Enabling cellular access for unmanned aerial vehicles (UAVs) is a practically appealing solution to realize their high-quality communications with the ground for ensuring safe and efficient operations. In this paper, we study the trajectory design for a cellular-connected UAV that needs to fly from given initial to final locations, while communicating with the ground base stations (GBSs) subject to a minimum signal-to-noise ratio (SNR) requirement along its flight. However, due to various practical considerations such as GBSs' locations and coverage range as well as UAV's trajectory and mobility constraints, the SNR target may not be met at certain time periods during the flight, each termed as an outage duration. In this paper, we first propose a general outage cost function in terms of outage durations in the flight, which includes the two commonly used metrics, namely total outage duration and maximum outage duration as special cases. Based on it, we formulate a UAV trajectory optimization problem to minimize its mission completion time, subject to a constraint on the maximum tolerable outage cost. To tackle this challenging (non-convex) optimization problem, we first transform it into a tractable form and thereby reveal some useful properties of the optimal trajectory solution. Based on these properties, we further simplify the problem and propose efficient algorithms to check its feasibility and obtain optimal as well as low-complexity suboptimal solutions for it by leveraging graph theory and convex optimization techniques. Numerical results show that our proposed trajectory designs outperform that by the conventional method of dynamic programming, in terms of both performance and complexity.
This paper studies unmanned aerial vehicle (UAV)-enabled wireless communication, where a rotary-wing UAV is dispatched to communicate with multiple ground nodes (GNs). We aim to minimize the total UAV energy consumption, including both propulsion energy and communication related energy, while satisfying the communication throughput requirement of each GN. To this end, we first derive a closed-form propulsion power consumption model for rotary-wing UAVs, and then formulate the energy minimization problem by jointly optimizing the UAV trajectory and communication time allocation among GNs, as well as the total mission completion time. The problem is difficult to be optimally solved, as it is non-convex and involves infinitely many variables over time. To tackle this problem, we first consider the simple fly-hover-communicate design, where the UAV successively visits a set of hovering locations and communicates with one corresponding GN while hovering at each location. For this design, we propose an efficient algorithm to optimize the hovering locations and durations, as well as the flying trajectory connecting these hovering locations, by leveraging the travelling salesman problem with neighborhood and convex optimization techniques. Next, we consider the general case, where the UAV also communicates while flying. We propose a new path discretization method to transform the original problem into a discretized equivalent with a finite number of optimization variables, for which we obtain a high-quality suboptimal solution by applying the successive convex approximation technique. The numerical results show that the proposed designs significantly outperform the benchmark schemes.
Opportunistic spectrum access (OSA) is a key technique enabling the secondary users (SUs) in a cognitive radio (CR) network to transmit over the "spectrum holes" unoccupied by the primary users (PUs). In this paper, we focus on the OSA design in the presence of reactive PUs, where PU's access probability in a given channel is related to SU's past access decisions. We model the channel occupancy of the reactive PU as a 4-state discrete-time Markov chain. We formulate the optimal OSA design for SU throughput maximization as a constrained finite-horizon partially observable Markov decision process (POMDP) problem. We solve this problem by first considering the conventional short-term conditional collision probability (SCCP) constraint. We then adopt a long-term PU throughput (LPUT) constraint to effectively protect the reactive PU transmission. We derive the structure of the optimal OSA policy under the LPUT constraint and propose a suboptimal policy with lower complexity. Numerical results are provided to validate the proposed studies, which reveal some interesting new tradeoffs between SU throughput maximization and PU transmission protection in a practical interaction scenario.
Intelligent reflecting surface (IRS) is a cost-effective solution for achieving high spectrum and energy efficiency in future wireless communication systems by leveraging massive low-cost passive elements that are able to reflect the signals with adjustable phase shifts. Prior works on IRS mostly consider continuous phase shifts at each reflecting element, which however, is practically difficult to realize due to the hardware limitation. In contrast, we study in this paper an IRS-aided wireless network, where an IRS with only a finite number of phase shifts at each element is deployed to assist in the communication from a multi-antenna access point (AP) to a single-antenna user. We aim to minimize the transmit power at the AP by jointly optimizing the continuous transmit beamforming at the AP and discrete reflect beamforming at the IRS, subject to a given signal-to-noise ratio (SNR) constraint at the user receiver. We first propose a suboptimal and low-complexity solution to the problem by applying the alternating optimization technique. Then, we analytically show that as compared to the ideal case with continuous phase shifts, the IRS with discrete phase shifts achieves the same squared power gain in terms of asymptotically large number of reflecting elements, while a constant performance loss is incurred that depends only on the number of phase-shift levels. Simulation results verify our analytical result as well as the effectiveness of our proposed design as compared to different benchmark schemes.
Intelligent reflecting surface (IRS) has emerged as a cost-effective solution to enhance wireless communication performance via passive signal reflection. Existing works on IRS have mainly focused on investigating IRS's passive beamforming/reflection design to boost the communication rate for users assuming that their channel state information (CSI) is fully or partially known. However, how to exploit IRS to improve the wireless transmission reliability without any CSI, which is typical in high-mobility/delay-sensitive communication scenarios, remains largely open. In this paper, we study a new IRS-aided communication system with the IRS integrated to its aided access point (AP) to achieve both functions of transmit diversity and passive beamforming simultaneously. Specifically, we first show an interesting result that the IRS's passive beamforming gain in any direction is invariant to the common phase-shift applied to all of its reflecting elements. Accordingly, we design the common phase-shift of IRS elements to achieve transmit diversity at the AP side without the need of any CSI of the users. In addition, we propose a practical method for the users to estimate the CSI at the receiver side for information decoding. Meanwhile, we show that the conventional passive beamforming gain of IRS can be retained for the other users with their CSI known at the AP. Furthermore, we derive the asymptotic performance of both IRS-aided transmit diversity and passive beamforming in closed-form, by considering the large-scale IRS with an infinite number of elements. Numerical results validate our analysis and show the performance gains of the proposed IRS-aided simultaneous transmit diversity and passive beamforming scheme over other benchmark schemes.
A millimeter-wave (MMW) windmill-shaped magnetoelectric-dipole antenna with broadband circular polarization and wide axial-ratio (AR) beamwidth is presented. Good circular polarization performances of the proposed antenna are contributed to excellent performances of the magnetoelectric-dipole, the benefit of windmill shape and the sequential feed scheme by a circular microstrip line. Simulation results show that the antenna obtains a wide impedance bandwidth of 58.8% over the band 30～55 GHz, a stable 3-dB AR bandwidth of 30% across the band 34～46 GHz. And the gain is higher than 6 dBic within the AR bandwidth, with the highest gain of 9.2 dBic at 45 GHz. In addition, a unidirectional and stable pattern has also been achieved.
Multi-antenna precoding effectively mitigates the interference in wireless networks. However, the resultant performance gains can be significantly compromised in practice if the precoder design fails to account for the inaccuracy in the channel state information (CSI) feedback. This paper addresses this issue by considering finite-rate CSI feedback from receivers to their interfering transmitters in the two-user multiple-input-multiple-output (MIMO) interference channel, called cooperative feedback, and proposing a systematic method for designing transceivers comprising linear precoders and equalizers. Specifically, each precoder/equalizer is decomposed into inner and outer components for nulling the cross-link interference and achieving array gain, respectively. The inner precoders/equalizers are further optimized to suppress the residual interference resulting from finite-rate cooperative feedback. Furthermore, the residual interference is regulated by additional scalar cooperative feedback signals that are designed to control transmission power using different criteria including fixed interference margin and maximum sum throughput. Finally, the required number of cooperative precoder feedback bits is derived for limiting the throughput loss due to precoder quantization.
This paper considers wireless downlink transmission when multiple antennas are employed at both the base station (BS) and the mobile terminals. Assorted dynamic transmission schemes based on TDMA and SDMA are described and their associated system throughput limits are compared, under different assumptions on the availability of channel side information (CSI) at the BS. The trade-off between scheme complexity and system throughout is discussed.
Unmanned aerial vehicles (UAVs) have found numerous applications and are expected to bring fertile business opportunities in the next decade. Among various enabling technologies for UAVs, wireless communication is essential and has drawn significantly growing attention in recent years. Compared to the conventional terrestrial communications, UAVs' communications face new challenges due to their high altitude above the ground and great flexibility of movement in the three-dimensional (3D) space. Several critical issues arise, including the line-of-sight (LoS) dominant UAV-ground channels and resultant strong aerial-terrestrial network interference, the distinct communication quality of service (QoS) requirements for UAV control messages versus payload data, the stringent constraints imposed by the size, weight and power (SWAP) limitations of UAVs, as well as the exploitation of the new design degree of freedom (DoF) brought by the highly controllable 3D UAV mobility. In this paper, we give a tutorial overview of the recent advances in UAV communications to address the above issues, with an emphasis on how to integrate UAVs into the forthcoming fifth-generation (5G) and future cellular networks. In particular, we partition our discussions into two promising research and application frameworks of UAV communications, namely UAV-assisted wireless communications and cellular-connected UAVs,where UAVs serve as aerial communication platforms and users, respectively. Furthermore, we point out promising directions for future research and investigation.
In this paper, we study a cellular-enabled unmanned aerial vehicle (UAV) communication system consisting of one UAV and multiple ground base stations (GBSs). The UAV has a mission of flying from an initial location to a final location, during which it needs to maintain reliable wireless connection with the cellular network by associating with one of the GBSs at each time instant. We aim to minimize the UAV mission completion time by optimizing its trajectory, subject to a quality of connectivity constraint of the GBS-UAV link specified by a minimum received signal-to-noise ratio (SNR) target, which needs to be satisfied throughout the mission. This problem is non-convex and difficult to be optimally solved. We first propose an effective approach to check its feasibility based on graph connectivity verification. Then, by examining the GBS-UAV association sequence during the UAV mission, we obtain useful insights on the optimal UAV trajectory, based on which an efficient algorithm is proposed to find an approximate solution to the trajectory optimization problem by leveraging techniques in convex optimization and graph theory. Numerical results show that our proposed trajectory design achieves near-optimal performance.
This paper considers a cooperative and cognitive radio (CCR) system where two secondary users (SUs) exchange their information through a "cognitive" relay station (RS) via the two-way relaying. The SUs share the same spectrum with a primary user (PU) while maintaining the interference power at the PU under a certain level. In order to enhance the achievable sum rate, the cognitive RS exploits the channel state information (CSI) of channel links from the RS to PU and from the RS to SUs to determine the relay beamforming (BF) and the transmit power levels for the RS and SUs. The structures of the optimal relay BF and suboptimal BF schemes based on the subspace projection are presented. Both orthogonal and non-orthogonal projection are considered. In addition, power control algorithms are proposed to satisfy the transmit power constraints as well as the interference power constraints. Numerical results are provided to compare the performance of the optimal relay BF with that of the suboptimal ones and also to justify the benefit of the optimal power allocation.
