Intelligent reflecting surface (IRS) is envisioned as a promising hardware solution to hardware cost and energy consumption in the fifth-generation (5G) mobile communication network. It exhibits ...great advantages in enhancing data transmission, but may suffer from performance degradation caused by inherent hardware impairment (HWI). For analysing the achievable rate (ACR) and optimizing the phase shifts in the IRS-aided wireless communication system with HWI, we consider that the HWI appears at both the IRS and the signal transceivers. On this foundation, first, we derive the closed-form expression of the average ACR and the IRS utility. Then, we formulate optimization problems to optimize the IRS phase shifts by maximizing the signal-to-noise ratio (SNR) at the receiver side, and obtain the solution by transforming non-convex problems into semidefinite programming (SDP) problems. Subsequently, we compare the IRS with the conventional decode-and-forward (DF) relay in terms of the ACR and the utility. Finally, we carry out simulations to verify the theoretical analysis, and evaluate the impact of the channel estimation errors and residual phase noises on the optimization performance. Our results reveal that the HWI reduces the ACR and the IRS utility, and begets more serious performance degradation with more reflecting elements. Although the HWI has an impact on the IRS, it still leaves opportunities for the IRS to surpass the conventional DF relay, when the number of reflecting elements is large enough or the transmitting power is sufficiently high.
This paper is concerned with the analysis of proportionally fair scheduling (PFS), and we provide an analytical approximation for the PFS throughput over Rayleigh fading channels. Though quite ...accurate, the ordinary differential equation (ODE) analysis, typically used to analyze the PFS throughput, is highly time-consuming when there are lots of users. On the other hand, due to the intricate interplay among these ODE equations, the ODE analysis generally fails to provide a closed-form approximation for estimating the PFS throughput unless with simplified models such as the linear rate model to characterize channel capacity. Our aim is to provide a novel framework to evaluate PFS in Rayleigh fading without the above-mentioned limitations. To put our work on a firm base, we use results of stochastic approximation in the analysis and take the Gaussian approximation for capacity modeling for fading channels. Simulations validate this approach and show that our analytic result provides highly accurate estimate of the PFS throughput. Compared to existing studies, our work advances the state of the art in three ways. First, it goes beyond the linear rate model and applies to the commonly used Shannon rate model. Second, it provides accurate estimate of the PFS throughput without the need for the time-consuming ODE analysis. Third, it provides a unified closed-form expression for estimating the PFS throughput for both the linear rate model and the Shannon rate model. It is interesting to note that our analysis provides the same result as existing studies when assuming the linear rate model. More importantly, our formula is intuitive yet easy to evaluate numerically.
The canonical scale-free model to describe complex networks is BA model with an power-law exponent γ = 3. Researchers further propose DS model (1 <; γ ≤ 4) to consider link failure besides node ...growth in preferential attachment. However, both models assume globally preferential attachment which is difficult to achieve in real networks. This paper proposes a new scale-free model, i.e. Neighborhood Log-on and Log-off model (NLL) which considers locally preferential connectivity. NLL incorporates both node growth and removal in topology evolvement. Unlike BA and DS, NLL adds compensation mechanism to enhance connectivity. The analysis shows that NLL has 1 <; γ ≤ 3. We conduct simulations to evaluate NLL performance and show that, NLL has short average path length and large clustering coefficient, compared with BA and DS models.
This paper studies the connectivity of large cooperative ad hoc networks. Unlike existing work where all nodes are assumed to transmit cooperatively, the cooperative network we consider is realistic ...as we assume that not all nodes are willing to collaborate when relaying other nodes' traffic. For such selfish, cooperative network, we use stochastic geometry and percolation theory to analyze the connectivity and provide an upper bound of critical node density when the network percolates.
This paper applies device-to-device technique to vehicular-to-vehicular communications (D2D-V) in an underlay fashion. Different from conventional D2D systems focusing on smart phones, whose ...positions are usually assumed to be statical, the D2D-V system suffers more mobility and uncertainties. These dynamic features indeed complicate the design of D2D-V system, but also, give more potential to accomplish some geographic based algorithms. Armed with the geographic knowledge of highway and the dynamic GPS information in each vehicle, we propose a geographic based reuse cellular user selection scheme and two different distributed power control schemes to serve various applications in vehicular networks. We model the problem firstly, then formulate two metrics: the sum rate and the minimum-achievable rate to evaluate the performance of the proposed schemes. Simulations are proposed to validate our schemes and analysis. Influences of different system settings are also discussed here.
In this paper, we study the outage probability minimizing problem in a two-hop cooperative relay network. To reduce outage probability, existing studies propose many schemes for relay selection and ...power allocation, which are usually based on the assumption of exact channel state information (CSI). However, it is difficult to obtain perfect instantaneous CSI in practical situations where channel states change rapidly, and thus traditional methods would not perform well. Considering these factors, we turn to the emerging reinforcement learning (RL) methods for solutions. RL methods do not need any prior knowledge of CSI, but use neural network for approximation and decision after interacting with communication environment. Nevertheless, conventional RL methods, including most deep reinforcement learning (DRL) methods, cannot perform well when the search space is too large. In addition, non-stationarity is a common problem when using hierarchical reinforcement learning (HRL), which is caused by the changing behavior in different hierarchies. Therefore, we first propose a DRL framework with an outage-based reward function, which is then used as a baseline. Then, we further design an HRL framework and training algorithm. By decomposing relay selection and power allocation into two hierarchical optimization objectives, and combining on- policy and off-policy methods in the HRL framework, our method successfully address the sparse reward and non-stationary problem. Simulation results reveal that compared with traditional DRL method, the proposed HRL training algorithm can converge faster and reduce the outage probability by 8% in two-hop relay network with the same outage threshold.
The intelligent reflecting surface (IRS) is promising in assisting user localization and wireless communication in the future wireless networks. In this paper, a novel IRS-aided joint localization ...and communication (L&C) scheme is designed in a millimeter-wave transmission system. For the proposed scheme, the user position/orientation estimation error bound (POEB) and the effective achievable data rate (EADR) are derived in closed-form as L&C performance metrics, which reveal the inherent trade-off between L&C capabilities. To achieve the joint optimal point of the POEB and EADR in consideration of the localization errors, a worst-case robust beamforming and time allocation optimization problem is formulated. To solve the original non-convex problem, a novel joint optimization approach is developed. Specifically, from an equivalent minimax problem, the local optimal solutions of the transceiver beamformers, the IRS phase-shift matrix, and the time allocation ratio between user localization stage (ULS) and effective data transmission stage (EDTS), are obtained in closed-form with respect to the localization errors. Then, the worst-case localization error is iteratively found by a dedicated majorize-minimization (MM) based algorithm. Subsequently, potential extensions to general wireless channels and discrete phase-shift models are discussed in detail. Finally, simulations are carried out to show the optimization results and the L&C performance trade-off. In comparison with the conventional non-robust method, the proposed approach is validated to be robust against the user localization uncertainty.
Recent years have witnessed growing interests in vehicular ad hoc network (VANET) topology. Existing works assume that the networks generated at each time are independent. In these works, the VANET ...topologies are formed based on simple traffic and communication models such as unit disk graph model. Interestingly, some works find that VANETs are scale-free ones characterized by strong connectivity and survivability, while some argue that they are not. This study analyzes VANETs in more realistic settings, trying to find the answer to the paradox. Specifically, in this paper we propose a Dynamically Evolving Networking (DEN) model and take as input realistic vehicular traces to make the research and results more practical. We consider the effect of node addition, node deletion and link loss due to node mobility and keep the network evolving by including preferential attachment and link compensation mechanisms. We find that the evolved VANET exhibits a scale-invariant feature under certain conditions. We also find that the emergence of such phenomenon has no relation to communication range if the range is large. Furthermore, we apply complex network theory to capture the dynamics of the VANET. Theoretically for the first time we show that VANET would evolve into the scale-free topology when the probability of node addition is relatively large or the probability of link compensation is properly set, the presence of which would help establish a strongly connected VANET topology.
Caching is a promising approach to address the backhaul traffic congestion problem and boost throughput in fog radio access networks. In this correspondence paper, we investigate a proactive ...probabilistic caching optimization in wireless fog radio access network where multiple users request different files from multiple base stations (BSs). To assess the performance, we first derive the analytical results of successful transmission probability (STP) using tools of stochastic geometry. With the derived closed-form STP expressions, our objective is to optimize the proactive probabilistic caching distribution to maximize the STP. To reduce the computational complexity, we specially discuss the optimization in high signal-to-noise ratio (SNR). We propose a projection gradient method to get a local optimal solution. Simulation results show that the caching placement optimized by our proposed algorithm increases the STP by about <inline-formula><tex-math notation="LaTeX">18\%</tex-math></inline-formula> over the best existing caching distribution when <inline-formula><tex-math notation="LaTeX">{\rm SNR}=10</tex-math></inline-formula>dB with user density <inline-formula><tex-math notation="LaTeX">\lambda _u=0.09</tex-math></inline-formula>, BS density <inline-formula><tex-math notation="LaTeX">\lambda _b=0.1</tex-math></inline-formula> and file number <inline-formula><tex-math notation="LaTeX">N=100</tex-math></inline-formula>.
Not only has information technology evolved rapidly, but the spatial cognition theory for blind and visually impaired (BVI) people has also made great strides, which has opened up a new opportunity ...for indoor travel assistance systems (ITASs). However, there are still some issues that have not been effectively addressed due to the lack of guidance of the spatial cognition theory. Thus, this article presents a comparative survey among ITASs proposed in the last four years in an effort to inform researchers and developers about system problems and challenges and inform BVI people about the various types and functions of the ITAS. This article will also make researchers and developers aware of the importance of the spatial cognition theory. Furthermore, we give predictions for future trends based on a detailed analysis of 17 ITASs.
PDF
