This paper studies the achievable rates of the multi-antenna or multiple-input multiple-output (MIMO) secrecy channel with multiple single-/multi-antenna eavesdroppers. It is known that the maximum achievable secrecy rate is obtained with the optimal transmit covariance matrix that maximizes the minimum difference between the channel mutual information of the secrecy user and those of the eavesdroppers. The maximum secrecy rate computation can thus be formulated as a non-convex max-min problem, which cannot be solved efficiently by existing methods. To handle this difficulty, this paper explores a new relationship between the secrecy channel and the recently developed cognitive radio (CR) channel, in which the secondary user transmits over the same spectrum simultaneously with multiple primary users, subject to the received interference power constraints at the primary users, or the so-called ¿interference temperature (IT)¿ constraints. By constructing an auxiliary multi-antenna CR channel that has the same channel responses as the secrecy channel, this paper shows that the optimal transmit covariance to achieve the maximum secrecy rate is the same as that to achieve the CR spectrum sharing capacity with properly selected IT constraints. Thereby, finding the optimal complex transmit covariance matrix for the secrecy channel becomes equivalent to searching over a set of real IT constraints in the auxiliary CR channel. Based on this relationship, efficient algorithms are proposed to solve the non-convex secrecy rate maximization problem by transforming it into a sequence of convex CR spectrum sharing capacity computation problems, under various setups of the secrecy channel.
In this letter, we study a wireless communication system with a fixed-wing unmanned aerial vehicle (UAV) employed to collect information from a group of ground nodes (GNs). Our objective is to maximize the UAV's energy efficiency (EE), which is defined as the achievable rate among all GNs per unit propulsion energy consumption of the UAV. To efficiently solve this problem with continuous-time functions, we propose a new method based on receding horizon optimization (RHO), which significantly reduces the computational complexity compared to the conventional time discretization method. Specifically, we sequentially solve the EE maximization problem over a moving time-window of finite duration, for each of which the number of optimization variables is greatly reduced. Simulation results are provided to show the effectiveness of the proposed method.
Taking Shenzhen stock market as example,using data and mathematical model,the authors quantitatively analyze the impact of the enactment of regulation fair disclosure on Shenzhen main board market and Shenzhen SME board market respectively.The empirical results are:on the Shenzhen motherboard market,the promulgation of regulation fair disclosure is a result of the negative impact because the abnormal excess return is negative;on the Shenzhen SME board market,the promulgation of regulation fair disclosure had a positive impact because the abnormal excess return is positive.The different empirical results between main board market and SME board of are largely due to differences in the two markets,business groups,and the target service.
This paper puts forward two novel algebraic construction of quasi-cyclic low-density parity-check(QC-LDPC) codes. The two constructions are achieved on multiplicative and additive matrix dispersions of two specified base matrices, which are constructed based on multiplicative and cyclic groups, respectively. In addition, making technique in code construction can also be applied to the two constructions. Simulation results show that codes generated in this paper perform well with iterative decoding over AWGN channel. The codes have advantages over Maykay codes in some aspects of code performances. Meanwhile, a code constructed in this paper converges very fast in iterative decoding, which is an important property in high throughput communication system.
Industrial Internet of Things (IIoT), which contains different types of devices with heterogeneous Quality-of-Service (QoS) requirements, has encountered significant challenges on guaranteeing the needs of heterogeneous data collection utilizing limited resources. In this article, we investigate the joint frame structure and resource optimization for the hybrid long- and short-packet nonorthogonal multiple access (NOMA)-based data collection with imperfect successive interference cancellation (SIC) in IIoT, where a number of short and long packets can multiplex the same time-frequency resource simultaneously to guarantee their respective heterogeneous QoS requirements. Specifically, the short packet is first decoded to guarantee low latency, afterward the superposed long packet can be decoded to maintain high signal-to-interference-plus-noise-ratio (SINR) performance. A joint short-packet scheduling, pilot length, blocklength, and dynamic power allocation (JSLP) algorithm is proposed to minimize the maximum block error probability among short packets and mitigate the impact of SIC error propagation in NOMA transmission while maintaining a high SINR of long packet, with the assistance of the derived optimal closed-form short-packet scheduling results and pilot and block length expressions. Thanks to the closed-form expressions, the proposed JSLP algorithm demonstrates near-optimal performance and a significant complexity reduction compared to the exhaustive search, leading to fast convergence. Numerical results demonstrate that the designed hybrid NOMA-based frame structure and JSLP algorithm are robust against the SIC error propagation, and can maintain a high level of fairness by significantly mitigating the maximum block error probability gap among short packets.
In recent years, continuous growing interests have been seen in bringing artificial intelligence capabilities to mobile devices. However, the related work still faces several issues, such as constrained computation and memory resources, power drain, and thermal limitation. To develop deep learning (DL) algorithms on mobile devices, we need to understand their behaviors. In this article, we explore the architectural behaviors of some mainstream DL frameworks on mobile devices by performing a comprehensive characterization of performance, accuracy, energy efficiency, and thermal behaviors. We experimentally choose four model compression methods to perform on networks and in addition, analyze the related impact on the nodes amount, memory, execution time, model size, inference time, energy consumption, and thermal distribution. With insights into DL-based mobile application characteristics, we hope to guide the design of future smartphone platforms for lower energy consumption.
Intelligent reflecting surface (IRS) that enables the control of wireless propagation environment has recently emerged as a promising cost-effective technology for boosting the spectral and energy efficiency of future wireless communication systems. Prior works on IRS are mainly based on the ideal phase shift model assuming full signal reflection by each of its elements regardless of the phase shift, which, however, is practically difficult to realize. In contrast, we propose in this paper a practical phase shift model that captures the phase-dependent amplitude variation in the element-wise reflection design. Based on the proposed model and considering an IRS-aided multiuser system with one IRS deployed to assist in the downlink communications from a multi-antenna access point (AP) to multiple single-antenna users, we formulate an optimization problem to minimize the total transmit power at the AP by jointly designing the AP transmit beamforming and the IRS reflect beamforming, subject to the users' individual signal-to-interference-plus-noise ratio (SINR) constraints. Iterative algorithms are proposed to find suboptimal solutions to this problem efficiently by utilizing the alternating optimization (AO) as well as penalty-based optimization techniques. Moreover, to draw essential insight, we analyze the asymptotic performance loss of the IRS-aided system that employs practical phase shifters but assumes the ideal phase shift model for beamforming optimization, as the number of IRS elements goes to infinity. Simulation results unveil substantial performance gains achieved by the proposed beamforming optimization based on the practical phase shift model as compared to the conventional ideal model.
In this paper, we consider a cognitive radio (CR) network where a secondary (cognitive) user shares the spectrum for transmission with a primary (non-cognitive) user over block-fading (BF) channels. It is assumed that the primary user has a constant-rate, constant-power transmission, while the secondary user is able to adapt transmit power and rate allocation over different fading states based on the channel state information (CSI) of the CR network. We study a new type of constraint imposed over the secondary transmission to protect the primary user by limiting the maximum transmission outage probability of the primary user to be below a desired target. We derive the optimal power allocation strategies for the secondary user to maximize its ergodic/outage capacity, under the average/peak transmit power constraint along with the proposed primary user outage probability constraint. It is shown by simulations that the derived new power allocation strategies can achieve substantial capacity gains for the secondary user over the conventional methods based on the interference temperature (IT) constraint to protect the primary transmission, with the same resultant primary user outage probability.
In mobile wireless networks, dynamic allocation of resources such as transmit powers, bit-rates, and antenna beams based on the channel state information of mobile users is known to be the general strategy to explore the time-varying nature of the mobile environment. This paper looks at the problem of optimal resource allocation in wireless networks from different information-theoretic points of view and under the assumption that the channel state is completely known at the transmitter and the receiver. In particular, the fading multiple-access channel (MAC) and the fading broadcast channel (BC) with additive Gaussian noise and multiple transmit and receive antennas are focused. The fading MAC is considered first and a complete characterization of its capacity region and power region are provided under various power and rate constraints. The derived results can be considered as nontrivial extensions of the work done by Tse and Hanly from the case of single transmit and receive antenna to the more general scenario with multiple transmit and receive antennas. Efficient numerical algorithms are proposed, which demonstrate the usefulness of the convex optimization techniques in characterizing the capacity and power regions. Analogous results are also obtained for the fading BC thanks to the duality theory between the Gaussian MAC and the Gaussian BC.
