced to provide powerful representation capacities of semantic features. Furthermore, a rate adaption module, which can flexibly adjust feature length based on the entropy of semantic features, is proposed to integrate with PQ to improve rate-distortion performance. The experimental results demonstrate that VPQ-SemCom shows 32.4% improvement at high SNRs and 62.2% improvement at SNR = 2dB in Learned Perceptual Image Patch Similarity (LPIPS) compared to current state-of-the-art vector quantization (VQ) based digital SemCom systems.

Semantic communications (SemComs) which utilize the inherent meanings and relationships of data, have shown significant advantages for information transmission in recent years. In this paper, a novel semantic base (Seb) enabled SemCom framework is proposed, where Sebs, the basic units of fine-grained image semantics, are explicitly shared among transceivers to support image transmission. Specifically, first, to improve source coding efficiency, a Seb-based image codec is proposed, where semantics in each image patch are encoded with synchronized Sebs, that are generated from recent images. To ensure semantic consistency among the embeddings of Sebs, Gray coding is used to establish the projection, enhancing the framework’s robustness against channel noise. The details of images are further refined by a residual codec for high-quality reconstructions. Second, to enhance transmission reliability with overhead as small as possible, a semantic-aware fine-grained hybrid automatic repeat request (SAFG-HARQ) is proposed, where only erroneous Sebs are retransmitted to precisely refine corrupted semantics using contextual correlations. Extensive simulations demonstrate that the proposed framework outperforms state-of-the-art works, where the image reconstruction quality is improved by 20% in learned perceptual image patch similarity (LPIPS), with a 60% reduction in transmission costs.

Multiple access is one of the primary issues for multi-user semantic communication systems. In this paper, we propose a novel pair of semantic difference (SeD) aware NOMA transceivers for downlink semantic-based image transmission, which mitigates the semantic-level interference among semantic streams. In specific, a SeD-aware superposition coding (SC) technique is proposed to suppress the semantic-level interference by coupling the semantic symbols of higher inter-feature semantic difference, which makes the interfering semantic symbols be identified and filtered out by the corresponding decoding function. The SeD-aware successive interference cancellation (SIC) technique further reduces the semantic-level interference by estimating the transmitted semantic symbols with the joint semantic and channel (JSC) autoencoder. Simulation results show that the proposed transceivers achieve comparable performance with benchmarks of OMA-aided transmission, while outperforming the benchmark of SeD-unaware NOMA transceivers in terms of the quality of reconstructed images and outperforming both benchmarks in terms of semantic transmission efficiency.

In this paper, we propose a novel interference-suppressed semantic-aware non-orthogonal multiple access (IS-SNOMA) framework for downlink image transmission in the semantic-aware communication networks. The proposed IS-SNOMA is able to mitigate the inter-user interference in the non-orthogonal transmission for multiple semantic-oriented users (SU) or the coexistence of SUs and bit-oriented users (BU). 1) For the homogeneous transmission of semantic streams, we propose a pair of novel semantic difference (SeD) aware IS-SNOMA transceivers to accommodate multiple SUs over the same resource block. A novel SeD-aware superposition coding (SeDSC) technique and SeD-aware successive interference cancellation (SeDSIC) technique are specially designed to mitigate the semantic-level interference. 2) For the heterogeneous transmission of semantic-bit streams, we develop a pair of syntactic difference (SyD) aware IS-SNOMA transceivers to multiplex channels for SUs and BUs. The heterogeneous semantic symbols and bit sequences are superposed and separated with the proposed SyD-aware SC technique (SyDSC) and SyD-aware SIC technique (SyDSIC), respectively. Simulation results demonstrate the advantages of the proposed frameworks in improving the communication efficiency of both SUs and BUs, compared with OMA and NOMA-aided transmission benchmarks.

Semantic communications, aiming at ensuring the successful delivery of the meaning of information, are expected to be one of the potential techniques for the next generation communications. However, the knowledge forming and synchronizing mechanism that enables semantic communication systems to extract and interpret the semantics of information according to the communication intents is still immature. In this paper, we propose a semantic image transmission framework with explicit semantic base (Seb), where Sebs are generated and employed as the knowledge shared between the transmitter and the receiver with flexible granularity. To represent images with Sebs, a novel Seb-based reference image generator is proposed to generate Sebs and then decompose the transmitted images. To further encode/decode the residual information for precise image reconstruction, a Seb-based image encoder/decoder is proposed. The key components of the proposed framework are optimized jointly by end-to-end (E2E) training, where the loss function is dedicatedly designed to tackle the problem of non-differentiable operation in Seb-based reference image generator by introducing a gradient approximation mechanism. Extensive experiments show that the proposed framework outperforms state-of-art works by 0.5 - 1.5 dB in peak signal-to-noise ratio (PSNR) w.r.t. different signal-to-noise ratios (SNR).

Semantic communications are expected to be an innovative solution to the emerging intelligent applications in the era of connected intelligence. In this paper, a novel scalable multi-task semantic communication system with feature importance ranking (SMSC-FIR) is explored. Firstly, the multi-task correlations are investigated by a joint semantic encoder to extract relevant features. Then, a new scalable coding method is proposed based on feature importance ranking, which dynamically adjusts the coding rate and guarantees that important features for semantic tasks are transmitted with higher priority. Simulation results show that SMSC-FIR achieves performance gain w.r.t. individual intelligent tasks, especially in the low SNR regime.

Deep learning (DL) enabled semantic communication has been demonstrated to be efficient in speech transmission under fixed channels by equivalently transmitting all the reconstruction-related semantic features, which however, neglects the effects of distinct semantic features under dynamic channels, constraining the further improvement of communication efficiency. In this paper, we propose a signal-to-noise ratio (SNR)-adaptive multi-layer joint semantic-channel coding framework for speech transmission. Specifically, to achieve the consistency between the source speeches and the reconstructed ones at the semantic level, a multi-layer semantic representation framework is specially designed for speeches, where not only the content-related semantic feature, but also the acoustic-related semantic features are extracted to combat the semantic distortion caused by channel noise and attenuation.By jointly considering the effect of different semantic features and the dynamic channel conditions, an SNR-adaptive channel encoder is proposed to fuse the multi-layer semantic features, where an attention-based gating network is adopted to adjust the proportion of fusion with aim of efficiency maximization. Simulation results show that the proposed system can significantly improve the communication efficiency and robustness under dynamic SNRs.

In future 6G communications, unmanned aerial vehicle (UAV) will play a vital role in urban area communication system. However, the link between low-altitude UAV and base station (BS) will inevitably be blocked by buildings, resulting in severe performance degradation. Intelligent reflecting surface (IRS) is a cost-effective technique that can improve the link quality by intelligently reflecting signals. Motivated by this, an IRS-assisted UAV communication system is studied in this paper to enhance the communication quality between BS and UAV within UAV’s flying area. Specifically, the regional signal-to-noise ratio (R-SNR) is maximized for the blocked area. Due to the non-convexity of the problem, we first decouple the parameters in the formulated problem, and then propose a gradient descent algorithm with suspensive condition (GDSC) to obtain an asymptotic-optimal solution. The convergence property of the proposed GDSC is proved in this paper. Furthermore, a particle swarm optimization-aided alternating (PSOA) algorithm is then proposed, in which we screen the starting points to reduce the complexity. The simulation results reveal the two proposed algorithms achieve near-optimal performance and approach the optimal solution provided by the exhaustive search scheme, and outperform the traditional gradient descent algorithm by up to 28.5%.

Unmanned aerial vehicles (UAVs) are deployed in emergency disaster-relief operations to provide communication services as substitutes for damaged ground base stations (BSs), as well as to offload computational tasks for applications such as target recognition. In view of the limited computing power of a single UAV, we focus on the edge computing offloading problem with multiple-UAV cooperation. As a single UAV is not enough to offload massive delay-sensitive computing tasks in the emergency communication scenarios, we have built up a multi-UAV cooperation computing architecture. By exploring the multiple-UAV cooperation computing offloading capacity, we formulated an optimization problem of minimizing the total time slot size. Since the proposed problem is relevant to mixed integer nonlinear programming, it can be decomposed into two sub-problems: computing task scheduling and UAV trajectory. To handle the formulated problems, we developed a joint optimization algorithms by invoking the penalty method and successive convex approximation (SCA) method. The simulation results show that, compared with the benchmark algorithms, the proposed algorithm can significantly reduce the computation task delay and improve the execution efficiency of the UAVs.

With the advantages of low signaling overhead and latency, grant-free random access (GFRA) becomes a promising technology for supporting massive machine-type communications (mMTCs), but poses new challenges for active user detection (AUD) and channel estimation (CE), whose performance mainly depends on the preamble detection. In this article, we design the composite preamble based on differential phase rotations by aggregating orthogonal Zadoff-Chu (ZC) sequences and multiple root ZC sequences with differential phase rotations to reduce the probability of preamble collisions, thereby improving the performance of AUD and CE. In particular, differential phase rotations extend the preamble set size so that users colliding in orthogonal sequences can be distinguished by phase rotations. In addition, it also reduces nonorthogonal interference and thus reduces CE errors. The preamble detection algorithm and CE scheme are proposed, along with the theoretical analysis of AUD and CE performance to verify the effectiveness of the designed preamble. In addition, the proposed preamble is extended to combine phase rotations with cyclic shifts to further enlarge the preamble set size with low nonorthogonality. Simulation results show that the proposed composite preamble outperforms existing preambles in terms of the probability of detection and CE accuracy.

In a multi-user system, system resources should be allocated to different users. In traditional communication systems, system resources generally include time, frequency, space, and power, so multiple access technologies such as time division multiple access (TDMA), frequency division multiple access (FDMA), space division multiple access (SDMA), code division multiple access (CDMA), and non-orthogonal multiple access (NOMA) are widely used. In semantic communication, which is considered a new paradigm of the next-generation communication system, we extract high-dimensional features from signal sources in a model-based artificial intelligence approach from a semantic perspective and construct a model information space for signal sources and channel features. From the high-dimensional semantic space, we excavate the shared and personalized information of semantic information and propose a novel multiple access technology, named model division multiple access (MDMA), which is based on the resource of the semantic domain. From the perspective of information theory, we prove that MDMA can attain more performance gains than traditional multiple access technologies. Simulation results show that MDMA saves more bandwidth resources than traditional multiple access technologies, and that MDMA has at least a 5-dB advantage over NOMA in the additive white Gaussian noise (AWGN) channel under the low signal-to-noise (SNR) condition.

Deep learning (DL) enabled semantic communications have been developed to improve the offline communication efficiently and intelligently by exploring the semantic information, while constraining their applications in real-time online scenarios. In this work, we propose SemAudio, the first DL-based streaming semantic communication system for real-time audio processing. To better extract the semantic features of the audio signal, SemAudio employs the Transformer-XL due to its potential to capture long-distance dependency. Moreover, the system works based on a chunk-based mask attention strategy to enable real-time streaming. By incorporating the novel Transformer-XL and chunk-wise approach, SemAudio can effectively learn and extract semantic features from real-time audio data. Furthermore, to alleviate the channel distortion and attenuation, the semantic and channel encoder/decoder are jointly designed by minimizing the mean error in both time and frequency domains rather than the merely time domain. The extensive experimental results suggest that our proposed SemAudio outperforms the traditional communications. Besides, the proposed SemAudio compromises the quality and latency to meet real-time requirements, which obtains satisfactory performance with significantly higher accuracy and lower latency under multiple channel conditions for real-time audio communication.

With the popularity and application of the Industrial Internet of Things (1IoT), device anomaly detection is considered as one of the important challenges in IloT implementation. However, the privacy sensitivity of device data and the high heterogeneity of IloT devices make it impossible for traditional schemes to achieve efficient, accurate, and privacy-protected device anomaly detection in IloT networks. In this study, we propose an intelligent anomaly detection architecture for IloT networks based on federated optimization algorithms and deep learning (DL). In particular, an online, adaptive, and semi-supervised device anomaly detection model is designed, and a heterogeneity-aware federated learning algorithm, called Clustered-FedProx, is presented. The Clustered-FedProx algorithm considers the differences in computational power and data statistical distribution among IloT devices, whereby multiple devices can be coordinated to train a global DL model in highly heterogeneous networks. Simulation results show that the proposed scheme can achieve more stable and accurate performance than conventional schemes.

This article studies the collision avoidance and interference mitigation for unmanned aerial vehicle (UAV) swarm where many UAVs track a common target. The considered problem is formulated to jointly minimize the average interference and ensure the collision avoidance among UAVs. By exploiting the problem characteristics, our major contributions are summarized as follows. First, the singular case tolerance (SCT)-artificial potential field (APF) is proposed to overcome the failure of traditional APFs in collision avoidance, where the repulsive force gain coefficient among UAVs is dynamically controlled by the corresponding interferences. Second, the mean-field game (MFG) model is established to control communication power, where the instantaneous interferences among flying UAVs are represented by the mean-field approximation. Third, considering the tight coupling of trajectory and interference of UAVs, a cooperative control approach enabled by dual-fields is proposed to jointly adjust the trajectories and power of UAVs. Simulation results validate the significant performance improvement of the cooperative control approach enabled by dual-fields. Compared with separate APF and MFG, the proposed dual-field-cooperation approach can achieve about 117% throughput gain and 88% interference reduction when the UAV swarm is close to the target.

The cache-enabling unmanned aerial vehicle (UAV) non-orthogonal multiple access (NOMA) networks for mixture of augmented reality (AR) and normal multimedia applications are investigated, which is assisted by UAV base stations. The user association, power allocation of NOMA, deployment of UAVs and caching placement of UAVs are jointly optimized to minimize the content delivery delay. A branch and bound (BaB) based algorithm is proposed to obtain the per-slot optimization. To cope with the dynamic content requests and mobility of users in practical scenarios, the original optimization problem is transformed to a Stackelberg game. Specifically, the game is decomposed into a leader level user association sub-problem and a number of power allocation, UAV deployment and caching placement follower level sub-problems. The long-term minimization was further solved by a deep reinforcement learning (DRL) based algorithm. Simulation result shows that the content delivery delay of the proposed BaB based algorithm is much lower than benchmark algorithms, as the optimal solution in each time slot is achieved. Meanwhile, the proposed DRL based algorithm achieves a relatively low long-term content delivery delay in the dynamic environment with lower computation complexity than BaB based algorithm.

As an enabling technology to address spectrum shortage, spectrum sensing has been investigated a lot. However, the uncertainties in the detection environment, including noise uncertainty and carrier frequency (CF) mismatch, still remain as the main challenges of spectrum sensing, which greatly degrades the sensing performance of typical sensing methods, such as energy detection and cyclostationary detection. To this end, this paper proposes two robust spectrum sensing schemes by leveraging the difference between the phase difference (PD) distribution of noise-perturbed signal and that of Gaussian noise. Specifically, the compact approximation of the PD distribution is first derived to enable the extraction of the features of PD distributions, which are robust to noise uncertainty and CF mismatch. Based on these features, two sensing schemes based on the deep neural network (DNN), referred to as DNN-based PD distribution detection (PDD) and blind PDD (BPDD), are proposed to detect spectrum holes in cases with known CF and unknown CF, respectively. Simulation results show that our proposed schemes are more robust to CF mismatch and noise uncertainty in comparison with the existing sensing schemes. Furthermore, when the CF of the sensed signal is unknown, the proposed BPDD significantly outperforms existing blind sensing schemes.

The Internet of Things (IoT) pervades every aspect of our daily lives and industrial productions since billions of interconnected devices are deployed everywhere of the globe. However, the seamless IoT unveils a number of physical-layer threats, such as jamming and spoofing that decrease the communication performance and the reliability of the IoT systems. As the process of identifying the modulation format of signals corrupted by noise and fading, automatic modulation classification (AMC) plays a vital role in physical-layer security as it can detect and identify the pilot jamming, deceptive jamming, and sybil attacks. In this article, we propose a novel cyclic correntropy vector (CCV)-based AMC method using long short-term memory densely connected network (LSMD). Specifically, cyclic correntropy model-driven feature CCV is first extracted using the received signals as it contains both the second-order and the higher order characteristics of cyclostationary. Then, the extracted CCV feature is put into the data-driven LSMD which mainly consists of long short-term memory (LSTM) network and dense network (DenseNet). Moreover, an additive cosine loss is utilized to train the LSMD for maximizing the interclass feature differences and minimizing the intraclass feature variations. Simulations demonstrate that the proposed CCV-LSMD method yields superior performance than other recent schemes.

In this article, we propose the Raptor coded random access (RCRA) scheme to enable reliable transmission in the decentralized unmanned aerial vehicle (UAV) network. The considered network is composed of several overlapped random access systems with interference nodes, and the proposed RCRA scheme reduces bit-error ratio (BER) of the random access systems by three steps. First, we choose the number of slots based on a derived lower bound, which is necessary for reliable random access. Second, error-correcting codes are incorporated as the precode before random access, and then the access probability is optimized to achieve the minimum BER. Third, by correlating two consecutive slots, an idle-slot-filling approach is designed to further improve the efficiency of the random access systems. Numerical results show that the proposed RCRA scheme reduces significantly both block-error ratio (BLER) and BER at moderate- and high-signal-to-noise ratio (SNR) region. With$E_{s}/N_{0}$equal to 0 dB, the RCRA scheme saves 20% slots, compared with the existing frameless ALOHA scheme, to achieve a target BER of 10−4.

The recent success of single-agent reinforcement learning (RL) in Internet of Things (IoT) systems motivates the study of multiagent RL (MARL), which is more challenging but more useful in large-scale IoT. In this article, we consider a voting-based MARL problem, in which the agents vote to make group decisions and the goal is to maximize the globally averaged returns. To this end, we formulate the MARL problem based on the linear programming form of the policy optimization problem and propose a primal-dual algorithm to obtain the optimal solution. We also propose a voting mechanism through which the distributed learning achieves the same sublinear convergence rate as centralized learning. In other words, the distributed decision making does not slow down the process of achieving global consensus on optimality. Finally, we verify the convergence of our proposed algorithm with numerical simulations and conduct case studies in practical multiagent IoT systems.

Millimeter wave (mmWave) communications can potentially meet the high data-rate requirements of unmanned-aerial-vehicle (UAV) networks. However, as the prerequisite of mmWave communications, the narrow directional beam tracking is very challenging because of the 3-D mobility and attitude variation of UAVs. Aiming to address the beam tracking difficulties, we propose to integrate the conformal array (CA) with the surface of each UAV, which enables the full spatial coverage and the agile beam tracking in highly dynamic UAV mmWave networks. More specifically, the key contributions of our work are threefold: 1) a new mmWave beam tracking framework is established for the CA-enabled UAV mmWave network; 2) a specialized hierarchical codebook is constructed to drive the directional radiating element (DRE)-covered cylindrical CA, which contains both the angular beam pattern and the subarray pattern to fully utilize the potential of the CA; and 3) a codebook-based multiuser beam tracking scheme is proposed, where the Gaussian process machine learning-enabled UAV position/attitude prediction is developed to improve the beam tracking efficiency in conjunction with the tracking-error aware adaptive beamwidth control. Simulation results validate the effectiveness of the proposed codebook-based beam tracking scheme in the CA-enabled UAV mmWave network, and demonstrate the advantages of CA over the conventional planner array in terms of spectrum efficiency and outage probability in the highly dynamic scenarios.

This paper considers the blind recognition of the type and the encoding parameters of channel codes from the Gaussian noisy signals. Specifically, based on the recurrent neural network (RNN), the attention mechanism, and the residual neural network (ResNet), three universal recognizers are proposed to identify the type, rate, and length of the target channel codes, with a training set generated by a small portion of all the possible code parameters. The proposed architectures need near zero a priori knowledge about the target channel code, and only require the length of the received signal to be dozen times of the codeword length. Numerical experiments show that the proposed deep learning methods own strong generalization to identify channel codes from the testing samples not generated by the encoding parameters utilized for the training set.

In this paper, an energy-efficient design for massive multiple-input multiple-output (MIMO) systems is studied with the consideration of hardware impairments. The objective is to maximize the system energy efficiency of both uplink and downlink transmissions by jointly optimizing the number of antennas at the base station, the number of served users, and the transmit power. Firstly, by considering the linear distortion due to hardware impairments, the resultant channel estimation error from both distortion and noise is analyzed, following which closed-form approximations for the average achievable uplink/downlink rates are derived. Then, a massive MIMO energy efficiency optimization problem considering hardware impairments is formulated. By applying the techniques of relaxation and change of variables, an alternative optimization with build-in bisection search (AO-BS) algorithm is proposed with the quasi-concavity of the transformed objective function theoretically proved. The performance of the proposed AO-BS algorithm is validated through numerical simulations, which shows fast convergence to near-optimal solutions. Compared with existing approaches, the proposed scheme improves the system energy efficiency greatly when the hardware impairments are considered. Furthermore, the system design guideline in terms of the number of antennas, the number of served users, and the transmit power is provided.

By fully exploiting the cooperative communication capacities among mobile terminals (MTs), the MTs can adapt the offloading designs well to the practical network with dynamic features. In this paper, joint multi-user cooperative partial offloading, transmission scheduling and computation allocating is discussed for device-to-device (D2D) underlay mobile edge computing (MEC). By considering stochastic application requests, unpredictable MTs states, time-varying channel states and computation resources, a customized application offloading model, which aims to minimize the network-wide response latency and energy consumption simultaneously, is formulated. In order to solve this non-convex and non-smooth optimization problem, an online resource coordinating and allocating scheme (ORCAS) is proposed by exploiting Lyapunov optimization theory, variable substitution technique and resource provisioning priority mechanism. Both theoretical analyses and simulation results demonstrate that the proposed ORCAS can 1) drive the application response cost converge to the minimum; 2) achieve superior performance (e.g., the average network-wide response cost under ORCAS is approximately 19.14% lower than that under partial offloading directly); 3) adapt to dynamic situations in terms of stochastic user demands and channel states.

Unmanned aerial vehicles (UAVs) have extensive civilian and military applications, but establishing a UAV network providing high data rate communications with low delay is a challenge. Millimeter wave (mmWave), with its high bandwidth nature, can be adopted in the UAV network to achieve high speed data transfer. However, it is difficult to establish and maintain the mmWave communication links due to the mobility of UAVs. In this paper, a beam management scheme utilizing angular domain information (ADI) is proposed to rapidly establish and reliably maintain the communication links for the mmWave UAV network. Firstly, Gaussian process machine learning (GPML)-enabled position prediction is proposed to facilitate coarse-ADI acquisition through the proposed UAV clustering algorithm. Then, with the proposed confined-ADI acquisition which removes the redundancy in the coarse-ADI acquisition, fast beam tracking with respectively the single-beam pattern and the multi-beam pattern is achieved. Finally, a data-driven beam pattern selection scheme is proposed for improving the spectrum efficiency. Simulation results verify the outstanding performance of the proposed beam management for mmWave UAV networks.

The cache-enabling unmanned aerial vehicle (UAV) cellular networks with massive access capability supported by non-orthogonal multiple access (NOMA) are investigated in this paper. The delivery of multi-media contents for the mixed augmented reality (AR) and normal multi-media application is assisted by multiple mobile UAV base stations, which cache popular contents for wireless backhaul link traffic offloading. To cope with the dynamic content requests and mobility of users in practical scenarios, the dynamic optimization problem for user association, caching placement of UAVs, real-time deployment of UAVs, and power allocation of NOMA is modeled as a stackelberg game to minimize the long-term content delivery delay. Specifically, the game is decomposed into a leader level problem and a number of follower level problems. A correction mechanism is added in deep reinforcement learning (DRL) to optimize the user association in leader level. A meta actor network is proposed in DRL to jointly optimize the UAVs caching placement, real-time UAVs deployment and power allocation of NOMA in follower level. Then, a dynamic caching placement and resource allocation algorithm based on multi-agent meta deep reinforcement learning is proposed to minimize the long-term content delivery delay. Finally, we demonstrate that the considerable gains are achieved by the proposed algorithm.

The development of 5G network and rapid growth of mobile traffic bring lucrative opportunities for Micro Operators (μOs), the novel local operators who own local spectrum, deploy and manage small cells (e.g., femtocells) in a specific area. Collaborating with traditional mobile network operators (MNOs), μOs gain profits from helping to offload the traffic carried by macro base stations and providing the MNOs customers with seamless service. However, due to the demand uncertainty and the sensitivity of individual user's demand profile, it is challenging for μOs to allocate the resource properly to avoid the under-and over-utilized situations. To address this issue, in this paper, we propose to employ data-driven approach to characterize the demand uncertainty, exploit differential privacy protocols to protect user's demand profile, and formulate the small cell planning problem into two-stage stochastic programming optimization with the objective of minimizing the capital and operational costs of μOs. Based on the formulated problem, we develop feasible solutions and conduct extensive simulations using real-world base station accessing real cellular data (i.e., data of 4G LTE network in Zhengzhou city, China) to verify the effectiveness of the proposed model.

In this paper, we study the high-resolution channel estimation problem for intelligent reflecting surface (IRS)-assisted millimeter wave (mmWave) multiple-input-multiple-output (MIMO) communications, which is a prerequisite to guarantee further high-rate data transmission. Considering the typical sparsity of mmWave channels, we formulate the cascaded channel estimation problem from a sparse signal recovery perspective, and then propose a novel two-step cascaded channel estimation protocol to estimate the cascaded user-IRS-base station channel with high-resolution for IRS-assisted mmWave MIMO communications. More specifically, the first step is to estimate the coarse angular domain information (ADI) and further establish the robust uplink by beam training. In the second step, by exploiting the coarse ADI, an adaptive grid matching pursuit (AGMP) algorithm is proposed to estimate the high-resolution cascaded channel state information (CSI) with low complexity. Simulation results verify that the proposed two-step channel estimation protocol significantly outperforms the state-of-the-art scheme, i.e., beam training based channel estimation, and meanwhile can reap near-optimal system performance achieved by perfect CSI.

We study the optimized dynamic antenna parameters configuration for the 3D massive multiple-input multiple- out (MIMO) system in a heterogeneous network (HetNet) with overlaid macrocells and smallcells. In particular, we propose a deep reinforcement learning (DRL) approach to jointly adjust three key antenna parameters, namely, downtilt angle, vertical and horizontal half-power beamwidths of the macro base stations (mBSs) automatically in a dynamic environment with strong user mobility. More specifically, employing the gridded user location information (ULI), we propose a novel mix Q-learning algorithm to efficiently address the challenging joint optimization problem, which integrates a parallel hyper-parameter updating mechanism in dual sub-networks and a technique of prioritized replay buffer. The resultant neural network can efficiently learn the historical experience in an online fashion and achieve excellent sum-rate performance with affordable trials. Moreover, thanks to the proposed gridded ULI, our DRL-empowered antenna configuration framework can easily fit various HetNet deployments with variable user densities. Numerical results show that the average weighted sum-rate is increased by 4.59 bit/s/Hz, and the average performance improvement is up to 24.82% as compared to the reference scheme without gridded ULI.

Motivated to understand the increasingly severe threat of unmanned aerial vehicles (UAVs) to the confidentiality of terrestrial radio links, this paper analyzes the ergodic and E-outage secrecy capacities of the links in the presence of multiple cooperative aerial eavesdroppers flying autonomously in three-dimensional (3D) spaces and exploiting selection combining (SC) or maximal ratio combining (MRC). The “cut-off” density of the eavesdroppers under which the secrecy capacities vanish is identified. By decoupling the analysis of the random trajectories from the random channel fading, closed-form approximations with almost sure convergence to the secrecy capacities are devised. The analysis is extended to study the impact of the oscillator phase noises and finite memories of the aerial eavesdroppers on the secrecy performance of the ground link. Validated by simulations, the cut-off density only depends on the range of the link in the case of SC eavesdropping, while it depends on the flight region of the eavesdroppers in the case of MRC eavesdropping.

In this article, we investigate the content distribution in the hotspot area, whose traffic is offloaded by the combination of the unmanned aerial vehicle (UAV) communication and edge caching. In cache-enabling UAV-assisted cellular networks, the network deployment and resource allocation are vital for quality of experience (QoE) of users with content distribution applications. We formulate a joint optimization problem of UAV deployment, caching placement and user association for maximizing QoE of users, which is evaluated by mean opinion score (MOS). To solve this challenging problem, we decompose the optimization problem into three sub-problems. Specifically, we propose a swap matching based UAV deployment algorithm, then obtain the near-optimal caching placement and user association by greedy algorithm and Lagrange dual, respectively. Finally, we propose a low complexity iterative algorithm for the joint UAV deployment, caching placement and user association optimization problem, which achieves good computational complexity-optimality tradeoff. Simulation results reveal that: i) the MOS of the proposed algorithm approaches that of the exhaustive search method and converges within several iterations; and ii) compared with the benchmark algorithms, the proposed algorithm achieves better performance in terms of MOS, content access delay and backhaul traffic offloading.

We study an energy-efficient unmanned aerial vehicle (UAV) multicast system, in which ground terminals (GTs) requiring a common information (CI) are grouped and a UAV flies to each group to deliver the CI using minimum energy consumption. A machine learning (ML) empowered joint multicast grouping and UAV trajectory optimization framework is proposed to tackle the challenging joint optimization problem. In this framework, we first propose the compressed-feature regression and clustering machine learning (C2ML) for multicast grouping. A support vector regression (SVR) is trained with the silhouette coefficient, a one- dimensional compressed feature regarding the distribution of GTs, to efficiently determine the number of groups that guides the K-means clustering to approach the optimal multicast grouping. With the C2ML- enabled multicast grouping, we solve the UAV trajectory optimization problem by formulating an equivalent centroid-adjustable traveling salesman problem (CA- TSP). An efficient CA-TSP inspired iterative optimization algorithm is proposed for UAV trajectory planning. The proposed ML-empowered joint optimization framework, which integrates the offline C2ML-enabled multicast grouping and the online CA-TSP inspired UAV- trajectory optimization, is shown to achieve excellent energy-saving performance.

Gaussian process (GP) for machine learning has been well studied over the past two decades and is now widely used in many sectors. However, the design of low-complexity GP models still remains a challenging research problem. In this paper, we propose a novel scalable GP regression model for processing big datasets, using a large number of parallel computation units. In contrast to the existing methods, we solve the classic maximum likelihood based hyper-parameter optimization problem by a carefully designed distributed alternating direction method of multipliers (ADMM). The proposed method is parallelizable over a large number of computation units. Simulation results confirm the benefits of the proposed scalable GP model over the state-of-the-art distributed methods.

Unmanned aerial vehicle (UAV) millimeter-wave (mmWave) communication is emerging as a promising technique for future networks with flexible network topology and ultra-high data transmission rate. Within such full-dimensionally dynamic mmWave network, beam-tracking is challenging and critical, especially when all the UAVs are in motion for some collaborative tasks that require high-quality communications. In this paper, we propose a fast beam tracking scheme, which is built on an efficient position prediction of multiple moving UAVs. In particular, a Gaussian process based machine learning scheme is proposed to achieve fast and accurate UAV position prediction with quantifiable positional uncertainty. Based on the prediction results, the beam-tracking can be confined within some specific spatial regions centered on the predicted UAV positions. In contrast to the full-space searching based scheme, our proposed position prediction based beam tracking requires little system overhead and thus achieves high net spectrum efficiency. Moreover, we also propose a practical communication protocol embedding our beam-tracking scheme, which monitors the channel evolution and triggers the UAV position prediction for beam-tracking, transmit-receive beam pair selection and data transmission. Simulation results validate the advantages of our scheme over the existing works.

Spectrum efficiency can be greatly improved through high-accuracy spectrum sensing in cognitive unmanned aerial vehicle (UAV) networks. However, the traditional centralized cooperative spectrum sensing (CCSS) methods are not applicable to the spectrum sensing of cognitive UAV networks, since the mobility of nodes and the dynamicity of network topology make it challenging to gather all the sensing information into a fusion center (FC) quickly enough. To overcome the challenge, this paper proposes a clustering-based distributed cooperative spectrum sensing (c-DCSS) scheme. Specifically, the considered cognitive UAV network is first clustered based on Max-Min distance clustering methods by jointly taking the position, velocity, and moving direction of UAVs in account, and then a two-stage fusion scheme is adopted to execute hierarchical sensing information fusion. Simulation results show that compared to the unclustered DCSS (u-DCSS) scheme, the proposed scheme significantly enhances the spectrum detection performance of cognitive UAV networks, especially when the number of UAV nodes is relatively large.

Unmanned aerial vehicles (UAVs) can now be considered as aerial base stations (BSs) to support ultra-reliable and low-latency communications by establishing line-of-sight (LoS) connections to ground users. Moreover, combining UAVs with millimeter wave (mmWave) massive multiple-input multiple-output (MIMO) will be a promissing solution. It can provide potentially high capacity wireless services due to their aerial positions and their ability to deploy on demand at specific locations. In this paper, we propose a low-cost and energy-efficient hybrid precoding architecture for UAVs, where the antenna part is realized by lens array. We investigate an efficient and energy-saving hybrid precoding scheme with robustness, which is inspired by the cross-entropy (CE) optimization in machine learning and the relative error estimation optimization. As for each selection of the hybrid precoders for obtaining the optimized precoder, we regarded it as a training process in machine learning, in which the training target is the CE-loss function between the predicted precoders and the target precoders. It aims to minimize the relative error between the predicted and actual values for optimizing the probability distributions of the elements in the analog hybrid precoder. Simulation results show that our proposed scheme can achieve higher sum rate and energy efficiency.

UAV has attracted a significant amount of attention for its low-cost and diverse applications like video surveillance, auxiliary communication, etc. In this paper, the UAV fire surveillance network is proposed and the maximization of the UAV-centric energy efficiency (EE) is investigated by jointly taking the source/channel rate control and flow routing into account. The design is cast into a cross-layer optimization problem, which is proven to be difficult to solve. In light of it, a parametric transformation approach is adopted to convert the original problem into a tractable form and further decouple it into two independent subproblems. An efficient algorithm consisting of a two-layer iterative algorithm with an inner loop and an outer loop is proposed to solve the transformed problem. Simulation results show the impact of the network configuration on the network-wide EE and the performance of the proposed algorithm.

The mobility load balancing (MLB) in self-organizing networks (SONs) is designed to automatically resolve the mismatch between network resource distribution and network traffic demand. In this paper, we propose an off-policy deep reinforcement learning (DRL) based MLB framework to balance the load distribution among all the cells. Our main contribution is three-fold. First, we propose to use off-policy RL with multiple behavior policies to autonomously learn the optimal MLB policy without any prior knowledge over the underlying wireless environments. Second, we propose a corresponding DRL-based MLB model by using deep neural networks as the function approximators to improve the generalization ability over complex system states. Third, we propose an asynchronous parallel learning framework for MLB to improve the training efficiency in a collaborative manner. Experimental results show that our proposed DRL-based MLB model can outperform the existing approaches considerably.

Distributed Gaussian Process (GP) is a scalable Bayesian method that is promising for handling big data. Our contribution in applying GP for traffic prediction is two-fold. First, to the best of our knowledge, this paper is the first to empower GP regression with the alternating direction method of multipliers (ADMM) for distributed hyper-parameter optimization in the training phase, where the ADMM training framework well balances local estimation and information consensus in a principled way. Second, in the prediction phase, we fuse local predictions obtained from distributed computing units via a cross-validation based optimal strategy, which demonstrates itself to be reliable and robust for general regression tasks. Moreover, the cross-validation based optimal fusion strategy is built upon a well acknowledged probabilistic model to retain the valuable closed-form GP prediction properties. Experimental results show that our proposed distributed GP model can outperform the state-of-the-art distributed GP models considerably, in terms of wireless traffic prediction performance.

Millimeter wave offers large bandwidth for high data-rate unmanned aerial vehicle (UAV)-to-UAV communications. Because of high mobility and attitude variations, it is challenging to maintain the communication link among the navigating UAVs with narrow beam in the mmWave band. To the best of our knowledge, this is the first paper to establish a transmission-oriented UAV attitude prediction model for the UAV-to-UAV mmWave communication link. In particular, a position-attitude prediction based beam tracking algorithm is proposed. First, a Guassian Process (GP) based learning algorithm is presented for the transmitting UAV to predict the position and attitude of the receiving UAV by using the previous position-attitude data and exploiting the relationship between the position and attitude. Then, the analog beamforming vectors are derived by using the predicted spatial angles. Simulation results demonstrate that the proposed learning algorithm can achieve high accurate position-attitude prediction, and the beam tracking algorithm considering UAV attitude variations significantly outperforms the existing algorithms with only position information.

In the coming fifth generation (5G) or beyond 5G next generation networks (NGNs), the small cell deployment is a promising solution to meet the ever increasing demands of mobile devices, and the proliferation of wireless services. The low power base station (BS), such as femtocell BS, is a cost-effective and environmental friendly substitution for the power-hungry macrocell BS. One potentially effective way to deploy those small cells is to use two-tier NGN architecture, where the first-tier carrier can authorize the second-tier carrier's access to users' transmission information database (e.g., uplink/downlink service demands), and thereafter the second-tier carrier can decide how to place small cell BSs according to the mobile users' requirements locally. However, the second-tier carriers/operators for small cell placement may not be trustworthy, and the NGN users' data privacy might be compromised. To address this issue, we integrate differential privacy (DP) preserving techniques into data-driven optimization, and propose a novel scheme that not only preserves the privacy of NGN users' transmission information, but also maximizes the revenue of small cell deployment. Briefly, differential private noises are intentionally added into the users' transmission information database. Based on queries, the second-tier carrier can aggregate a given set of users' differentially private historical data, estimate the users' demands, and formulate the data-driven revenue maximization problem. Given the stochastic programming optimization formulation, we develop feasible solutions and conduct extensive simulations with real-world transmission datasets (i.e., transmission data collected hourly from 3072 4G eNBs deployed in several southern cities of China in 2015) to verify the effectiveness of the proposed scheme.

Fountain codes are promising for unmanned aerial vehicle (UAV) communications with intermittent transmission links caused by high UAV mobility. However, it is challenging to estimate the transmission delay of UAV communication systems with fountain codes due to the uncertainty of the coding rate and the dynamic channel quality. In this paper, we propose a delay estimation method based on a joint buffer-decoder queuing model for UAV communication systems with LT codes, and show that the complexity of the proposed delay estimation method can be reduced from O(n3) to O(n2). Simulation results validate the effectiveness of the proposed delay estimation method.

In this paper, a novel three timeslots frame structure is proposed for Energy-Harvesting Cognitive Radio Networks, where the frame structure includes spectrum sensing, energy harvesting and non-orthogonal multiple access uplink transmission. Our goal is to maximize the sum-capacity of secondary users (SUs) by jointly optimizing spectrum sensing duration, energy harvesting duration and data transmission duration. To solve the challenging problem, we first derive the closed form expressions for optimal energy harvesting and data transmission durations by fixing the spectrum sensing duration, and then optimize the spectrum sensing duration by golden section search method. Finally, we obtain a sub-optimal solution through the alternate iteration of two previous steps. Simulation results show that the sum-capacity of SUs under the proposed scheme significantly increases compared to the time division multiple access (TDMA) uplink transmission protocol, especially when the transmitted power of cognitive base station (CBS) increases and the number of SUs is large enough.

The transportation network company (TNC) services efficiently pair the passengers with the vehicles/drivers through mobile applications, such as Uber, Lyft, Didi, etc. TNC services definitely facilitate the traveling of passengers, while it is equally important to effectively and intelligently schedule the routes of cruising TNC vehicles to improve TNC drivers' revenues. From the TNC drivers' side, the most critical question to address is how to reduce the cruising time, and improve the efficiency/earnings by using their own vehicles to provide TNC services. In this paper, we propose a deep reinforcement learning-based TNC route scheduling approach, which allows the TNC service center to learn about the dynamic TNC service environment and schedule the routes for the vacant TNC vehicles. In particular, we jointly consider multiple factors in the complex TNC environment, such as locations of the TNC vehicles, different time periods during the day, the competition among TNC vehicles, etc., and develop a deep Q-network-based route scheduling algorithm for vacant TNC vehicles based on distributed framework, which makes the server closer to the terminal users and accelerates the training speed. Furthermore, we apply the geo-indistinguishability scheme based on differential privacy to preserve the sensitive location information uploaded by the passengers. We evaluate the proposed algorithm's performance via simulations using open data sets from Didi Chuxing. Through extensive simulations, we show that the proposed scheme is effective in reducing the cruising time of vacant TNC vehicles and improving the earnings of TNC drivers.

In this article, we propose a deep reinforcement learning (DRL)-based mobility load balancing (MLB) algorithm along with a two-layer architecture to solve the large-scale load balancing problem for ultradense networks (UDNs). Our contribution is threefold. First, this article proposes a two-layer architecture to solve the large-scale load balancing problem in a self-organized manner. The proposed architecture can alleviate the global traffic variations by dynamically grouping small cells into self-organized clusters according to their historical loads, and further adapt to local traffic variations through intracluster load balancing afterwards. Second, for the intracluster load balancing, this article proposes an off-policy DRL-based MLB algorithm to autonomously learn the optimal MLB policy under an asynchronous parallel learning framework, without any prior knowledge assumed over the underlying UDN environments. Moreover, the algorithm enables joint exploration with multiple behavior policies, such that the traditional MLB methods can be used to guide the learning process thereby improving the learning efficiency and stability. Third, this article proposes an offline-evaluation-based safeguard mechanism to ensure that the online system can always operate with the optimal and well-trained MLB policy, which not only stabilizes the online performance but also enables the exploration beyond current policies to make full use of machine learning in a safe way. Empirical results verify that the proposed framework outperforms the existing MLB methods in general UDN environments featured with irregular network topologies, coupled interferences, and random user movements, in terms of the load balancing performance.

The cloud radio access network (C-RAN) is a promising paradigm to meet the stringent requirements of the fifth generation (5G) wireless systems. Meanwhile, the wireless traffic prediction is a key enabler for C-RANs to improve both the spectrum efficiency and energy efficiency through load-aware network managements. This paper proposes a scalable Gaussian process (GP) framework as a promising solution to achieve large-scale wireless traffic prediction in a cost-efficient manner. Our contribution is three-fold. First, to the best of our knowledge, this paper is the first to empower GP regression with the alternating direction method of multipliers (ADMM) for parallel hyper-parameter optimization in the training phase, where such a scalable training framework well balances the local estimation in baseband units (BBUs) and information consensus among BBUs in a principled way for large-scale executions. Second, in the prediction phase, we fuse local predictions obtained from the BBUs via a cross-validation-based optimal strategy, which demonstrates itself to be reliable and robust for general regression tasks. Moreover, such a cross-validation-based optimal fusion strategy is built upon a well acknowledged probabilistic model to retain the valuable closed-form GP inference properties. Third, we propose a C-RAN-based scalable wireless prediction architecture, where the prediction accuracy and the time consumption can be balanced by tuning the number of the BBUs according to the real-time system demands. The experimental results show that our proposed scalable GP model can outperform the state-of-the-art approaches considerably, in terms of wireless traffic prediction performance.

The advance in mobile communications has escalated the use of transportation network company (TNC) services by residents. The TNC services efficiently pair the passengers with the vehicles/drivers through mobile applications such as Uber, Lyft, Didi, etc. TNC services definitely facilitate the traveling of passengers, while it is equally important to effectively and intelligently schedule the routes of cruising TNC vehicles to improve TNC drivers' revenues. From the TNC drivers' side, the most critical question to address is how to reduce the cruising time, and improve the efficiency/earnings of using their own vehicles to provide TNC services. In this paper, we propose a deep reinforcement learning based TNC route scheduling approach, which allows the TNC service center to learn about the dynamic TNC service environment and schedule the routes for the vacant TNC vehicles. In particular, we jointly consider multiple factors in the complex TNC environment such as locations of the TNC vehicles, different time periods during the day, the competition among TNC vehicles, etc., and develop a deep Q-network (DQN) based route scheduling algorithm for vacant TNC vehicles. We evaluate the proposed algorithm's performance via simulations using open data sets from Didi Chuxing. Through extensive simulations, we show that the proposed scheme is effective in reducing the cruising time of vacant TNC vehicles and improving the earnings of TNC drivers.

This paper studies an energy beamforming and time duration joint optimization problem for maximizing the sum-rate of a battery-free-multi-antenna-relay-assisted wireless powered communication network (WPCN). The considered problem is non-convex due to the strongly coupled optimization variables. By introducing new optimization variables and reformulating the problem into a convex problem, the optimal solution is obtained based on convex optimization methods. Furthermore, an alternate iteration algorithm is proposed by decoupling the problem into two subproblems, and a semi-closed form solution of the optimal energy beamforming matrix is derived with given time allocation. Simulation results indicate that the proposed battery-free multi- antenna relay architecture can deliver a significant sum-rate gain for the WPCN. In addition, the proposed suboptimal algorithm only has 5% sum-rate loss compared to the optimal solution.

The distinctive characteristics of unmanned aerial vehicle networks (UAVNs), including highly dynamic network topology, high mobility, and open-air wireless environments, may make UAVNs vulnerable to attacks and threats. In this study, we propose a novel trust model for UAVNs that is based on the behavior and mobility pattern of UAV nodes and the characteristics of inter-UAV channels. The proposed trust model consists of four parts: direct trust section, indirect trust section, integrated trust section, and trust update section. Based on the trust model, the concept of a secure link in UAVNs is formulated that exists only when there is both a physical link and a trust link between two UAVs. Moreover, the metrics of both the physical connectivity probability and the secure connectivity probability between two UAVs are adopted to analyze the connectivity of UAVNs. We derive accurate and analytical expressions of both the physical connectivity probability and the secure connectivity probability using stochastic geometry with or without Doppler shift. Extensive simulations show that compared with the physical connection probability with or without malicious attacks, the proposed trust model can guarantee secure communication and reliable connectivity between UAVs and enhance network performance when UAVNs face malicious attacks and other security risks.

In this paper, the non-orthogonal multiple access (NOMA) technology is integrated into cognitive orthogonal frequency-division multiplexing (OFDM) systems, called cognitive OFDM-NOMA, to boost the system capacity. First, a capacity maximization problem is considered in half-duplex cognitive OFDM-NOMA systems with two accessible users on each subcarrier. Due to the intractability of the considered problem, we decompose it into three subproblems, i.e., the optimization of, respectively, sensing duration, user scheduling, and power allocation. By investigating and exploiting the characteristics of each subproblem, the optimal sensing duration adaptation, a matching-theory-based user scheduling, and the optimal power allocation are proposed correspondingly. An alternate iteration framework is further proposed to jointly optimize these three subproblems, with its convergence proved. Moreover, based on the non-cooperative game theory, a generalized power allocation algorithm is proposed and then used in the framework to accommodate half-duplex cognitive OFDM-NOMA systems with multiple users on each subcarrier. Finally, the proposed framework is extended to solve the capacity maximization problem in full-duplex cognitive OFDM-NOMA systems. Simulation results validate the superior performance of the proposed algorithms. For example, for the case of two accessible users, the proposed framework approaches the optimal solution with less than 1% capacity loss and 120 times lower complexity compared with exhaustive search.

Wireless traffic prediction can effectively reduce the uncertainty in network demand and supply, and thus is a key enabler of smart management in next-generation wireless networks. To the best of our knowledge, this paper is the first to establish a wireless traffic prediction model by applying the Gaussian Process (GP) method based on real 4G traffic data. Our work is two-fold: First, based on the observed wireless traffic patterns, the kernel in our proposed GP model is designed accordingly to capture both the periodic trend and dynamic deviations; second, by leveraging the Toeplitz structure in the covariance matrix, the computational complexity of hyperparameter learning is significantly reduced from O(n3) to O(n2) and that of inference is reduced from O(n3) to O(n \log n), without any loss of prediction accuracy. Experimental results show that the proposed GP model can attain up to 97% prediction accuracy, and outperform the state-of-the-art algorithms considerably.

In this paper, the problem of joint dynamic spectrum access and multi-relay selection is investigated in relayenabled cooperative communication systems to maximize the system sum-capacity. Since the considered problem is a mixed integer nonlinear program, which is generally intractable to find the optimal solution, two matching theory-based suboptimal algorithms are proposed to reduce the computational complexity for two different cases. For the case that each source node can only be assisted by one relay, a cyclic three-sided matching algorithm is firstly proposed to attain the stable matching results for the selection of the source node and the relay with the spectrum band used. Then, for the case that each source node can be assisted by more than one relay, a two-step matching algorithm is proposed to perform joint dynamic spectrum access and multi-relay selection. Simulation results show that the proposed algorithms, with much lower complexity compared to the optimal exhaustive search, can achieve the near-optimal performance with a gap to the optimum being less than 5%.

In this paper, we study the throughput performance of LTE-licensed-assisted access (LAA) networks coexisting with wireless local area networks (WLAN) in the presence of imperfect spectrum sensing. By considering the false-alarm probability and miss-detection probability of widely-used energy detection, we analyze the potential impact of imperfect spectrum sensing on the access performance of unsaturated LTE-LAA networks along with binary slotted exponential backoff. The access probabilities of LTE-LAA networks and WLAN systems are derived based on the discrete-time Markov chain (DTMC) model. Furthermore, the throughput of LTE-LAA networks is maximized by jointly optimizing the sensing duration and threshold. Numerical results confirm the great impact of imperfect spectrum sensing on the LTE-LAA system throughput, and indicate the optimized sensing duration and threshold can achieve a significant performance gain compared to the fixed ones.

In this paper, the non-orthogonal multiple access technology is integrated into cognitive orthogonal frequency division multiplexing (OFDM) systems, referred to as NOMA-OFDM, to boost the system capacity as well as the number of accessible users. The considered problem is formulated as jointly optimizing the sensing duration, user selection, and power allocation under the constraints of maximum transmitted power and maximum allowable interference. In order to overcome the non- convexity, we decompose the formulated problem into three subproblems, i.e., the sensing duration optimization, user selection optimization and power allocation optimization. By exploiting the individual characteristic of each subproblem, three efficient algorithms, i.e., bisection search method, matching-theory-based user selection and difference of convex (DC) programming, are proposed to solve the corresponding subproblems, respectively. Moreover, an alternate iteration algorithm is also provided to perform joint optimization of three subproblems. Simulation results validate the fast convergence and considerable performance gain of the proposed algorithms.

This paper studies the spectrum efficiency (SE) maximization problem for cooperative multi-antenna power beacon (PB)-enabled wireless powered communication networks (WPCN), where each transmitter harvests energy from surrounding PBs and then transmits data to the corresponding receiver within its allocated duration. The considered problem is formulated as jointly optimizing the energy beamforming vectors of PBs, the transmission duration, and the transmit power of users to maximize the total SE. In order to derive an efficient algorithm, the SE maximization problem is decomposed into two subproblems: the SE maximization problem for data transmission and the energy consumption minimization problem for energy transfer.We prove that the optimal SE is concave with the harvested energy of each user, and based on this concavity, an efficient algorithm is proposed to achieve the optimal solution. Finally, simulation results validate the optimality of the proposed algorithm, and verify the superiority of the proposed scheme-more than 150% performance gain is obtained, compared with the scheme of single PB with single antenna.

In this paper, an energy-incentive cooperative transmission (EICT) scheme is proposed for wireless ad hoc networks, where a node uses energy as reward to seek for cooperative transmission from neighboring nodes and then the cooperative node adopts a decode-and-forward (DF) protocol to relay data. An optimal time slot and power allocation algorithm is proposed to maximize the sum-rate under the constraints of peak power, energy consumption, and individual data rate when the channel state information (CSI) is perfectly known at transmitters. Furthermore, the scenario that only the statistical CSI is available at transmitters is investigated, and an alternative algorithm is presented to optimize time slot and power allocation. Simulation results confirm the superiority of the proposed scheme over existing ones, demonstrating a more effective mechanism to stimulate cooperation in wireless ad hoc networks.

In this paper, we investigate power allocation for simultaneous wireless information-and-energy multicast in cognitive OFDM systems. Our objective is to maximize the energy efficiency (EE) subject to the maximum power constraint at cognitive base station (CBS), maximum receiver interference constraint at each primary user (PU) and minimum harvested energy constraint at each energy receiver (ER). Due to the non-convexity of objective function, fractional programming is adopted to transform the nonconvex problem to a convex one. However, the complexity of the traditional optimization method, i.e., interior point method, is still too high to solve the transformed problem. To this end, a bisection-search-based suboptimal algorithm is proposed. Simulation results show that the proposed algorithm can greatly reduce the complexity (up to 1/12 at most) at the cost of tiny performance loss (less than 2%) compared with traditional convex optimization algorithms.

This paper investigates an energy efficiency optimization problem in cognitive orthogonal frequency division multiplexing systems. The goal is to maximize the energy efficiency by adapting the sensing duration, detection threshold, and transmit power to the constraints of the energy consumption of the secondary network and the interference to the primary network in a statistical manner. First, the case of identical detection threshold for all subcarriers is considered. In order to circumvent the intractability of the resulting problem, an alternate iteration framework is proposed to iteratively solve the three decoupled subproblems: sensing duration optimization, detection threshold optimization, and power allocation optimization. By exploiting the characteristics of each subproblem, the proposed framework is proved to be convergent. Then, the case with individual detection threshold for each subcarrier is explored. By proving that the optimal detection threshold is the root of a quadratic equation with one unknown variable, the proposed framework can be applied with minor modification. Simulation results show that the proposed alternating optimization framework can approach rapidly to the optimal solution, with less than 1% gap. Compared with the existing schemes, both the cases with identical and individual detection thresholds can achieve a considerable energy efficiency gain, with the latter further outperforming the former.

Considering the dynamics of vacant spectrum in cognitive radio networks, spectrum sensing is one of the most challenging technologies. However, traditional spectrum sensing technologies fail to resolve the contradiction between accuracy and complexity. To solve this paradox, this paper proposes a novel spectrum sensing scheme based on the distribution of phase difference (PD) between noise-perturbed signal and Gaussian noise. By using the variance of PD as the test statistics, the proposed PD variance detection (PDVD) is formulated for efficient spectrum sensing and its performance is analyzed under Rayleigh fading and Gaussian noise, which has a low complexity of O(K)and is immune to the noise uncertainty in contrast to the energy detection scheme. Both simulations and field measurement results show that the proposed PDVD can achieve a performance gain of 2-4dB for SNR requirement compared to the energy detection scheme when the sample length reaches 500.

In this paper, we analyze the outage probability of a three-node decode-and-forward (DF) relay network, where the relay adopts a power-splitting protocol to harvest energy from the received signal, and utilizes the harvested energy to forward information. First of all, the theoretical expression of outage probability is derived, and then the closed forms of the optimal amount of harvested energy and the best relay location are achieved analytically in order to minimize the outage probability. Furthermore, the condition that the outage performance with energy harvesting (EH) surpasses that without EH is also deduced to figure out when EH is indispensable to relay enhancements. Numerical results verify the correctness of our theoretical derivation, and validate that there exist the optimal amount of harvested energy and the best relay location to reach the minimum outage probability in the energy harvesting relay network. The work of this paper will provide valuable insights into the effect of harvested energy and relay location on outage probability, and be instrumental in how to deploy relays with RF energy harvesting functions.

In this paper, we focus on whether and how the quality-of-service (QoS) requirements can be guaranteed in radio frequency (RF)-harvesting-powered cognitive orthogonal frequency division multiplexing (OFDM) relay systems, where the available energy is extremely limited by power transfer capability, and the available spectrum is greatly restricted by primary networks. Thus, the joint power splitting ratio, subcarrier assignment and power allocation problem is formulated under the interference constraints and QoS requirements. In addition, in order to maximize the capacity of the secondary network, an optimal power splitting and resource allocation scheme is proposed based on dual decomposition as well as time sharing conditions. Simulation results show that the proposed scheme can perfectly satisfy the QoS requirements in spite of limited energy and spectrum, and outperforms conventional methods in terms of capacity and QoS satisfaction.

In this paper, we consider the simultaneous information and power transfer (SIPT) in cognitive radio networks (CRNs) where the secondary receiver (SR) can harvest energy from both the primary transmitter (PT) and the secondary transmitter (ST). The SIPT-enabled SR turns the harmful interference from the PT into energy and uses it to prolong battery life. We assume that the ST and SR adopt orthogonal frequency division multiplexing (OFDM) modulation and our goal is to find the optimal power allocation and power splitting ratio that maximize the energy efficiency of the secondary system. To settle this non-convex problem, we utilize the fractional programming to transform the objective function into the counterpart subtractive form and then take advantage of an approximation to turn the problem convex. After that, the equivalent convex problem is solved by an efficient iterative algorithm. Numerical results illustrate that the proposed method performs well and achieves a good tradeoff between energy efficiency and capacity of the secondary system, especially in high signal interference-plus-noise-ratio (SINR) regions.

In this paper, we investigate resource allocation in OFDM based CRNs with cyclic prefix power transfer (CPPT). In this system, the secondary receiver (SR) can extract power from the cyclic prefix (CP) of the received signal and use the harvested energy for its own energy supply. Our objective is to find the optimal power allocation and CP size that maximize the energy efficiency (EE) of the secondary system, under the maximum primary user (PU) interference and the minimum harvested energy constraints. As the CP size will impact the interference constraints, the relationship between optimal power allocation and CP size is hard to be expressed by a function. In this paper, we solve the problem by two steps. First, we propose an efficient iterative algorithm to achieve the optimal power allocation for the EE maximization problem with a certain CP size, and then find the optimal CP size through a full-search method. The influence caused by the CP size on power allocation is illustrated by simulations and numerical results prove CPPT can improve the system utilization greatly compared with traditional methods.

Nowadays energy efficiency (EE) of wireless communication systems has become a hot issue, yet the nonlinear effect and inefficiency of power amplifier (PA) have posed practical challenges for system designs to achieve high EE. However, most of previous work only considered linear PA. In this paper, we studies EE optimization concerning with I-way Doherty PA which has widespread use with I = 1 and I = 2 in orthogonal frequency division multiplex (OFDM)-based cognitive radio (CR) systems. The aim is to maximize EE with nonlinear PA subject to the total power budget, the interference constraint and the minimum rate requirement. Other than traditional methods, the problem is hard to solve directly due to the nonlinearity of PA, and a bisection search method tailored for nonlinear PA is adopted to achieve the sub-optimal solution. Numerical results demonstrate that if the PA's nonlinearity is not considered, the EE performance of OFDM-based CR systems can be severely overestimated by 34% at P max out = 80 W for 1-way Doherty PA. Meanwhile, the EE performance can be enhanced by approximate 32% if 2-way Doherty PA is utilized instead of 1-way Doherty PA.

Unlicensed spectrum, around 5 GHz, will be introduced to Long Term Evolution (LTE) systems, referred to as LTE-Unlicensed (LTE-U), to combat the explosive growth of traffic volume in next 10 years. In this paper, the interference-controlled power allocation problem is studied for LTE-U systems, which can be inherently classified as orthogonal frequency division multiplexing (OFDM)-based cognitive radio (CR) systems, where optimal power allocation algorithms are currently available by resorting to computation-intensively numerical iterations with a moderate risk of divergence. In order to satisfy the rigorous algorithm requirements, i.e., convergent outputs of power allocation and running time of milliseconds, for practical LTE-U deployments, a cap-limited waterfilling method is proposed to regulate the interference to primary users one by one successively, by which not only a near-optimal solution can be obtained, but also the intractable iteration divergence and computation complexity issues can be excluded completely. Simulation results indicate the capacity performance of the proposed low-complexity method approaches to the optimal solution with a slight loss less than 5%, and is remarkably superior to the existing suboptimal methods.

This paper investigates the energy-efficient power loading with intercarrier and intersymbol interference considerations for OFDM-based cognitive systems. The objective is to maximize the energy efficiency (EE) as well as to balance the tradeoff between intercarrier interference (ICI) and intersymbol interference (ISI) by jointly optimizing the subcarrier bandwidth and power allocation in a mobile scenario, under the power budget and the interference constraint. First, the primal problem is converted into a convex optimization problem by fractional programming. Then, the Lagrange dual function and the sub-gradient method are adopted to achieve the optimal power allocation and the golden section method is employed to search for the optimal subcarrier number (i.e, subcarrier bandwidth). Numerical results show that the proposed algorithm can realize ICI control by choosing an optimal subcarrier number, and simultaneously the EE can be significantly improved by 139%.

In this letter, cooperative multicast in cognitive relay systems is investigated, where multiple multi-antenna relay nodes help forward the transmitted message from different sources to the corresponding destinations. Our objective is to maximize the global transmission rate scaling factor by cooperatively optimizing the forwarding matrix for each relay node. To solve the problem effectively, we first prove that the optimal forwarding matrix at each relay node is the combination of a multi-stream matched-filter receiver and a multi-stream adaptive beamformer, and then demonstrate that the beamformer design problem can be further transformed into the cognitive multi-group multicast beamforming problem. Finally, a bisection-based algorithm is proposed to find the optimal beamforming vector. Compared to the existing cooperative multicast schemes, the proposed scheme can achieve higher transmission rate and provide better protection for primary nodes.

Simultaneous wireless information and power transfer (SWIPT), which processes information and scavenges energy from the same ambient radio frequency signals, has recently drawn significant attention. In addition, the integration of cognitive radio and two-way relay transmission has emerged as a promising approach for improving spectral efficiency. In this paper, we consider a cognitive two-way relaying network, where the SWIPT-enabled-relay helps two secondary nodes exchange information with the energy harvested from the received signals. The rate maximization problem for the relaying network is formulated under the constraint that interference to the primary user resulting from the transmission of the secondary relaying network cannot exceed the threshold. Based on the mathematical analysis of the problem, we propose a suboptimal joint relay selection and power allocation scheme with the Bisection Search method. Numerical simulations confirm the near-optimality of the proposed scheme by comparing with the optimal solution. Specially, the best two-way relay location is also revealed in simulations.

Recent advances in network coding have shown great potential for efficient information transfer. In this paper, exploiting inter-layer and inter-session network coding, we address the distributed control problem in heterogeneous-service networks (HSNs) with booming multi-rate multicast (MRM) and unicast (UC) services. Different from the literatures on inter-layer/inter-session schemes, heterogeneity and fairness among MRM users and between different services are jointly considered. With the Lagrangian and subgradient method, a decentralized rate control algorithm is developed with little coordination among intermediate nodes, in which only local information is needed to achieve rate, congestion, and fairness balance control. Numerical examples are provided to verify the effectiveness and convergence of the proposed algorithm. Furthermore, we demonstrate the performance improvement and implementation advantages of the proposed algorithm compared with the previous solutions considering layered coding for an MRM service or inter-session coding limited for UC services.

We consider a spectrum sharing protocol in which a secondary system can operate on the same spectrum with a primary user. In the protocol, the secondary system helps the primary system achieve its target rate by acting as a relay for the primary system. As a reward, the remaining subcarriers can be used for the secondary transmission. In order to alleviate the disadvantages of amplify-and-forward (AF) and decode-and-forward (DF) relaying techniques, an adaptive relaying scheme is proposed. In this paper, we study energy efficient resource allocation for the cognitive cooperation system. The considered problem is modeled as a non-convex optimization problem which takes into account the total transmit power of the secondary system and the minimum required data rate of the primary system. The optimal set of subcarriers used for cooperation, subcarrier power allocation and relaying technique are derived for maximization of the energy efficiency (EE) of the secondary system by using fractional programming and dual theory. Simulation results demonstrate that significant performance gains can be achieved by the devised scheme.

This paper investigates an energy-efficient transmission scheme in cognitive radio networks, where primary users (PUs) may reoccupy the spectrum when secondary users (SUs) is transmitting data. We aim to maximize the energy efficiency by jointly optimizing the transmission power, the fusion rule threshold and the sensing/transmission durations. Firstly, it is derived that for a given fusion rule threshold, the objective function is unimodal while only one optimization parameter varies. Furthermore, we provide the corresponding closed-form expressions of the optimal data transmission duration and transmission power. Finally, the globally unimodal property is proven, and hence, the globally optimal point can be easily found with the proposed algorithm based on the alternating direction method (ADM). Numerical simulation results show that our proposed scheme is much better than the existing ones.

This letter investigates the throughput optimization of multiple description coding multicast (MDCM) in cognitive radio networks (CRNs) by taking into account both statistical channel state information (CSI) and the interference from the primary network. A statistical-CSI-based MDCM scheme with low complexity is proposed, which is shown to be able to approach the perfect-CSI performance with large multicast group size. For comparison, conventional multicast (CM) is also analyzed. A tight upper bound is derived, which decays to zero rapidly when the multicast group size grows. Numerical results are also presented to validate the proposed scheme.

This paper investigates the energy efficient resource allocation strategy for OFDM-based cognitive radio (CR) networks with imperfect spectrum sensing. The interference model taking the sensing errors into account is formulated at first. And the objective is to maximize the energy efficiency of the multiuser CR system subject to the total transmission power budget and each primary user's (PU) interference constraints. As the primal problem is a mixed integer nonlinear programming issue, we will separate the resource allocation scheme into two steps, i.e., subcarrier assignment and power allocation. After the suboptimal subcarrier assignment, an optimal power allocation algorithm is proposed based on fractional programming and sub-gradient method. The simulation results show that the proposed resource allocation scheme can achieve higher energy efficiency than the one maximizing the capacity of the CR networks. Meanwhile, it can protect the normal communication of each PU compared to the scheme without considering sensing errors.

In this paper, cooperative multicast with the help of short-range data sharing is studied in the cognitive radio networks (CRNs). The original multicast data is split into many segments, and the transmission of each segment is divided into two stages. In the first stage, cognitive base station transmits each segment to the corresponding cooperative user, and in the second stage, the cooperative user decodes the received data and broadcasts it to other multicast users. Based on this transmission model, cooperative multicast is formulated as an optimization problem with the aim of maximizing the total transmission rate. Afterwards, we first propose a cooperative user selection strategy, which is meaningful when the multicast size is large, and then implement the resource allocation with dual translation and subgradient updating. The simulation results show that the proposed cooperative multicast scheme can achieve much higher spectrum efficiency than both conventional multicast scheme and multiple description coding multicast scheme.

This paper proposes an energy-efficient multicast scheme for downlink orthogonal frequency division multiplexing (OFDM) system in which the base station (BS) is equipped with multiple antennas. We employ the multiple description coding multicast (MDCM) model and beamforming technique to maximize the energy efficiency (EE) with the constraint on total transmit power. In MDCM, the transmission rate is not limited by the user with the minimum channel quality any more. And the beamforming technique can enhance the signal strength of the weakest user. In this paper, a two-step suboptimal scheme is studied. Firstly, the beamforming weighted vector (BWV) is obtained by a vector splitting algorithm, and then the power and subcarrier allocation is realized by fractional programming and subgradient method. Numerical results reveal that the proposed scheme can achieve near optimal EE and greatly improve the EE compared with two spectrum-efficient schemes. In addition, the energy-efficient scheme can render a good performance in EE as well as throughput when the total transmit power is small.

Scheduling with single carrier property restriction has a significant impact on system performance in Long Term Evolution (LTE) uplink (UL). Due to the unavoidable delay of control signaling, the scheduling decisions have great influence on the variation of inter-cell interference (ICI) which affects the performance of adaptive modulation and coding (AMC). Many studies have been carried out on the topic of resource allocation in single-carrier frequency division multiple access (SC-FDMA) system. Some of them deal with the ICI variation with inter-cell measurements or coordination. In this paper, we present the problem of frequency domain packet scheduling (FDPS), analyze the negative effects of ICI variation and develop our improved proportional fair (PF) scheduling algorithm based on the traditional one for LTE UL. Our proposed algorithm decreases the ICI variation and improves AMC accuracy without any inter-cell coordination. Compared with the traditional way, system level performance shows that at least 29% gain can be obtained at both the cell user average spectral efficiency and the cell edge user spectral efficiency with comparable proportional fairness in typical interference-limited scenario.

With the continuous growth of the wireless communication business, energy issues and environmental problems are becoming increasingly grim. Therefore, this paper investigates the energy efficient transmission scheme with cooperative sensing in cognitive radio networks, in which AND fusion rule is introduced to determine the presence of the primary user. It is proved that the energy efficiency is a quasi-concave function with sensing time when the number of cooperative users satisfies certain constraints. Aiming at maximizing the energy efficiency, the transmission power is selected at first, then a scheme of jointly optimizing sensing time, energy detector threshold and the number of cooperative users is proposed based on the related theory analysis. From the simulations, it can be found that the optimal sensing time is only about half of that consumed in single user sensing, and the proposed scheme has significant improvement in energy efficiency.

In this paper, resource allocation problem with imperfect channel information in cognitive radio networks (CRNs) is studied concerning the multiple description coding (MDC) multicast transmission. The traditional unicast model is extended to MDC multicast in CRNs, which aims to maximize the total received rate of all cognitive radio (CR) users. Primarily, a new auxiliary variable, named as normalized channel power gain in this paper, is introduced to substitute the transmission rate as the optimization variable. Then a two-stage method is proposed to conduct the resource allocation: first the multicast group (MG) selection and the normalized channel power gain setting, second the optimal power allocation. Meanwhile, as only estimated channel gain for the interference channel gain is obtained, the primary users' interference can not be accurately estimated when we carry out the power allocation. Consequently, we suitably enlarge the estimated channel gain for the purpose of interference control. It has been verified in the simulation results that the proposed scheme can improve the system performance apparently in terms of both throughput maximization and interference control.

In this paper, we introduce multiple description coding multicast (MDCM) into orthogonal frequency division multiplexing based (OFDM-based) multi-cell cognitive radio networks (CRNs) and investigate the resource allocation problem aiming to maximize the weighted sum rate (WSR). An efficient distributed scheme including subcarrier assignment and power allocation is proposed. During subcarrier assignment, each cell heuristically selects the multicast group (MG) and the associated set of scheduled cognitive radio users (CRUs) for each subcarrier. During power allocation, each cell allocates power to subcarriers by a proposed iterative scheme considering pricing to enhance the efficiency of selfish iteration. The distributed scheme does not require global network information and each cognitive radio (CR) cell performs its own resource allocation through limited interaction with other CR cells. The effectiveness of the proposed scheme is illustrated by extensive simulation results.

This paper investigates the resource allocation problem for multiple description coding (MDC) multicast in OFDM-based cognitive radio (CR) systems, where secondary users (SUs) share the primary spectrum under the interference constraints of primary users (PUs). The previous multicast model is usually based on the full buffer traffic in which there are sufficient data for multicast groups (MGs) to receive. However, it does not consider the nature of limited traffic in practical systems. Taking this case into consideration, saturation rate (SR) is introduced to describe the characteristic of non-full buffer traffic. Aiming at maximizing the weighted sum rate (WSR), a modified resource allocation algorithm based on the Lagrangian dual decomposition is proposed. Simulation results show that the proposed scheme significantly outperforms the conventional multicast. Furthermore, our scheme is meaningful in non-full buffer traffic scenarios, because it can avoid the resource redundancy for users with good channel conditions and the resource starvation for users with bad channel conditions, and hence will reduce the resource wasting.

We study a repeated auction for the resource allocation problem in OFDM-based cognitive radio networks (CRNs), in which secondary users (SUs) share the primary spectrum under the interference constraints of primary users (PUs). With the inter-cell interference and mutual interference between PUs and SUs, the resource allocation problem is formulated as a non-convex optimization problem. Auction performs well in solving non-convex problems, therefore the interference auction with cooperative bidding is proposed. Moreover, with the theoretical analysis of equilibrium, an implementation algorithm for the auction is developed and the convergence is proved. Simulation results show that the interference auction obtains a good spectrum efficiency improvement and a rapid convergence rate.

In this paper, we propose a distributed call admission control scheme for multiservice OFDMA system. In our proposed call admission control (CAC) scheme, a cell dynamically adjusts optimal new call and handoff acceptance ratios by three steps. First, exchange numbers of calls in handoff area with its adjacent cells. Then, dynamically allocate resource for different types of traffic. Finally, use a Markov queue model and a bidirectional iterative search method (BIS) to calculate the new call and handoff acceptance ratios to obtain a near-maximal network utility which depends on both packets delay and the number of ongoing calls. Simulation results show the performance gain of the proposed scheme.

This paper investigates the energy-efficient transmission under hybrid spectrum sharing scenario, where secondary users (SUs) can select a proper spectrum sharing method based on the state of primary users (PUs). Firstly, an optimization model to evaluate the energy-efficiency, as measured by the "throughput per Joule" metric, is proposed. Then, we show that there exists a unique globally optimal transmission power scheme for SU to achieve the maximum energy-efficiency by decomposing the optimization problem into two sub-problems. It's usually difficult to directly solve the optimal problem which is equivalent to solve joint nonlinear equations. So we propose a one-dimension low-complexity search algorithm, considering the unimodal characteristic of energy-efficiency function. Our simulation results show that the proposed transmission scheme can greatly improve energy savings with energy-efficiency maximization objective.

In this paper, we develop a distributed sub-carrier allocation algorithm with a low complexity in OFDMA multi-cell system , which is decomposed into two sub-problems: inter-cell and intra-cell sub-carrier allocation. During inter-cell process, based on time-variant characteristics and performance differ-ences among cells of sub-carriers, each base station applies Ant Colony Optimization (ACO) to choose available sub-carriers dynamically, which contributes to reduce co-channel interference. According to pheromone associated with channel capacity, the probability of choosing sub-carrier with better capability is higher. Then during intra-cell process, each base station sufficiently uses multi-user diversity to satisfy all users' QoS requirements and greatly increase system throughput. Simulation results show that this algorithm exhibits substantial gains over existing frequency reuse schemes.

We study in this paper a sub-optimal beamforming algorithm for the MISO interference channel based on interference pricing, defined as user's marginal decrease in its utility due to interference. An iterative approach is considered given a set of interference prices and beams. Combining the interference price from other users and channel state information, the transmitter can update the beams to maximize its pure utility, which is defined as its own utility minus loss from other users' utility caused by its interference. Meanwhile, the receiver can update its interference price according to the total received interference. Our results from comprehensive simulations show that this algorithm is vastly superior to the existing beamforming algorithms, e.g. the maximum-ratio transmission (MRT) beamforming scheme, the zero-forcing (ZF) beamforming scheme and the algorithm in in terms of efficiency, convergence and robustness, which can get close to the Pareto edge of the available rate region in.

In this work we consider the rate control issue for network coding based multicast among multiple sessions, which can be formulated as a network utility maximization problem. To solve the problem we propose a distributed optimization decomposition approach, which is different from the previous work in the literature in that (1) it is a hierarchical decomposition approach where the primal problem is decomposed recursively, until an independent rate control module is obtained and the decomposed subproblems can be solved by the distributed max-flow algorithm and we emphasize the layered functionality allocation of decomposed subproblems following the framework of "Layering as Optimization Decompositions"; (2) we first separate the primal problem by relaxing the capacity constraint among sessions; (3) to implement end-to-end control, we separate the independent rate control module at end node from the operation in the interior of the network. In this work we intend to propose not only a rate control algorithm but also a possible choice of network architecture for network coding based communication with rate control capability.

To meet the challenges in sensing the spectrum in a reliable and timely manner, an approach of multi-antenna spectrum sensing based on spectral correlation property is proposed in this paper. Without any prior information of the licensed system, it extracts the frequency-domain channel information from the spectral correlation functions (SCF) of multiple antenna signals, and then generates the SCF of multi-antenna combining (SCF-MAC) over the frequency-cycle frequency plane. The expressions for the detection and false alarm probabilities of a decision based on SCF-MAC are derived for frequency selective channel, which indicate the proposed approach is capable of achieving full spatial diversity in spectrum sensing. Simulation results corroborate the theoretical analysis and show the significant performance improvement.

A novel resource allocation problem for min-rate guaranteed services is studied in orthogonal frequency division multiplexing (OFDM) distributed antenna systems (DAS) by this paper. In addition to multiuser diversity as well as conventional space diversity (such as multiple-input multiple-output (MIMO) -OFDM), distributed multiantenna diversity and time diversity are also exploited by the proposed algorithm. The proposed allocation algorithm iteratively assigns subcarriers to users to maximize rate-sum capacity subject to min-rate constraints. Simulated results show the proposed algorithm can fully utilize all degrees of freedom including antenna, user, time, and frequency to ensure users' min-rate transmission. Consequently, OFDM DAS outperforms OFDM co-located antenna systems (CAS) in terms of rate-sum capacity due to distributed antenna deployment.

This paper formulates resource allocation problem in orthogonal frequency division multiplexing (OFDM) distributed antenna systems (DAS) by extending conventional one in OFDM co-located antenna systems (CAS). The algorithm to achieve maximum rate-sum capacity is derived, and the achievable capacity of a single user is also given by theoretical analysis. Simulated and theoretical results show that OFDM DAS can achieve much more rate-sum capacity than OFDM CAS, and the performance gain increases with the number of antennas in systems.