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.

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.

The balance between system performance and power consumption has always been a top priority, particularly in massive multiple-input multiple-output (MIMO) system. Besides, as one of the most critical modules in the radio-frequency (RF) chain, analog-to-digital converters (ADCs) hold significant importance. To find that tradeoff, mixed-ADC architecture, combining high-resolution ADCs and low-resolution ADCs, emerges as an effective approach. In this paper, we propose a novel three-stage near maximum likelihood (nML) detector to perform signal detection for the mixed architecture. In the first step, high-resolution ADCs are exploited to roughly find the transmitted signal vector. Subsequently, a convex optimization problem is further constructed based on the rough vector and projection gradient method is used to estimate the more likely one. An approximate expression for the cumulative distribution function is derived to simplify the optimization problem. Finally, the size of the transmitted signal vector collection is effectively minimized on the basis of the most possible vector and exhaustive search is carried out on the collection. Numerical results show that the proposed three-stage nML detector is efficient enough to simultaneously obtain high accuracy and low complexity.

5G networks and Subband Full Duplex (SBFD) technology aim to enhance uplink transmission efficiency in IoT environments. However, large-scale random access users in SBFD systems exacerbates cross-link interference (CLI), which could undermine its potential performance advantages. To address this challenge, we propose a priority-aware random access (PA-RA) strategy, which comprises two key components. First, access users are selected through a dynamic priority evaluation method that considers Quality of Service (QoS), real-time resources availability, and historical access attempts. Second, a centralized subband allocation method preferentially assigns resources to central regions of the uplink subband to minimize CLI, while adaptively utilizing edge resources under low-interference conditions. The simulation results demonstrate that PA-RA strategy could significantly reduce CLI compared to conventional methods, achieving high success rates of access and low latency. Furthermore, it provides a robust framework for interference management in SBFD networks with high-density IoT devices.

In massive MIMO mmwave systems, the inherent conflict between high-latency conventional beam scanning in millimeter-wave communications and the critical need for user location privacy protection is addressed. In this paper, an implicit location-based beam selection method driven by deep learning is proposed, where raw user coordinates are transferred into irreversible implicit feature vectors through a triple protection mechanism involving Gaussian noise injection, differential privacy perturbation, and nonlinear transformation. A lightweight deep neural network integrated with channel attention is designed to establish end-to-end mapping from implicit location to optimal beam indices for latency-sensitive selection. Experiments with DeepMIMO dataset prove that the proposed method reduces beam search complexity by nearly 90% with comparable accuracy to exhaustive search. This work provides a novel framework for jointly optimizing privacy preservation and beam management efficiency in mmWave communication systems.

One-bit analog-to-digital converters (ADCs) reduce cost and power consumption in massive MIMO systems but introduce signal distortion that complicates data detection. Temporal oversampling at the receiver can mitigate performance loss from coarse quantization. In this paper, we analyze the performance of temporally oversampled massive MIMO systems with one-bit quantization in the presence of correlated noise, where the noise correlation arises from both filtered thermal noise and inter-symbol interference (ISI). Specifically, we propose a linear symbol-wise MMSE receiver and reveal how noise correlation affects its resulting MSE. Two approximations of the MSE are developed to offer valuable insights. The first approximation suggests that the effects of different noise correlation pairs on the MSE can be treated as independent. The second approximation demonstrates that the MSE variations induced by noise correlation are jointly determined by the pulse-shaping and receive filters. For a system employing a raised cosine pulse, noise correlation degrades the receiver performance. Moreover, the trade-off between noise correlation and the oversampling ratio is analyzed. The case of imperfect channel state information (CSI) is also considered, where the analysis developed for data detection is extended to a linear MMSE channel estimator. Finally, numerical results show that for a system employing a raised cosine pulse, an oversampling ratio of 2 is beneficial, offering practical guidelines for system design.

Although reconfigurable intelligent surface (RIS) can improve the secrecy communication performance of wireless users, it still faces challenges such as limited coverage and double-fading effect. To address these issues, in this paper, we utilize a novel multi-functional RIS (MF-RIS) to enhance the secrecy performance of wireless users, and investigate the physical layer secrecy problem in non-orthogonal multiple access (NOMA) networks. Specifically, we derive the secrecy outage probability (SOP) and secrecy throughput expressions of users in MF-RIS-assisted NOMA networks with external and internal eavesdroppers. The asymptotic expressions for SOP and secrecy diversity order are also analyzed under high signal-to-noise ratio (SNR) conditions. Additionally, we examine the impact of receiver hardware limitations and error transmission-induced imperfect successive interference cancellation (SIC) on the secrecy performance. Numerical results indicate that: i) under the same power budget, the secrecy performance achieved by MF-RIS significantly outperforms active RIS and simultaneously transmitting and reflecting RIS; ii) with increasing power budget, residual interference caused by imperfect SIC surpasses thermal noise as the primary factor affecting secrecy capacity; and iii) deploying additional elements at the MF-RIS brings significant secrecy enhancements for the external eavesdropping scenario, in contrast to the internal eavesdropping case.

With the emergence of diverse services and the development of native artificial intelligence (AI), 6G network is expected to satisfy higher performance requirement by supporting deep convergence of computing and communication. As an important development trend, AI-embedded base station (BS) with computing resources should be considered. However, in some busy-traffic cells, it is difficult for a single BS to serve all users due to its limited computing resources. In this paper, a novel collaborative-computing based system model is proposed for computing-constrained BSs, in which the shared computing resources are introduced to finish both communication protocol and application processing. In addition, a new user association (UA) scheme based on a hierarchical double auction model is designed to improve the long-term network capacity. Simulation results demonstrate that, compared with several typical UA algorithms, the proposed scheme could increase the served user number and throughput significantly without energy efficiency degradation, which is proved a promising approach for wireless networks with integrated computing and communication.

In massive Multiple-Input Multiple-Output (MIMO) systems, the high power consumption of radio frequency (RF) chains is unbearable. Low-resolution analog-to-digital converters (ADCs) serve as a potential method, but they may lead to performance degradation. To get a tradeoff, mixed- ADC architecture is considered as a promising approach to balance performance and power consumption. In this paper, an optimal bits allocation scheme is proposed for uplink massive MIMO systems with mixed ADCs. Based on the derivation of closed-form expressions for achievable sum rate and energy efficiency (EE), optimization problems are formulated to reflect the relationship between system performance and different ADC resolutions on antennas with total bits constraint. Moreover, a hierarchical traversal algorithm is further provided to reduce the computation complexity. Numerical results validate that the proposed bits allocation scheme shows significant performance gain compared with other schemes. Furthermore, it has been proved feasible for practical massive MIMO systems due to its much lower complexity.

Millimeter Wave (mmWave) is usually combined with large-scale multiple antenna technology to produce a large number of narrow beams for better coverage. In order to improve the network throughput, an optimal beam or beam pair is selected to achieve high data rate. However, it is challenging to perform beam selection in vehicular systems with dynamic blocking scenario due to its complex geometric environment and variable number of multipaths. In this paper, a two-stage prediction-based beam selection (TS-PBBS) algorithm is proposed to find the optimal beam accurately and quickly in blocking scenarios. A deep neural network (DNN) is constructed to perform the mapping between the performance of predesigned probing beams and narrow beams. Furthermore, a local search method is introduced to improve the beam selection accuracy. Simulation results reveal that the proposed algorithm is able to recognize the blockage in the environment and achieve high accuracy for beam selection with relatively low searching complexity. In addition, it also appears to have the capability of resisting channel estimation error because the deep learning model weakens the effects of noise, which provides an effective solution for practical deployment of mmWave massive MIMO systems.

Multi-user multiple-input multiple-output (MU-MIMO) is one of the key techniques to meet high spectral efficiency requirements for the fifth generation (5G) and beyond 5G wireless network. To achieve the high capacity of uplink MU-MIMO, a generalized polar precoding scheme based on rotation and permutation (GP-RP) is proposed in this paper, which can enhance the reliability distinctions between users’ channels. Via the three-stage channel transformation, the binary polar coding, signal modulation and the original multi-user channel are successively partitioned into multiple bit polarized channels. To further amplify the polarization effect, an unitary precoding scheme is proposed for the uplink MU-MIMO system without changing the communication capacity. The simulation results show that the proposed precoding scheme can further improve the polarization between the synthesized user channels and achieve lower block error ratio (BLER).

Non orthogonal multiple access (NOMA) is a potential solution for communication systems with massive connection requirement. It enables multiple users to share the same time-frequency resource block through user pairing, so as to improve the spectrum efficiency of the system. However, the advantage of NOMA can only be fully utilized when the difference of user channels are quite large, while it is rarely achieved when the user channels are similar due to the poor performance of successive interference cancellation (SIC) receiver. In this paper, a user pairing scheme based on power boosting is proposed for NOMA systems, which enables users with similar channel gains to get paired and obtain NOMA advantage. In addition to a feasibility analysis on promoting more paired users by increasing certain power, the relationship between the required power boosting and channel difference is also derived. Simulation results demonstrate that, compared with the hybrid transmission of NOMA and orthogonal multiple access (OMA), our proposed scheme can significantly improve the spectrum efficiency with little power efficiency degradation.

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.

The sensitive information of vehicles which is closely related to the safety of transportation makes the privacy problems in the vehicular networks a popular concern. The development of artificial intelligence (AI) has led the Internet of Vehicles (IoV) to the next phase of intelligence, the Social IoV (SIoV). For social purposes, vehicles may upload captured images with more sensitive information. As a result, the privacy problem is even more serious in SIoV. The exited studies of privacy-preserving methods in vehicular networks mainly consider the spontaneous privacy disclosure. However, the main privacy leakage in real life comes from peer disclosure rather than spontaneous privacy disclosure, which is usually ignored. Therefore, this article innovatively presents a decentralized scheme for solving the peer disclosure issues in SIoV, which, to the best of our knowledge, is the first research in SIoV peer disclosure discussion. A directed acyclic graph (DAG)-based mutual supervision (Dmsv) algorithm is designed for mutually distrustful vehicles. It is verified that our proposed algorithm reduces at least 72% multidimentional privacy loss of image entropy leakage probability when compared with nonpeer disclosure prevention. The decentralized scheme also achieves a relatively low delay of around 24 s from the transaction generation to a network-wide consensus. This article provides a feasibility of applying DAG in a mobile system and provides guidance on how the transportation situation influences the confirmation delay and how to adjust the incentive mechanism according to the transportation situation to maintain robustness.

In recent years, the privacy issue in Vehicular Edge Computing (VEC) has gained a lot of concern. The privacy problem is even more severe in autonomous driving business than the other businesses in VEC such as ordinary navigation. Federated learning (FL), which is a privacy-preserved strategy proposed by Google, has become a hot trend to solve the privacy problem in many fields including VEC. Therefore, we introduce FL into autonomous driving to preserve vehicular privacy by keeping original data in a local vehicle and sharing the training model parameter only with the help of MEC server. Moreover, different from the common assumption of honest MEC server and honest vehicle in former studies, we take the malicious MEC servers and malicious vehicles into account. First, we consider honest-but-curious MEC server and malicious vehicles and propose a traceable identity-based privacy preserving scheme to protect the vehicular message privacy where improved Dijk-Gentry-Halevi-Vaikutanathan (DGHV) algorithm is proposed and a blockchain-based Reputation-based Incentive Autonomous Driving Mechanism (RIADM) is adopted. Further, when the case comes to the non-credibility of both parties where semi-honest MEC server and malicious vehicles are considered, we propose an anonymous identity-based privacy preserving scheme to protect the identity privacy of vehicles with Zero-Knowledge Proof (ZKP). Based on the simulation of virtual autonomous driving based on real-world road images, it is verified that our proposes scheme can reduce 73.7 % training loss of autonomous driving, increase the accuracy to around 5.55 % while keeps effective privacy of message and identity under the threat of dishonest MEC server and vehicles.

With the support of massive multiple input multiple output (MIMO), millimeter wave (mmWave) communication technology has emerged as a promising solution to meet the increasing demand on data traffic. This paper addresses the joint optimization of beam selection and power allocation to maximize the spectral efficiency of multiple users across multiple base stations (BSs), where the number of available beams has spatial differences (i.e., nonuniform cardinalities) for different BSs. A mixed-integer programming problem is formulated under the considerations of complex interference environments (i.e., including intra-BS interference and inter-BS interference) and nonuniform cardinality, which is transformed into the convex optimization problem by relaxing the binary indicator of beam selection and utilizing the logarithmic approximation of spectral efficiency. The Lagrangian dual method and the gradient descent method are then exploited to solve the transformed optimization problem. Corroborated by simulation results, the proposed algorithm can be converged iteratively and is superior to the state of the art in terms of spectral efficiency.

Backhaul capability is usually considered as a bottleneck for system performance in dense wireless networks, caching popular file in base stations is an effective solution. In the dense deployed network, it is necessary to consider the interaction between cache size and file placement, which will lead to a rise of cache deployment cost. In order to solve this problem, this paper introduces the concept of user group and proposes a scheme with joint optimization of cache size and file placement. In this scheme, considering constraint of file delivery latency and other factors, joint optimization for minimizing the total cache size is established, with iteration of the coordinate axis descent algorithm and cache value competition algorithm. Simulation results show that, compared with the greedy algorithm, the proposed algorithm can effectively reduce the total cache size and and is very close to the optimal algorithm. Influences of user group size and preference on file placement are also discussed.

This paper exploits the inter beam handover(IBH) mechanism for 5G mmWave mobile communication system in high speed rail(HSR) scenario. Considering higher speed and narrower coverage of serving beam in mmWave system, more frequent handover(HO) will happen, so in order to reduce inter beam handover failure rate of the network, a novel jointly optimized dynamic inter beam handover(JOD-IBH) scheme based on multiple beams cooperation was proposed in this paper, where handover failure rate and resource occupation rate were jointly optimized. Simulation results show that the proposed JOD-IBH brings improvements to the inter beam handover performance for HSR scenario, which can be observed in reducing handover failure rate and achieving the balance between handover failure rate and resource occupation rate.

In this paper, we study the secrecy performance in a hybrid wiretapping wireless system, where the half-duplex (HD) or full-duplex (FD) eavesdroppers may wiretap the confidential signal and/or transmit a jamming signal. To evaluate the secrecy performance, we derive the approximate closed-form results for the secrecy outage probability and mean secrecy rate by means of the random matrix theory (RMT). The RMT method can greatly simplify the complicated mathematical analysis with high accuracy, and can provide a useful analytical framework for other researches. The Monte Carlo simulations and numerical results are provided to validate the theoretical analysis and demonstrate the impacts of the system parameters. From the perspective of BS transmission, increasing BS transmission power can greatly improve the secrecy performance in the low transmit power region, but the secrecy performance is constant in the high BS transmit power region. Moreover, in terms of adversaries, FD eavesdroppers have better wiretapping performance than HD eavesdroppers when the jamming power is relatively low; otherwise, this result is reversed.

Massive Multi-Input Multi-Output (MIMO) is one of the key technologies for the fifth generation (5G) cellular networks. As the number of antennas increases, pilot contamination (PC) is considered as one of the main limiting factors for the performance of massive MIMO systems. In this paper, a new dynamic pilot allocation scheme is proposed to mitigate the pilot contamination with low complexity. In this scheme, a pilot reuse indicator is introduced and each cell is divided into center zone and edge zone. When the pilots are allocated, the pilots for edge zone users are dynamically selected according to the pilot reuse indicator. So the scheme can mitigate the pilot contamination by reducing pilot repetition in the target cell and neighbor cells, improve the system capacity. Our simulation results show that the proposed scheme can significantly increase the uplink achievable rate and provide better stability in the scenario with nonuniform distributed users, especially with more users and less pilots.

Artificial noise (AN) assisted secure communication is a promising technique in the area of physical layer security. In this paper, we propose a new AN method to enhance the secrecy performance of a time reverse (TR) transmission system. Based on the proposed AN model, we first derive the closed-form secrecy rate, whose accuracy is verified through simulations. Then using it as an objective function, we further study the power allocation issue between useful information signals and the AN with the aid of maximizing the secrecy rate. The optimal power allocation is first derived in closed form and then analyzed in some special cases such as Rayleigh channel. Analytical and simulation results show that the secrecy performance can be considerably improved by over 10% even with a small amount of AN. And when there are less multi-paths in the channel or when the transmit power is small, more power for AN is required.

This paper addresses the cell association issue in the downlink of 3-dimension (3D) multiple input multiple output (MIMO) enabled heterogeneous networks (HetNets). In order to serve specific users more efficiently, the downtilts of cell-center users and cell-edge users are configured through dynamic vertical beamforming. Therefore, it is necessary to consider the factor of downtilt in cell association for 3D MIMO enabled HetNets, so that the users are more likely to access to an appropriate cell. In this paper, a novel joint processing algorithm based on Lagrange dual decomposition (JP-LDD) is proposed, where cell association and downtilts adjustment are jointly optimized. The objective of JP-LDD algorithm is to maximize the spectral efficiency of HetNets and NP-hard cell association problem is transformed into a linear format to facilitate the analysis, with access control principle considered as well. Finally, simulation results show the superiority of JP-LDD algorithm in both performance of spectral efficiency and offloading effects in HetNets. Furthermore, the feasibility of its application in massive MIMO systems is also verified.

In this paper, we investigate the performance of artificial noise assisted secure communication in the presence of a full-duplex active eavesdropper who can simultaneously perform eavesdropping and jamming. An approximate closed-form expression for the secrecy rate is derived. With this result, we obtain a new result for the optimal power allocation factor maximizing the secrecy rate, and analyze the impact of self-interference coefficient and jamming power on it. We further consider a more practical scenario where the legitimate user aims to maintain a given target data rate and uses all remaining power for artificial noise (AN) to interfere with the eavesdropper. While the eavesdropper tries to compel the transmitter to reduce the AN by sending jamming signals to the legitimate receiver. To solve this conflict, we introduce a power cost parameter to describe the impact of jamming on the eavesdropper itself and then formulate a game-theoretic framework. We also derive the closed-form equilibrium solutions for both sides. These results show the optimal jamming strategy of the eavesdropper and provide insights into the low bound secrecy performance. Finally, simulation results are provided to verify our analytical results.

This paper studies the performance of a Massive MIMO system with pilot contamination in the multi-cell multiuser network. RCI (Regularized Channel Inversion) precoding is considered due to its good performance. Instead of using a large system method which assumes the number of antennas and users tends to infinity with a fixed ratio, we analyze the system performance in a more general situation where the number of transmit antennas M and number of users K are finite. We first derive the closed-form expression of the optimal RCI factoramp;#945;opt,and then study the impact of pilot contamination on the system performance. Result shows that more factors will be considered in finite situation. It is shown that our analysis is more accurate especially in small values of M and K. And our derived amp;#945;opt enables much better performance than the traditional large system result.

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.

Massive MIMO can significantly improve diversity/multiplexing gain, channel capacity and spectrum efficiency. An efficient signal detection algorithm plays a very important role for system performance. However, the BER, complexity and delay performance of present signal detection algorithms are far from optimal with the increasing number of antennas. This paper presents an improved mixed Gibbs sampling algorithm based on multiple random parallel Markov chains (MGS-MRPMC). It has lower complexity than traditional signal detection algorithms in massive MIMO systems. The simulation shows that the proposed algorithm can alleviate the stalling problem encountered in the mixed Gibbs sampling (MGS) algorithm with higher order QAM. Moreover, it can reduce the serious delay of the mixed Gibbs sampling with multiple restarts (MGS-MR) algorithm. The use of parallel strategy makes it suitable for hardware implementation in practice.

Coordinated MIMO (co-MIMO) can mitigate inter-ceil interference (ICI) and significantly improve spectral efficiency with accurate channel estimation. However, channel estimation error (CEE) is inevitable and may cause obvious performance degradation for coordinated scheduling algorithms due to the inaccurate information. In this paper, a novel coordinated scheduling algorithm is proposed based on an adaptive CEE model for the uplink co-MIMO system. Based on a new defined CEE sensitivity criterion, users are divided into different groups and applied to different scheduling approaches. Simulation results show that the proposed algorithm outperforms traditional coordinated scheduling algorithms in practical scenario with CEE. Furthermore, it can also achieve a better tradeoff between throughput and fairness compared to the robust user pairing algorithm.

Abstract In the paper, we study the problem of secure connectivity for colluding eavesdroppers using relay selection in random wireless networks, where the relay nodes and eavesdroppers are all randomly distributed according to two independent Poisson point process. The decode‐and‐forward and randomize‐and‐forward two relay strategies are considered, and a new metric is defined for best relay selection and random relay selection. We derive closed‐form expressions for the secrecy outage probability for the two relay strategies. In particular, the effect of power allocation ratio and the maximum ratio combing at the destination node on the secrecy outage probability is demonstrated for the decode‐and‐forward relay strategy. Numerical results illustrate the secrecy performance gains with collaborative transmit diversity. © 2016 The Authors. Wireless Communications and Mobile Computing Published by John Wiley & Sons Ltd.

This paper investigates transmit antenna selection (TAS) in a multiple-input multiple-output (MIMO) wiretap channel based on a measure with dual thresholds at the legitimate receiver and the eavesdropper, respectively. Due to the fact that pilots are separated from transmit signals in time, weighting errors at the combiners are considered here. Closed-form expressions for the exact and asymptotic secure connection outage probability are derived and analysed. It is demonstrated that antenna channel hopping with fountain codes exploitation achieves a desirable secrecy diversity order, which is related to the weighting correlation coefficient and the additional transmit packet number. At last, simulation results validate the security enhancement and the actual impact of weighting errors.

Multiple-input multiple-output (MIMO) technology has been widely used as an efficient way to improve the capacity of wireless channels. As an optimal detection for MIMOOFDM systems, the maximum likelihood (ML) algorithm whose complexity rises exponentially with the number of transmit and receive antennas is not practical for real-time detection. Instead, the sphere decoding (SD) algorithm was proposed to find the solution to the ML detection problem, however, the complexity is still large. The choice of the initial radius for SD detector has a significant impact on the complexity. In this paper, an improved depth-first- search sphere decoding (IDFS) algorithm based on the optimization of likelihood ascent search (LAS) predetection is proposed. The simulation results demonstrate that the proposed algorithm can not only achieve almost the same performance as ML detector but also efficiently decrease the complexity. For example, for about 49 visiting nodes for 16QAM and 30 visiting nodes for 4QAM, IDFS algorithm has about 4dB SNR gain than Schnorr-Euchner sphere decoding (SESD) algorithm as Fig.5 shows.

Wireless communication is particularly susceptible to eavesdropping due to its broadcast nature. Security and privacy issues have become increasingly critical for wireless networks. This paper considers the problem of secret communication in the multiuser downlink eavesdropped by a passive eavesdropper (EVE). The well-known zero-forcing preceding is adopted at the transmitter to produce concurrent data streams to the users, and at the same time an artificial noise (AN) is generated to prevent EVE from intercepting the information. We first derive an analytical closed-form expression for the secrecy sum-rate in the large system limit. We then use it as the objective function to optimize the power allocation (PA) between the information signals and the AN to maximize the secrecy sum-rate. A simple analytical expression of the optimal PA is derived in the high transmit power regime, which proves to be a near-optimal and generic strategy. The large system results are quite accurate for finite-size systems and thus can provide useful insights into system analysis and design. Our analytical and simulation results show that the AN assisted strategy achieves the same multiplexing gain as in the multiuser downlink without eavesdropping. Moreover, more power should be allocated to AN when the system serves fewer users and EVE has more antennas.

In the paper, we study the problem of secure connectivity in general Multiple input and Multiple output (MIMO) stochastic networks, where legitimate users and eavesdroppers are equipped with multi antennas, and designed to distribute on a two- dimensional plane according to two independent Poisson point process(PPP)s. The non-colluding and colluding eavesdroppers scenarios are considered. Transmit-receive diversity (TRD) is explored to derive a closed-form expression of the upper bound of secrecy connection probability. For colluding eavesdroppers, we consider the worst cases where the received signals of eavesdroppers are Nakagami-m distributed with integer parameter m. Compared with MISO system, our analysis and simulation shows that a significant improvement of secrecy connection probability can be received by performing TRD in MIMO system.

Owning to the advantage of an increase in spectral efficiency by reducing the transmitted pilot tones, compressed sensing has been widely applied to pilot-aided sparse channel estimation in OFDM systems. In this paper, we focus on increasing the accuracy of channel estimation and propose a novel sparse channel estimation method that takes the effect of the additive noise into consideration. In the proposed method, the channel impulse response (CIR) is represented as a combination of ideal channel paths and additive noise. We firstly use the Orthogonal Matching Pursuit (OMP) algorithm to estimate the path delays and path gains. Then, the Mahalanobis distance discriminant analysis is applied to channel paths identification. The proposed method could effectively distinguish the significant channel paths especially those with small amplitude from the noise. Simulation results demonstrate the effectiveness of the proposed sparse channel estimation method. Compared with the conventional CS-based channel estimation algorithms, the new method proposed here enjoys superior performance in terms of bit error rate (BER) and mean square error (MSE).

In heterogeneous networks, the evolved NodeBs (eNBs) have different downlink transmission power and the power imbalance problem can not be avoided. In order to solve this problem, Uplink/Downlink (UL/DL) split for users in Cell Range Expansion (CRE) region of small cells has been proposed in 3GPP. UL/DL split users are connected with macro cells and small cells simultaneously. In this paper, we propose an uplink power control scheme based on game theory for UL/DL split users. In the scheme, the convex pricing function is an exponential pricing function of users' transmission power, which reflects the interference that macro users are suffering from UL/DL split users. It can ensure that UL/DL split users will be penalized when they cause serious interference to macro users. The power control scheme based on non-linear convex pricing obtains more improvements than the linear one with numerical simulations. Furthermore, a Dynamic Power Adjustment (DPA) algorithm is added to the scheme in order to mitigate interference in uplink and to speed up the convergence process.

In this paper, we contribute to the secrecy outage analysis of transmit antenna selection (TAS) with switch-and-examine combining (SEC) in the Multiple-input multiple-output (MIMO) wiretap channel. The complexity of SEC is lower than maximum-ratio combining (MRC) and selection combining (SC). The transmit antenna which maximizes the instantaneous signal-to-noise ratio (SNR) at the receiver is selected. Two scenarios are studied here: 1) SEC at both the receiver's side and the eavesdropper's side (SEC/SEC); 2) SEC at the receiver's side while MRC at the eavesdropper's side (SEC/MRC). Expressions for the exact and asymptotic secrecy outage probability are derived and validated by simulation results. It is demonstrated that SEC may achieve competitive secrecy performance in the low SNR regime with less estimation cost. In the high SNR regime, it achieves full secrecy diversity order NaNb with careful threshold's design even under the condition of MRC applied at the eavesdropper.