The informatization of cities has been further promoted, and the construction of smart cities supported by technological innovation has been upgraded. The financial industry and data are closely related. Whether the financial industry can make good use of new information technology is the key to its successful transformation. The development of smart cities has a significant effect on the development of people's livelihood, the process of urbanization, the use of technology, the solution of urban problems, and the improvement of economic levels. This also provides a good choice for the development of cities in each country. For better development, it needs technical support. Therefore, it is very important to improve the technical level. This research mainly discusses the risk assessment and regulation algorithms of financial technology platforms in smart cities. This study divides the risk decision channels into two paths based on the smart city theory, considers the internal risk factors and external risk factors of the robo-advisory service platform from the three perspectives of platform characteristics, corporate characteristics, and investor characteristics and exploring the construction of a robo-advisory service platform risk prediction model based on the machine learning perspective. The design and implementation of a personalized financial investment prototype system, a Python-based web development framework Django, and a variety of toolkits have realized a B/S architecture robo-advisor. Among them, the function of buying and selling ETF and the trend recording function after buying are realized by simulating the transaction data collected by the data collection module. The study found that the key potential characteristics that constitute platform risks are mainly the listing year of the background company, the age of the platform, the investment threshold, and the search index. To a certain extent, this provides data support for investors and regulatory authorities to evaluate platform capabilities and platform selection. Investors should comprehensively consider platform qualifications when making platform decisions and pay attention to information such as the age of listing of companies with platform background, platform age, and investment thresholds. Only when the quality of people is improved, the quality of the population of this city improves, so that the development of the city has a broad room for growth. The accuracy of the similar formula calculation method in the big data proposed in this study reached 88%. This research provides new ideas for perfecting the black box regulatory system of robo-advisory algorithms.
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.
In this paper, we study a new intelligent refracting surface (IRS)-assisted high-mobility communication with the IRS deployed in a high-speed moving vehicle to assist its passenger's communication with a static base station (BS) on the roadside. The vehicle's high Doppler frequency results in a fast fading channel between the BS and the passenger/user, which renders channel estimation for the IRS with a large number of refracting elements a more challenging task as compared to the conventional case with low-mobility users only. In order to mitigate the Doppler effect and reap the full IRS passive beamforming gain with low training overhead, we propose a new and efficient transmission protocol to execute channel estimation and IRS refraction design for data transmission. Specifically, by exploiting the quasi-static channel between the IRS and user both moving at the same high speed, we first estimate the cascaded BS-IRS-user channel with the Doppler effect compensated. Then, we estimate the instantaneous BS-user fast fading channel (without IRS refraction) and tune the IRS refraction over time accordingly to align the cascaded channel with the BS-user direct channel, thus maximizing the IRS's passive beamforming gain as well as converting their combined channel from fast to slow fading. Simulation results show the effectiveness of the proposed channel estimation scheme and passive beamforming design as compared to various benchmark schemes.
In this paper, we consider a mmWave massive multiple-input multiple-output (MIMO) communication system with one static base station (BS) serving a fast-moving user, both equipped with a very large array. The transmitted signal arrives at the user through multiple paths, each with a different angle of-arrival and hence Doppler frequency offset, thus resulting in a fast time-varying multipath fading MIMO channel. In order to mitigate the Doppler-induced channel aging for reduced pilot overhead, we propose a new angular-domain selective channel tracking and Doppler compensation scheme at the user side. We formulate the joint estimation of partial angular-domain channel and DFO parameters as a dynamic compressive sensing problem. Then we propose a Doppler-aware-dynamic variational Bayesian inference (DD-VBI) algorithm to solve this problem efficiently. Finally, we propose a practical DFO compensation scheme which selects the dominant paths of the fast time-varying channel for DFO compensation and thereby converts it into a slow time-varying effective channel. Compared with the existing methods, the proposed scheme can enjoy the huge array gain provided by the massive MIMO and also balance the tradeoff between the CSI signaling overhead and spatial multiplexing gain. Simulation results verify the advantages of the proposed scheme over various baseline schemes.
This paper studies a new and challenging wireless surveillance problem where a legitimate monitor attempts to eavesdrop two suspicious communication links simultaneously. To facilitate concurrent eavesdropping, our multi-antenna legitimate monitor employs a proactive eavesdropping via jamming approach, by selectively jamming suspicious receivers to lower the transmission rates of the target links. In particular, we are interested in characterizing the achievable eavesdropping rate region for the minimum-mean-squared-error (MMSE) receiver case, by optimizing the legitimate monitor's jamming transmit covariance matrix subject to its power budget. As the monitor cannot hear more than what suspicious links transmit, the achievable eavesdropping rate region is essentially the intersection of the achievable rate region for the two suspicious links and that for the two eavesdropping links. The former region can be purposely altered by the monitor's jamming transmit covariance matrix, whereas the latter region is fixed when the MMSE receiver is employed. Therefore, we first analytically characterize the achievable rate region for the two suspicious links via optimizing the jamming transmit covariance matrix and then obtain the achievable eavesdropping rate region for the MMSE receiver case. In addition, we also consider the MMSE with successive interference cancellation (MMSE-SIC) receiver case and characterize the corresponding achievable eavesdropping rate region by jointly optimizing the time-sharing factor between different decoding orders. Furthermore, extensions to the imperfect channel state information case and the more than two suspicious links scenario are also examined. Finally, numerical results are provided to corroborate our analysis and evaluate the eavesdropping performance.
Simultaneous wireless information and power transfer (SWIPT) has recently drawn significant interests for its dual use of radio signals to provide wireless data and energy access at the same time. However, a challenging secrecy communication issue arises as the messages sent to the information receivers (IRs) may be eavesdropped by the energy receivers (ERs), which are presumed to harvest energy only from the received signals. To tackle this problem, we propose in this paper an artificial noise (AN) aided transmission scheme to facilitate the secrecy information transmission to IRs and yet meet the energy harvesting requirement for ERs, under the assumption that the AN can be cancelled at IRs but not at ERs. Specifically, the proposed scheme splits the transmit power into two parts, to send the confidential message to the IR and an AN to interfere with the ER, respectively. Under a simplified three-node wiretap channel setup, the transmit power allocations and power splitting ratios over fading channels are jointly optimized to minimize the outage probability for delay-limited secrecy information transmission, or to maximize the average rate for no-delay-limited secrecy information transmission, subject to a combination of average and peak power constraints at the transmitter as well as an average energy harvesting constraint at the ER. Both the secrecy outage probability minimization and average rate maximization problems are shown to be non-convex, for each of which we propose the optimal solution based on the dual decomposition as well as suboptimal solution based on the alternating optimization. Furthermore, two benchmark schemes are introduced for comparison. Finally, the performances of proposed schemes are evaluated by simulations in terms of various trade-offs for wireless (secrecy) information versus energy transmissions.
This paper studies wireless spectrum sharing between a primary radio (PR) link and a secondary radio or so-called cognitive radio (CR) link. Assuming that the PR adapts its transmit power and/or rate upon receiving an interference signal from the CR, and such transmit adaptations are observable by the CR, this phenomenon leads to a new form of feedback from PR to CR, termed as hidden PR feedback, whereby the CR obtains the information of PR transmit adaptations without the need of a dedicated feedback channel from the PR. This interesting interaction between PR and CR is investigated in this paper to design new learning and transmission strategies for the CR, named as active learning and supervised transmission, respectively. For the active learning, this paper shows that via exploiting the hidden PR feedback, the CR is able to estimate the channel gain from the CR transmitter to the PR receiver, which is essential for the CR to predict the resulting PR performance degradation as a function of the CR transmit power level. Moreover, this paper shows that the hidden PR feedback enables the CR to design a supervised transmission to effectively control the "feedback" interference from the PR by properly setting the CR transmit power level. This paper analyzes the CR achievable rate with/without the operation of canceling the PR feedback interference at the CR receiver.
Prior studies on Intelligent Reflecting Surface (IRS) have mostly assumed perfect channel state information (CSI) available for designing the IRS passive beamforming as well as the continuously adjustable phase shift at each of its reflecting elements, which, however, have simplified two challenging issues for implementing IRS in practice, namely, its channel estimation and passive beamforming designs both under the constraint of discrete phase shifts. To address them, we consider in this paper an IRS-aided single-user communication system with discrete phase shifts and design the IRS training reflection matrix for channel estimation as well as the passive beamforming for data transmission, both subject to the constraint of discrete phase shifts. We show that the training reflection matrix design for discrete phase shifts greatly differs from that for continuous phase shifts, and thus the corresponding passive beamforming should be optimized by taking into account the correlated channel estimation error due to discrete phase shifts. Specifically, we consider a practical block-based transmission, where each block has a finite (insufficient) number of training symbols for channel estimation. A novel hierarchical training reflection design is proposed to progressively estimate IRS elements' channels over multiple blocks by exploiting IRS-elements grouping and partition. Based on the resolved IRS channels in each block, we further design the progressive passive beamforming at the IRS with discrete phase shifts to improve the achievable rate for data transmission over the blocks.
In this letter, we consider the fundamental problem of jointly designing the transmit beamformers and the phase-shifts of the intelligent reflecting surface (IRS) / reconfigurable intelligent surface (RIS) to minimize the transmit power, subject to quality-of-service constraints at individual users in an IRS-assisted multiuser multiple-input single-output downlink communication system. In particular, we propose a new successive convex approximation based second-order cone programming approach in which all the optimization variables are simultaneously updated in each iteration. Our proposed scheme achieves superior performance compared to state-of-the-art benchmark solutions. In addition, the complexity of the proposed scheme is $O(N_{\mathrm s}^{3.5})$, while that of state-of-the-art benchmark schemes is $O(N_{\mathrm s}^{7})$, where $N_{\mathrm s}$ denotes the number of reflecting elements at the IRS.
Wireless localization and sensing technologies are essential in modern wireless networks, supporting applications in smart cities, the Internet of Things (IoT), and autonomous systems. High-performance localization and sensing systems are critical for both network efficiency and emerging intelligent applications. Integrating channel state information (CSI) with deep learning has recently emerged as a promising solution. Recent works have leveraged the spatial diversity of multiple input multiple output (MIMO) systems and the frequency granularity of orthogonal frequency division multiplexing (OFDM) waveforms to improve spatial resolution. Nevertheless, the joint modeling of localization and sensing under the high-dimensional CSI characteristics of MIMO-OFDM systems remains insufficiently investigated. This work aims to jointly model and optimize localization and sensing tasks to harness their potential synergy. We first formulate localization and sensing as a mixed-integer bilevel deep learning problem and then propose a novel stochastic proximal gradient-based mixed-integer bilevel optimization (SPG-MIBO) algorithm. SPG-MIBO is well-suited for high-dimensional and large-scale datasets, leveraging mini-batch training at each step for computational and memory efficiency. The algorithm is also supported by theoretical convergence guarantees. Extensive experiments on multiple datasets validate its effectiveness and highlight the performance gains from joint localization and sensing optimization.
Intelligent reflecting surface (IRS) is a cost-effective solution for achieving high spectrum and energy efficiency in future wireless networks by leveraging massive low-cost passive elements that are able to reflect the signals with adjustable phase shifts. Prior works on IRS mainly consider continuous phase shifts at reflecting elements, which are practically difficult to implement 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 multiple single-antenna users. We aim to minimize the transmit power at the AP by jointly optimizing the continuous transmit precoding at the AP and the discrete reflect phase shifts at the IRS, subject to a given set of minimum signal-to-interference-plus-noise ratio (SINR) constraints at the user receivers. The considered problem is shown to be a mixed-integer non-linear program (MINLP) and thus is difficult to solve in general. To tackle this problem, we first study the single-user case with one user assisted by the IRS and propose both optimal and suboptimal algorithms for solving it. Besides, 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 proportional power loss is incurred that depends only on the number of phase-shift levels. The proposed designs for the single-user case are also extended to the general setup with multiple users among which some are aided by the IRS. Simulation results verify our performance analysis as well as the effectiveness of our proposed designs as compared to various benchmark schemes.
This paper studies an unmanned aerial vehicle (UAV)-enabled wireless powered communication network (WPCN), in which a UAV is dispatched as a mobile access point (AP) to serve a set of ground users periodically. The UAV employs the radio frequency (RF) wireless power transfer (WPT) to charge the users in the downlink, and the users use the harvested RF energy to send independent information to the UAV in the uplink. Unlike the conventional WPCN with fixed APs, the UAV-enabled WPCN can exploit the mobility of the UAV via trajectory design, jointly with the wireless resource allocation optimization, to maximize the system throughput. In particular, we aim to maximize the uplink common (minimum) throughput among all ground users over a finite UAV's flight period, subject to its maximum speed constraint and the users' energy neutrality constraints. The resulted problem is non-convex and thus difficult to be solved optimally. To tackle this challenge, we first consider an ideal case without the UAV's maximum speed constraint, and obtain the optimal solution to the relaxed problem. The optimal solution shows that the UAV should successively hover above a finite number of ground locations for downlink WPT, as well as above each of the ground users for uplink communication. Next, we consider the general problem with the UAV's maximum speed constraint. Based on the above multi-location-hovering solution, we first propose an efficient successive hover-and-fly trajectory design, jointly with the downlink and uplink wireless resource allocation, and then propose a locally optimal solution by applying the techniques of alternating optimization and successive convex programming (SCP). Numerical results show that the proposed UAV-enabled WPCN achieves significant throughput gains over the conventional WPCN with fixed-location AP.
Constant envelope (CE) precoding is an appealing transmission technique which enables the use of highly efficient nonlinear radio frequency (RF) power amplifiers (PAs). For CE precoding in a single-user multiple-input single-output (MISO) channel, a desired constellation is feasible at the receiver if and only if it can be scaled to lie in an annulus, whose boundaries are characterized by the instantaneous channel realization. Therefore, if a fixed receiver constellation is used for CE precoding in fading channel, where the annulus is time-varying, there is a non-zero probability of encountering a channel that makes CE precoding infeasible. To tackle this problem, we study the fixed-rate adaptive receiver constellation design for CE precoding to minimize symbol error rate (SER) in a single-user MISO flat-fading channel with an arbitrary number of antennas at the transmitter. Specifically, this paper proposes an efficient algorithm to find the optimal two-ring amplitude-and-phase shift keying (APSK) constellation that is both feasible and of the maximum minimum Euclidean distance (MED), for any given constellation size and instantaneous channel realization. Numerical results show that by using the optimized adaptive receiver constellation, our proposed scheme achieves significantly improved SER performance than CE precoding with fixed receiver constellation. Furthermore, with the PA efficiency gain achieved by CE precoding, our proposed scheme requires less transmitter power consumption to achieve a desired SER level than linear precoding schemes under the less-stringent average per-antenna power constraint (PAPC).
The peculiar line-of-sight (LoS) propagation in air-to-ground channel provides both opportunities and challenges for the emerging cellular-connected unmanned aerial vehicle (UAV) communications. On one hand, the LoS channels make more cellular base stations (BSs) visible to a UAV as compared to ground users, which leads to a higher macro-diversity gain as compared to the ground users. On the other hand, the LoS channels also render the UAV to generate/receive more severe uplink/downlink interference to/from the BSs, thus requiring more sophisticated inter-cell interference coordination (ICIC) techniques with more BSs involved. To draw essential insight, this paper studies the uplink transmission from a UAV to cellular BSs. To mitigate the UAV's interference effect, we aim to maximize the sum-rate of the UAV and all ground users in its resulted ICIC region by jointly optimizing the UAV's cell association, resource block (RB) allocation, and transmit power. We first propose a centralized ICIC design that achieves the optimal performance. As the centralized ICIC requires global information of the network and substantial information exchange among an excessively large number of BSs, we further propose a decentralized ICIC scheme of significantly lower complexity and overhead for implementation. Specifically, we divide the cellular BSs into clusters, each with a dedicated cluster head for collecting information from its cluster BSs and exchanging information with the UAV by exploiting the LoS-induced macro-diversity. Numerical results show that the proposed decentralized ICIC scheme achieves a performance close to the optimal centralized design, and also outperforms the traditional ICIC scheme for cellular networks with terrestrial interference only.
The rate maximization for the $K$-user interference channels (ICs) has been investigated extensively in the literature. However, the dual problem of minimizing the error probability with given signal modulations and/or data rates of the users is less exploited. In this paper, by utilizing the additional degrees of freedom attained from the improper signaling (versus the conventional proper signaling), we optimize the precoding matrices for the $K$-user single-input single-output (SISO) ICs to achieve minimal pair-wise error probability (PEP) and symbol error rate (SER) with two proposed algorithms, respectively. Compared to conventional proper signaling as well as other state-of-the-art improper signaling designs, our proposed improper signaling schemes achieve notable SER improvement in SISO-ICs under both additive white Gaussian noise (AWGN) channel and cellular system setups. Our study provides another viewpoint for optimizing transmissions in ICs and further justifies the practical benefit of improper signaling in interference-limited communication systems.
