While integrated sensing and communication (ISAC) beamforming has become a research hotspot recently, most proposed approaches remain scenario-specific and suffer from limited generalizability. In this paper, we propose a flexible beamforming design framework for multiple-input multiple-output ISAC (MIMO-ISAC) systems. To characterize the optimal performance tradeoff between the sensing and communication (S&C), the objective is to maximize the weighted sum of S&C mutual information (MI). An integrated fractional programming (IFP) framework is first proposed, from which a semi-closed-form solution for the optimal beamformer is derived. The proposed framework is further extended to several representative ISAC scenarios, including fairness-aware tradeoffs, imperfect channel state information (CSI), dynamic radar cross-section (RCS) variations, and massive MIMO systems. By incorporating advanced optimization techniques, the IFP framework effectively addresses the challenges arising in these scenarios with only minor modifications while still yielding semi-closed-form solutions. Moreover, we show that the IFP framework is closely related to the projected gradient descent (PGD) method, and its computational efficiency can be further enhanced through gradient acceleration techniques. Simulation results demonstrate that the IFP framework and its variants consistently outperform benchmark schemes across diverse system settings. The proposed low-complexity and accelerated schemes reduce computation time by approximately 65% and 89%, respectively, with only marginal performance degradation. These results highlight the flexibility and generality of the proposed IFP framework for ISAC beamforming design and are expected to provide valuable insights for the development of future 6G systems.

Integrated sensing and communication (ISAC) has gained traction in academia and industry. Recently, multipath components (MPCs), as a type of spatial resource, have the potential to improve the sensing performance in ISAC systems, especially in richly scattering environments. In this paper, we propose to leverage MPC and Khatri-Rao space-time (KRST) code within a single ISAC system to realize high-accuracy sensing for multiple dynamic targets and multi-user communication. Specifically, we propose a novel MPC-enhanced sensing processing scheme with symbol-level fusion, referred to as the “SL-MSP” scheme, to achieve high-accuracy localization of multiple dynamic targets and empower the single ISAC system with a new capability of absolute velocity estimation for multiple targets with a single sensing attempt. Furthermore, the KRST code is applied to flexibly balance communication and sensing performance in richly scattering environments. To evaluate the contribution of MPCs, the closed-form Cramér-Rao lower bounds (CRLBs) for location and absolute velocity estimation are derived. Simulation results illustrate that the proposed SL-MSP scheme is more robust and accurate in localization and absolute velocity estimation compared with the existing state-of-the-art schemes.

This paper investigates an explicit polar coding scheme for a two-user discrete memoryless multiple access wiretap channel with partial rate-limited feedback (MAC-WT-PLF). Feedback is exploited to increase channel-input correlation, inject dummy messages, and encrypt messages using a one-time pad. Existing MAC-WT polar coding schemes assume independent channel inputs, which cannot support the correlation requirement. Therefore, we propose an explicit polar mapping that leverages feedback to introduce correlation between the channel inputs. This mapping allows both the transmitter and the receiver to restructure the correlation in opposite decoding directions. The proposed scheme relies on source polarization, block Markov coding, superposition coding, lossy source coding, and ratesplitting, without making symmetry or degradation assumptions on the channel model. Rigorous information-theoretic asymptotic analysis establishes that the proposed scheme ensures both reliability and strong secrecy, and attains the entire achievable secrecy rate region given in prior work.

Integrated sensing and communication (ISAC) has emerged as a pivotal enabling technology for sixth-generation (6G) mobile communication system. The ISAC research in dense urban areas has been plaguing by severe multipath interference, propelling the thorough research of ISAC multipath interference elimination. However, transforming the multipath component (MPC) from enemy into friend is a viable and mutually beneficial option. In this paper, we preliminarily explore the MPC-aided ISAC signal processing and apply a space-time code to improve the ISAC performance. Specifically, we propose a symbol-level fusion for MPC-aided localization (SFMC) scheme to achieve robust and high-accuracy localization, and apply a Khatri-Rao space-time (KRST) code to improve the communication and sensing performance in rich multipath environment. Simulation results demonstrate that the proposed SFMC scheme has more robust localization performance with higher accuracy, compared with the existing state-of-the-art schemes. The proposed SFMC would benefit highly reliable communication and sub-meter level localization in rich multipath scenarios.

Emerging applications, such as autonomous driving and Internet of Things (IoT) services put forward the demand for simultaneous sensing and communication functions in the same system. Integrated sensing and communication (ISAC) has the potential to meet the demands of ubiquitous communication and high-precision sensing due to the advantages of spectrum and hardware resource sharing, as well as the mutual enhancement of sensing and communication. However, the ISAC system faces severe interference requiring effective interference suppression, avoidance, and exploitation techniques. This article provides a comprehensive survey on the interference management techniques in ISAC systems, involving network architecture, system design, signal processing, and resource allocation. We first review the channel modeling and performance metrics of the ISAC system. Then, the methods for managing self-interference (SI), mutual interference (MI), and clutter in a single base station (BS) system are summarized, including interference suppression, interference avoidance, and interference exploitation methods. Furthermore, cooperative interference management methods are studied to address the cross-link interference (CLI) in a coordinated multipoint ISAC (CoMP-ISAC) system. Finally, future trends are revealed. This article may provide a reference for the study of interference management in ISAC systems.

Integrated sensing and communication (ISAC) is expected to play a crucial role in the sixth-generation (6G) mobile communication systems, offering potential applications in the scenarios of intelligent transportation, smart factories, etc. The performance of radar sensing in ISAC systems is closely related to the characteristics of radar sensing and communication channels. Therefore, ISAC channel modeling serves as a fundamental cornerstone for evaluating and optimizing ISAC systems. This article provides a comprehensive survey on the ISAC channel modeling methods. Furthermore, the methods of target radar cross section (RCS) modeling and clutter RCS modeling are summarized. Finally, we discuss the future research trends related to ISAC channel modeling in various scenarios.

The massive access in Internet of Things (IoT) introduces significant communication and computation overheads. Besides, the widespread IoT terminals placed in unattended area and with limited capabilities are vulnerable to various attacks such as physical attack. To alleviate the huge communication and computation overheads and to resist physical attacks, we propose a physically unclonable function (PUF)-based group authentication protocol in this paper, where a pre-stored PUF challenge scheme with PUF acting as the root key is proposed to limit the size of signalings in a group. Different from existing work with assumptions on secure communication channels and trusted group leader (GL), we consider untrusted GL and insecure communication channels among the device, the GL, and the home network (HN) and propose a simplified PUF-based device-to-device authentication scheme to perform mutual authentication and key sharing between the devices and the untrusted GL. Finally, the proposed protocol is evaluated with formal security analysis, where a novel threat model is presented for the physical attacker to overhear the secret in device’s memory. Results show that the proposed protocol can achieve desired authentication and confidentiality goals even the GL is under physical attacks and the communication channels are insecure. Further, simulations are demonstrated to show the outperformance of the proposed protocol in communication overhead, computation overhead, and security, compared with baseline solutions.

Interactive multimodal applications (IMAs), such as route planning in the Internet of Vehicles, enrich users’ personalized experiences by integrating various forms of data over wireless networks. Recent advances in large language models (LLMs) utilize mixture-of-experts (MoE) mechanisms to empower multiple IMAs, with each LLM trained individually for a specific task that presents different business workflows. In contrast to existing approaches that rely on multiple LLMs for IMAs, this paper presents a novel paradigm that accomplishes various IMAs using a single compositional LLM over wireless networks. The two primary challenges include 1) guiding a single LLM to adapt to diverse IMA objectives and 2) ensuring the flexibility and efficiency of the LLM in resource-constrained mobile environments. To tackle the first challenge, we propose ContextLoRA, a novel method that guides an LLM to learn the rich structured context among IMAs by constructing a task dependency graph. We partition the learnable parameter matrix of neural layers for each IMA to facilitate LLM composition. Then, we develop a step-by-step fine-tuning procedure guided by task relations, including training, freezing, and masking phases. This allows the LLM to learn to reason among tasks for better adaptation, capturing the latent dependencies between tasks. For the second challenge, we introduce ContextGear, a scheduling strategy to optimize the training procedure of ContextLoRA, aiming to minimize computational and communication costs through a strategic grouping mechanism. Experiments on three benchmarks show the superiority of the proposed ContextLoRA and ContextGear. Furthermore, we prototype our proposed paradigm on a real-world wireless testbed, demonstrating its practical applicability for various IMAs. We will release our code to the community.

Physical layer (PHY) secret key generation (SKG) has been widely studied as a promising approach to achieving One-Time-Pad security. The improvement of SKG rate is quite a huge challenge, especially in scenarios with untrusted internal helpers or eavesdroppers that aim to wiretap the negotiated secret keys between legitimate parties. In this paper, we propose a token-based SKG scheme to deal with the problem of information leakage with internal eavesdropping attacks. The basic idea is to cover random pilots with protective tokens to confuse eavesdroppers. Three scenarios including passive external eavesdropping, active internal eavesdropping with a reconfigurable intelligent surface (RIS)-assisted untrusted helper, and active internal eavesdropping with an untrusted relay are considered and analyzed to evaluate the performance of the proposed anti-eavesdropping scheme. Theoretical analysis shows that the proposed token-based SKG scheme can perfectly secure the key negotiation, achieving zero information leakage even in the untrusted relaying scenario without a direct link between Alice and Bob. Moreover, closed-form expressions for secret key capacity (SKC) are obtained. Finally, numerical results indicate that the proposed scheme outperforms the state-of-the-art methods. Using a token-generation mapping function with greater diversity in amplitude and phase, our approach achieves enhanced SKC performance across various scenarios, including those with a passive eavesdropper, a RIS-assisted untrusted helper, and an untrusted relay.

Integrated sensing and communication (ISAC) is a potential technology of the sixth-generation (6G) mobile communication system, which enables communication base station (BS) with sensing capability. However, the performance of single-BS sensing is limited, which can be overcome by multi-BS cooperative sensing. There are three types of multi-BS cooperative sensing, including cooperative active sensing, cooperative passive sensing, and cooperative active and passive sensing, where the multi-BS cooperative passive sensing has the advantages of low hardware modification cost and large sensing coverage. However, multi-BS cooperative passive sensing faces the challenges of synchronization offset mitigation and sensing information fusion. To address these challenges, a non-line of sight (NLoS) and line of sight (LoS) signal cross-correlation (NLCC) method is proposed to mitigate carrier frequency offset (CFO) and time offset (TO). Besides, a symbol-level fusion method of multi-BS sensing information is proposed. The discrete samplings of echo signals from multiple BSs are matched independently and coherently accumulated to improve sensing accuracy. Moreover, a low-complexity joint angle-of-arrival (AoA) and angle-of-departure (AoD) estimation method is proposed to reduce the computational complexity. Simulation results show that symbol-level multi-BS cooperative passive sensing scheme has an order of magnitude higher sensing accuracy than single-BS passive sensing. This work provides a reference for the research on multi-BS cooperative passive sensing.

In the evolution towards the forthcoming era of sixth-generation (6G) mobile communication systems characterized by ubiquitous intelligence, integrated sensing and communication (ISAC) is in a phase of burgeoning development. However, the capabilities of communication and sensing within single frequency band fall short of meeting the escalating demands. To this end, this paper introduces a carrier aggregation (CA)-enabled multi-input multi-output orthogonal frequency division multiplexing (MIMO-OFDM) ISAC system fusing the sensing data on high and low-frequency bands by symbol-level fusion for ultimate communication experience and high-accuracy sensing. The challenges in sensing signal processing introduced by CA include the initial phase misalignment of the echo signals on high and low-frequency bands due to attenuation and radar cross section, and the fusion of the sensing data on high and low-frequency bands with different physical-layer parameters. To this end, the sensing signal processing is decomposed into two stages. In the first stage, the problem of initial phase misalignment of the echo signals on high and low-frequency bands is solved by the angle compensation, spatial filtering and cyclic cross-correlation operations. In the second stage, this paper realizes symbol-level fusion of the sensing data on high and low-frequency bands through sensing vector rearrangement and cyclic prefix adjustment operations, thereby obtaining high-precision sensing performance. Then, the closed-form communication mutual information (MI) and sensing Cramér-Rao lower bound (CRLB) for the proposed ISAC system are derived to explore the theoretical performance bound with CA. Simulation results validate the feasibility and superiority of the proposed ISAC system.

This paper studies a multiple-input multiple-output (MIMO) orthogonal frequency division multiplexing (OFDM) networked integrated sensing and communication (ISAC) system, in which multiple base stations (BSs) perform beam tracking to communicate with a mobile device. In particular, we focus on the beam tracking over a number of tracking time slots (TTSs) and suppose that these BSs operate at non-overlapping frequency bands to avoid the severe inter-cell interference. Under this setup, we propose a new cooperative sensing-assisted predictive beam tracking design. In each TTS, the BSs use echo signals to cooperatively track the mobile device as a sensing target, and continuously adjust the beam directions to follow the device for enhancing the performance for both communication and sensing. First, we propose a cooperative sensing design to track the device, in which the BSs first employ the two-dimensional discrete Fourier transform (2D-DFT) technique to perform local target estimation, and then use the extended Kalman filter (EKF) method to fuse their individual measurement results for predicting the target parameters. Next, based on the predicted results, we obtain the achievable rate for communication and the predicted conditional Cramér-Rao lower bound (PC-CRLB) for target parameters estimation in the next TTS, as a function of the beamforming vectors. Accordingly, we formulate the predictive beamforming design problem, with the objective of maximizing the achievable communication rate in the following TTS, while satisfying the PC-CRLB requirement for sensing. To address the resulting non-convex problem, we first propose a semi-definite relaxation (SDR)-based algorithm to obtain the optimal solution, and then develop an alternative penalty-based algorithm to get a high-quality low-complexity solution. Simulation results indicate that the proposed cooperative sensing design achieves higher target tracking accuracy than other benchmark schemes. The results also validate the benefits of multi-BS cooperative sensing in improving tracking performance compared with the conventional single-BS sensing.

Addressing the communication and sensing demands of sixth-generation (6G) mobile communication system, integrated sensing and communication (ISAC) has garnered traction in academia and industry. With the sensing limitation of single base station (BS), multi-BS cooperative sensing is regarded as a promising solution. The coexistence and overlapped coverage of macro BS (MBS) and micro BS (MiBS) are common in the development of 6G, making the cooperative sensing between MBS and MiBS feasible. Since MBS and MiBS work in low and high frequency bands, respectively, the challenges of MBS and MiBS cooperative sensing lie in the fusion method of the sensing information in high and low-frequency bands. To this end, this paper introduces a symbol-level fusion method and a grid-based three-dimensional discrete Fourier transform (3D-GDFT) algorithm to achieve precise localization of multiple targets with limited resources. Simulation results demonstrate that the proposed MBS and MiBS cooperative sensing scheme outperforms traditional single BS (MBS/MiBS) sensing scheme, showcasing superior sensing performance.

Over-the-air (OTA) attacks, such as flooding and jamming, significantly compromise the availability and reliability of radio access networks, especially with Quality of Service (QoS)-aware scheduling. Despite extensive research aimed at enhancing network performance and efficiency, the impact of OTA attacks on QoS performance has been insufficiently addressed. This paper conducts a thorough analysis of how OTA attacks influence the network performance. By leveraging a multi-class M/M/1 queuing model with selection and feedback mechanisms, the complex relationship between attack strategies and prioritized network scheduling is analyzed. Expressions for the delay and throughput are derived based on the result of the stationary distribution of Continuous Time Markov Chain (CTMC) model. Finally, numerical and simulation results are demonstrated to validate the theoretical analysis and to analyze the impact of OTA attack on the scheduling performance. Results show that the attacker can compromise the QoS of low-priority traffic by blocking high-priority traffic. Moreover, the QoS of high-priority traffic can be compromised by jamming low-priority traffic.

Vehicular social network (VSN) has been emerged in recent years with the prosperity of Internet of Vehicles (IoV). VSN service providers can exploit the social relationships of vehicles to improve the performance of vehicular networks, such as improving the order-taking efficiency of taxis. However, when social relationships are captured by attackers, other contact information is passively disclosed without their knowledge, seriously threatening user privacy. This article proposes a$(\theta ,\epsilon)$privacy technique for protecting the social relationships of vehicles that can be used to shorten the average order-taking distance of taxis during order allocation. Two variants are included in the proposed technique, which combines graph theory and graph differential privacy (GDP) mechanisms. 1)$(\theta ,\epsilon)$-GDP–a projection-based GDP algorithm is proposed to protect social relationships among the vehicles. 2) Furthermore, an improved order allocation scheme named$(\theta ,\epsilon)$-PrivOT is proposed to reduce the average order-taking distance. Real data evaluations are provided to verify the outperformance of the proposed technique. The privacy level of the proposed algorithm is improved by up to 8.77% while its data utility is improved by up to 22.94% compared with the edge removal scheme. The proposed dispatching scheme can shorten the average order-taking distance by up to 6.89% compared with the scheme of original data with 100 vehicles.

Utilizing widely distributed communication nodes to achieve environmental reconstruction is one of the significant scenarios for integrated sensing and communication (ISAC) and a crucial technology for 6G. To achieve this crucial functionality, we propose a deep learning-based multinode ISAC 4D environment reconstruction method with the uplink-downlink (UL-DL) cooperation, which employs virtual aperture technology, constant false alarm rate (CFAR) detection, and mutiple signal classification (music) algorithm to maximize the sensing capabilities of single sensing nodes. Simultaneously, it introduces a cooperative environmental reconstruction scheme involving the multinode cooperation and UL-DL cooperation to overcome the limitations of single-node sensing caused by occlusion and limited viewpoints. Furthermore, the deep learning models attention gate gridding residual neural network (AGGRNN) and multiview sensing fusion network (MVSFNet) to enhance the density of the sparsely reconstructed point clouds are proposed, aiming to restore as many original environmental details as possible while preserving the spatial structure of the point cloud. Additionally, we propose a multilevel fusion strategy incorporating both the data-level and feature-level fusion to fully leverage the advantages of the multinode cooperation. Experimental results demonstrate that the environmental reconstruction performance of this method significantly outperforms the other comparative method, enabling high-precision environmental reconstruction using the ISAC system.

Internet of Things (IoT) is one of the most representative application scenarios in the 5G and 6G era. The concurrent access of massive IoT devices definitely poses enormous communication, computation, and certificate management challenges to the wireless authentication. Moreover, the emergence of quantum computing makes classical cryptography-based authentication protocols, such as 5G-AKA, more easier to be broken. Facing the challenges posed by the massive concurrent authentication and quantum attacks, this paper proposes a lattice cryptography based group authentication scheme, where lattice-based aggregate signature algorithm and identity-based encryption (IBE) are leveraged to achieve simultaneous authentication of concurrent accessed devices. The proposed authentication scheme eliminates the process of public key certificate management, greatly reducing the storage overhead of core network. Moreover, the utilization of lattice cryptography enables the resistance of quantum attacks. The proposed solution does not rely on additional security assumptions such as security channel or trusted group center, making it more flexible to be deployed in actual network scenario. Finally, formal security analysis of the proposed protocol is provided with the tool ProVerif. It is demonstrated that the proposed protocol can satisfy the goals of identity privacy, authentication, data confidentiality and forward secrecy. In addition, compared with existing advanced solutions, the outperformance of the proposed scheme in terms of computation overhead, signaling overhead, communication overhead, and security properties is validated with simulations.

Many interconnected terminals in the Internet of Things (IoT) networks raise significant security risks. From a competitive perspective, the many heterogeneous nodes, including various wireless terminals, access points, and base stations are the competing targets between the defenders and potential attackers. This article proposes a security competition model based on the game theory to address the security competition problem. The model has two players, a defender and an attacker, who allocate resources to each IoT node according to their strategies. A novel metric, security entropy, derived from the security probability, quantifies each node’s security status. Based on the node heterogeneity, the overall security performance of the considered IoT network is evaluated with a topology-determined weighted security entropy. The defender and the attacker, respectively, aim to decrease and increase the weighted security entropy while balancing the cost, which constitutes their utility functions. The existence of the unique Nash equilibrium is proved. A best response selection algorithm for the optimal solutions is designed. The experimental results demonstrate that the proposed model effectively represents the goal orientation and interaction between the attackers and defenders in various scenarios. Additionally, increasing the cost for the attackers significantly reduces their resource allocation, especially to the attackers leading to a decrease in the system’s security entropy.

Integrated sensing and communication (ISAC) is regarded as the enabling technology in the future 5th-Generation-Advanced (5G-A) and 6th-Generation (6G) mobile communication system. ISAC waveform design is critical in ISAC system. However, the difference of the performance metrics between sensing and communication brings challenges for the ISAC waveform design. This paper applies the unified performance metrics in information theory, namely mutual information (MI), to measure the communication and sensing performance in multicarrier ISAC system. In multi-input multi-output orthogonal frequency division multiplexing (MIMO-OFDM) ISAC system, we first derive the sensing and communication MI with subcarrier correlation and spatial correlation. Then, we propose optimal waveform designs for maximizing the sensing MI, communication MI and the weighted sum of sensing and communication MI, respectively. The optimization results are validated by Monte Carlo simulations. Our work provides effective closed-form expressions for waveform design, enabling the realization of MIMO-OFDM ISAC system with balanced performance in communication and sensing.

Channel state information (CSI) is differently available at each terminal in state-dependent wiretap channels (SD-WTCs). Considering a random channel state non-causally available only at the encoder, this paper investigates an explicit polar coding scheme for the discrete and memoryless SD-WTC, where Alice aims to transmit a secret message (SM) and a secret key (SK) to Bob while concealing them from Eve. Based on a two-layer superposition coding, which includes an inner layer and an outer layer, the proposed polar coding scheme can achieve the inner bound of the current optimal SM-SK capacity region for the discrete and memoryless SD-WTC with CSI non-causally available only at the encoder. A cross-layer construction is proposed to address the issue where the regular chaining construction used in wiretap polar codes cannot be applied in the inner layer. Results show that the decoding error probability (DEP) of the legitimate decoder vanishes to zero as the block length increases. In addition, the proposed scheme is proven to satisfy strong secrecy.

This paper investigates a reconfigurable intelligent surface (RIS) assisted cooperative multicell integrated sensing and communication (ISAC) system with multiple users and targets. In particular, the RIS is leveraged to assist the joint transmission of the multiple base stations (BSs) to multiple users, while assisting cooperative sensing by multiple BSs to perform multiple targets sensing. We formulate a problem for the purpose of minimizing the transmit power via jointly designing the transmit beamforming of the BSs and phase shifts of the RIS, while guaranteeing the achievable communication rate requirements and the sensing mutual information requirements. To address this non-convex problem, a high-quality alternating optimization algorithm is developed to split the intractable problem into two sub-problems. Specifically, with the given phase shifts of the RIS, the transmit beamforming sub-problem is addressed by semidefinite relaxation-based algorithm. A successive convex approximation (SCA) method-based and penalty function-based convex-concave procedure algorithm is proposed to tackle the RIS phase-shift optimization sub-problem. To reduce the computational complexity, an efficient low-complexity alternating optimization algorithm is developed. For the transmit beamforming design, an SCA method-based second-order cone programming algorithm is proposed, while for the RIS phase-shift design, a circle manifold optimization-based algorithm is introduced by utilizing penalty function. Simulation results validate the advancement of deploying RIS in enhancing the performance of cooperative multicell ISAC systems in terms of transmit power. Furthermore, our results illustrate the significant superiority of the proposed algorithms over the benchmark schemes.

This paper studies a networked integrated sensing and communication (ISAC) system, where distributed base stations (BSs) implement coordinated transmit beamforming to communicate with their respective user and cooperatively perform multi-static target sensing. To fully reap the performance gains provided by the networked ISAC system, accurate channel state information (CSI) and time synchronization (TS) among distributed BSs are crucial. However, CSI errors and TS errors are inevitable in practice due to the imperfect channel training and the inaccurate synchronization. To reveal the effect of CSI errors on communication, a Gaussian distributed CSI error model is formulated based on the channel estimation process, and accordingly, the users’ achievable rates with CSI errors are derived. To characterize the effect of TS errors on multi-static sensing, the Cramér-Rao lower bound (CRLB) for estimating target position in the presence of TS errors is derived. It is shown that due to the existence of CSI errors and TS errors, additional terms are introduced in the achievable rate and CRLB formulas, degrading the communication and sensing performance, respectively. Based on the above derivations, we aim at maximizing the sum-rate of users by designing the coordinated transmit beamforming at the BSs, while guaranteeing the CRLB requirements for target sensing. In particular, we consider two cases with and without TS errors, for which the corresponding non-convex optimization problems are solved via a penalty-based algorithm and an alternating optimization algorithm, respectively. Simulation results show that the proposed algorithms significantly outperform benchmark schemes for both cases with and without CSI/TS errors, thus validating the robustness in ISAC performance optimization.

Reconfigurable intelligent surface (RIS) is acknowledged as a promising technique in improving physical layer (PHY) secret key generation (SKG) performance by enhancing the randomness of radio electromagnetic environment. However, RIS can also be adopted by illegal eavesdropper to destroy the channel reciprocity between key-sharing parties. Thus, there is a tradeoff between the enhanced randomness and the decreased channel reciprocity with the application of RIS. This paper aims to answer the question that whether the adversarial RIS is harmful to SKG by studying the SKG performance in the RIS scenario where Alice and Eve each applies a RIS. To this end, the closed-form expression for the upper bound of secret key rate (SKR) and expressions for the autocorrelation coefficients of two probing channel samples within a coherent duration are provided. Results show that the relative positions of Eve and adversarial RIS to Alice and Bob play a key role in determining whether the adversarial RIS is harmful or beneficial to the PHY SKG system.

As the uplink sensing has the advantage of easy implementation, it attracts great attention in integrated sensing and communication (ISAC) system. This paper presents an uplink ISAC system based on multi-input multi-output orthogonal frequency division multiplexing (MIMO-OFDM) technology. The mutual information (MI) is introduced as a unified metric to evaluate the performance of communication and sensing. In this paper, firstly, the upper and lower bounds of communication and sensing MI are derived in details based on the interaction between communication and sensing. And the ISAC waveform is optimized by maximizing the weighted sum of sensing and communication MI. The Monte Carlo simulation results show that, compared with other waveform optimization schemes, the proposed ISAC scheme has the best overall performance.

Integrated sensing and communication (ISAC) will greatly enhance the efficiency of physical resource utilization. The design of ISAC signal based on the orthogonal frequency division multiplex (OFDM) signal is the mainstream. However, when detecting the long-range target, the delay of echo signal exceeds CP duration, which will result in inter-symbol interference (ISI) and inter-carrier interference (ICI), limiting the sensing range. Facing the above problem, we propose to increase useful signal power through coherent compensation and improve the signal to interference plus noise power ratio (SINR) of each OFDM block. Compared with the traditional 2D-FFT algorithm, the improvement of SINR of range-doppler map (RDM) is verified by simulation, which will expand the sensing range.

Integrated sensing and communication (ISAC) has the advantages of efficient spectrum utilization and low hardware cost. It is promising to be implemented in the fifth-generation-advanced (5G-A) and sixth-generation (6G) mobile communication systems, having the potential to be applied in intelligent applications requiring both communication and high-accurate sensing capabilities. As the fundamental technology of ISAC, ISAC signal directly impacts the performance of sensing and communication. This article systematically reviews the literature on ISAC signals from the perspective of mobile communication systems, including ISAC signal design, ISAC signal processing, and ISAC signal optimization. We first review the ISAC signal design based on 5G, 5G-A, and 6G mobile communication systems. Then, radar signal processing methods are reviewed for ISAC signals, mainly including the channel information matrix method, spectrum lines estimator method, and super-resolution method. In terms of signal optimization, we summarize peak-to-average power ratio (PAPR) optimization, interference management, and adaptive signal optimization for ISAC signals. This article may provide the guidelines for the research of ISAC signals in 5G-A and 6G mobile communication systems.

Achieving seamless global coverage is one of the ultimate goals of space-air-ground integrated network, as a part of which High Altitude Platform (HAP) network can provide wide-area coverage. However, deploying a large number of HAPs will lead to severe congestion of existing frequency bands. Spectrum sharing improves spectrum utilization. The coverage performance improvement and interference caused by spectrum sharing need to be investigated. To this end, this paper analyzes the performance of spectrum sharing between HAP network and terrestrial network. We firstly generalize the Poisson Point Process (PPP) to curves, surfaces and manifolds to model the distribution of terrestrial Base Stations (BSs) and HAPs. Then, the closed-form expressions for coverage probability of HAP network and terrestrial network are derived based on differential geometry and stochastic geometry. We verify the accuracy of closed-form expressions by Monte Carlo simulation. The results show that HAP network has less interference to terrestrial network. Low height and suitable deployment density can improve the coverage probability and transmission capacity of HAP network.

Unmanned aerial vehicles (UAVs) are anticipated to be a potential data collection solution for wireless sensor networks (WSNs). The main challenges of integrating UAVs in WSNs are security threats and UAV’s onboard energy limitation. To cope with these two challenges, this article examines the secrecy energy efficiency (SEE) maximization problem in UAV-enabled WSN. Specifically, a full-duplex (FD) UAV gathers confidential information from ground sensor nodes (SNs) in the uplink while sending jamming signals to confound a ground eavesdropper (Eve) in the downlink. Considering a passive eavesdropping scenario lacking Eve’s instantaneous channel state information (CSI), the resulting problem is subject to the constraints of connection outage probability (COP), secrecy outage probability (SOP), securely collected bits, and flight trajectory. To tackle the intractable nonconvex problem, we first derive the optimal codeword rate and redundancy rate in closed-form expressions and then develop a low-complexity algorithm using the block coordinate descent (BCD) approach to alternatively optimize the SN scheduling, SN transmit power, UAV transmit power, and UAV trajectory. Simulation results verify the performance gains of the proposed scheme compared with the benchmark schemes. In particular, the proposed scheme achieves nearly the same secrecy rate gains at a lower UAV’s energy consumption cost than the sum secrecy rate maximization (SSRM) baseline. Moreover, it is revealed that trajectory optimization of the proposed scheme plays a crucial role in improving SEE performance compared with the circle trajectory (CT) baseline.

Neighbor discovery (ND) is a key initial step of network configuration and prerequisite of vehicularad hocnetwork (VANET). However, the convergence efficiency of ND is facing the requirements of multivehicle fast networking of VANET with frequent topology changes. This article proposes the gossip-based information dissemination and sensing information-assisted ND with multipacket reception (GSIM-ND) algorithm for VANET. The GSIM-ND algorithm leverages efficient gossip-based information dissemination in the case of multipacket reception (MPR). Besides, through the multitarget detection function of multiple sensors installed in roadside unit (RSU), RSU can sense the distribution of vehicles and help vehicles to obtain the distribution of their neighbors. Thus, the GSIM-ND algorithm leverages the dissemination of sensing information as well. The expected number of discovered neighbors within a given period is theoretically derived and used as the critical metric to evaluate the performance of the GSIM-ND algorithm. The expected bounds of the number of time slots when a given number of neighbors needs to be discovered are derived as well. The simulation results verify the correctness of theoretical derivation. It is discovered that GSIM-ND algorithm proposed in this article can always reach the short-term convergence quickly. Moreover, the GSIM-ND algorithm is more efficient and stable compared with the completely random algorithm (CRA), scan-based algorithm (SBA), and gossip-based algorithm. The convergence time of the GSIM-ND algorithm is 40%–90% lower than that of these existing algorithms for both low density and high density networks. Thus, GSIM-ND can improve the efficiency of ND algorithm.

Unmanned aerial vehicles (UAVs) are widely applied in civil applications, such as disaster relief, agriculture and cargo transportation, and so on. With the massive number of UAV flight activities, the anti-collision technologies aiming to avoid the collisions between UAVs and other objects have attracted much attention. The anti-collision technologies are of vital importance to guarantee the survivability and safety of UAVs. In this article, a comprehensive survey on UAV anti-collision technologies is presented. We firstly introduce laws and regulations on UAV safety which prevent a collision at the policy level. Then, the process of anti-collision technologies is reviewed from three aspects, i.e., obstacle sensing, collision prediction, and collision avoidance. We provide a detailed survey and comparison of the methods of each aspect and analyze their pros and cons. Besides, the future trends on UAV anti-collision technologies are presented from the perspective of fast obstacle sensing and fast wireless networking. Finally, we summarize this article.

Thanks to the advantages of flexible deployment and high mobility, unmanned aerial vehicles (UAVs) have been widely applied in the areas of disaster management, agricultural plant protection, environment monitoring, and so on. With the development of UAV and sensor technologies, UAV-assisted data collection for the Internet of Things (IoT) has attracted increasing attention. In this article, the scenarios and key technologies of UAV-assisted data collection are comprehensively reviewed. First, we present the system model, including the network model and the mathematical model of UAV-assisted data collection for IoT. Then, we review the key technologies, including clustering of sensors, UAV data collection mode as well as joint path planning and resource allocation. Finally, the open problems are discussed from the perspectives of efficient multiple access as well as joint sensing and data collection. This article hopefully provides some guidelines and insights for researchers in the area of UAV-assisted data collection for IoT.

Internet of Things (IoT) is vulnerable to various cyber attacks due to the massive deployment of IoT devices and the openness of wireless environments. In this article, taking IoT devices as the network resources competed between an attacker and a defender, we study the modeling and analysis of network resource competition in an attack-defense game. The attacker and defender inject different competition strength in each IoT device as their strategies. As a result, the security state of each IoT device will change, which is captured by differential equations. To study the interaction between the attacker and defender and the evolution of the system security states, a zero-sum differential game is formulated by modeling the competition of IoT devices. To achieve the equilibrium of the formulated differential game, optimal control theory is employed to solve the optimization problems of players. Further, a Gauss–Seidel-like implicit finite-difference method is utilized to obtain the saddle point strategy. Finally, numerical results are provided to demonstrate the evolution of network resource competition between the attacker and defender. The results show that our formulated model can effectively and accurately characterize the evolution of the system security states with strategic interactions between the attacker and defender.

Unmanned Aerial Vehicles (UAVs) hold great potential to support a wide range of applications due to the high maneuverability and flexibility. Compared with single UAV, UAV swarm carries out tasks efficiently in harsh environment, where the network resilience is of vital importance to UAV swarm. The network topology has a fundamental impact on the resilience of UAV network. It is discovered that scale-free network topology, as a topology that exists widely in nature, has the ability to enhance the network resilience. Besides, increasing network throughput can enhance the efficiency of information interaction, improving the network resilience. Facing these facts, this paper studies the throughput of UAV network with scale-free topology. Introducing the hybrid network structure combining both ad hoc transmission mode and cellular transmission mode into UAV network, the throughput of UAV network is improved compared with that of pure ad hoc UAV network. Furthermore, this work also investigates the optimal setting of the hop threshold for the selection of ad hoc or cellular transmission mode. It is discovered that the optimal hop threshold is related with the number of UAVs and the parameters of scale-free topology. This paper may motivate the application of hybrid network structure into UAV network.

Despite its advantages of flexility and low-cost networking, unmanned aerial vehicle (UAV) communications face various attacks such as eavesdropping. Existing studies on secure UAV communications assume fixed-location eavesdroppers and rarely consider interactions between legitimate nodes and eavesdroppers. In this paper, we investigate eavesdropping and anti-eavesdropping interaction between a UAV-enabled eavesdropper (UAV-E) and a UAV-enabled base station (UAV-BS) in a downlink wiretap system. The UAV-E aims to wiretap downlink signals by adaptively adjusting its trajectory while the UAV-BS aims to maximize secrecy-sum-rate with minimum power consumption by jointly optimizing user scheduling, power control, and trajectory. Dynamic differential equations are formulated to characterize motions of UAVs, following which a zero-sum differential game is formulated to model the “pursuit-evasion” interaction between the UAV-BS and the UAV-E. Definition and existence of Nash equilibrium (NE) are provided. To obtain the NE, Pontryagins minimum principle is leveraged to solve the trajectory design problem. Further, Gauss-Seidel-like implicit finite-difference method is leveraged to obtain saddle-point strategies at NE. Finally, numerical results are provided to verify the effectiveness of the proposed game model. It is revealed that the differential game can well-characterize the strategy interactions between UAVs. Moreover, results show that the initial positions and weights of UAVs, the energy consumption factor, and the user scheduling have key impacts on motion interactions between the UAV-BS and the UAV-E and further on UAV-BS’s power control.

Due to energy- and computation-efficiency, physical layer authentication has been acknowledged as a powerful approach in verifying the identity of mobile terminals, especially in the massive machine type communication (mMTC) scenario with resource-constraint terminals. In existing literatures, most of works are mainly focused on point-to-point verification, where only one terminal can be authenticated at a time. In this paper, we propose a novel physical layer group authentication mechanism exploiting the benefits of non-orthogonal multiple-access (NOMA) and the irreversibility of hash operation. The proposed mechanism is especially suitable for group authentication of multiple terminals in the mMTC networks with massive connections to one access point. In the authentication procedure, challenge-response signals are multiplied with upper layer keys with hash operations and then exchanged at the physical layer. Binary hypothesis test is employed to verify multiple terminals. Missing rate and false alarm rate are investigated to evaluate the performance of the proposed authentication mechanism. Signal to noise ratio(SNR) is the ratio of the power of total transmission signal and noise. The proposed scheme can achieve a missing rate of 0.8% with the false alarm rate below 1% under the SNR of 25dB.

Internet of Things (IoT) is regarded as the fundamental platform for many emerging services, such as smart city, smart home, and intelligent transportation systems. With ever-increasing penetration of IoT, it becomes of great importance to ensure the IoT security, as the security threats are extended from the cyber world to the physical world. In this article, we investigate physical-layer authentication to help verify the identity of IoT entities for preventing unauthorized access to information or service. Specifically, we propose a Gaussian-tag-embedded physical-layer authentication (GTEA) scheme by using a weighted fractional Fourier transform (WFRFT). Through the superimposition of a low-power Gaussian WFRFT tag onto the message signal, the legitimate receiver can verify the authenticity of the received signal at the physical layer, without being detected by adversaries. Moreover, security analysis shows that with the deliberately designed Gaussian tag, the GTEA scheme is robust against spoofing and replaying attacks. In addition, tradeoff analysis and simulation results are provided to demonstrate the capability of the GTEA scheme in achieving reliability of the message delivery, stealth of the embedded tag signal, and balancing the tradeoff among the robustness of user authentication. Moreover, a prototype is further developed using FPGA and experiments are conducted to demonstrate the effectiveness and performance improvement of the proposed GTEA scheme.

Facing the dramatic increase of mobile devices and the scarcity of spectrum resources, grant-free nonorthogonal multiple access (NOMA) emerges as an enabling technology for massive access, which also reduces signaling overhead and access latency effectively. However, in grant-free NOMA systems, the collisions resulting from uncoordinated resource selection can cause severe interference and reduce system throughput. In this article, we apply deep reinforcement learning (DRL) in the decision making for grant-free NOMA systems, to mitigate collisions and improve the system throughput in an unknown network environment. To reduce collisions in the frequency domain and the computational complexity of DRL, subchannel and device clustering are first designed, where a cluster of devices compete for a cluster of subchannels following grant-free NOMA. Furthermore, discrete uplink power control is proposed to reduce intracluster collisions. Then, the long-term cluster throughput maximization problem is formulated as a partially observable Markov decision process (POMDP). To address the POMDP, a DRL-based grant-free NOMA algorithm is proposed to learn about the network contention status and output subchannel and received power-level selection with less collisions. The numerical results verify the effectiveness of the proposed algorithm and reveal that DRL-based grant-free NOMA outperforms slotted ALOHA NOMA with 32.9% and 156% performance gain on the system throughput when the number of devices is twice and five times that of the subchannels, respectively. When the number of devices is five times that of the subchannels, the success access probability of DRL-based grant-free NOMA is above 85%, compared to 33% in the slotted ALOHA NOMA system.

Providing reliable and efficient coverage for cell-edge mobile users (MUs) is a key issue in wireless communication networks. With non-uniform structure and heterogeneity of network topology in the 5G/B5G networks, performance improvement of cell-edge MUs becomes even more challenging. Unmanned aerial vehicle (UAV) exhibits a comparable advantage in enhancing cell edge performance due to its flexible mobility and line-of-sight air-to-ground links. In this paper, we study UAV-assisted cell-edge MU offloading in the non-uniform heterogeneous cellular networks. A base station (BS) coordination and ground-to-air offloading scheme is proposed to enhance the cell-edge MUs' performance, whereby cell-edge MUs are periodically scheduled between coordinated ground BSs and a flying UAV. Furthermore, a theoretical framework is developed to analyze the average spectral efficiency (SE) and average network throughput. Specifically, closed-form expressions for the average SE are derived for MUs associated with the ground BSs. Upper and lower bounds for the average SE are also obtained when the MU is offloaded to the flying UAV. Finally, numerical and simulation results are provided to validate the theoretical analysis and investigate the impact of key system parameters on the system performance, which also demonstrate the advantages of the UAV-assisted offloading scheme, compared with benchmark solutions.

This paper studies the secrecy energy efficiency (SEE) maximization problem in a full-duplex (FD) bidirectional system, where an FD base station (BS) communicates with an FD user equipment (UE) with the existence of an external user who harvests energy from the ambient radio frequency (RF) signal with SWIPT and acts as a potential eavesdropper. To balance the secrecy rate and the energy efficiency, an SEE maximization problem is formulated subject to the minimum secrecy rate, the maximum transmitted power and the minimum harvested energy. The formulated non-convex problem is solved with a two-layer optimization algorithm, where Dinkelbach method is employed to deal with the fractional programming of the outer problem and a convex approximation method based on Taylor expansion is applied to transfer the inner problem into a convex iterative program. Numerical results demonstrate the effectiveness of the proposed algorithm and the advantages of FD operation in improving SEE.

Blockchain has been considered as a promising technology to improve the efficiency of data sharing in Vehicular Edge Computing (VEC) by data mining among RSUs and vehicles. In order to obtain more mining reward, RSUs and vehicles will compete to accomplish the data sharing mining task. However, it is easier for RSUs equipped with stronger computing capabilities to win the mining reward. In this condition, some vehicles provide their own computing resources to form a shared resource pool for the competition with RSUs. Meanwhile, the competition among the vehicles sharing the resource pool is non-negligible since some selfish vehicles divide the group reward but without providing resources. In this way, we provide a multilayer game model that involving a deformed N Iterated Prisoners Dilemma (NIPD) among vehicles and a bargain game between resource pool and RSUs. Further, we use multi-agent Deep Q Network (DQN) to achieve the equilibrium between vehicles and RSUs in mining. Finally, numerical results show the optimal strategy can attain a stable data sharing mining system in VEC network.

With emergence of Internet of Things (IoT), wireless traffic has grown dramatically, posing severe strain on core network and backhaul bandwidth. Proactive caching in mobile edge computing systems can not only efficiently mitigate the traffic congestion and relieve burden of backhaul but also can reduce the service latency for end devices. However, proactive caching heavily relies on the prediction accuracy of content popularity, which is typically unknown and change over time. In this paper, we propose an online proactive caching scheme based on bidirectional deep recurrent neural network (BRNN) model to predict time-series content requests and update edge caching accordingly. Specifically, on the first layer, a 1-D convolution neural network (CNN) is devised to reduce the computational costs. Then, BRNN is employed to predict time-varying requests from users. Afterward, a fully connected neural network (FCNN) is harnessed to learn and sample predicts from the BRNN. Finally, we conduct experiments based on real datasets, which demonstrate that the proposed approach can achieve considerably high prediction accuracy and significantly improve content hit rate of end devices.

Unmanned aerial vehicles (UAVs) are widely exploited in environment monitoring, search-and-rescue, etc. However, the mobility and short flight duration of UAVs bring challenges for UAV networking. In this paper, we study the UAV networks with n UAVs acting as aerial sensors. UAVs generally have short flight duration and need to frequently get energy replenishment from the control station. Hence, the returning UAVs bring the data of the UAVs along the returning paths to the control station with a store-carry-and-forward (SCF) mode. A critical range for the distance between the UAV and the control station is discovered. Within the critical range, the per-node capacity of the SCF mode is θ(n/logn) times higher than that of the multihop mode. However, the per-node capacity of the SCF mode outside the critical range decreases with the distance between the UAV and the control station. To eliminate the critical range, a mobility control scheme is proposed such that the capacity scaling laws of the SCF mode are the same for all UAVs, which improves the capacity performance of UAV networks. Moreover, the delay of the SCF mode is derived. The impact of the size of the entire region, the velocity of UAVs, the number of UAVs and the flight duration of UAVs on the delay of SCF mode is analyzed. This paper reveals that the mobility and short flight duration of UAVs have beneficial effects on the performance of UAV networks, which may motivate the study of SCF schemes for UAV networks.

Unmanned aerial vehicles (UAVs), featured by flexible configuration, robust deployment, and line-of-sight links, has a great potential to provide ubiquitous wireless coverage and high-speed transmission. In this paper, we aim to analyze the coverage performance of UAV-assisted terrestrial cellular networks, where partially energy-harvesting-powered caching UAVs are randomly deployed in the 3-D space with a minimum and maximum altitude, i.e., Hland Hh. A novel cooperative UAV clustering scheme is proposed to offload ground mobile terminals (GMTs) from ground cellular base stations to cooperative UAV clusters. A cooperative UAV cluster is developed within a cylinder with projection centered on a GMT, based on their energy states, the cached contents, and the cell loads. With tractable Poisson point process and Gamma approximation, explicit expressions for the successful transmission probabilities are obtained. A theoretical analysis reveals that the cooperative probability of a UAV and the offloading probability of a GMT have bell-shaped relation with respect to the radius of the cylinder and the cache hit probability (the matching probability of a content request and content cache). Numerical results are provided to demonstrate the impacts of the system parameters on the cooperative UAV cluster. The results also give the optimal average altitude (Hl+ Hh/2) and altitude difference (Hh-Hl) in maximizing the coverage performance with the proposed cooperative transmission scheme.

In this paper, we study the performance of base station (BS) coordination in heterogeneous networks (HetNets) with cache-enabled and renewable energy-powered small cell BSs (SBSs). Macrocell base stations (MBSs) provide basic coverage, while the SBSs, powered by harvested energy, conduct content-aware coordinated transmission to provide high data rate and further improve the network coverage. Specifically, a joint transmission strategy is performed based on the knowledge of the energy states and the cached contents of SBSs, along with the awareness of the availability of channel resources and the average received signal strength (RSS) of the corresponding link. Stochastic geometry is applied to characterize the statistics of the cell load at MBSs and SBSs, as well as the aggregated information and interference signal strength. Then, the average user capacity for the joint transmission is obtained. Additionally, the coverage probability is derived with gamma approximation for the aggregated information and interference signal strength. Analytical results reveal that the average user capacity and coverage probability can be maximized with optimal cache size, energy harvesting rate and cooperative RSS threshold. Finally, extensive numerical and simulation results are provided.

To alleviate the burden on power grid in heterogeneous cellular networks, energy harvesting (EH) has inspired thorough research in both academia and industry. However, the mismatched distribution of energy storage and traffic load leads to a bad result that the renewable energy can not be utilized sufficiently. To tackle this issue, we propose an energy-biased-received-power (EBRP) scheme (i.e., users incline to associate with BSs at higher energy level) for energy-load balancing and on-grid energy saving. Taking into account the dynamic EH behavior, a performance analysis of coverage probability and energy efficiency is conducted in a two-tier heterogeneous network with EH enabled personal cells. Numerical and simulation results are provided along with the maximum-biased-received-power (MBRP) scheme. Numerical results show that the reduction of maximum transmit power at base stations is favorable for enhancing energy efficiency. Moreover, it is revealed that the proposed scheme shows superiority in energy-load balancing when the energy efficiency is comparable with that of the MBRP scheme.

This paper investigates the secrecy rate maximization problem for the multiple-input-single-output multiple-antenna-eavesdropper (MISOME) wiretap channel with multiple randomly located jammers. The multi-antenna base station (BS) transmits information signals along with artificial noise (AN) to disturb the eavesdropper. Moreover, the friendly jammers are properly selected to assist the legitimate link for better secure transmission with some payoffs. With this system model, we first formulate a Stackelberg game between the BS and the assisting jammers with full channel state information. Stackelberg equilibriums, including optimal fraction of transmit power for AN, optimal transmit power, and asking prices of assisting jammers, are first proved to exist and then analytically derived. A policy iterative algorithm is also proposed to obtain the optimal solutions. We then extend the Stackelberg game to the case of MISOME broadcast wiretap channel with channel distribution information of eavesdropper. Numerical results verify the accuracy of the derived results and the efficiency of the proposed algorithm. The results reveal that the proposed jammer-assisted secure transmission can greatly improve the secrecy performance and meanwhile save more energy for information signals, which is significant for future wireless communication.

This paper investigates secrecy and connection performance for uplink transmission in a two-tier heterogeneous network with non-uniformly deployed low- power small base stations (BSs). All BSs and the eavesdropper are equipped with multiple antennas. We propose an aggregate interference approximation approach to characterize the statistics of interference generated by users associated with small BSs to facilitate our analysis. Then the secrecy outage probability and successful connection probability for a randomly located macro user are derived. In addition, we characterize them for a special case where macro BSs and eavesdropper are equipped with single antenna. Numerical results validate the effectiveness and accuracy of the aggregate interference approximation approach. Besides, the theoretical results fit well with the numerical results.

This paper studies the physical layer security in a multi-user downlink, where a single user is selected for secret transmission during each time frame. Current works usually assume a worst case where all unselected users are curious and act as eavesdroppers, and conclude that no multiuser diversity is achievable for secrecy when the number of users is pretty large. However, the worst case may happen rarely in practice. A general scenario is that several (maybe all) of the unselected users act as eavesdroppers. In this case, selecting the user with the largest SNR (i.e., signal to noise ratio) does not necessarily achieve the maximum secrecy rate. For the general case, we derive the new closed-form expression of the secrecy outage probability, which increases with the number of curious users, and tends to converge in the high-SNR and large-user-number regime. When the number of curious users is supposed to be small, the secrecy outage probability could be any value smaller than one even in the large-user-number regime. These results provide additional insights into the system performance.