arXiv subjects

Qingqing Wu

Publications and source records attributed to Qingqing Wu.

At least 289 records · Page 16Linked to original sources

arXiv2021

A Comprehensive Overview on 5G-and-Beyond Networks with UAVs: From Communications to Sensing and Intelligence

Due to the advancements in cellular technologies and the dense deployment of cellular infrastructure, integrating unmanned aerial vehicles (UAVs) into the fifth-generation (5G) and beyond cellular networks is a promising solution to achieve safe UAV operation as well as enabling diversified applications with mission-specific payload data delivery. In particular, 5G networks need to support three typical usage scenarios, namely, enhanced mobile broadband (eMBB), ultra-reliable low-latency communications (URLLC), and massive machine-type communications (mMTC). On the one hand, UAVs can be leveraged as cost-effective aerial platforms to provide ground users with enhanced communication services by exploiting their high cruising altitude and controllable maneuverability in three-dimensional (3D) space. On the other hand, providing such communication services simultaneously for both UAV and ground users poses new challenges due to the need for ubiquitous 3D signal coverage as well as the strong air-ground network interference. Besides the requirement of high-performance wireless communications, the ability to support effective and efficient sensing as well as network intelligence is also essential for 5G-and-beyond 3D heterogeneous wireless networks with coexisting aerial and ground users. In this paper, we provide a comprehensive overview of the latest research efforts on integrating UAVs into cellular networks, with an emphasis on how to exploit advanced techniques (e.g., intelligent reflecting surface, short packet transmission, energy harvesting, joint communication and radar sensing, and edge intelligence) to meet the diversified service requirements of next-generation wireless systems. Moreover, we highlight important directions for further investigation in future work.

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arXiv2021

Joint Beamforming Design and Power Splitting Optimization in IRS-Assisted SWIPT NOMA Networks

This paper proposes a novel network framework of intelligent reflecting surface (IRS)-assisted simultaneous wireless information and power transfer (SWIPT) non-orthogonal multiple access (NOMA) networks, where IRS is used to enhance the NOMA performance and the wireless power transfer (WPT) efficiency of SWIPT. We formulate a problem of minimizing base station (BS) transmit power by jointly optimizing successive interference cancellation (SIC) decoding order, BS transmit beamforming vector, power splitting (PS) ratio and IRS phase shift while taking into account the quality-of-service (QoS) requirement and energy harvested threshold of each user. The formulated problem is non-convex optimization problem, which is difficult to solve it directly. Hence, a two-stage algorithm is proposed to solve the above-mentioned problem by applying semidefinite relaxation (SDR), Gaussian randomization and successive convex approximation (SCA). Specifically, after determining SIC decoding order by designing IRS phase shift in the first stage, we alternately optimize BS transmit beamforming vector, PS ratio, and IRS phase shift to minimize the BS transmit power. Numerical results validate the effectiveness of our proposed optimization algorithm in reducing BS transmit power compared to other baseline algorithms. Meanwhile, compared with non-IRS-assisted network, the IRS-assisted SWIPT NOMA network can decrease BS transmit power by 51.13\%.

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arXiv2021

Throughput Maximization for IRS-Assisted Wireless Powered Hybrid NOMA and TDMA

The high reflect beamforming gain of the intelligent reflecting surface (IRS) makes it appealing not only for wireless information transmission but also for wireless power transfer. In this letter, we consider an IRS-assisted wireless powered communication network, where a base station (BS) transmits energy to multiple users grouped into multiple clusters in the downlink, and the clustered users transmit information to the BS in the manner of hybrid non-orthogonal multiple access and time division multiple access in the uplink. We investigate optimizing the reflect beamforming of the IRS and the time allocation among the BS's power transfer and different user clusters' information transmission to maximize the throughput of the network, and we propose an efficient algorithm based on the block coordinate ascent, semidefinite relaxation, and sequential rank-one constraint relaxation techniques to solve the resultant problem. Simulation results have verified the effectiveness of the proposed algorithm and have shown the impact of user clustering setup on the throughput performance of the network.

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arXiv2021

A Novel Wireless Communication Paradigm for Intelligent Reflecting Surface Based Symbiotic Radio Systems

This paper investigates a novel intelligent reflecting surface (IRS)-based symbiotic radio (SR) system architecture consisting of a transmitter, an IRS, and an information receiver (IR). The primary transmitter communicates with the IR and at the same time assists the IRS in forwarding information to the IR. Based on the IRS's symbol period, we distinguish two scenarios, namely, commensal SR (CSR) and parasitic SR (PSR), where two different techniques for decoding the IRS signals at the IR are employed. We formulate bit error rate (BER) minimization problems for both scenarios by jointly optimizing the active beamformer at the base station and the phase shifts at the IRS, subject to a minimum primary rate requirement. Specifically, for the CSR scenario, a penalty-based algorithm is proposed to obtain a high-quality solution, where semi-closed-form solutions for the active beamformer and the IRS phase shifts are derived based on Lagrange duality and Majorization-Minimization methods, respectively. For the PSR scenario, we apply a bisection search-based method, successive convex approximation, and difference of convex programming to develop a computationally efficient algorithm, which converges to a locally optimal solution. Simulation results demonstrate the effectiveness of the proposed algorithms and show that the proposed SR techniques are able to achieve a lower BER than benchmark schemes.

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arXiv2021

Securing NOMA Networks by Exploiting Intelligent Reflecting Surface

This paper investigates the security enhancement of an intelligent reflecting surface (IRS) assisted non-orthogonal multiple access (NOMA) network, where a distributed IRS enabled NOMA transmission framework is proposed to serve users securely in the presence of a passive eavesdropper. Considering that eavesdropper's instantaneous channel state information (CSI) is challenging to acquire in practice, we utilize secrecy outage probability (SOP) as the security metric. A problem of maximizing the minimum secrecy rate among users, subject to the successive interference cancellation (SIC) decoding constraints and SOP constraints, by jointly optimizing transmit beamforming at the BS and phase shifts of IRSs, is formulated. For special case with a single-antenna BS, we derive the exact closed-form SOP expressions and propose a novel ring-penalty based successive convex approximation (SCA) algorithm to design power allocation and phase shifts jointly. While for the more general and challenging case with a multi-antenna BS, we adopt the Bernstein-type inequality to approximate the SOP constraints by a deterministic convex form. To proceed, an efficient alternating optimization (AO) algorithm is developed to solve the considered problem. Numerical results validate the advantages of the proposed algorithms over the baseline schemes. Particularly, two interesting phenomena on distributed IRS deployment are revealed: 1) the secrecy rate peak is achieved only when distributed IRSs share the reflecting elements equally; and 2) the distributed IRS deployment does not always outperform the centralized IRS deployment, due to the tradeoff between the number of IRSs and the reflecting elements equipped at each IRS.

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arXiv2021

Intelligent Reflecting Surface Enhanced D2D Cooperative Computing

This paper investigates a device-to-device (D2D) cooperative computing system, where an user can offload part of its computation task to nearby idle users with the aid of an intelligent reflecting surface (IRS). We propose to minimize the total computing delay via jointly optimizing the computation task assignment, transmit power, bandwidth allocation, and phase beamforming of the IRS. To solve the formulated problem, we devise an alternating optimization algorithm with guaranteed convergence. In particular, the task assignment strategy is derived in closed-form expression, while the phase beamforming is optimized by exploiting the semi-definite relaxation (SDR) method. Numerical results demonstrate that the IRS enhanced D2D cooperative computing scheme can achieve a much lower computing delay as compared to the conventional D2D cooperative computing strategy.

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arXiv2021

Max-Min Fair Energy-Efficient Beamforming Design for Intelligent Reflecting Surface-Aided SWIPT Systems with Non-linear Energy Harvesting Model

This paper considers an intelligent reflecting sur-face (IRS)-aided simultaneous wireless information and power transfer (SWIPT) network, where multiple users decode data and harvest energy from the transmitted signal of a transmit-ter. The proposed design framework exploits the cost-effective IRS to establish favorable communication environment to improve the fair energy efficient. In particular, we study the max-min energy efficiency (EE) of the system by jointly designing the transmit information and energy beamforming at the base station (BS), phase shifts at the IRS, as well as the power splitting (PS) ratio at all users subject to the minimum rate, minimum harvested energy, and transmit power constraints. The formulated problem is non-convex and thus challenging to be solved. We propose two algorithms namely penalty-based and inner approximation (IA)-based to handle the non-convexity of the optimization problem. As such, we divide the original problem into two sub-problems and apply the alternating optimization (AO) algorithm for both proposed algorithms to handle it iteratively. In particular, in the penalty-based algorithm for the first sub-problem, the semi-definite relaxation (SDR) technique, difference of convex functions (DC) programming, majorization-minimization (MM) approach, and fractional programming theory are exploited to transform the non-convex optimization problem into a convex form that can be addressed efficiently. For the second sub-problem, a penalty-based approach is proposed to handle the optimization on the phase shifts introduced by the IRS with the proposed algorithms. For the IA-based method, we optimize jointly beamforming vectors and phase shifts while the PS ratio is solved optimally in the first sub-problem...

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arXiv2021

Intelligent Reflecting Surface based Passive Information Transmission: A Symbol-Level Precoding Approach

Intelligent reflecting surfaces (IRS) have been proposed as a revolutionary technology owing to its capability of adaptively reconfiguring the propagation environment in a cost-effective and hardware-efficient fashion. While the application of IRS as a passive reflector to enhance the performance of wireless communications has been widely investigated in the literature, using IRS as a passive transmitter recently is emerging as a new concept and attracting steadily growing interest. In this paper, we propose two novel IRS-based passive information transmission systems using advanced symbol-level precoding. One is a standalone passive information transmission system, where the IRS operates as a passive transmitter serving multiple receivers by adjusting its elements to reflect unmodulated carrier signals. The other is a joint passive reflection and information transmission system, where the IRS not only enhances transmissions for multiple primary information receivers (PIRs) by passive reflection, but also simultaneously delivers additional information to a secondary information receiver (SIR) by embedding its information into the primary signals at the symbol level. Two typical optimization problems, i.e., power minimization and quality-of-service (QoS) balancing, are investigated for the proposed IRS-based passive information transmission systems. Simulation results demonstrate the feasibility of IRS-based passive information transmission and the effectiveness of our proposed algorithms, as compared to other benchmark schemes.

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arXiv2021

RIS-Aided Next-Generation High-Speed Train Communications: Challenges, Solutions, and Future Directions

High-speed train (HST) communication is a very challenging scenario in wireless communication systems due to its fast time-varying channels and significant penetration loss. Thanks to the low cost and smart channel reconfiguration ability, reconfigurable intelligent surface (RIS) has been proposed as a promising technology to solve the aforementioned challenges cost-effectively and to achieve high spectral and energy-efficiency in the next-generation HST communication systems. In this article, we first provide a novel RIS-aided HST wireless communication paradigm, including its main challenges and application scenarios. Then, we propose practical solutions are provided to realize efficient signal processing and resource management in the considered system. Finally, future directions of RIS-aided HST communication systems are pointed out to inspire further investigation in future work.

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arXiv2021

Vertical Beamforming in Reconfigurable Intelligent Surface-aided Cognitive Radio Networks

In this letter, we investigate joint application of reconfigurable intelligent surface (RIS) and vertical beamforming in cognitive radio networks (CRN). After properly modeling the network, an optimization problem is formed to jointly design the beamforming vector and tilt angle at the secondary base station (BS) as well as the phase shifts at the RIS with the objective of maximizing spectral efficiency of the secondary network. The optimization problem is non-convex; thus, we propose an efficient solution method for it. Numerical results show that adding a RIS and optimizing the radiation orientation, can significantly improve performance of the CRNs.

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arXiv2021

IRS-Assisted Wireless Powered NOMA: Do We Really Need Different Phase Shifts in DL and UL?

Intelligent reflecting surface (IRS) is a promising technology to improve the performance of wireless powered communication networks (WPCNs) due to its capability to reconfigure signal propagation environments via smart reflection. In particular, the high passive beamforming gain promised by IRS can significantly enhance the efficiency of both downlink wireless power transfer (DL WPT) and uplink wireless information transmission (UL WIT) in WPCNs. Although adopting different IRS phase shifts for DL WPT and UL WIT, i.e., dynamic IRS beamforming, is in principle possible but incurs additional signaling overhead and computational complexity, it is an open problem whether it is actually beneficial. To answer this question, we consider an IRS-assisted WPCN where multiple devices employ a hybrid access point (HAP) to first harvest energy and then transmit information using non-orthogonal multiple access (NOMA). Specifically, we aim to maximize the sum throughput of all devices by jointly optimizing the IRS phase shifts and the resource allocation. To this end, we first prove that dynamic IRS beamforming is not needed for the considered system, which helps reduce the number of IRS phase shifts to be optimized. Then, we propose both joint and alternating optimization based algorithms to solve the resulting problem. Simulation results demonstrate the effectiveness of our proposed designs over benchmark schemes and also provide useful insights into the importance of IRS for realizing spectrally and energy efficient WPCNs.

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arXiv2021

Exploiting Amplitude Control in Intelligent Reflecting Surface Aided Wireless Communication with Imperfect CSI

Intelligent reflecting surface (IRS) is a promising new paradigm to achieve high spectral and energy efficiency for future wireless networks by reconfiguring the wireless signal propagation via passive reflection. To reap the potential gains of IRS, channel state information (CSI) is essential, whereas channel estimation errors are inevitable in practice due to limited channel training resources. In this paper, in order to optimize the performance of IRS-aided multiuser systems with imperfect CSI, we propose to jointly design the active transmit precoding at the access point (AP) and passive reflection coefficients of IRS, each consisting of not only the conventional phase shift and also the newly exploited amplitude variation. First, the achievable rate of each user is derived assuming a practical IRS channel estimation method, which shows that the interference due to CSI errors is intricately related to the AP transmit precoders, the channel training power and the IRS reflection coefficients during both channel training and data transmission. Then, for the single-user case, by combining the benefits of the penalty method, Dinkelbach method and block successive upper-bound minimization (BSUM) method, a new penalized Dinkelbach-BSUM algorithm is proposed to optimize the IRS reflection coefficients for maximizing the achievable data transmission rate subjected to CSI errors; while for the multiuser case, a new penalty dual decomposition (PDD)-based algorithm is proposed to maximize the users' weighted sum-rate. Simulation results are presented to validate the effectiveness of our proposed algorithms as compared to benchmark schemes. In particular, useful insights are drawn to characterize the effect of IRS reflection amplitude control (with/without the conventional phase shift) on the system performance under imperfect CSI.

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arXiv2021

Scoring the Terabit/s Goal:Broadband Connectivity in 6G

This paper explores the road to vastly improving the broadband connectivity in future 6G wireless systems. Different categories of use cases are considered, with peak data rates up to 1 Tbps. Several categories of enablers at the infrastructure, spectrum, and protocol/algorithmic levels are required to realize the intended broadband connectivity goals in 6G. At the infrastructure level, we consider ultra-massive MIMO technology (possibly implemented using holographic radio), intelligent reflecting surfaces, user-centric cell-free networking, integrated access and backhaul, and integrated space and terrestrial networks. At the spectrum level, the network must seamlessly utilize sub-6 GHz bands for coverage and spatial multiplexing of many devices, while higher bands will be mainly used for pushing the peak rates of point-to-point links. Finally, at the protocol/algorithmic level, the enablers include improved coding, modulation, and waveforms to achieve lower latency, higher reliability, and reduced complexity.

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arXiv2021

Spectral Graph Theory Based Resource Allocation for IRS-Assisted Multi-Hop Edge Computing

The performance of mobile edge computing (MEC) depends critically on the quality of the wireless channels. From this viewpoint, the recently advocated intelligent reflecting surface (IRS) technique that can proactively reconfigure wireless channels is anticipated to bring unprecedented performance gain to MEC. In this paper, the problem of network throughput optimization of an IRS-assisted multi-hop MEC network is investigated, in which the phase-shifts of the IRS and the resource allocation of the relays need to be jointly optimized. However, due to the coupling among the transmission links of different hops caused by the utilization of the IRS and the complicated multi-hop network topology, it is difficult to solve the considered problem by directly applying existing optimization techniques. Fortunately, by exploiting the underlying structure of the network topology and spectral graph theory, it is shown that the network throughput can be well approximated by the second smallest eigenvalue of the network Laplacian matrix. This key finding allows us to develop an effective iterative algorithm for solving the considered problem. Numerical simulations are performed to corroborate the effectiveness of the proposed scheme.

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arXiv2021

UAV-Assisted Intelligent Reflecting Surface Symbiotic Radio System

This paper investigates a symbiotic unmanned aerial vehicle (UAV)-assisted intelligent reflecting surface (IRS) radio system, where the UAV is leveraged to help the IRS reflect its own signals to the base station, and meanwhile enhance the UAV transmission by passive beamforming at the IRS. First, we consider the weighted sum bit error rate (BER) minimization problem among all IRSs by jointly optimizing the UAV trajectory, IRS phase shift matrix, and IRS scheduling, subject to the minimum primary rate requirements. To tackle this complicated problem, a relaxation-based algorithm is proposed. We prove that the converged relaxation scheduling variables are binary, which means that no reconstruct strategy is needed, and thus the UAV rate constraints are automatically satisfied. Second, we consider the fairness BER optimization problem. We find that the relaxation-based method cannot solve this fairness BER problem since the minimum primary rate requirements may not be satisfied by the binary reconstruction operation. To address this issue, we first transform the binary constraints into a series of equivalent equality constraints. Then, a penalty-based algorithm is proposed to obtain a suboptimal solution. Numerical results are provided to evaluate the performance of the proposed designs under different setups, as compared with benchmarks.

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arXiv2021

Intelligent reflecting surface enhanced wideband MIMO-OFDM communications: From practical model to reflection optimization

Intelligent reflecting surface (IRS) is envisioned as a revolutionary technology for future wireless communication systems since it can intelligently change radio environment and integrate it into wireless communication optimization. However, most existing works adopted an ideal IRS reflection model, which is impractical and can cause significant performance degradation in realistic wideband systems. To address this issue, we first study the dual phase- and amplitude-squint effect of reflected signals and present a simplified practical IRS reflection model for wideband signals. Then, an IRS enhanced wideband multiuser multi-input single-output orthogonal frequency division multiplexing (MU-MISO-OFDM) system is investigated. We aim to jointly design the transmit beamformer and IRS reflection for the case of using both continuous and discrete phase shifters to maximize the average sum-rate over all subcarriers. By exploiting the relationship between sum-rate maximization and mean square error (MSE) minimization, the original problem is equivalently transformed into a multi-block/variable problem, which can be efficiently solved by the block coordinate descent (BCD) method. Complexity and convergence for both cases are analyzed or illustrated. Simulation results demonstrate that the proposed algorithm can offer significant average sum-rate enhancement compared to that achieved using the ideal IRS reflection model, which confirms the importance of the use of the practical model for the design of wideband systems.

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arXiv2020

Intelligent Reflecting Surface Aided MISO Uplink Communication Network: Feasibility and Power Minimization for Perfect and Imperfect CSI

In this paper, we consider the weighted sum-power minimization under quality-of-service (QoS) constraints in the multi-user multi-input-single-output (MISO) uplink wireless network assisted by intelligent reflecting surface (IRS). We perform a comprehensive investigation on various aspects of this problem. First, when users have sufficient transmit powers, we present a new sufficient condition guaranteeing arbitrary information rate constraints. This result strengthens the feasibility condition in existing literature. Then, we design novel penalty dual decomposition (PDD) based and nonlinear equality constrained alternative direction method of multipliers (neADMM) based solutions to tackle the IRS-dependent-QoS-constraints, which effectively solve the feasibility check and power minimization problems. Besides, we further extend our proposals to the cases where channel status information (CSI) is imperfect and develop an online stochastic algorithm that satisfy QoS constraints stochastically without requiring prior knowledge of CSI errors. Extensive numerical results are presented to verify the effectiveness of our proposed algorithms.

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arXiv2020

Deep Reinforcement Learning Based Intelligent Reflecting Surface for Secure Wireless Communications

In this paper, we study an intelligent reflecting surface (IRS)-aided wireless secure communication system for physical layer security, where an IRS is deployed to adjust its surface reflecting elements to guarantee secure communication of multiple legitimate users in the presence of multiple eavesdroppers. Aiming to improve the system secrecy rate, a design problem for jointly optimizing the base station (BS)'s beamforming and the IRS's reflecting beamforming is formulated given the different quality of service (QoS) requirements and time-varying channel condition. As the system is highly dynamic and complex, and it is challenging to address the non-convex optimization problem, a novel deep reinforcement learning (DRL)-based secure beamforming approach is firstly proposed to achieve the optimal beamforming policy against eavesdroppers in dynamic environments. Furthermore, post-decision state (PDS) and prioritized experience replay (PER) schemes are utilized to enhance the learning efficiency and secrecy performance. Specifically, PDS is capable of tracing the environment dynamic characteristics and adjust the beamforming policy accordingly. Simulation results demonstrate that the proposed deep PDS-PER learning-based secure beamforming approach can significantly improve the system secrecy rate and QoS satisfaction probability in IRS-aided secure communication systems.

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