With the unprecedented capability of configuring antenna positions, fluid antenna systems (FASs) have been recognized as a key enabler for secure communications. However, it is challenging to optimize the secrecy rate by reconfiguring positions of fluid antennas based on the fast-changing instantaneous channel state information (CSI). Considering the effectiveness of regularized zero-forcing (RZF) and zero-forcing (ZF) precoding in mitigating information leakage in physical layer security, we propose a two-timescale design to maximize ergodic secrecy sum rate (ESSR), where only the statistical CSI is utilized for the port selection of FASs. For that purpose, we first derive the analytical expression for the ESSR of FASs with RZF/ZF precoding by utilizing random matrix theory (RMT). Then, based on the evaluation results, we propose a two-timescale algorithm to maximize the ESSR by optimizing both port selection of FASs and regularization factor of RZF. Numerical simulations validate the accuracy of the proposed ESSR evaluation and show that the proposed two-timescale design could improve the ESSR performance significantly when compared with the uniform port selection.

The integration of blockchain technology with the sixth generation (6G) networks offers a promising approach to enhance the reliability and trustworthiness of industrial Internet of Things (IIoT) systems. Since IIoT devices typically lack the capability to directly participate in blockchain consensus, drone networks offer a viable alternative by providing dynamic coverage and reducing dependence on fixed infrastructure such as centralized servers. Sharding is an effective method to improve the scalability of blockchain systems, yet existing sharding schemes overlook the complexity and dynamic nature of drone network topologies. These networks frequently experience changes due to drone mobility, task variations, and energy constraints, all of which can disrupt consensus communications. To address these challenges, we propose a time-varying graph-based blockchain sharding scheme (BSTVG) tailored for large-scale drone blockchain networks. The time-varying graph-based model captures the temporal dynamics of drone communications. We adopt an improved K-Means++ clustering algorithm that incorporates communication conditions to adapt network sharding. Additionally, we develop mechanisms for intra-shard consensus and cross-shard transaction processing. To accommodate node joins, exits, and significant topological changes, we introduce a slot–epoch coupling mechanism that dynamically adjusts the epoch length. We analyze the security of the proposed scheme and validate its performance through simulations. Experimental results demonstrate that our scheme not only enhances the throughput but also reduces energy consumption of the drone blockchain network.

The reconfigurability of fluid antenna systems (FASs) and reconfigurable intelligent surfaces (RISs) provides significant flexibility in optimizing channel conditions by jointly adjusting the positions of fluid antennas and the phase shifts of RISs. However, it is challenging to acquire the instantaneous channel state information (CSI) for both fluid antennas and RISs, while frequent adjustment of antenna positions and phase shifts will significantly increase the system complexity. To tackle this issue, this paper investigates the two-timescale design for FAS-RIS multi-user systems with linear precoding, where only the linear precoder design requires instantaneous CSI of the end-to-end channel, while the FAS and RIS optimization relies on statistical CSI. The main challenge comes from the complex structure of channel and inverse operations in linear precoding, such as regularized zero-forcing (RZF) and zero-forcing (ZF). Leveraging on random matrix theory (RMT), we first investigate the fundamental limits of FAS-RIS systems with RZF/ZF precoding by deriving the ergodic sum rate (ESR). This result is utilized to determine the minimum number of selected antennas to achieve a given ESR. Based on the evaluation result, we propose an algorithm to jointly optimize the antenna selection, regularization factor of RZF, and phase shifts at the RIS. Numerical results validate the accuracy of performance evaluation and demonstrate that the performance gain brought by joint FAS and RIS design is more pronounced with a larger number of users.

With its capability to obtain a good tradeoff between complexity and performance, regularized zero-forcing (RZF) has been widely investigated to enhance the physical layer security. However, the associated reliability performance, i.e., secrecy outage probability (SOP), is not yet available in the literature. In this paper, we characterize the secrecy performance of RZF in the multi-user, downlink multiple-input single-output system. For this purpose, we first set up a central limit theorem for the joint distribution of users’ signal-to-interference-plus-noise ratio and eavesdropper’s signal-to-noise ratio by leveraging random matrix theory. The result is then utilized to obtain a closed-form approximation for the ergodic secrecy rate and SOP of three typical scenarios: the case with only external Eves, the case with only internal Eves, and that with both. The derived results are then used to evaluate the percentage of users in secrecy outage and the required number of transmit antennas to achieve a positive secrecy rate. It is shown that, with equally-capable Eves, the secrecy loss caused by external Eves is higher than that caused by internal Eves. Numerical simulations validate the accuracy of the theoretical results and demonstrate the advantage of RZF over other linear transmitters such as ZF.

Unmanned aerial vehicles (UAVs) have been extensively employed in the low-altitude intelligent network (LAIN) on data collection and transmission, enabling predictive maintenance, enhanced safety, and improved operational efficiency. However, the openness of wireless communication networks makes UAVs vulnerable to numerous security threats. To secure the critical transmitted data, many authenticated key agreement (AKA) schemes have been developed. Nevertheless, most existing AKA schemes fail to efficiently and securely authenticate communications between a single user and multiple UAVs in IIoT environments. To this end, we propose an extensible multi-party AKA scheme for LAINs. Specifically, we employ the physical unclonable functions and the Chinese remainder theorem to facilitate efficient authentication and data aggregation. Furthermore, by leveraging the additive homomorphic cryptography and blockchain, our scheme ensures privacy even in the presence of semi-trusted mobile operators. Formal security analyses and performance evaluations indicate that the proposed scheme meets the security requirements for LAINs while maintaining lightweight and extensible energy consumption.

This paper considers a passive intelligent reflecting surface (IRS)-assisted integrated radar and communication system for multi-target detection and multi-user communications. To balance the communication and sensing performance, we propose an alternating optimization algorithm to optimize the worst-case weighted sum of the radar waveform minimum mean square error (MSE) and Multiuser interference (MUI) in Communication, under the spectrum compatibility and power constraints. The proposed algorithm utilizes a novel Tchebycheff optimization framework that decomposes the multi-objective optimization problem into three subproblems by optimizing the radar transmitted sequences, communication transmitted sequences, and IRS phase configuration. We propose an alternating optimization algorithm which incorporates alternating direction penalty method (ADPM) and element-wise block coordinate descent (E-BCD) frameworks to efficiently solve the optimization problem. Extensive numerical simulations validate the effectiveness of the proposed method, demonstrating significant performance improvements in both minimizing radar MSE and communication MUI and better convergence speed.

The reconfigurability of fluid antenna systems (FASs) and reconfigurable intelligent surfaces (RISs) can be jointly utilized to achieve unprecedented degrees of freedom for wireless communication systems. However, adjusting fluid antennas and RISs based on instantaneous channel state information (CSI) is highly challenging. To tackle this challenge, we propose a two-timescale approach for FAS-RIS-aided multi-user systems with regularized zero-forcing (RZF)/zero-forcing (ZF) precoding, where only statistical CSI is required for FAS and RIS optimization. To achieve this goal, we first obtain the closed-form evaluation for the ergodic sum rate (ESR) of FAS-RIS aided multi-user systems with RZF/ZF precoding by exploiting random matrix theory (RMT). Then, we propose an ESR maximization algorithm by jointly optimizing the port selection for FASs, phase shifts at the RIS, and regularization factor of RZF. Numerical results validate the approximation accuracy of the derived ESR evaluation and demonstrate that the performance enhancement benefiting from the joint design of FASs and RISs becomes more prominent when the number of users becomes larger.

With a massive number of antennas densely deployed in a compact area, holographic multiple-input multiple-output (HMIMO) systems are envisioned to be a key enabling technology for improving the data rate and coverage of 6 G networks. Unfortunately, the reduced spacing between radiation elements, which enables HMIMO to better exploit the channel propagation characteristics, also causes increased mutual coupling (MC) and reduced radiation efficiency. It is thus critical to understand the effect of MC on the capacity of HMIMO systems, which is not yet available in the literature. In this paper, we investigate the ergodic mutual information (EMI) and associated capacity-achieving transmit covariance design for HMIMO systems with MC. To this end, we first derive the closed-form expression for the EMI of HMIMO systems with MC, by leveraging random matrix theory (RMT). Then, based on the derived results, we propose an MC-aware algorithm to maximize the EMI by optimizing the transmit covariance matrix. Numerical simulations validate the accuracy of the theoretical analysis and the effectiveness of the proposed MC-aware algorithm. It is observed that the halfwavelength antenna spacing is not optimal especially with low signal-to-noise ratio.

The spiked covariance model, characterized by a population covariance matrix perturbed by a low-rank matrix, plays a crucial role in data analysis. In this context, the low-rank deformation typically signifies the underlying signal composition, while the extreme eigenvalues and eigenvectors of the sample covariance matrix contain valuable information about the signal. While the spiked covariance model has been extensively studied, its behavior under dimension reduction techniques, such as random projection, remains largely unexplored. These dimension reduction methods are commonly employed to manage the computational complexity associated with high-dimensional data. In this work, we study the behavior of the extreme eigenvalues and eigenvectors of the spiked covariance model with random projection. Specifically, we identify the exact critical threshold for the empirical eigenvalues to be out of the main bulk of the spectrum. Additionally, we determine the asymptotic positions of the isolated eigenvalues, as well as the projections of the isolated eigenvectors. It is quantitatively shown that the signal strength decreases under projection, and the isolated eigenvectors carry the information of the projected signal. Based on the above results, we propose a linear detection method for strong signals and analyze its performance limits. Simulation results validate the accuracy of the theoretical analysis.

Flying ad hoc networks (FANETs) provide high flexibility and real-time wireless communication solutions for multiunmanned aerial vehicle (UAV) systems by utilizing UAVs as routers. However, conventional routing protocols are inadequate for FANETs due to high mobility and dynamic topology of UAV networks. To address these challenges, this paper proposes an enhanced on-demand predictive (EDP) routing protocol for UAV networks. The EDP protocol incorporates a neighbor-coverage-based predictive flooding mechanism and an adaptive link-quality-based route maintenance method. The flooding mechanism utilizes Kalman filter theory to predict mobility and the route maintenance method selects optimal route by evaluating multiple factors. Simulation results demonstrate that the EDP protocol significantly improves the packet delivery rate while reducing network delay and overhead under varying environments, outperforming benchmark routing protocols for FANETs.

Semantic communication (SC), by compressing raw data at the semantic level, significantly improves the information entropy of transmitted data and is considered as one of the key enabling technologies for the next-generation communication. However, most current research underestimates the impact of channel interference on SC systems. As an innovative generative artificial intelligence technique, the diffusion model (DM) has demonstrated remarkable performance in image denoising and enhancement. In this paper, we focus on the effects of wireless channels on SC image transmission and propose an unmanned aerial vehicle (UAV)-enhanced SC framework, termed diffusion joint source-channel coding (D-JSCC). Initially, we deploy a ground-to-air SC system on UAVs, utilizing the aerial advantage to provide favorable channels. Subsequently, we employ DM for intelligent signal processing, adaptively denoising channel interferences and optimizing received images with respect to numerical errors and perceptual loss. The results show that DJSCC consistently exhibits superior performance across various metrics over different channel conditions.

Semantic communication (SC) is considered a critical technology for breaking through the Shannon limit and achieving low-latency, high-capacity 6 G transmission. However, previous SC systems have typically employed a symmetrical architecture to enhance data recovery capabilities, resulting in a strong coupling between the encoder and decoder. In this paper, we introduce a novel asymmetric SC system, termed masked joint source-channel coding (M-JSCC), which significantly enhances the encoder's versatility by allowing it to adapt to different decoder models tailored to specific task requirements. Moreover, we abandon traditional convolutional neural networks and adopt the innovative transformer to increase model capacity further. Additionally, we empower the model with data generation capabilities to combat interference and distortion during wireless transmission, achieving robust semantic transmission. As a result, extensive experiments verify that our M-JSCC achieves better semantic understanding and performance across various tasks and different channel conditions.

Machine learning (ML) enhanced unmanned aerial vehicle (UAV) networks are envisioned to facilitate extensive applications in next-generation wireless networks. Due to the privacy concern and communication overhead in cloud-centric ML, federated reinforcement learning (FRL) enables UAVs to collaboratively train a policy model without disclosing raw observation data. However, the model aggregator in centralized FRL architecture poses various potential threats such as a single point of failure and is inappropriate to distributed networks with unreliable links and nodes. In this paper, we propose AirFRL, a topology-aware decentralized federated reinforcement learning framework for UAV-enabled networks. In AirFRL, we consider the topology dynamics influenced by nodes' mobility and communication quality and its impact on AirFRL. To accelerate training process and guarantee the model performance, we also incorporate the model compression to lighten the local model and introduce the consensus distance and data correlation to reflect the discrepancy between local models and local data. Furthermore, we propose an efficient algorithm to decide the optimal neighbour node selection and model compression ratio. A case study and numerical results demonstrate that AirFRL can achieve linear training speedup and guarantee the learning performance for UAV-enabled networks.

The integration of blockchain technology with unmanned aerial vehicles (UAVs) offers considerable potential, enhancing cybersecurity and driving innovation within the UAV industry. However, due to the dynamic nature of UAVs and limited resources, existing blockchain consensus technologies cannot be directly applied to UAVs. To this end, we propose a chainbased optimized blockchain consensus protocol designed for UAV ad hoc networks, which employs the particle swarm optimization (PSO) algorithm to optimize chain consensus. We design several sub-protocols to cope with malicious nodes in the UAV network, node changes during UAV missions, topology changes. Numerical results show that our protocol increases throughput, reduces communication overhead, and enhances operation efficiency in UAV networks.

Drone networks are becoming increasingly significant in industrial Internet of Things (IIoT) applications. The limited resources of drones pose challenges in implementing robust security mechanisms that require substantial computation and power resources. Specifically, the inherent complexity of drone networks makes traditional intrusion detection systems (IDS) ineffective due to data imbalance and data scarcity. To address these challenges, this paper proposes a novel IDS framework that integrates conditional generative adversarial networks (CGANs) and utilizes the benefits from the systematic integration of foundation models within a federated learning (FL) paradigm. It leverages the CGANs to address the data issues ensures reliable performance and stable convergence against the foundation model. Moreover, our approach enhances data privacy relying on the differential privacy in FL and protects global model integrity through secure aggregation and updating. Simulation results show that the proposed framework achieve the accuracy rates of 91% and 99% on cyber and physical datasets, respectively. This framework achieves improvement ranging from 0.47% to 3.24% for cyber datasets and from 0.93% to 4.84% for physical datasets, which yields its superior performance in drone networks intrusion detection.

Effective conflict resolution is crucial to ensure the safety of unmanned aerial vehicles (UAVs) in increasingly congested low-altitude airspace. However, the inefficiency in representing large-scale UAV information hinders the performance of existing learning-based methods. To overcome this challenge, we propose an enhanced graph-based reinforcement learning (GRL) approach that models multi-agent interactions relationships. Specifically, a novel multi-scale graph reinforcement learning (MS-GRL) approach is utilized to learn UAV avoidance strategies in a dense environment. MS-GRL utilizes the time-evolving intensity of local conflicts to evaluate the global attention weights for UAVs in conflict, while aggregating UAV observation data through graph embedding and requisite feature refinement. In addition, to adaptively limit the safety region of the action space while minimize deviations from the original trajectory, a safety-constrained maneuver strategy is proposed. Experimental results demonstrate that MS-GRL outperforms state-of-the-art GRL methods when there are up to 100 UAVs and multiple obstacles.

Reconfigurable intelligent surfaces (RISs) have been shown effective in strengthening the physical layer security of wireless systems, and the two-timescale design was proposed to tackle the challenges in channel estimation and phase-shift control. However, existing maximum ratio transmission (MRT) based precoding design is not efficient in mitigating information leakage. To this end, this paper considers the performance analysis and two-timescale design for RIS-aided multiple-input single-output (MISO) secure communications with regularized zero-forcing (RZF) and zero-forcing (ZF) precoding, which is not available in the literature. The major challenges come from the two-hop channel and the inverse structure in the precoding matrix. By utilizing random matrix theory, we first evaluate the fundamental limits of the considered system by deriving a closed-form expression for the ergodic secrecy sum rate (ESSR). Then, we determine the optimal regularization factor of the RZF precoder and evaluate the ESSR over independent and identically distributed (i.i.d.) channels in the high SNR regime. The results indicate that when the number of reconfigurable elements at the RIS is overwhelmingly larger than that of transmit antennas and users, the ESSR of the two-hop channel approaches that of the single-hop channel. Based on the performance analysis, we propose a two-timescale algorithm to maximize the ESSR by optimizing the regularization factor of RZF and the phase shifts of the RIS alternatively. Simulation results validate the accuracy of the theoretical analysis and the effectiveness of the proposed algorithm.

The classical Rician Weichselberger channel and the emerging holographic multiple-input multiple-output (MIMO) channel share a common characteristic of non-separable correlation, which captures the interdependence between transmit and receive antennas. However, this correlation structure makes it very challenging to characterize the fundamental limits of non-centered (Rician), non-separable MIMO channels. In fact, there is a dearth of existing literature that addresses this specific aspect, underscoring the need for further research in this area. In this paper, we investigate the mutual information (MI) of non-centered non-separable MIMO channels, where both the line-of-sight and non-line-of-sight components are considered. By utilizing random matrix theory (RMT), we set up a central limit theorem for the MI and give the closed-form expressions for its mean and variance. The derived results are then utilized to determine the ergodic MI and outage probability of holographic MIMO channels. Numerical simulations validate the accuracy of the theoretical results.

Multi-antenna relays and intelligent reflecting surfaces (IRSs) have been utilized to construct favorable channels to improve the performance of wireless systems. A common feature between relay systems and IRS-aided systems is the two-hop multiple-input multiple-output (MIMO) channel. As a result, the mutual information (MI) of two-hop MIMO channels has been widely investigated with very engaging results. However, a rigorous investigation on the fundamental limits of two-hop MIMO channels, i.e., the first and second-order analysis, is not yet available in the literature, due to the difficulties caused by the two-hop (product) channel and the noise introduced by the relay (active IRS). In this paper, we employ large random matrix theory, specifically Gaussian tools, to derive the closed-form deterministic approximation for the mean and variance of the MI. Additionally, we determine the convergence rate for the mean, variance and the characteristic function of the MI, and prove the asymptotic Gaussianity. Furthermore, we also investigate the analytical properties of the fundamental equations that describe the closed-form approximation and prove the existence and uniqueness of the solution. An iterative algorithm is then proposed to obtain the solutions for the fundamental equations. Numerical results validate the accuracy of the theoretical analysis.

The multiple-input and multiple-output (MIMO) technique is considered as a promising approach for improving the throughput and reliability of optical fiber communications. However, the finite blocklength (FBL) analysis of optical fiber MIMO systems is not available in the literature. Considering the Jacobi MIMO channel, which was proposed to model the nearly lossless propagation and the crosstalks in optical fiber channels, this paper studies the optimal average error probability (OAEP) of optical fiber multicore/multimode systems in the FBL regime. In particular, we consider the case where the coding rate is in the ${\mathcal{O}}\left({\frac{1}{{\sqrt {LM} }}}\right)$ proximity of the capacity, with M and L denoting the number of transmit channels and blocklength, respectively. To this end, a central limit theorem (CLT) for the information density is first established in the asymptotic regime where the blocklength and the number of transmit, receive, and available channels approach infinity with fixed ratios. With the aid of the CLT, the closed-form upper and lower bounds for the OAEP with the concerned rate are then derived. It is shown that the derived bounds could degenerate to those for Rayleigh MIMO channels if the number of available channels goes to infinity. Numerical simulations indicate that the derived bounds are closer to the performance of low-density parity check (LDPC) coding schemes than outage probability, thus providing a better characterization with the concerned the rate.

Intelligent reflecting surfaces (IRSs) have emerged as a promising technology to improve the efficiency of wireless communication systems. However, passive IRSs suffer from the “multiplicative fading” effect, where the transmit signal will go through two fading hops. With the ability to amplify and reflect signals, active IRSs offer a potential way to tackle this issue, where the amplification energy only experiences the second hop. However, the fundamental limit and system design for active IRSs have not been fully understood, especially for multiple-input multiple-output (MIMO) systems. In this paper, we consider the analysis and design for the large-scale active IRS-aided MIMO system assuming only statistical channel state information (CSI) at the transmitter and the IRS. The characterization of the fundamental limit, i.e., ergodic rate, turns out to be a very difficult problem. To this end, we leverage random matrix theory (RMT) to derive the deterministic approximation (DA) for the ergodic rate, and then design an algorithm to jointly optimize the transmit covariance matrix at the transmitter and the reflection matrix at the active IRS. Numerical results demonstrate the accuracy of the derived DA and the effectiveness of the proposed optimization algorithm. Interesting physical insights regarding the advantage of active IRSs over their passive counterparts and the optimal power allocation between the transmitter and IRS are unveiled.

The Rayleigh-product channel model is utilized to characterize the rank deficiency caused by keyhole effects. However, the finite blocklength analysis for Rayleigh-product channels is not available in the literature. In this paper, we will characterize the mutual information density (MID) and perform the FBL analysis to reveal the impact of rank-deficiency in Rayleigh-product channels. To this end, we first set up a central limit theorem for the MID over Rayleigh-product MIMO channels in the asymptotic regime where the number of scatterers, number of antennas, and blocklength go to infinity at the same pace. Then, we utilize the CLT to obtain the upper and lower bounds for the packet error probability, whose approximations in the high and low signal to noise ratio regimes are then derived to illustrate the impact of rank-deficiency. One interesting observation is that rank-deficiency degrades the performance of MIMO systems with FBL and the fundamental limits of Rayleigh-product channels degenerate to those of the Rayleigh case when the number of scatterers approaches infinity.

Intelligent reflecting surface (IRS) provides an energy-efficient way to construct channels and enables the joint transceiver and channel design. In this paper, we consider the analysis and design of IRS-aided multiple-input multiple-output (MIMO) secure communications. We first investigate the fundamental limits of IRS-aided wiretap MIMO communications by determining the ergodic secrecy rate (ESR) and secrecy outage probability (SOP), which are not yet available in the literature. For that purpose, we derive the central limit theorem (CLT) for the joint distribution of the mutual information (MI) statistics over IRS-aided MIMO wiretap channels by utilizing random matrix theory (RMT). The CLT is then used to obtain the closed-form expressions for ESR and SOP, which are also extended to the scenario with multiple multi-antenna eavesdroppers. Based on the theoretical results, algorithms for maximizing the artificial noise (AN)-aided ESR and minimizing SOP are proposed. Numerical simulations validate the accuracy of the theoretical results and effectiveness of the proposed optimization algorithms.

The asymptotic mutual information (MI) analysis for multiple-input multiple-output (MIMO) systems over double-scattering channels has achieved engaging results, but the convergence rates of the mean, variance, and the distribution of the MI are not yet available in the literature. In this paper, by utilizing the large random matrix theory (RMT), we give a central limit theory (CLT) for the MI and derive the closed-form approximation for the mean and the variance by a new approach—Gaussian tools. The convergence rates of the mean, variance, and the characteristic function are proved to be${\mathcal {O}}\left({\frac {1}{N}}\right)$for the first time, where$N$is the number of receive antennas. Furthermore, the impact of the number of effective scatterers on the mean and variance was investigated in the moderate-to-high SNR regime with some interesting physical insights. The proposed evaluation framework can be utilized for the asymptotic performance analysis of other systems over double-scattering channels.

Intelligent reflecting surfaces (IRSs) are promising enablers for next-generation wireless communications due to their reconfigurability and high energy efficiency in improving poor propagation condition of channels, e.g., limited scattering environment. However, most existing works assumed full-rank channels requiring rich scatters, which may not be available in practice. To analyze the impact of rank-deficient channels and mitigate the ensued performance loss, we consider a large-scale IRS-aided MIMO system with statistical channel state information (CSI), where the double-scattering channel is adopted to model rank deficiency. By leveraging random matrix theory (RMT), we first derive a deterministic approximation (DA) of the ergodic rate with low computational complexity and prove the existence and uniqueness of the DA parameters. Then, we propose an alternating optimization algorithm for maximizing the DA with respect to phase shifts and signal covariance matrices. Numerical results will show that the DA is tight and our proposed method can effectively mitigate the performance loss induced by channel rank deficiency.

The mutual information (MI) of Gaussian multi-input multi-output (MIMO) channels has been evaluated by utilizing random matrix theory (RMT) and shown to asymptotically follow Gaussian distribution, where the ergodic mutual information (EMI) converges to a deterministic quantity. However, with non-Gaussian channels, there is a bias between the EMI and its deterministic equivalent (DE), whose evaluation is not available in the literature. This bias of the EMI is related to the bias for the trace of the resolvent in large RMT. In this paper, we first derive the bias for the trace of the resolvent, which is further extended to compute the bias for the linear spectral statistics (LSS). Then, we apply the above results on non-Gaussian MIMO channels to determine the bias for the EMI. It is also proved that the bias for the EMI is −0.5 times of that for the variance of the MI. Finally, the derived bias is utilized to modify the central limit theory (CLT) and calculate the outage probability. Numerical results show that the modified CLT significantly outperforms previous methods in approximating the distribution of the MI and improves the accuracy for the outage probability evaluation.

The key factor for training a good neural network lies in both model capacity and large-scale training data. As more datasets are available nowadays, one may wonder whether the success of deep learning descends from data augmentation only. In this paper, we propose a new dataset, namely, Extended ImageNet Classification (EIC) dataset based on the original ILSVRC CLS 2012 set to investigate if more training data is a crucial step. We address the problem of object localization where given an image, some boxes (also called anchors) are generated to localize multiple instances. Different from previous work to place all anchors at the last layer, we split boxes of different sizes at various resolutions in the network, since small anchors are more prone to be identified at larger spatial location in the shallow layers. Inspired by the hourglass work, we apply a conv-deconv network architecture to generate object proposals. The motivation is to fully leverage high-level summarized semantics and to utilize their up-sampling version to help guide local details in the low-level maps. Experimental results demonstrate the effectiveness of such a design. Based on the newly proposed dataset, we find more data could enhance the average recall, but a more balanced data distribution among categories could obtain better results at the cost of fewer training samples.

Dynamic spectrum access has become a focal issue recently. Lots of works have been done concerning secondary users (SU) synchronously accessing primary users' (PU) network. However, on one hand, SU have to periodically synchronize with PU's time tables, which will bring lots of unnecessary overhead. On the other hand, it is possible that SU have no idea about PU's communication scheme at all or even communications among PU are not based on synchronous scheme. In order to address such problems, this paper advances an asynchronous algorithm, called A-DSA, for SU to asynchronously access CR-based Ad-Hoc network. We focus on three questions in this paper: 1) how to choose channels to sense; 2) how to sense the chosen channels; 3) how to determine the final access channel after sense. Three strategies are proposed towards each question. Our simulations show that choosing sense channels by A-DSA can attain 20% more success rate than randomly choosing. Moreover, sensing by A-DSA can not only achieve nearly 50% less interference probability than equal allocation of the overall sense time, but also well adapt to time-varying channels.