Both passive and active intelligent reflecting surfaces (IRSs) can be deployed in complex environments to enhance wireless network coverage by creating multiple blockage-free cascaded line-of-sight (LoS) links. In this paper, we study a multi-passive/active-IRS (PIRS/AIRS) aided wireless network with a multi-antenna base station (BS) in a given region. First, we divide the region into multiple non-overlapping cells, each of which may contain one candidate location that can be deployed with a single PIRS or AIRS. Then, we show several trade-offs between minimizing the total IRS deployment cost and enhancing the signal-to-noise ratio (SNR) performance over all cells via direct/cascaded LoS transmission with the BS. To reconcile these trade-offs, we formulate a joint multi-PIRS/AIRS deployment problem to select an optimal subset of all candidate locations for deploying IRS and also optimize the number of passive/active reflecting elements deployed at each selected location to satisfy a given SNR target over all cells, such that the total deployment cost is minimized. However, due to the combinatorial optimization involved, the formulated problem is difficult to be solved optimally. To tackle this difficulty, we first optimize the reflecting element numbers with given PIRS/AIRS deployed locations via sequential refinement, followed by a partial enumeration to determine the PIRS/AIRS locations. Simulation results show that our proposed algorithm achieves better cost-performance trade-offs than other baseline deployment strategies.
In this paper, we consider an intelligent reflecting surface (IRS)-aided single-user system where an IRS with discrete phase shifts is deployed to assist the uplink communication. A practical transmission protocol is proposed to execute channel estimation and passive beamforming successively. To minimize the mean square error (MSE) of channel estimation, we first formulate an optimization problem for designing the IRS reflection pattern in the training phase under the constraints of unit-modulus, discrete phase, and full rank. This problem, however, is NP-hard and thus difficult to solve in general. As such, we propose a low-complexity yet efficient method to solve it sub-optimally, by constructing a near-orthogonal reflection pattern based on either discrete Fourier transform (DFT)-matrix quantization or Hadamard-matrix truncation. Based on the estimated channel, we then formulate an optimization problem to maximize the achievable rate by designing the discrete-phase passive beamforming at the IRS with the training overhead and channel estimation error taken into account. To reduce the computational complexity of exhaustive search, we further propose a low-complexity successive refinement algorithm with a properly-designed initialization to obtain a high-quality suboptimal solution. Numerical results are presented to show the significant rate improvement of our proposed IRS training reflection pattern and passive beamforming designs as compared to other benchmark schemes.
This paper considers an unmanned aerial vehicle (UAV)-enabled wireless sensor network (WSN) in urban areas, where a UAV is deployed to collect data from distributed sensor nodes (SNs) within a given duration. To characterize the occasional building blockage between the UAV and SNs, we construct the probabilistic line-of-sight (LoS) channel model for a Manhattan-type city by using the combined simulation and data regression method, which is shown in the form of a generalized logistic function of the UAV-SN elevation angle. We assume that only the knowledge of SNs' locations and the probabilistic LoS channel model is known a priori, while the UAV can obtain the instantaneous LoS/Non-LoS channel state information (CSI) with the SNs in real time along its flight. Our objective is to maximize the minimum (average) data collection rate from all the SNs for the UAV. To this end, we formulate a new rate maximization problem by jointly optimizing the UAV three-dimensional (3D) trajectory and transmission scheduling of SNs. Although the optimal solution is intractable due to the lack of complete UAV-SNs CSI, we propose in this paper a novel and general design method, called hybrid offline-online optimization, to obtain a suboptimal solution to it, by leveraging both the statistical and real-time CSI. Essentially, our proposed method decouples the joint design of UAV trajectory and communication scheduling into two phases: namely, an offline phase that determines the UAV path prior to its flight based on the probabilistic LoS channel model, followed by an online phase that adaptively adjusts the UAV flying speeds along the offline optimized path as well as communication scheduling based on the instantaneous UAV-SNs CSI and SNs' individual amounts of data received accumulatively. Extensive simulation results are provided to show the significant rate performance improvement of our proposed design as compared to various benchmark schemes.
Intelligent reflecting/refracting surface (IRS) has recently emerged as a promising solution to reconfigure wireless propagation environment for enhancing the communication performance. In this paper, we study a new IRS-aided high-mobility communication system by employing the intelligent refracting surface with a high-speed vehicle to aid its passenger's communication with a remote base station (BS). Due to the environment's random scattering and vehicle's high mobility, a rapidly time-varying channel is typically resulted between the static BS and fast-moving IRS/user, which renders the channel estimation for IRS with a large number of elements more challenging. In order to reap the high IRS passive beamforming gain with low channel training overhead, we propose a new and efficient transmission protocol to achieve both IRS channel estimation and refraction optimization for data transmission. Specifically, by exploiting the quasi-static channel between the IRS and user both moving at the same high speed as well as the line-of-sight (LoS) dominant channel between the BS and IRS, the user first estimates the LoS component of the cascaded BS-IRS-user channel, based on which IRS passive refraction is designed to maximize the corresponding IRS-refracted channel gain. Then, the user estimates the resultant IRS-refracted channel as well as the non-IRS-refracted channel for setting an additional common phase shift at all IRS refracting elements so as to align these two channels for maximizing the overall channel gain for data transmission. Simulation results show significant performance improvement of the proposed design as compared to various benchmark schemes. The proposed on-vehicle IRS system is further compared with a baseline scheme of deploying fixed intelligent reflecting surfaces on the roadside to assist high-speed vehicular communications, which achieves significant rate improvement.
Due to the passive nature of Intelligent Reflecting Surface (IRS), channel estimation is a fundamental challenge in IRS-aided wireless networks. Particularly, as the number of IRS reflecting elements and/or that of IRS-served users increase, the channel training overhead becomes excessively high. To tackle this challenge, we propose in this paper a new anchor-assisted two-phase channel estimation scheme, where two anchor nodes, namely A1 and A2, are deployed near the IRS for helping the base station (BS) to acquire the cascaded BS-IRS-user channels. Specifically, in the first phase, the partial channel state information (CSI), i.e., the element-wise channel gain square, of the BS-IRS link is obtained by estimating the BS-IRS-A1/A2 channels and the A1-IRS-A2 channel, separately. Then, in the second phase, by leveraging such partial knowledge of the BS-IRS channel that is common to all users, the individual cascaded BS-IRS-user channels are efficiently estimated. Simulation results demonstrate that the proposed anchor-assisted channel estimation scheme is able to achieve comparable mean-squared error (MSE) performance as compared to the conventional scheme, but with significantly reduced channel training time.
Intelligent reflecting surface (IRS) is a promising technology to improve the performance of backscatter communication systems by smartly reconfiguring the multi-reflection channel. To fully exploit the passive beamforming gain of IRS in backscatter communication, channel state information (CSI) is indispensable but more practically challenging to acquire than conventional IRS-assisted systems, since IRS passively reflects signals over both the forward and backward (backscattering) links between the reader and tag. To address this issue, we propose in this letter a new and efficient channel estimation scheme for the IRS-assisted backscatter communication system. To minimize the mean-square error (MSE) of channel estimation, we formulate and solve an optimization problem by designing the IRS training reflection matrix for channel estimation under the constraints of unit-modulus elements and full rank. Simulation results verify the effectiveness of the proposed channel estimation scheme as compared to other baseline schemes.
This paper studies the optimal resource allocation problem for the relay-assisted orthogonal frequency-division multiple-access (OFDMA) -based multiuser system. A new transmission protocol, named hierarchical OFDMA, is proposed to support two-way transmissions between the base station (BS) and each mobile user (MU) with or without an assisting relay station (RS) in "relay" or "direct" mode, respectively. In particular, two-way relaying is applied to MUs in relay mode with two relay-operations: decode-and-forward (DF) and amplify- and-forward (AF). Applying convex optimization techniques, efficient algorithms are developed for optimizing the resource allocation at the BS, RSs, and MUs. Simulation results show that substantial throughput gains are achievable by the proposed two- way relaying and optimal resource allocation schemes over the conventional one-way relaying and fixed resource allocation for relay-assisted OFDMA-based wireless networks.
Intelligent reflecting surface (IRS) is a promising new technology for achieving spectrum and energy efficient wireless communication systems in the future. By adaptively varying the incident signals' phases/amplitudes and thereby establishing favorable channel responses through a large number of reconfigurable passive reflecting elements, IRS is able to enhance the communication performance of mobile users in its vicinity cost- effectively. In this paper, we study an IRS- enhanced orthogonal frequency division multiplexing (OFDM) system in which an IRS is deployed to assist the communication between a nearby user and its associated base station (BS). We aim to maximize the downlink achievable rate for the user by jointly optimizing the transmit power allocation at the BS and the passive array reflection coefficients at the IRS. Although the formulated problem is non-convex and thus difficult to solve, we propose an efficient algorithm to obtain a high-quality suboptimal solution for it, by alternately optimizing the BS's power allocation and the IRS's passive array coefficients in an iterative manner, along with a customized method for the initialization. Simulation results show that the proposed design significantly improves the OFDM link rate performance as compared to the cases without the IRS or with other heuristic IRS designs.
This paper studies a novel mobile relaying technique, where relays of high mobility are employed to assist the communications from source to destination. By exploiting the predictable channel variations introduced by relay mobility, we study the throughput maximization problem in a mobile relaying system via dynamic rate and power allocations at the source and relay. An optimization problem is formulated for a finite time horizon, subject to an information-causality constraint, which results from the data buffering employed at the relay. It is found that the optimal power allocations across the different time slots follow a "stair-case" water filling (WF) structure, with non-increasing and non-decreasing water levels at the source and relay, respectively. For the special case where the relay moves unidirectionally from source to destination, the optimal power allocations reduce to the conventional WF with constant water levels. Numerical results show that with appropriate trajectory design, mobile relaying is able to achieve tremendous throughput gain over the conventional static relaying.
Relaying is an effective technique to achieve reliable wireless connectivity in harsh communication environment. However, most of the existing relaying schemes are based on relays with fixed locations, or \emph{static relaying}. In this paper, we consider a novel \emph{mobile relaying} technique, where the relay nodes are assumed to be capable of moving at high speed. Compared to static relaying, mobile relaying offers a new degree of freedom for performance enhancement via careful relay trajectory design. We study the throughput maximization problem in mobile relaying systems by optimizing the source/relay transmit power along with the relay trajectory, subject to practical mobility constraints (on the relay speed and initial/final relay locations), as well as the \emph{information-causality constraint} at the relay owing to its decode-store-and-forward (DSF) strategy. It is shown that for fixed relay trajectory, the throughput-optimal source/relay power allocations over time follow a "staircase" water filling (WF) structure, with \emph{non-increasing} and \emph{non-decreasing} water levels at the source and relay, respectively. On the other hand, with given power allocations, the throughput can be further improved by optimizing the relay trajectory via successive convex optimization. An iterative algorithm is thus proposed to optimize the power allocations and relay trajectory alternately. Furthermore, for the special case with free initial and final relay locations, the jointly optimal power allocation and relay trajectory are derived. Numerical results show that by optimizing the trajectory of the relay and power allocations adaptive to its induced channel variation, mobile relaying is able to achieve significant throughput gains over the conventional static relaying.
Channel estimation is a practical challenge for intelligent reflecting surface (IRS) aided wireless communication. As the number of IRS reflecting elements or IRS-aided users increases, the channel training overhead becomes excessively high, which results in long delay and low throughput in data transmission. To tackle this challenge, we propose in this paper a new anchor-assisted channel estimation approach, where two anchor nodes, namely A1 and A2, are deployed near the IRS for facilitating its aided base station (BS) in acquiring the cascaded BS-IRS-user channels required for data transmission. Specifically, in the first scheme, the partial channel state information (CSI) on the element-wise channel gain square of the common BS-IRS link for all users is first obtained at the BS via the anchor-assisted training and feedback. Then, by leveraging such partial CSI, the cascaded BS-IRS-user channels are efficiently resolved at the BS with additional training by the users. While in the second scheme, the BS-IRS-A1 and A1-IRS-A2 channels are first estimated via the training by A1. Then, with additional training by A2, all users estimate their individual cascaded A2-IRS-user channels simultaneously. Based on the CSI fed back from A2 and all users, the BS resolves the cascaded BS-IRS-user channels efficiently. In both schemes, the channels among the fixed BS, IRS, and two anchors are estimated in a large timescale, which greatly reduces the real-time training overhead. Simulation results demonstrate that our proposed anchor-assisted channel estimation schemes achieve superior performance as compared to existing IRS channel estimation schemes, under various practical setups. In addition, the first proposed scheme outperforms the second one when the number of antennas at the BS is sufficiently large, and vice versa.
Unmanned aerial vehicles (UAVs) are expected to be an important new class of users in the fifth generation (5G) and beyond 5G cellular networks. In particular, there are emerging UAV applications such as aerial photograph and data relaying that require high-speed communications between the UAVs and the ground base stations (GBSs). Due to the high UAV altitude, the strong line-of-sight (LoS) links generally dominate the channels between the UAVs and GBSs, which brings both opportunities and challenges in the design of future wireless networks supporting both terrestrial and aerial users. Although each UAV can associate with more GBSs for communication as compared to terrestrial users thanks to the LoS-dominant channels, it also causes/suffers more severe interference to/from the terrestrial communications in the uplink/downlink. This paper studies the uplink communication from a multi-antenna UAV to a set of GBSs within its signal coverage by considering a practical yet challenging scenario when the number of antennas at the UAV is smaller than that of co-channel GBSs. To achieve high-rate transmission yet avoid interfering with any of the existing terrestrial communications at the co-channel GBSs, we propose a novel multi-beam transmission strategy by exploiting the nonorthogonal multiple access (NOMA) technique. Specifically, the UAV sends each data stream to a selected subset of the GBSs, which can decode the UAV's signals and then cancel them before decoding the messages of their served terrestrial users, and in the meanwhile nulls its interference at the other GBSs via zero-forcing (ZF) beamforming. To draw essential insight, we first characterize in closed-form the degrees-of-freedom (DoF) achievable for the UAV's sum-rate maximization under the proposed strategy. Then, we propose an efficient algorithm to jointly optimize the number of UAV data streams, the data stream-GBS association, and the transmit beamforming to maximize the UAV's transmit rate subject to the interference avoidance constraints for protecting the terrestrial users. Numerical examples are provided to verify the effectiveness of the proposed NOMA-based multi-beam transmission strategy.
