When uncrewed aerial vehicles (UAVs) perform high-precision communication tasks, such as searching for users and providing emergency coverage, positioning errors between base stations and users make it challenging to deploy trajectory planning algorithms. To address these challenges caused by positioning errors, we propose a compensation framework based on channel knowledge map (CKM), a site-specific database that stores and manages channel state information (CSI). By taking the positions with errors as input, the generated CKM could give a prediction of signal attenuation which is close to true positions. Based on that, the predictions are utilized to calculate the received power and a proximal policy optimization-based algorithm is applied to optimize the compensation. After training, the framework is able to find a strategy that minimize the flight time under communication constraints and positioning error. Besides, the confidence interval is calculated to assist the allocation of power and the update of CKM is studied to adapt to the dynamic environment. Simulation results show the robustness of CKM to positioning error and environmental changes, and the superiority of CKM-assisted UAV communication design.

Intelligent Reflective Surface (IRS) is a promising technique for Beyond 5G and 6G wireless communications. IRS is comprised of plenty of passive reflecting elements that an external controller is able to control by software. To develop the great potential of IRS-aided wireless communication system, we should get a full understanding of Channel State Information (CSI). However, it’s an exceedingly challengeable task for channel estimation in IRS-aided system. In this paper, we propose a residual neural network to achieve cascaded channel estimation for an IRS-aided multiple-input multiple-output (MIMO) communication system. Numerical results prove the effectiveness and high robustness of our Deep Learning (DL) method.

In this work, we consider a robust transmission design of Intelligent Reflecting Surface (IRS)-assisted secure Cognitive radio communication system base on imperfect channel state information(ICSI). We aim to minimize the transmit power under the constraint of the secure rate of the secondary user and the interference temperature of the primary user. To solve this problem, we use an alternate optimization method to decouple the problem into two sub-problems that can be solved by iterative optimization. Particularly, the successive convex approximation (SCA) method is used to optimize the active beamforming for the second station, while the penalty convex-concave procedure(CCP) is used to obtain the reflected phase of the IRS. Simulation results illustrate robust design is necessary and can improve the energy efficiency and spectrum efficiency of the system.

As one of the key technologies in the sixth-generation (6G) wireless network, reconfigurable intelligent surfaces (RIS) have numerous applications in wireless communication. This study focuses on the RIS-assisted secure wireless communication system. Specifically, we deploy double-RIS in the system, which takes into account the hardware impairments of the transceivers. By jointly designing the transmit beamforming of the base station (BS) and the phase shift matrix of the RIS, we can enhance the signal strength at the legitimate user and suppress the signal strength at the eavesdropper to maximize the secrecy rate. To address the high coupling of the optimization variables, the block coordinate descent (BCD) method is employed to decompose the problem into three sub-problems for optimization. The sub-problem of optimizing the beamforming variables is transformed into a convex problem using the successive convex approximation (SCA) method. Furthermore, the sub-problem of optimizing the RIS phase shift is solved using the semi-definite relaxation (SDR) method. Simulation results illustrate that the proposed robust design scheme has better performance than the traditional non-robust scheme, which ignores hardware impairments.

Reconfigurable intelligent surface (RIS) has recently been regarded as a potential technique to enhance the performance of wireless communication systems by creating additional communication links. However, it is almost impossible to get the perfect channel state information (CSI) from the base station (BS) to the Internet of Things Devices (IoTDs) and the RIS-related channels. Furthermore, residual transceiver hardware impairments inevitably affect the performance of wireless communication systems. Hence, we study the robust design for an RIS-aided wireless communication system based on the imperfect CSI and hardware impairments. Minimizing the power consumption of BS is formulated by ensuring the minimum signal-to-interference-plus-noise ratio (SINR) demands of the IoTDs and the unit-modulus constraints of the RIS. Specifically, after approximating the nonconvex constraints by using the S-procedure and the successive convex approximation (SCA) methods, we adopt the block coordinate descent (BCD) technique to iteratively optimize one set of variables while keeping the other variables fixed in various channel uncertainty scenarios. Simulation results demonstrate that the influence of transceiver hardware impairments can be effectively decreased by deploying RIS even with channel uncertainty, which is more advantageous than increasing the number of BS’s antennas.

Reconfigurable intelligent surface (RIS) has become a crucial technology for the next generation wireless communication due to its good performance on low power consumption and high energy efficiency. In this paper, we investigate maximizing the sum of achievable data rates (SADR) of all the users by considering hardware impairments in the RIS-aided multiple-input multiple-output (MIMO) wireless communication system. Owing to the coupling effect of the multiple variables of the optimization problem (OP), we adopt the block coordinate descent algorithm (BCDA) to decouple the original problem into two sub-problems where the transmitting precoding matrix (TPM) and phase shifts are alternately optimized. Specifically, the upper bound optimal solution of the TPM can be obtained in closed form through the Lagrangian multiplier method, and majorization-minimization algorithm (MMA) is applied to solve the sub-problem of optimizing phase shifts. Simulation results demonstrate the necessity of considering hardware impairments into beamforming optimization and the effectiveness of introducing RIS into the MIMO system.

In this article, we aim to minimize the total power of all the Internet of Things Devices (IoTDs) by jointly optimizing user association, computational capacity, transmit power, and the location of unmanned aerial vehicles (UAVs) in an UAV-assisted cloud radio access network (C-RAN). In order to solve this nonconvex problem, we propose an effective algorithm by solving four subproblems iteratively. For the user association and the computational capacity subproblems, the nonconvex constraints are relaxed and the optimal solutions are obtained. For the transmit power control and the location planning subproblems, the successive convex approximation (SCA) technique is used to transform the nonconvex constraints into convex ones. Moreover, to obtain the suboptimal solutions, slack variables are also introduced to deal with the feasibility-check problems. The simulation results demonstrate that the proposed algorithm can greatly reduce the total power consumption of IoTDs.

This paper investigates a UAV-enabled wireless downlink system, where a UAV-enabled base station flies above a group of Internet of things devices (IoTDs) and transmits data to them. In order to ensure the fairness among all IoTDs, we jointly optimize scheduling association and UAV trajectory to maximize the minimal throughput of all IoTDs under the 3dimensional (3-D) channel model. Meanwhile, to ensure the stability of communication, we aim to guarantee a high probability of line-of-sight (LoS) links between the UAV and all IoTDs. The optimization problem is non-convex and we propose an iterative algorithm based on the block coordinate descent and successive convex approximation to solve it. Furthermore, we show the convergence of the algorithm through simulations. It is shown that the proposed algorithm achieves higher data rate than the traditional scheme without guaranteeing the LoS probability.

Intelligent reflecting surface (IRS) is a promising technology for its capability of enhancing the desired signals and suppressing the interference. In this paper, we introduce IRS into a device-to-device (D2D) underlaying cellular system and investigate its application on physical layer security. The secrecy rate is maximized by jointly designing the received beamforming vector, power allocation and phase shift matrix. We apply the block coordinate descent (BCD) algorithm to decouple the optimization problem, and the semidefinite relaxation (SDR) technique is adopted to optimize the phase shift matrix. Simulation results show that the IRS is able to improve the system secrecy performance of the D2D underlying cellular system.

Wireless-powered communication networks (WPCNs) are a promising technology supporting resource-intensive devices in the Internet of Things (IoT). However, their transmission efficiency is very limited over long distances. The newly emerged intelligent reflecting surface (IRS) can effectively mitigate the propagation-induced impairment by controlling the phase shifts of passive reflection elements. In this article, we integrate IRS into WPCNs to assist both the energy and information transmission. We aim to maximize the uplink (UL) sum rate of all IoT devices by jointly optimizing the time allocation variable, energy beam matrix at the power transmitting base station (PTBS), receive beamforming matrix at the information receiving base station, and the phase shifts of the IRS both in the UL and downlink (DL) subject to time allocation constraint, together with transmit power constraint for the PTBS and unit modulus constraints. This problem is very difficult to solve directly due to the highly coupled variables, which results in the optimization problem taking neither linear nor convex form. Hence, we decouple this problem into three subproblems by using the block coordinate descent method. The UL receive beamforing matrix and phase shift are alternatively optimized in the UL optimization subproblem with fixed time allocation and the DL variables. The DL optimization subproblem is solved by the proposed successive convex approximation algorithm. Simulation results demonstrate that the performance of integrating IRS and WPCNs outperforms traditional WPCNs. Besides, the results show that IRS is an effective method to preserve the tradeoff of energy efficiency and transmission efficiency in the IoT.

The emerging Internet of Things (IoT) applications, such as smart manufacturing and smart home, lead to a huge demand on the provisioning of low-cost and high-accuracy positioning and navigation solutions. Inertial measurement unit (IMU) can provide an accurate inertial navigation solution in a short time but its positioning error increases fast with time due to the cumulative error of accelerometer measurement. On the other hand, ultrawideband (UWB) positioning and navigation accuracy will be affected by the actual environment and may lead to uncertain jumps even under line-of-sight (LOS) conditions. Therefore, it is hard to use a standalone positioning and navigation system to achieve high accuracy in indoor environments. In this article, we propose an integrated indoor positioning system (IPS) combining IMU and UWB through the extended Kalman filter (EKF) and unscented Kalman filter (UKF) to improve the robustness and accuracy. We also discuss the relationship between the geometric distribution of the base stations (BSs) and the dilution of precision (DOP) to reasonably deploy the BSs. The simulation results show that the prior information provided by IMU can significantly suppress the observation error of UWB. It is also shown that the integrated positioning and navigation accuracy of IPS significantly improves that of the least squares (LSs) algorithm, which only depends on UWB measurements. Moreover, the proposed algorithm has high computational efficiency and can realize real-time computation on general embedded devices. In addition, two random motion approximation model algorithms are proposed and evaluated in the real environment. The experimental results show that the two algorithms can achieve certain robustness and continuous tracking ability in the actual IPS.

In this paper, we investigate a resource allocation scheme for fully loaded distributed antenna system (DAS) with Device-to-Device (D2D) communication. A framework of resource allocation for D2D communications under laying a fully loaded DAS is presented, where the objective is to maximize the throughput of shared spectrum channel. First, we set up DAS with D2D communication model. Then, a dual threshold power control scheme is studied for each D2D pair and its cellular user (CU) partner to maximize the throughput of shared spectrum channel. Finally, the optimal solution is obtained according to the convex optimization theory. Numerical results show that the proposed scheme can significantly improve the throughput and communication quality of communication network system compared to the co- located antenna system (CAS) with D2D communication.

Orthogonal Frequency Division Multiplexing (OFDM) is designed to combat the effect of multipath reception, by dividing the wide band frequency selective fading channel into many narrow flat sub- channels, which improves the spectral efficiency and significantly mitigates the intersymbol interference (ISI). However, OFDM can not meet the demand for 5G heterogeneous service scenarios since it has a high out-of-band emission and a large peak-to-average power ratio (PAPR), and it only supports one kind of waveform parameter in the whole bandwidth. The Filtered-OFDM (F-OFDM) is proposed as a candidate technique for 5G high-data rate wireless communication system. This paper proposes a finite impulse response (FIR) digital filter based on window function method to achieve the F-OFDM, and discusses the performance of different window functions implemented in the F-OFDM. Simulation results show that the proposed F-OFDM is easy to be implemented and has a very low out-of-band emission with the same bit error rate (BER) performance compared to the conventional OFDM.

In this paper, we discuss three different design criteria for a distributed antenna system (DAS). They are maximizing throughput under the constraint of the overall transmit power, minimizing the overall transmit power while guaranteeing the minimum spectral efficiency (SE) requirements, and maximizing energy efficiency (EE) under the constraints of minimum SE requirements and overall transmit power. We use sub-gradient iteration approach to solve the first two optimization problems and exploit fractional programming method to deal with the third one. Based on these design criteria, three power allocation algorithms are developed for the downlink multi-user DAS. Depending on application enviroments, we can use the first and second criteria to achieve the highest throughput and to save the most energy, respectively, while we can balance throughput and energy consumption using the third criterion.

In this paper, we propose an optimal scheme for a distributed antenna system (DAS) to maximize energy efficiency (EE) under a constraint of overall transmit power of each remote access unit (RAU). We exploit the multicriteria optimization method to systematically investigate the relationship between EE and spectral efficiency (SE). Using the weighted sum method, we first convert the multicriteria optimization function, which is extremely complex, into a simpler single objective optimization function. Then an optimal algorithm is developed to allocate the available power to tradeoff EE and SE effectively. Furthermore, we also illustrate the effectiveness of the proposed method and demonstrate there is a tradeoff between energy-efficient and spectral-efficient transmission through computer simulation of a downlink multiuser DAS.

Energy efficiency(EE) has caught more and more attention in future wireless communications due to steadily rising energy costs and environmental concerns. In this paper, we propose an EE scheme with proportional fairness for the downlink multiuser distributed antenna systems (DAS). Our aim is to maximize EE, subject to constraints on overall transmit power of each remote access unit (RAU), bit-error rate (BER), and proportional data rates. We exploit multi-criteria optimization method to systematically investigate the relationship between EE and spectral efficiency (SE). Using the weighted sum method, we first convert the multi-criteria optimization problem, which is extremely complex, into a simpler single objective optimization problem. Then an optimal algorithm is developed to allocate the available power to balance the tradeoff between EE and SE. We also demonstrate the effectiveness of the proposed scheme and illustrate the fundamental tradeoff between energy- and spectral-efficient transmission through computer simulation.

In this paper, we compare EE of the distributed MIMO (D-MIMO) and co-located MIMO (C-MIMO) in the uplink cellular systems since mobile stations are battery powered. The total energy consumption includes both the circuit energy consumption and the transmission energy. We get the closed-form expression for EE of D-MIMO and C-MIMO systems. What's more, an optimization algorithm is proposed to get the optimal EE values while satisfying given spectral efficiency (SE) requirement for both D-MIMO and C-MIMO systems. Simulation results show that the D-MIMO systems are more energy efficient than C-MIMO systems in composite fading channel, and the optimal EE value can be obtained by the proposed algorithm while satisfying given SE requirement.

Transmit beamforming and receive combining, which is used in multiple-input multiple-output orthogonal frequency division multiplexing (MIMO-OFDM) systems, is investigated in this paper. The complexity of linear spherical interpolation algorithm is too large and it can only be applied to unquantized beamforming, so we propose a general spherical interpolation method. This method feeds back pilot subcarrier beamforming vectors to the transmitter, then using the channel correlation in frequency domain, the beamforming vectors for non-pilot subcarriers are reconstructed according to that of the two neighboring pilot subcarriers using the general spherical interpolation algorithm. Simulation results show that this algorithm is applicable not only for unquantized beamforming, but also for quantized beamforming. Considering the realization of the actual system, we propose a phase quantized method. Simulations demonstrate that the performance of the phase quantization method outperforms existing MIMO-OFDM beamforming interpolation algorithms, and is easy to realize in actual system.