Phased array (PA) has received considerable attention as a representative multiantenna technique due to its inherent advantages of superior directionality, spatial multiplexing capabilities, and robust anti-jamming characteristics. However, PA suffers from relatively high hardware complexity and power consumption. As a low-complexity array technology with excellent beamforming capability, time modulated array (TMA) has attracted much attention in recent years. In this article, we exploit a TMA for enhancing the Age of Information (AoI) of covert communication. Specifically, we first propose the transmitter structures and the corresponding beamforming methods for the TMA scheme and the PA scheme as a benchmark. Subsequently, the closed-form expressions of the Kullback-Leibler (KL) divergence is derived to serve as the quantitative measure of communication covertness under both schemes, based on which the average covert AoI (CAoI) is derived to jointly characterize the covertness and timeliness performance. Then, to minimize the average CAoI, the optimization problems of the block-length and beamforming parameters for both the TMA and PA schemes are formulated and solved. Finally, the numerical results are provided to show that the proposed TMA scheme surpasses the PA scheme in terms of both the convergence rate and the average CAoI.

Abstract This paper investigates an intelligent reflecting surface (IRS) assisted downlink short packet transmission system, where an access point sends short packets to multiple devices with the help of an IRS. Specifically, a performance comparison between the frequency division multiple access and time division multiple access is conducted for the considered system, from the perspective of average age of information (AoI). To minimize the maximum average AoI among all devices, the resource allocation and passive beamforming are jointly optimized. However, the formulated problem is difficult to solve due to the non‐convex objective function and coupled variables. Thus, an alternating optimization based algorithm is proposed by exploiting the semidefinite relaxation and bisection search techniques. Simulation results show that time division multiple access can achieve lower AoI by exploiting the time‐selective passive beamforming of IRS for maximizing the signal to noise ratio of each device consecutively. Moreover, it also shows that as the length of information bits becomes sufficiently large as compared to the available bandwidth, the proposed frequency division multiple access transmission scheme becomes more favourable due to more flexible power allocation.

In the cellular Internet of Things (CIoT), it is crucial to ensure the information freshness for status update applications. Considering the centralized access methods could cause large access delay and hamper timely status updates, this paper exploits the random access method and studies decentralized status update schemes to minimize the average age of information (AoI) for CIoT. However, due to the non-cooperation among machine type communication devices (MTCDs) in random access, packet collisions are inevitable, which makes it tricky to improve the AoI performance. In this regard, we design novel age-based status update schemes to control the transmission behavior of MTCDs, where the AoI at the MTCDs and the base station (BS) is used. We first model the AoI minimization problem as a Markov decision process. Then, through variable substitution and linear programming, we get a slightly more computationally complex status update scheme, where the dual threshold structure of the scheme is proved theoretically. To facilitate system design and reduce computational complexity, we further design a low-complexity scheme, where the age thresholds at both the MTCDs and BS are optimized. Simulation results verify that the proposed schemes significantly outperform the common access scheme.

Complex status updates have attracted widespread attention in real-time monitoring services (e.g., real-time fire gas monitoring and wildfire spread prediction). In complex status updates, the status information needs to be obtained by processing the perceived original data. However, as lightweight terminals, temporarily deployed Internet of Things (IoT) devices have no computing modules. Unmanned aerial vehicle (UAV) can act as a edge server to help IoT devices complete computing tasks by mobile-edge computing (MEC). To this end, this article considers a complex status update in UAV-assisted IoT, where an UAV moves in hovering-flight-hovering mode to ensure that it can serve IoT devices in different areas. When the UAV hovers, it obtains the status information based on the original data transmitted by the IoT device and sends it to the control center. During the complex status update, the short packet communication and time-varying channel are considered. To realize the tradeoff optimization of the average Age of Information (AoI) and average power consumption of both IoT device and UAV within a long time, we formulate a location and dynamic status update strategy optimization problem for UAV hovering-flight-hovering mode. In order to solve the problem with Markov properties, we derive the state probability equations and further establish the linear programming problem with fixed UAV location. Then, we propose a probability-based algorithm to obtain UAV location and dynamic status update strategy. To adapt to more urgent scenarios, we propose an AoI threshold-based strategy to reduce the complexity of the problem. State probability equations are derived under the strategy and a linear programming problem with a fixed AoI threshold is established. Next, we propose a low-complexity algorithm to obtain the optimal AoI threshold. Simulation results show that the proposed algorithms can optimize the three performance metrics in a balanced way and we need to select the appropriate transmit power of the IoT device and the computing capacity of the UAV to achieve better performance.

This article is motivated by the requirement of high information freshness in the industrial Internet of Things (IIoT). An industrial robot sends short status packets to a control center (CC), and the timeliness of status updates is measured by the Age of Information (AoI). Due to the dynamic change of the wireless channel, the robot needs to send a pilot for channel estimation during each coherence time. Considering the robot is energy-limited, we investigate the average AoI minimization scheme for short-packet communications under the average power consumption constraint. By rationally analyzing state transitions, we first formulate the problem as a constrained Markov decision process and obtain the optimal solution through linear programming (LP). Then, for the problem of high computational complexity caused by too many variables in LP, we propose a heuristic threshold-based status update scheme by exploiting the threshold structure of the optimal solution. Simulation results show that the LP scheme can effectively minimize the average AoI and the threshold-based scheme can achieve near-optimal performance. Interestingly, we find that when the channel suffers from severe fading, at the end of a coherence time, the robot does not send status packets even if the information at the CC is particularly stale.

Motivated by the high information freshness requirement in the Internet of Things (IoT), this paper investigates adaptive packet length adjustment schemes to minimize the average age of information (AoI) for status update systems, where a machine type communication device adaptively adjusts the packet length in real time by exploiting the channel state information and AoI. Since the status packets in the IoT are often short, a significant packet error rate is introduced. Due to the instability of channel fading and the high packet error rate, optimizing the AoI performance is tricky. Under a power consumption constraint, the AoI minimization problem is modeled as a constrained Markov decision process (CMDP), and the structure of the optimal scheme is revealed. Then, under the CMDP framework, this paper transforms the AoI minimization problem into a linear programming problem and proposes a probabilistic packet length adjustment scheme, which can lead to the optimal solution. When the power consumption constraint is loose, a low-complexity suboptimal scheme is further proposed, where the expected average AoI of one period length is minimized. Simulation results verify the superiority of the proposed optimal scheme and show that the proposed low-complexity scheme can reach near-optimal performance.

Unmanned aerial vehicles (UAVs) can utilize multiaccess edge computing (MEC) to help Internet of Things (IoT) devices complete the complex status update by efficient offloading and proper trajectory design. However, considering that IoT devices usually communicate with UAVs in the finite blocklength regime, the uplink transmission cannot be error free. Due to the nonzero packet error probability (PEP), it is difficult to evaluate the instantaneous system performance as in the case with small blocklength. Moreover, the PEP is simultaneously coupled with the offloading parameters and trajectories of UAVs, which makes the performance optimization even more challenging. To this end, we first derive the analytical expressions of the average peak Age of Information (AoI), the average energy consumption of IoT devices and the average energy consumption of UAVs. Then, we formulate a joint optimization problem aiming to minimize the weighted sum of the three performance metrics by jointly optimizing the offloading parameters and the UAV trajectories. By dividing the original problem into multiple subproblems, an alternating optimization-based algorithm is proposed to solve it suboptimally. Simulation results validate the effectiveness of our proposed algorithm and reveal that by properly setting the transmit power and computing capacity of IoT devices, the desired tradeoff among the three performance metrics can be achieved and thus the system performance can be improved effectively.

Timely status updates are significant for some critical Internet of Things applications. However, due to the transmission delay of status packets, the status information arriving at the destination is not fresh enough. Especially, when the transmission delay is large, the destination cannot know the current status of the target well. In this article, the prediction technology is introduced into status update systems to solve this problem. First, we study a prediction-based status update scheme for machine-type communications (MTCs) and use the recently proposed metric, Age of Information (AoI), to characterize the status update performance. With the scarce spectrum resources, an MTC device (MTCD), as an unlicensed user, adopts the overlay mode to reuse the spectrum resource of the primary user and sends status packets to the central controller (CC). Then, we consider prediction errors and packet decoding errors to characterize the reliability of MTC, analyze the status update process in the prediction-based scheme, and derive the closed-form expression of the average AoI. Finally, in the proposed prediction-based scheme, we analyze the tradeoff between the status update performance and energy consumption and design an optimization algorithm to improve the status update performance by adjusting the transmit power and prediction horizon of the MTCD. Simulation results verify the correctness of the theoretical analysis and show that the proposed scheme can help the MTCD effectively improve the status update performance.

Abstract A proactive spectrum monitoring and spectrum monitoring data (SMD) transmission coexistence system is investigated in dynamic spectrum sharing network, where a spectrum monitor (SM) aims to monitor the spectrum information from the electromagnetic signal sent by the suspicious transmitter to the suspicious destination by using a portion of its antennas and transmit its SMD to the spectrum data fusion center by using the rest antennas. The SMD transmission of the SM can also be employed to jam the suspicious destination to realise the effective proactive spectrum monitoring. Thus, the precoding and antenna selection scheme are jointly designed at the SM to maximise the sum of the achievable spectrum monitoring rate and the SMD transmission rate. The scenario that the SM can assess the statistical channel state information of the suspicious links based on the spectrum information obtained from the spectrum database as the spectrum management node is then considered. The spectrum monitoring/SMD transmission success probability is also derived, and a trade‐off between them is further revealed. Simulation results show that the proposed schemes can obtain higher sum rate of the spectrum monitoring and the SMD transmission, which also verifies its effectiveness and above “security‐reliability” trade‐off.

In this article, we investigate physical layer security enhancement methods for millimeter-wave (mmWave) simultaneous wireless information and power transfer (SWIPT) unmanned aerial vehicle (UAV) networks, where the power-limited destination decodes the information and harvests energy from the received radio-frequency signals. Considering the effect of beamforming design and actual 3-D antenna gain, the directional modulation (DM) technique based on random frequency diverse array (RFDA) is adopted to guarantee security. The closed-form expressions of the lower bound of average secrecy rates with uniform linear array (ULA) and uniform planar array (UPA) are derived, respectively. Furthermore, based on the theoretical analysis results, we formulate the secrecy rate maximization problem subject to the energy harvesting constraint at the destination. Then, a suboptimal iterative optimization algorithm is proposed to solve the secrecy rate maximization problem by optimizing the transmit power, power splitting ratio, and UAV trajectory jointly. The simulation results show that the average secrecy rate of RFDA scheme is much larger than the conventional phase array scheme, especially when using ULA. The proposed optimization algorithm can achieve a higher average secrecy rate than other benchmark algorithms.

In this article, we investigate the performance of massive machine type communications (mMTC) in status update systems, where massive machine type communication devices (MTCDs) send status packets to the BS for system monitoring. However, massive MTCDs sending status packets to the BS will cause severe packet collisions, which will have a negative impact on status update performance. In this case, it is necessary to carry out reasonable access control and resource allocation scheme to improve the status update performance for mMTC. In this article, taking the features of mMTC into consideration, we first analyze access control, packet collisions and packet errors in mMTC respectively, and derive the closed-form expression of the average age of information for all MTCDs as the performance metric, and then propose a joint access control, frame division and subchannel allocation scheme to improve the overall status update performance. Simulation and numerical results verify the correctness of theoretical results and show that our proposed scheme can achieve almost the same performance as the exhaustive search method and outperforms benchmark schemes.

This paper employs hybrid automatic repeat request (HARQ) to assist the cognitive non-orthogonal multiple access (NOMA) scheme for secure transmission in Internet of Things networks where the security-required (SR) users with high-security requirements and quality of services (QoS)-sensitive (QS) users with real-time process requirements are paired to perform NOMA for increasing the network connectivity. In addition, the imperfect successive interference cancellation (SIC) is considered at both legitimate users and eavesdropper for providing a realistic analysis. The closed-form expressions for the connection outage probability (COP), the secrecy transmission probability (RSP), and effective secrecy throughput (EST) of the SR user are derived in the proposed lightweight but efficient randomized retransmission scheme (RRS). As benchmarks, the secrecy performances of the fixed retransmission scheme (FRS) and orthogonal multiple access (OMA) scheme are also investigated. Besides, the asymptotic secrecy analysis is given to gain a better insight into secrecy performance. The analytical results developed in the paper demonstrate that HARQ is beneficial to secrecy transmission and the RRS outperforms or equals to the FRS and OMA scheme when the transmit power and secrecy coding redundancy is sufficiently high.

Future wireless networks have requirements of large connections, low power consumption, high reliability, and strong security. Considering the abundant available bandwidth of millimeter wave (mmWave) and the large line-of-sight (LOS) link probability of air-ground channel, in this article, we develop a framework to study security, reliability and energy coverage performance of the downlink mmWave simultaneous wireless information and power transfer (SWIPT) unmanned aerial vehicle (UAV) networks under nonorthogonal multiple access (NOMA) and orthogonal multiple access (OMA) schemes, where a UAV serves two types of authorized Internet of Things (IoT) devices in the presence of multiple passive eavesdroppers. Directional modulation (DM) is also used to improve the physical layer security (PLS) performance. First, the analytical expressions for connection outage probability (COP), secrecy outage probability (SOP), and effective secrecy throughput (EST) of the users with high rate security requirement (HRSR) under NOMA and OMA schemes are derived using stochastic geometry. Then, we optimize the active antenna selection of DM using adaptive genetic simulated annealing algorithm (AGSA) to further improve the secrecy performance based on the obtained analytical results. Finally, the closed-form expressions for the COP and energy-information coverage probability (EICP) of the energy-constrained users with low-rate requirement (ECLR) under NOMA and OMA schemes are obtained. The numerical and simulation results provide interesting insights into the influence of various parameters on the tradeoff between the reliability and security for the HRSR user, and the tradeoff between the reliability and energy coverage for the ECLR user. Moreover, the EST of the NOMA scheme outperforms OMA at low transmit power and high codeword transmission rate.

Considering the diversified communication requirements in the Internet-of-Things (IoT) networks, this article proposes a nonorthogonal multiple access (NOMA) scheme which pairs two types of typical users, i.e., delay-sensitive user (DSU) and security-required user (SRU) to share the same nonorthogonal communication resources. We focus on the investigation of the secure transmission for the SRU while guaranteeing the communication requirements of the DSUs at the high priority. Specifically, the delay requirement of the DSUs and the security demand of the SRU are guaranteed by using short-packet communications and the maximal ratio transmission (MRT) scheme, respectively. Since the packets cannot always be detected correctly in the short-packet communications, a novel power allocation strategy is designed sophisticatedly to avoid the outage performance floor at the SRU caused by the short-packet communications. A set of closed-form expressions of the connection outage probability (COP), secrecy outage probability, and effective secrecy throughput (EST) of the SRU are derived over Nakagami-m channels in the proposed NOMA scheme and also the benchmark OMA scheme. Besides, we provide the security- reliability tradeoff (SRT) and security-efficiency tradeoff (SET) results for obtaining more insights into system performance. Results demonstrate the superiority of the proposed NOMA scheme over the benchmark OMA scheme in terms of the COP, EST, SRT, and SET when the transmitter is equipped with multiple antennas.

This letter studies the legitimate surveillance with one half-duplex legitimate monitor (E) over the suspicious multichannel relaying system, where the suspicious transmitter (ST) and relay (SR) implement the optimal power allocation over all orthogonal channels under a joint sum-power constraint to maximize the communication rate of the suspicious system. Under this setup and considering that SR operates in amplify-and-forward (AF) or decode-and-forward (DF) mode, E aims to i) determine the optimal set of sub-channels for jamming and ii) optimize its jamming power over these sub-channels in order to deliberately force ST and SR reallocating power to the unjammed sub-channels to the most, so as to increase the perceived power of E over the unjammed sub-channels and then maximize the eavesdropping rate. In particular, for the AF case, the formulated problem is non-convex, for which the slack variables are introduced and successive convex approximation is exploited. For the DF case, some insights are provided for the optimization process. Results present the considerable gain of the proposed strategy compared to intuitive schemes.

In this paper, we design a secure transmission scheme in hybrid automatic repeat request (HARQ) assisted cognitive non-orthogonal multiple access (NOMA) networks, where a security-required user (SRU) is paired with a quality of service (QoS)-sensitive user (QSU) to perform NOMA. To elaborate, the QoS requirement of the QSU is guaranteed by a cognitive power allocation scheme, while the HARQ technique is employed to mitigate the successive interference cancellation (SIC) errors and improve the secrecy performance of the SRU. For reducing information leakage, a randomized retransmission NOMA (RR-NOMA) scheme is designed, where the retransmitted signals are generated from independent randomized codebooks. In this scheme, the closed-form expressions for the connection outage probability (COP), the average number of transmission (ANT), the secrecy outage probability (SOP), and effective secrecy throughput (EST) of the SRU are derived. In addition, as benchmarks, the performance analyses for the fixed retransmission (FR-NOMA) scheme and the randomized retransmission orthogonal multiple access (RR-OMA) scheme are also provided. Results show a trade-off between SOP and COP or EST, which is denoted by security-reliability trade-off (SRT) or security-efficiency trade-off (SET). Furthermore, simulation results show that the HARQ technique improves SRT and the RR-NOMA scheme achieves better SET than the FR-NOMA scheme in the low SOP region. We further conduct asymptotic analysis in the RR-NOMA, FR-NOMA and RR-OMA schemes. Asymptotic results demonstrate that the three schemes achieve the same ANT and the RR-NOMA scheme obtains better secrecy performance than the RR-OMA scheme and equal secrecy performance to the FR-NOMA scheme in terms of both EST and SOP.

This paper investigates the secure transmission in hybrid automatic repeat request (HARQ)-assisted non-orthogonal multiple access (NOMA) networks, where a security-required (SR) user is paired with an opportunity-served (OS) user to perform NOMA. Different from the previous works where NOMA and HARQ are separately considered for secure transmissions, in this paper, both NOMA and HARQ are utilized to enhance the security of the SR user. We derive the closed-form expressions for connection outage probability (COP), secrecy outage probability (SOP), reliable and secure transmission probability (RSP) and effective secrecy throughput (EST) of the SR user in both maximum ratio combining (MRC) and selection combining (SC) schemes. The security-reliability trade-off (SRT) results of the SR user in both MRC and SC schemes are also provided. Analysis and simulation results show that HARQ improves secrecy performance in MRC and SC schemes in terms of RSP, especially in low transmit power region. Besides, since every retransmission is utilized in the MRC scheme, it strictly outperforms the SC scheme in terms of the SRT metric. However, the SC scheme has the potential to achieve higher EST than the MRC scheme by optimizing the transmit power or the power allocation factor. Significantly, the quality of service (QoS) of the OS user can be guaranteed by pairing an SR user who is far away from the transmitter and the secrecy performance of the SR user can be enhanced by pairing an OS user.

Content recommendation based on device-to-device (D2D) multicast communications is expected to become a promising approach to improve local area services. Importantly, two main challenges should be considered: i) user clustering-in order to be tailored to recommend contents, the members in the same cluster should have great similarity in multiple characteristics, and ii) link allocation-we should use as little resource consumption and information exchange as possible while keeping the recommendation accuracy. In this paper, we firstly quantify the degree of the similarity between two target users with regard to multiple characteristics. Guided by such similarity, we define a clustering validity index in terms of between-within proportion (BWP) to characterize the clustering performance. Then, the issue of user clustering is modeled as a sum BWP maximum problem, and a user clustering algorithm based on modified K-means algorithm is designed to solve it in a fast-operating and low-complexity way. After user clustering, we model the issue of link allocation as a weighted aggregate interference minimization problem, and then transform it to an exact potential game. As such, a link allocation algorithm based on stochastic learning algorithm is proposed which helps to obtain the result of this game in a distributed way without complete information. Also, we analyze its convergence and optimality performance. Simulation results demonstrate the effectiveness of our proposed algorithms.

The millimeter-wave (mmWave) ultra-dense networks are more suitable for wireless power transfer, since the short-distance transmissions experience less pathloss and the base station (BS) packed with large-scale antenna arrays can achieve significant array gains. However, the secrecy performance of the simultaneous wireless information and power transfer (SWIPT) mmWave ultra-dense networks has not been investigated so far. In this paper, we consider the secure communications in downlink SWIPT mmWave ultra-dense networks, where the energy-constrained users extract energy and information from the mmWave signals in the presence of multiple eavesdroppers. First, the analytical expressions of the energy-information coverage probability are derived for both power splitting and time switching policies using stochastic geometry. Then, we derive the closed-form expressions of the secrecy probability in the presence of multiple independent or colluding eavesdroppers. Finally, the effective secrecy throughput (EST), which can measure the network energy coverage, secure, and reliable transmission performance in a unified manner, is derived. Theoretical analysis and simulation results reveal that the EST first increases and then decreases with the increasing of the transmit power, power/time splitting ratio, codeword transmission rate, and confidential information rate. Furthermore, reducing the beamwidth of the signal at BSs can decrease the information leakage and improve the EST.

In this paper, we investigate the secrecy performance of energy-constrained wireless-powered networks with considering the passive eavesdropping scenario, where the simultaneous wireless information and power transfer-based full-duplex self-jamming (SWIPT-FDSJ) scheme is developed. The maximal ratio transmission protocol is applied at the multi-antenna source such that the wireless signals are designated to the destination directly. Besides, the energy harvesting and full-duplex self-jamming operations are adopted at the energy-constrained destination to prolong its lifetime as well as to confuse the eavesdropper. Specifically, the exact and asymptotic closed-form expressions of the connection outage probability (COP), the secrecy outage probability (SOP), and the secrecy throughput of the proposed system are obtained, based on which we optimize the time-switching ratio to maximize the secrecy throughput. We also degenerate the proposed SWIPT-FDSJ scheme to the reduced half-duplex with no self-jamming (HDNSJ) scheme. The finds suggest that in the HDNSJ scheme, adding the antenna number of the source only benefits the COP performance, but has no impact on the SOP performance. By contrast, it will promote the COP and SOP performance at the same time in the SWIPT-FDSJ scheme, which eventually results in the great improvement of secrecy throughput. In addition, we present the practical application condition of the SWIPT-FDSJ scheme. It is demonstrated that the secrecy throughput performance of the SWIPT-FDSJ scheme is much superior to the HDNSJ scheme on condition that the application condition is satisfied.

Driven by the growing popularity of mobile applications, mobile cloud computing has been envisioned as a promising approach to enhance computation capability of mobile devices and reduce the energy consumptions. In this paper, we investigate the problem of multi-user computation offloading for mobile cloud computing under dynamic environment, wherein mobile users become active or inactive dynamically, and the wireless channels for mobile users to offload computation vary randomly. As mobile users are self-interested and selfish in offloading computation tasks to the mobile cloud, we formulate the mobile users' offloading decision process under dynamic environment as a stochastic game. We prove that the formulated stochastic game is equivalent to a weighted potential game which has at least one Nash Equilibrium (NE). We quantify the efficiency of the NE, and further propose a multi-agent stochastic learning algorithm to reach the NE with a guaranteed convergence rate (which is also analytically derived). Finally, we conduct simulations to validate the effectiveness of the proposed algorithm and evaluate its performance under dynamic environment.

This paper studies the physical layer security performance of a Simultaneous Wireless Information and Power Transfer (SWIPT) millimeter wave (mmWave) ultra-dense network under a stochastic geometry framework. Specifically, we first derive the energy-information coverage probability and secrecy probability in the considered system under time switching policies. Then the effective secrecy throughput (EST) which can characterize the trade-off between the energy coverage, secure and reliable transmission performance is derived. Theoretical analyses and simulation results reveal the design insights into the effects of various network parameters like, transmit power, time switching factor, transmission rate, confidential information rate, etc, on the secrecy performance. Specifically, it is impossible to realize the effective secrecy throughput improvement just by increasing the transmit power.

Device-to-Device (D2D) multicast communication is becoming a promising technology to improve local area service. In this paper, we present a distributed social-aware clustering method to group the content requesters (CRs) into clusters. Specifically, a cluster head (CH) selection algorithm based on social maximum weight (SMW) is firstly proposed to select the CHs from the original CRs. After that, we propose a cluster formation optimization framework via a non-transferable utility coalition formation game (CFG), and a distributed coalition formation algorithm for clustering is proposed based on preference relationship and switch operations. Moreover, the final coalition structure is proved to be Nash-stable. Numerical results are presented to verify the effectiveness of our proposed schemes.

Joint link and power allocation for device‐to‐device (D2D) communication underlaying cellular networks are necessary to coordinate the mutual interference. Most of the works consider the sum interference from D2D pairs. However, mobile devices are carried by human beings who are connected with social ties. For one cellular user, the strengths of the social ties with D2D pairs are different. Thus, his interference tolerance temperatures (ITTs) are various. In this study, we distinguish D2D pairs through the social ties, and propose a social aware pricing‐based joint link and power allocation scheme. Specifically, the base station (BS) prices the interference on every link and updates the prices according to the ITTs of cellular users. Subsequently, with the prices, the competition of D2D pairs for the links is modelled as a non‐cooperative game. The authors prove the existence and the uniqueness of the Nash equilibrium (NE), and demonstrate that the NE is Pareto optimal. Moreover, an iterative decentralised algorithm is designed to solve the game. Especially, if the number of the price on one link for one D2D pair being updated is up to an upper bound, the power is forced to be 0. Numerical simulations verify the effectiveness of the authors' proposed scheme.

Physical layer security is a promising way to secure the wireless communications in the Internet of Things (IoT). Motivated by the fact that the limited feedback resources in the IoT network would degrade the secrecy advantage of the multiple-antenna technique, we attempt to investigate the problem of how many transmit antennas should be utilized to perform secure communications. In particular, we consider the heterogeneous IoT downlink network and design a multiuser secure transmission scheme. In this scheme, the zero-forcing beamforming technique is adopted to serve the IoT legitimate users, and the remaining spatial freedoms are utilized to send artificial noise (AN) for confusing the passive eavesdroppers. Given the secrecy outage constraints, we derive the closed-form expression for the network secrecy throughput and formulate a non-convex optimization problem with multiple parameters, e.g., the number of transmit antennas, the wiretap codes, the feedback bits allocation strategy, and the power allocation ratio between the information bearing signal and the AN. To effectively tackle this problem, we develop an optimization framework involving the block coordinate descent algorithm and the 1-D search method. Simulation results validate the effectiveness of our proposed optimization framework and show that the optimal number of transmit antennas increases as the secrecy outage constraints become stricter, or the feedback resources become scarcer.

This paper considers secure communication for amplify-and-forward (AF) relay networks with finite alphabet input. The joint optimization of power selection and beamforming design for improving the physical layer security of AF relay networks with single and multiple eavesdroppers is investigated. For the case with one eavesdropper, we transform the problem of multi-variable beamforming design into a single-variable optimization problem through semi-definite programming and solve it with one-dimensional optimization techniques. Moreover, the corresponding source power is obtained by utilizing the relation between the mutual information and minimum mean square error. Then, an iterative two-step algorithm is proposed to maximize the achievable secrecy rate. In the presence of multiple eavesdroppers, a zero-forcing beamforming scheme, where the confidential signal is nulled out in the direction of all eavesdroppers, is proposed to enhance the physical layer security. We decouple the source power and the beamforming vector by transforming the achievable secrecy rate into a single-variable function of the source power. Then, the suboptimal source power and the corresponding beamforming vector with low-complexity are derived. Numerical examples show that the proposed schemes significantly enhance the secrecy performance of the AF relay networks.

We investigate the secrecy performance in large‐scale cellular networks, where both Base Stations (BSs) and eavesdroppers follow independent and different homogeneous Poisson point processes (PPPs). Based on the distances between the BS and user, the intended user selects the nearest BS as serving BS to transmit the confidential information. We first derive closed‐formed expressions of secrecy outage probability and average secrecy rate of a single‐antenna system for both noncooperative and cooperative eavesdroppers scenarios. Then, to further improve the secrecy performance through additional spatial degrees of freedom, the above analyses generalize to the multiantenna scenario, where BSs employ the transmit antenna selection (TAS) scheme. Finally, the results show the small‐scale fading has a considerable effect on the secrecy performance in certain density of eavesdroppers and small path loss exponent environment, and when the interference caused by BS is considered, the secrecy performance will be reduced. Moreover, the gap of secrecy performance between noncooperative and cooperative eavesdroppers cases is nearly invariable as the number of antennas increases.

This paper exploits nonorthogonal multiple access (NOMA) to enhance the uplink secure transmission in Internet of Things (IoT) networks. Considering the different intercept ability of eavesdroppers (Eve), secrecy performances of both strong and weak Eve wiretap scenarios have been investigated. In strong Eve wiretap scenario (SWS), Eve is assumed to be powerful enough to decode message without interference and, in weak Eve wiretap scenario (WWS), Eve is assumed to have significant demodulation capability constraint. The new closed‐form expressions of joint connection outage probability (JCOP), joint secrecy outage probability (JSOP), and sum secrecy throughput (SST) are derived in these two scenarios to indicate the impact of parameters, i.e., transmit power, codeword rate, and the placement of devices, on security performance. In order to demonstrate the superiority of NOMA, we also investigate the secrecy performance of orthogonal multiple access (OMA) system as a benchmark. Analysis results show that the performance in WWS is always better than that in SWS and, in low signal‐to‐noise ratio (SNR) or high codeword rate region, the performances of these two scenarios are close. In addition, we present the condition that NOMA outperforms OMA in terms of SST. Moreover, the placements of devices are significant to the SST performance of NOMA system. The suboptimal device placement scheme has been designed to maximize SST. Analysis results demonstrate that when Eve is far away from legal users, the suboptimal results tend to optimal.

This work studies the secrecy outage performance of buffer-aided dual-hop multi-antenna relay systems without eavesdropper's channel state information (CSI). By modeling the dynamic buffer state transitions with the Markov chain, the secrecy outage probability at each state is investigated and the stationary distribution probabilities of all states are subsequently derived. Using the total probability theorem, the closed-form expression of the secrecy outage probability of the system is finally obtained. It demonstrates that due to the fully exploitation of the available channels, the buffer-aided relay selection yields to better performance than Best Relay Selection (BRS), even when less relays and antennas are utilized. It is also shown that the buffer-aided relaying only results in a small performance degradation when the buffers are constrained to finite size, thus can be well applied to practical relaying cooperative networks. Simulation results are given to verify the theoretical analysis.

This study considers linear precoding for secure transmission in a multiuser peer‐to‐peer relay network with finite alphabet input. Under the assumption that the global channel‐state‐information is available, the achievable secrecy rate is derived. However, the computational complexity to evaluate the achievable secrecy rate grows exponentially with respect to the number of pair users. To reduce the computational complexity caused by the multiuser interference, an accurate approximation of the achievable secrecy rate is derived. Based on Karush–Kuhn–Tucker analysis, necessary conditions for the optimal precoder which maximises the approximated achievable secrecy rate are presented. In light of this, an iterative gradient method is developed to find the optimal precoder. Numerical examples demonstrate that the proposed scheme achieves significant gains in terms of the secrecy rate over schemes designed for Gaussian input.

The cache‐enabled device‐to‐device (D2D) communication is a burgeoning technique to offload cellular traffic. In this study, the authors conducted the joint cache policy and transmit power to maximise the content‐related energy efficiency (CREE) for the cache‐enabled D2D network. Specifically, the authors model the random distribution of mobile users by the Poisson point process and derive the closed‐form CREE via considering the D2D establishment threshold and signal‐to‐interference ratio threshold at the same time. Then the authors aim to determine the optimal cache policy and transmit power for maximising the CREE in two practical cases, respectively. Since the optimisation problem in both cases are non‐linear non‐convex problems and share the same mathematical form, the authors focus on the first case and propose a two‐step iterative algorithm to find a stationary solution due to its intractability. This iterative algorithm builds on two subproblems which are proved to have optimal solutions. Numerical simulations show that the joint optimisation can obtain more than two times the CREE compared with either of the single optimisation for cache policy or transmit power.

This study investigates the physical‐layer security in random cognitive radio network (CRN) consisting of randomly distributed primary nodes, secondary nodes, relay nodes and eavesdroppers, where the secondary nodes share the spectrum of the primary nodes and the eavesdroppers attempt to intercept the secondary transmissions. The authors develop a framework to analysis the reliability–reliability tradeoff (RRT) and security–reliability tradeoff (SRT) in the random CRNs, where the security and reliability are quantified in terms of secrecy outage probability and connection outage probability. The RRT evaluates performance tradeoff between the primary and the secondary networks and the SRT evaluates the performance tradeoff inside the secondary network. Furthermore, they propose an opportunistic relay selection (ORS) scheme to enhance the secondary confidential transmission. It is demonstrated that the ORS scheme significantly improves the RRT and SRT as the density of relays increases, and outperforms the conventional direct transmission when the density of relays is larger than a certain value. This actually promotes the understandings of the performance tradeoffs and provides direct insights on system design for the practical CRNs.

In this paper, we investigate the secrecy performance of full-duplex multi-antenna spectrum-sharing wiretap networks in which a jamming signal is simultaneously transmitted by the full-duplex secondary receiver (Bob) based on the zero forcing beamforming (ZFB) algorithm. For the security enhancement, we propose the two antenna reception schemes: 1) random selection combining (RSC) where Bob selects LB antennas at random to combine the received signals and 2) generalized selection combining (GSC) where Bob selects LB strongest antennas to combine the received signals. We derive the exact closed-form expressions for the secrecy outage probability of full-duplex multi-antenna spectrum-sharing wiretap networks with ZFB algorithm. In order to explore a new design of the proposed schemes, we provide tractable asymptotic approximations for the secrecy outage probability in high signal-to-noise ratio regime under two distinct scenarios. From the analysis, we demonstrate that: 1) when the main channel is much better than the eavesdropper's channel, GSC/ZFB scheme achieves full diversity NB, while RSC/ZFB scheme only achieves partial diversity LB and 2) GSC/ZFB scheme achieves better secrecy performance than RSC/ZFB with different antenna numbers at Bob.

Device-to-device (D2D) content sharing has become a promising solution to support the growing popularity of multimedia contents for local services. Considering the randomness of content location, the limited storage and transmission capability of devices, and the coexistence of altruistic and selfish user behaviors, how to optimally match the demanders to the providers of contents and how to stimulate an efficient cooperation are of importance for achieving the full benefits of D2D content sharing. Especially when the base-station-to-device (B2D), D2D, and novel multi-D2D sharing modes coexist, the issue of content sharing mode selection plays the predominant role in such matching. In this paper, we introduce a notion of social-aware rate, which combines the social selfishness from the social knowledge with the link rate to ensure the physical link quality and the effective cooperation together. Then, the social-aware rate-based content sharing mode selection problem is modeled as a maximum weighted mixed matching problem, which can be computationally reduced to a submodular welfare problem subject to a matroid constraint. Subsequently, we develop a best-effort distributed algorithm framework, which displays alternatives of various computation complexities and approximation ratios to satisfy the diverse practical needs.

We design a novel artificial-noise-aided secure ON-OFF transmission scheme in a wiretap channel. We consider a practical scenario, where the multi-antenna transmitter only obtains partial channel knowledge from the single-antenna receiver through limited training and feedback but has no channel knowledge about the single-antenna eavesdropper. In the design, we first propose a three-period block transmission protocol to capture the practical training and quantization features. We then characterize the statistics of the received signal-to-noise ratios at the receiver and the eavesdropper. Under the secrecy outage constraint, we exploit the ON-OFF scheme to perform secure transmission and derive a closed-form expression for the secrecy throughput. Moreover, we investigate the optimization problem of maximizing the secrecy throughput by proposing an iterative algorithm to determine the optimal power allocation between the information signal and artificial noise, as well as the optimal codeword transmission rate. Furthermore, we define the net secrecy throughput (NST), which takes the signaling overhead into account and address the problem of optimally allocating the block resource to the training and feedback overhead. Numerical results clearly demonstrate how the optimal signaling overhead changes with the number of transmit antennas, and there exists an optimal number of antennas that maximizes the NST.

Traffic learning and prediction is at the heart of the evaluation of the performance of telecommunications networks and attracts a lot of attention in wired broadband networks. Now, benefiting from the big data in cellular networks, it becomes possible to make the analyses one step further into the application level. In this paper, we first collect a significant amount of application-level traffic data from cellular network operators. Afterward, with the aid of the traffic “big data,” we make a comprehensive study over the modeling and prediction framework of cellular network traffic. Our results solidly demonstrate that there universally exist some traffic statistical modeling characteristics at a service or application granularity, including α-stable modeled property in the temporal domain and the sparsity in the spatial domain. But, different service types of applications possess distinct parameter settings. Furthermore, we propose a new traffic prediction framework to encompass and explore these aforementioned characteristics and then develop a dictionary learning-based alternating direction method to solve it. Finally, we examine the effectiveness and robustness of the proposed framework for different types of application-level traffic. Our simulation results prove that the proposed framework could offer a unified solution for application-level traffic learning and prediction and significantly contribute to solve the modeling and forecasting issues.

In this paper, we study the power control problem for interference management in the ultra-dense small cell networks, which is formulated to maximize the sum-rate of all the small cells while keeping tolerable interference to the macrocell users. We investigate the problem by proposing a novel game with dynamic pricing. Theoretically, we prove that the Nash equilibrium (NE) of the formulated game coincides with the stationary point of the original sum-rate maximization problem, which could be locally or globally optimal. Furthermore, we propose a distributed iterative power control algorithm to converge to the NE of the game with guaranteed convergence. To reduce the information exchange and computational complexity, we propose an approximation model for the original optimization problem by constructing the interfering domains, and accordingly design a local information-based iterative algorithm for updating each small cell's power strategy. Theoretic analysis shows that the local information-based power control algorithm can converge to the NE of the game, which corresponds to the stationary point of the original sum-rate maximization problem. Finally, simulation results demonstrate that the proposed approach yields a significant transmission rate gain, compared with the existing benchmark algorithms.

In this paper, we study secure transmission designs for underlay cognitive radio networks in the present of randomly distributed eavesdroppers. We consider the scenario where a secondary transmitter sends confidential messages to a secondary receiver subject to an interference constraint set by the primary user. We design two transmission protocols under different channel knowledge assumptions at the transmitter. For each protocol, we first give a comprehensive performance analysis to investigate the transmission delay, secrecy, and reliability performance. We then optimize the transmission design for maximizing the secrecy throughput subject to both secrecy and reliability constraints. Finally, we numerically compare the performance of the two transmission protocols.

In this paper, we analyze the secrecy performance of multi-antenna cognitive wiretap network, where the secondary transmitter (Alice) communicates with the secondary receiver (Bob) in the presence of an eavesdropper (Eve). Specifically, we investigate the cases of maximal-ratio combining (MRC) with half-duplex (HD) and selection combining/Zero forcing beamforming (SC/ZFB) scheme with full-duplex (FD) operation, respectively. Assuming the Rayleigh fading, closed-form expressions for the secrecy outage probability of cognitive wiretap channels with MRC and SC/ZFB are derived. Furthermore, we provide simple asymptotic approximations for the secrecy outage probability and find that both schemes achieve full diversity. In addition, simulation results reveal that MRC outperforms SC/ZBF in the low interference threshold regime, while the opposite holds in the high interference threshold regime.

The authors study the secure transmission design in random cognitive radio networks where the primary users, the secondary users and the eavesdroppers are randomly distributed according to Poisson point processes. Centring on this scenario, the authors propose a simple and decentralised secure transmission scheme by jointly incorporating the secrecy guard zone and artificial noise. In particular, this transmission scheme helps to enhance secrecy performance via differentiating between secondary transmitters in accordance with the eavesdropping environment. They then analyse the connection outage and secrecy outage performance of the secondary network, based on which they obtain the closed‐form expression of the secrecy throughput. They further determine the optimal transmission power of the secondary transmitters and the optimal power allocation between the information‐bearing signal and artificial noise to maximise the secrecy throughput of the secondary network under primary and secondary outage constraints. The authors’ analysis highlights that introducing secrecy guard zone provides better security performance, and artificial noise performs as additional interference to insert a control between the reliability and security. Numerical results show how the system parameters affect the achievable maximum secrecy throughput, the optimal transmission power and the optimal power allocation between the information‐bearing signal and the artificial noise.

In this study, the authors investigate the physical layer security for safeguarding secure transmission in multi‐antenna relaying networks with imperfect channel state information (CSI). Different from traditional strategies in multi‐antenna relaying systems, which would terminate the communication if the relay could not decode the signals successfully, the authors propose robust secure transmission strategy which switches between cooperative jamming (CJ) and decode‐and‐forward (DF) beamforming. Specifically, if the received signal‐to‐noise ratio at the relay is lower than a predefined threshold, it will impose CJ signals on potential eavesdropper, otherwise the relay would operate DF beamforming. The authors’ objective is to maximise secrecy rate and minimise secrecy outage probability in different practical communication scenarios with relaxed or strict time‐delay constraint. Thus, given the unavailability of the perfect eavesdropping CSI, the authors propose a worst‐case robust design and a statistical‐approach robust design based on secrecy rate and secrecy outage criterion, respectively. In addition, a traditional DF robust scheme and a non‐robust scheme are also considered as benchmarks. Simulation results verify that the proposed scheme significantly outperforms the traditional DF robust scheme and the non‐robust scheme.

This paper investigates the secure transmission in simultaneous wireless information and power transfer (SWIPT) system, where a source node communicates with a wireless‐powered full duplex destination node in the presence of a passive eavesdropping node. To take advantage of the benefits from SWIPT and full duplex techniques, we propose a two‐phase time‐sharing protocol: the destination node harvests energy form the source node in the first phase, while the information‐bearing signal is transmitted under the protection of artificial noise sent from the destination node in the second phase. The transmit power of the artificial noise is converted from the harvested energy and self‐interference exists at the destination. We derive the closed‐form expressions for the connection outage probability (COP), the secrecy outage probability (SOP) and the transmission outage probability (TOP), based on which the secrecy energy efficiency (SEE) is formulated. Furthermore, we determine the optimal time allocation factor and transmission power that maximize the SEE while satisfying the COP and SOP constraints. Finally, numerical results verify our analytical results and show that there are tradeoffs among security, reliability and energy efficiency. When the residual loopback interference is mitigated under some certain level, our proposed optimization scheme can significantly boost the SEE.

Spectrum efficiency (SE) and energy efficiency (EE) in secure communications have attracted much attention recently due to the fact that future wireless networks need to address the issues of high throughput, low power consumption, and high level of security. However, maximizing EE and SE are conflicting objectives, which can hardly be achieved simultaneously. In this paper, we develop a framework to study the SE and EE for secure transmission in underlay random cognitive radio (CR) networks where the primary, secondary, and eavesdropper nodes are randomly distributed according to Poisson point processes. We first analyze the connection outage probability and the secrecy outage probability of the typical links in the random CR network. Then, we evaluate the secure SE and EE in the secondary network based on the outage probability analysis. It is demonstrated that the scheduling scheme of the secondary network (i.e., the transmission power and the intensity of secondary transmitters arouses a tradeoff between the secure SE and EE. Furthermore, applying a unified secure SE-EE tradeoff metric, which condenses the secure SE and EE into a single utility function with a tradeoff factor, we formulate the joint secure SE and EE optimization problem with respect to the scheduling scheme of the secondary network. An iterative algorithm is proposed based on the separation optimization of the transmission power and the intensity. The analytical and simulation results show the effects of system parameters on the optimal solutions and secure SE-EE tradeoff. We also verify that the optimal scheduling scheme can improve the secure SE-EE tradeoff. Using these results, one can easily understand and make the secure SE-EE tradeoff in random CR networks.

In this paper, we study physical layer security in an underlay cognitive radio (CR) network. We consider the problem of secure communication between a secondary transmitter-receiver pair in the presence of randomly distributed eavesdroppers under an interference constraint set by the primary user. For different channel knowledge assumptions at the transmitter, we design four transmission protocols to achieve the secure transmission in the CR network. We give a comprehensive performance analysis for each protocol in terms of transmission delay, security, reliability, and the overall secrecy throughput. Furthermore, we determine the optimal design parameter for each transmission protocol by solving the optimization problem of maximizing the secrecy throughput subject to both security and reliability constraints. Numerical results illustrate the performance comparison between different transmission protocols.

With the emerging demands for local area services of popular content downloading, D2D communication is recognized as a promising technical support for cellular networks. In this work, we propose a social-aware content downloading mode selection scheme, which involves a novel mode, named MD2D, by collaborating multiple available D2D links. In particular, we construct a social-aware evaluation framework, which understands the interplay between physical transmission property and social networking characteristics for defining the performance metric with respect to the downloading mode selection. Accordingly, we formulate the social-aware content downloading mode selection problem based on the combinatorial auctions. By exploring the submodular approximation of this problem, we design a social-aware mode selection algorithm. We demonstrate that the solution achieves incentives for content contribution and resistance to misbehaving potential content providers, and also derive a theoretic upper bound of the corresponding performance loss.

This paper first provides a brief survey on existing traffic offloading techniques in wireless networks. Particularly as a case study, we put forward an online reinforcement learning framework for the problem of traffic offloading in a stochastic heterogeneous cellular network (HCN), where the time-varying traffic in the network can be offloaded to nearby small cells. Our aim is to minimize the total discounted energy consumption of the HCN while maintaining the quality-of-service (QoS) experienced by mobile users. For each cell (i.e., a macro cell or a small cell), the energy consumption is determined by its system load, which is coupled with system loads in other cells due to the sharing over a common frequency band. We model the energy-aware traffic offloading problem in such HCNs as a discrete-time Markov decision process (DTMDP). Based on the traffic observations and the traffic offloading operations, the network controller gradually optimizes the traffic offloading strategy with no prior knowledge of the DTMDP statistics. Such a model-free learning framework is important, particularly when the state space is huge. In order to solve the curse of dimensionality, we design a centralized Q-learning with compact state representation algorithm, which is named QC-learning. Moreover, a decentralized version of the QC-learning is developed based on the fact the macro base stations (BSs) can independently manage the operations of local small-cell BSs through making use of the global network state information obtained from the network controller. Simulations are conducted to show the effectiveness of the derived centralized and decentralized QC-learning algorithms in balancing the tradeoff between energy saving and QoS satisfaction.

We propose a new secure transmission scheme in the multi-input multi-output multi-eavesdropper wiretap channel. In this channel, the NA-antenna transmitter adopts transmit antenna selection (TAS) to choose the antenna that maximizes the instantaneous signal-to-noise ratio (SNR) at the receiver to transmit, while the NB-antenna receiver and the NE-antenna eavesdropper adopt maximal-ratio combining (MRC) to combine the received signals. We focus on the practical scenario where the channel state information (CSI) during the TAS process is outdated. In this scenario, we propose a new transmission scheme to prevent the detrimental effect of the outdated CSI on the wiretap codes design at the transmitter. To thoroughly assess the secrecy performance achieved by the proposed scheme, we derive new closed-form expressions for the exact secrecy outage probability and the probability of non-zero secrecy capacity for arbitrary SNRs. We also derive new compact expressions for the asymptotic secrecy outage probability at high SNRs. Notably, in the analysis, we take spatial correlation at the receiver into consideration. Apart from the advantage of our scheme over the conventional TAS/MRC scheme, we demonstrate that the outdated TAS reduces the secrecy diversity order from NANBto NB. We also demonstrate that antenna correlation improves the secrecy performance at low SNR but deteriorates the secrecy performance at medium and high SNRs, by affecting the secrecy array gain only.

In this work, we propose a dynamic distributed resource sharing scheme which jointly considers mode selection, resource allocation, and power control in a unified framework for general D2D communications. First, we model the joint issue of mode selection and resource allocation as a hedonic coalition formation game, while accounting for the tradeoff between the benefits in terms of available rate and the costs in terms of the mutual interference. Moreover, we develop a coalition formation process based on the switch rule, through which each cellular user makes an individual and distributed decision to form a Nash-stable partition. Second, we view the members of each coalition as a whole, and formulate a power control problem to share the aim of maximizing the sum-rate of cellular links in this coalition. To solve this NP-hard problem with online operation, we present a power control process, which employs the local piecewise-linear approach to take a locally and separately approximate optimal outcome. Finally, we present a dynamic resource sharing algorithm, which iteratively operates the coalition formation and power control processes.

This paper proposes a distributed cooperative framework for improving the energy efficiency of green cellular networks. Based on the traffic load, neighboring base stations (BSs) cooperate to optimize the BS switching (sleeping) strategies so as to maximize the energy saving while guaranteeing users' minimal service requirements. The inter-BS cooperation is formulated following the principle of ecological self-organization. An interaction graph is defined to capture the network impact of the BS switching operation. Then, we formulate the problem of energy saving as a constrained graphical game, where each BS acts as a game player with the constraint of traffic load. The constrained graphical game is proved to be an exact constrained potential game. Furthermore, we prove the existence of a generalized Nash equilibrium (GNE), and the best GNE coincides with the optimal solution of total energy consumption minimization. Accordingly, we design a decentralized iterative algorithm to find the best GNE (i.e., the global optimum), where only local information exchange among the neighboring BSs is needed. Theoretical analysis and simulation results finally illustrate the convergence and optimality of the proposed algorithm.

In this work, we study the problem of green uplink resource sharing over mobile device-to-device (D2D) communications underlaying cellular network. We first construct a analysis model of energy efficiency, which takes into account different sharing modes, as well as QoS requirement and spectrum utilization of each user. Then, we formulate the sharing problem as a non-transferable coalition formation game, with the characteristic function which accounts for the gains in terms of energy efficiency and the costs in terms of mutual interference. Then, the resulting coalition structure shows the energy-efficient sharing strategy on the joint mode selection, uplink link reusing allocation, and power management. Moreover, we develop a distributed coalition formation algorithm based on the merge-and-split rule and the Pare to order. The distributed solution is characterized through stability notions and is adapted to user mobility. Simulation results are provided to demonstrate the effectiveness of our proposed game model and algorithm.

In this paper, we investigate the secrecy outage probability (SOP) for the cooperative decode-and-forward (DF) underlay cognitive radio networks (CRNs). Duo to the spectrum sharing approach in underlay CRNs, the secondary transmitters (secondary source (SUS) and secondary relay (SUR)) are subjected to interference power constraint at primary user (PU), and the SU receivers (SURand secondary destination (SUD)) suffer from the co-channel interferences (CCIs) from PU. We focus on the practical scenario where the SU transmitter has no channel state information (CSI) of the eavesdropper's channel. Interference-power-confined selection scheme is proposed for the cooperative DF CRNs in the presence of an eavesdropper. The proposed scheme selects the “best” terminal (SUSor SUR) to enhance the secure performance which must firstly satisfy the interference power constraint. What's more, the effect of feedback delay on the selection scheme is studied. We derive an exact closed-form expression for the SOP. Simulation results validate our theoretical analysis and show that the proposed selection scheme determinately enhance the performance, and feedback delay decreases the SOP seriously, specially, the interference power constraint hugely confine the SOP.

It is well known that a cooperative communication technique can offer significant energy saving improvements. In particular, the efficient relay resource allocation makes energy saving practically appealing. In this work, we propose an auction-based relay power allocation scheme over multi-user relay networks from the energy-efficient perspective. In particular, during the relay resource allocation operation, three major design goals are considered: 1) efficient utilization of relay resources, in terms of Pareto optimal relay power allocation; 2) insurance of competitive fairness among competing users; and 3) guarantee of distributed implementation with relaxation of restrictions on complete private knowledge and accurate assessments of convergence. Specifically, we take full advantage of the auction mechanism, i.e., competitive fairness with the incomplete private information of other nodes, to model the interaction among the users as a multi-winner auction based on the optimal bidding decision. By treating the proposed auction mechanism as a non-cooperative game, we obtain the unique and Pareto optimal Nash equilibrium (NE), which yields the optimal bidding decision and allocation of the relay power. Moreover, we design a distributed algorithm based on best-response functions to reach the NE allocation. In particular, the convergence and the convergent rate of the algorithm are analyzed quantitatively to clarify the application scenarios.

Relay transmission can enhance coverage and throughput, whereas it can be vulnerable to eavesdropping attacks due to the additional transmission of the source message at the relay. Thus, whether or not one should use relay transmission for secure communication is an interesting and important problem. In this paper, we consider the transmission of a confidential message from a source to a destination in a decentralized wireless network in the presence of randomly distributed eavesdroppers. The source-destination pair can be potentially assisted by randomly distributed relays. For an arbitrary relay, we derive exact expressions of secure connection probability for both colluding and noncolluding eavesdroppers. We further obtain lower bound expressions on the secure connection probability, which are accurate when the eavesdropper density is small. Using these lower bound expressions, we propose a relay selection strategy to improve the secure connection probability. By analytically comparing the secure connection probability for direct transmission and relay transmission, we address the important problem of whether or not to relay and discuss the condition for relay transmission in terms of the relay density and source-destination distance. These analytical results are accurate in the small eavesdropper density regime.

This paper proposes a novel base station (BS) coordination approach for intercell interference mitigation in the orthogonal frequency-division multiple access based cellular networks. Specifically, we first propose a new performance metric for evaluating end user's quality of experience (QoE), which jointly considers spectrum efficiency, user fairness, and service satisfaction. Interference graph is applied here to capture and analyze the interactions between BSs. Then, a QoE-oriented resource allocation problem is formulated among BSs as a local cooperation game, where BSs are encouraged to cooperate with their peer nodes in the adjacent cells in user scheduling and power allocation. The existence of the joint-strategy Nash equilibrium (NE) has been proved, in which no BS player would unilaterally change its own strategy in user scheduling or power allocation. Furthermore, the NE in the formulated game is proved to lead to the global optimality of the network utility. Accordingly, we design an iterative searching algorithm to obtain the global optimum (i.e., the best NE) with an arbitrarily high probability in a decentralized manner, in which only local information exchange is needed. Theoretical analysis and simulation results both validate the convergence and optimality of the proposed algorithm with fairness improvement.

In this paper we investigate a cooperative jamming (CJ) strategy based Stackelberg security game to improve physical layer security over multiuser OFDMA networks. Each source node tries to deliver messages to its intended destination node securely in the presence of a passive eavesdropper. Fortunately, these node pairs can resort to a friendly jammer to enhance their communication. But the jamming service is not free and the friendly jammer charges each pair for some cost. Obviously there is a tradeoff between the friendly jammer and the source-destination pairs. Consequently, a two-level Stackelberg game model is proposed, where the friendly jammer plays the seller and the source-destination pairs are the buyers. We analyze the Stackelberg Equilibrium from two sides respectively and prove the existence and uniqueness of the Stackelberg Equilibrium. Correspondingly, we also develop a distributed iterative power allocation algorithm to reach the Stackelberg Equilibrium point. Numerical Results demonstrate that the system sum secrecy rate is really improved and the physical layer security performance is effectively meliorated.

In this work, with respect to the uncertainty about the individual information, we investigate the relay power allocation problem from the energy-efficient, Pareto optimal, and competitive fairness perspective. At first, we design an easy-implementation energy efficiency metric, which aims at striking a balance between the QoS provisioning and the energy consumption. Then, an auction mechanism is proposed for relay power allocation. By transferring the auction mechanism into a game, we prove the existence, uniqueness, and Pareto optimality of the Nash equilibrium (NE) for our auction game, and show that the allocation strategies from the NE can achieve the energy efficiency in terms of the proposed metric. Next, we develop a distributed relay power allocation algorithm based on our best-response functions to reach the Pareto optimal NE. Importantly, we not only certify the convergence of the proposed algorithm, but also provide quantitative analysis on it. Extensive simulations results are conducted to confirm the validity of the analytical results.

In this work, we propose a correlated equilibrium (CE)-based energy-efficient resource allocation scheme for uplink OFDMA systems. At first, we construct an energy-efficient resource allocation game, where each subcarrier is viewed as a player to choose the most satisfying user, and the objective is to balance the tradeoff between the total energy efficiency and the fairness. Since the CE can achieve better performance by helping the non-cooperative players coordinate their strategies, we employ the CE to analyze the proposed game. Next, we derive the condition under which the CE is Pareto optimal and employ linear programming duality to show its closed-form expressions. Furthermore, we present a linear programming method and a distributed algorithm based on the regret matching procedure to implement the CE, respectively. Simulation results demonstrate that our scheme is able to achieve good convergence, Pareto optimality, and fairness.

In this paper we analyze the effect of co-channel interference on the performance of cooperative decode-and-forward (DF) automatic-repeat-request (ARQ) scheme under Rayleigh fading channels. We assume that the relay node is affected by one interferer and the destination node is affected by another one. The outage probability of the cooperative DF ARQ scheme is derived based on the maximal ratio combining (MRC) technique. The closed-form expression clearly shows the outage probability has much to do with the maximum number of ARQ transmissions. Finally, Monte Carlo simulation results verify our theoretical analysis.

In this paper, the authors propose two cognitive relaying schemes based on overlay and underlay, which exploit the cooperation opportunities inherent in primary retransmission to improve secondary throughput. If a primary signal is not decoded by the primary receiver (PR), a secondary user (SU) can be selected to relay it invisibly along with the primary retransmission. For overlay cognitive relaying, SUs intend to reduce primary retransmission time by relaying primary message, so that more access opportunities are available. While in underlay cognitive relaying, SU allocates part of its power to help primary user (PU) and the remaining power is used to transmit secondary message simultaneously. By controlling the phase of the relay signal, signals retransmitted from primary transmitter (PT) and SU can constructively combine at the PR. In both relaying schemes, we consider the best-relay selection as well. We define some novel metrics to evaluate the performance of PU and SU. For PU, we study the improvements in outage performance and average transmitting time per packet, while for SU, we consider the cooperation gain and cooperation efficiency, respectively. Theoretical analysis and numerical results verify the validity of both schemes, and a comparison is made between them.

Owing to the evolution of green communications, energy efficiency is treated as an important performance metric of a uplink orthogonal frequency division multiple access (OFDMA) system. In this study, the authors propose an energy-efficient resource allocation scheme by using the correlated equilibrium (CE). At first, the authors construct an energy-efficient resource allocation game, where each subcarrier is viewed as a player to choose the most satisfying user, and the objective is to balance the tradeoff between the total energy efficiency and the fairness. Since the CE can achieve better performance by helping the non-cooperative players coordinate their strategies, the authors employ the CE to analyse the proposed game. Next, the authors derive the condition under which the CE is Pareto optimal and employ linear programming duality to show its closed-form expressions. Furthermore, the authors present a linear programming method and a distributed algorithm based on the regret matching procedure to implement the CE, respectively, which can help us determine the desired resource allocation. Simulation results demonstrate that our scheme is able to achieve good convergence, Pareto optimality and fairness.

In this work, we study the problem of how to strike a balance between the QoS provisioning and the energy efficiency when a cooperative communication scheme is applied to a clustered wireless sensor network. Specifically, we first characterize the tradeoff by a multi-variable optimization problem, with the goal of balancing the outage performance and the network lifetime. Then, we horizontally decompose the problem into the concatenation of two subproblems: i) the long-haul transmit power per sensor node, and ii) the set of assisting cluster nodes. For the former one, an optimal long-haul transmit power solution is proposed based on the Lambert W function. The latter one is modeled as a coalition formation game, where the characteristic function is designed based on the combination of the former subproblem's results. Furthermore, an optimal algorithm is proposed by using a dynamic coalition formation process based on the best-reply process with trial opportunity. Extensive simulation results are presented to demonstrate the effectiveness of our proposed scheme.

In this paper, we focus on the problem of how to strike a balance between the QoS provisioning and the energy consumption when a cooperative communication scheme is applied in a clustered wireless sensor network. We characterize this tradeoff by a multi-variable optimization problem, with the goals of jointly maximizing the outage performance and the network lifetime. Then, we horizontally decompose the problem into the concatenation of two subproblems: (i) the long-haul transmit power per sensor node, and (ii) the set of assisting cluster nodes (CNs). For the former one, the optimal long-haul transmit power solution is presented based on the Lambert W function, only with the knowledge of statistic channel state information. Moreover, a dynamic coalition formation game theoretical framework is modeled to solve the latter one. A corresponding algorithm is proposed by using a dynamic coalition formation process based on the best-reply process with trial opportunity. Through this algorithm, a stable coalition structure with a core allocation can be obtained, which can show the optimal set of assisting CNs. Extensive simulation results are provided to demonstrate the effectiveness of our proposed scheme.

In this paper, we consider a single-antenna OFDM-based relaying system over frequency selective fading channels employing subcarrier grouping and linear constellation precoding at the source node, and linear detection at the destination node. We derive the closed-form expressions of the diversitymultiplexing tradeoff (DMT) relation, and develop the optimal symbol loading strategy (the optimal number of symbols assigned to each group), and the optimal grouping strategy (the optimal partition of the subcarriers) to achieve the optimum DMT performance. Simulation results are presented to verify our theoretical analysis.

In this paper, we propose a hybrid cooperative scheme to improve the secrecy rate for a cooperative network in presence of multiple relays. Each relay node transmits a mixed signal consisting of weighted source signal and intentional noise. The problem of power allocation and joint design of beamforming and jamming weights are investigated, and an iterative solution for the secrecy rate maximization problem is presented. The numerical results demonstrate that the proposed hybrid scheme further improves secrecy rate, as compared to traditional cooperative schemes.

In this paper, we adopt the DMT framework to study a cooperative downlink OFDMA system with multiple relays. We derive the closed-form expression of the DMT curve for the cooperative OFDMA system employing linear constellation precoding at the base station, and linear detection at the mobile users, and develop the optimal symbol loading, relay selection and subcarrier allocation strategy. Simulation results are presented to verify the theoretical analysis and show that our proposed resource allocation strategy can improve the system performance significantly.

In this paper, we consider an AF relaying system without direct channel between the source node and the destination node, and use superimposed pilots to estimate the overall channel state information (CSI). Considering the superimposed pilot-interference (SPI) at the destination node is useful for channel estimation and data detection because the pilots and the data undergo the same channel fading due to superimposed sending, we utilize SPI to estimate CSI and detect the sending data. Simulation results show that our proposed method improves the spectral efficiency and obtain the satisfactory performance because of using superimposed pilots instead of traditional pilots.

This paper is concerned with decentralized selection of multimode precoding strategy for multiple-input multiple-output (MIMO) multiple access channels. We formulate it as a discrete noncooperative game. This game is shown to possess at least one pure strategy Nash equilibrium (NE) and the optimal strategy profile which maximizes the sum rate constitutes a pure strategy NE. Then we propose a decentralized algorithm based on learning automata to achieve the NE. A repeated mechanism is introduced to improve the sum rate performance and a mechanism for adapting step size is designed to control the convergence speed. Simulation results show that the proposed algorithm, which only requires limited feedback, can achieve near optimal or optimal sum rate performance.

It is conventionally supposed that the periodicity of superimposed training (ST) sequence designed for channel estimation has no impact on the ST system's performance, as long as the channel identification condition is satisfied. Accordingly the shortest sequence period equal to channel length is always preferred in ST-OFDM systems for its computational tractability. However, with additional consideration on extraction of the transmitted information, not only focusing on the accuracy of channel estimation, analysis and simulation indicate that the period length of ST sequence may have an unignorable influence on BER performance of ST-OFDM systems. As a special case, we show that ST with training sequence period equal to channel length can bring OFDM no benefit but BER performance degradation compared with its frequency-divided pilot counterpart.

Maximum likelihood detection for MIMO systems can be formulated as an integer quadratic programming problem. In this paper, we introduce depth-first branch and bound algorithm with variable dichotomy into MIMO detection. More nodes may be pruned with this structure. At each stage of the branch and bound algorithm, active set algorithm is adopted to solve the dual box-constrained quadratic programming subproblem. Numerical results show that the complexity of MIMO detection based on branch and bound algorithm is very low, especially in low SNR and large constellations.

Prior works of the antenna selection mainly studied the single user case. In this paper, we focus on the multi-user case which is more practical. The multi-user antenna selection problem is formulated as an N-person discrete non-zero sum finite strategy-form game. The capacity of each user is used as the utility function. The Nash equilibrium of the proposed game is studied by using stochastic learning and numerical results are given.

In this paper, we present a modified BP decoding algorithm based on select updating schedule by analyzing the properties of message-passing through cycles, we also proposed a concrete scheme to obtain the select updating schedule, the simulations prove that we could improve performance and reduce complexity at the same time for short regular LDPC codes which have a small difference between the maximal and minimal single bit node girths by select updating schedule compared with the conventional BP decoding algorithm.

We investigate the relay multiple access channels (RMAC) with two users and a destination. We determine the lower bound and upper bound on the capacity of RMAC under time-division (TD) decode-and-forward (DF) mode by using superposition modulation. A relay scheme with resource allocation is proposed to achieve the lower bound. Analytical results and simulation results show that the proposed scheme can achieve larger capacity region than that of direct transmission (DT) for the fixed channel gain case. But they provide the same maximum sum capacity. Whereas, the proposed relay scheme can provide higher outage capacity than DT scheme due to the fact that the two sources can share the resources from each other.

In this letter, we propose a novel quadratic programming model for soft-input soft-output (SISO) multiple-input multiple-output (MIMO) detection that is compact for QAM constellations and easy to analyze. A semidefinite relaxation for this model is derived that can be solved by the interior-point method. We also give the sufficient conditions and necessary conditions to speed up the interior-point method.

Abstract In this paper, we investigate the cooperative channels with one source‐destination pair and multiple partners (relays), where only the destination is equipped with multiple antennas. The achievable capacity with the optimal power allocation and partner selection is analyzed with the total transmit power constraints under the different cooperation modes, including amplify‐and‐forward (AF) and decode‐and‐forward (DF). With the partial channel state information (CSI) at the destination, we develop three algorithms to choose the possible best partner(s) for AF, repetition‐coded DF and Gaussian‐coded DF, which are called Selective AF (SAF), Selective repetition‐coded DF (SRDF), and Selective Gaussian‐coded DF (SGDF), respectively. In these algorithms, only one partner will be employed as relay for SAF and SRDF to maximize the capacity while multiple partners are selected for SGDF. An efficient quasi‐distributed protocol to support SAF, SRDF, and SGDF is also involved. We study the impact of the number of receive antennas and SNR on SAF, SRDF, and SGDF. Numerical results show that SAF and SRDF have higher spectral efficiency than direct transmission (DT), especially in low SNR regime and for the small number of receive antennas and large number of users cases, while SGDF outperforms DT evidently in every scenario. Copyright © 2007 John Wiley & Sons, Ltd.

All existing training signals designs for OFDM systems assume no guard subcarriers. In practice, guard subcarriers exist in almost all the OFDM systems. In this paper, we propose a Linear Programming(LP) method for optimal power distribution design and a certain matrix for optimal phase design in MIMO OFDM systems with guard subcarriers. Based on these methods, the optimal training signals for 802.11a and 802.16 are derived. Simulation results show that a significant performance improvement can be achieved with the proposed training signals.

The problem of random access (RA) in a wireless SIMO-OFDM system is addressed and a decentralized medium access control (MAC) strategy is proposed based on the so called random orthogonal frequency division multiple access (ROFDMA). By employing receive beamforming, the problem of packet collisions at the same subcarrier are solved by using random spatial division multiple access (RSDMA), which therefore provides a multipacket reception (MPR) capability. First, we study the optimal number of users allowed to transmit at the same subcarrier and the same slot to maximize the separable packets subject to their BER requirements. Then the transmission probability based on user's channel gain is designed to achieve the desired number of users with the highest probability. Finally, a MPR scheme under average transmit power constraint is proposed with modulation selection. The performance of the proposed scheme is analyzed analytically and evaluated through simulations.

In this paper, we propose a semi-blind channel estimation method based on first-order statistics of OFDM systems. The transmitter superimposes periodic pilot sequences on information sequences, where there is no loss in information rate but a controllable increase in transmission power. Employing the first-order statistics, it enables a closed-form channel estimation method to estimate the frequency-selective fading channel. Then, a mean square error (MSE) bound is derived to evaluate the proposed channel estimator. Computer simulations have been provided to compare the MSE performances of the proposed method with the LS estimation method based PSAM and the subspace method.

This paper addresses the optimal resource allocation problem for multiuser MIMO-OFDM systems. We apply an optimization algorithm to obtain a joint subcarrier and power allocation scheme based on orthogonal frequency division multiple access (OFDMA) combined with dirty paper coding (DPC) assuming instantaneous channel state information (CSI), which is called as DPC-OFDMA. The ultimate objective is to minimize the total transmit power subject to individual required data rates constraints. To reduce the complexity of the optimal solution, the analysis is considered in two stages. The first stage addresses subcarriers allocation, in which users are allowable to share subcarriers. The second stage employs DPC technology to deal with simultaneous transmissions of the users sharing the same subcarriers. An efficient algorithm to choose the best possible ordering for DPC and the optimal precoding design of each user are also involved. Simulation results show that DPC-OFDMA scheme has high spectral and power efficiency than conventional fixed schemes, where fixed power and subcarriers are allocated to each user.

In wireless communications, multiple receive antennas can be used with orthogonal frequency division multiplexing (OFDM) or single-carrier (SC), and frequency domain equalization (FDE) or frequency domain pre-equalization (pre-FDE) to significantly improve system capacity and performance. However, the complexity of such combination can be very large because multiple discrete Fourier transform (DFT) blocks, each for one receive antenna, are required to fully take advantage of the space diversity. Reference Defeng Huang et al., (2004) proposed a receive space diversity architecture for OFDM systems using orthogonal designs. Using this architecture, the number of DFT blocks can be reduced but performance degradation will be introduced. In this paper, we propose space diversity schemes for space-time block coding (STBC) based multiple-input multiple-output (MIMO) systems and frequency domain pre-equalization single-input multiple-output (SIMO) systems. These schemes can be used with OFDM and SC transmission techniques. Using these schemes, the total number of Fourier transform (FT) blocks can be further reduced and the performance can be significantly improved at higher signal-to-noise ratio. Simulation results show the benefits of the proposed schemes

Channel parameters in OFDM systems over fading channels are generally obtained by transmitting pilot symbols in given positions of the time-frequency grid. The pilot-based method usually involves the LS estimation step on the pilot subcarriers and the interpolation step over the entire time-frequency grid. Increasing the accuracy of the pilot estimation will accordingly increase the precision in the interpolation step. This can be achieved by subspace tracking method. We propose in this paper an improved subspace-tracking algorithm by the Givens plane rotation based delay-subspace tracking and the RLS filtering based amplitude tracking. Performed on a parameterized model of the channel, simulations prove that our channel estimation is more robust and accurate while the complexity of the algorithm is low.