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Reflection Resource Management for Intelligent Reflecting Surface Aided Wireless Networks

IEEE TRANSACTIONS ON COMMUNICATIONS 2021年第10期69卷 6971-6986页

作者： Gao, Yulan Yong, Chao Xiong, Zehui Zhao, Jun Xiao, Yue Niyato, Dusit Univ Elect Sci & Technol China UESTC Natl Key Lab Sci & Technol Commun Chengdu 611731 Peoples R China Nanyang Technol Univ NTU Sch Comp Sci & Engn Singapore 639798 Singapore Singapore Univ Technol & Design Pillar Informat Syst Technol & Design Singapore 487372 Singapore

In this paper, the adoption of an intelligent reflecting surface (IRS) for multiple user pairs in two-hop networks is investigated. Different from the existing studies on IRS that mainly focused on tuning the reflection coefficients of all elements, we consider the implementation of true reflection resource management (RRM) through the identification of the best triggered module subset. More precisely, the implementation of true RRM builds on the premise of our proposed modular IRS structure consisting of multiple independent and controllable modules. In the context of modular IRS structure, we investigate the signal-to-interference-plus-noise ratio (SINR)-based max-min problem subject to per source terminals (STs) power budgets and module size constraint, via joint triggered module subset identification, transmit power allocation, and the corresponding passive beamforming. Whereas this problem is NP-hard due to the module size constraint, which can be addressed by the convex sparsity-inducing approximation to the hard module size constraint using mixed l(1,F)-norm, where it yields a suitable semidefinite relaxation. Using techniques from separable convex programming, we provide a two-block alternating direction method of multipliers (ADMM) algorithm for the approximated problem. Numerical simulations are used to validate the analysis and assess the performance of the proposed algorithm as a function of the system parameters. Further energy efficiency (EE) performance comparison demonstrates the necessity and meaningfulness of the introduced modular IRS structure. Specifically, for a given network setting, there is an optimal value of the number of triggered modules for system, when the EE is considered.

关键词： Power demand Array signal processing Resource management Quality of service approximation algorithms Phase shifters Wireless networks Intelligent reflecting surface (IRS) transmit power allocation passive beamforming reflection resource management alternating direction and method of multipliers (ADMM) group sparsity

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Top-K Representative Search for Comparative Tree Summarization

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IEEE Transactions on Knowledge and Data Engineering 2025年

作者： Chen, Yuqi Huang, Xin Chen, Bilian Hong Kong Baptist University Hong Kong Xiamen University Department of Automation China

Data summarization aims at utilizing a small-scale summary to represent massive datasets as a whole, which is useful for visualization and information sipped generation. However, most existing studies of hierarchical summarization only work on one single tree by selecting k representative nodes, which neglects an important problem of comparative summarization on two trees. In this paper, given two trees with the same topology structure and different node weights, we aim at finding k representative nodes, where k1 nodes summarize the common relationship between them and k2 nodes highlight significantly different subtrees meanwhile satisfying k1 + k2 = k. To optimize summarization results, we introduce a scaling coefficient for balancing the summary view between two subtrees in terms of similarity and difference. Additionally, we propose a novel definition based on the Hellinger distance to quantify the node distribution difference between two subtrees. We present a greedy algorithm SVDT to find high-quality results with approximation guaranteed in an efficient way. Furthermore, we explore an extension of our comparative summarization to handle two trees with different structures. Extensive experiments demonstrate the effectiveness and efficiency of our SVDT algorithm against existing summarization competitors. © 1989-2012 IEEE.

关键词： approximation algorithms

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Efficient Hardware Implementation of DNN-Based Speech Enhancement Algorithm With Precise Sigmoid Activation Function

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IEEE TRANSACTIONS ON CIRCUITS AND SYSTEMS II-EXPRESS BRIEFS 2021年第11期68卷 3461-3465页

作者： Chiluveru, Samba Raju Gyanendra Chunarkar, Snehit Tripathy, Manoj Kaushik, Brajesh Kumar Indian Inst Technol Roorkee Instrumentat & Signal Proc Grp Dept Elect Engn Roorkee 247667 Uttar Pradesh India Indian Inst Technol Roorkee Microelect & VLSI Grp Dept Elect & Commun Engn Roorkee 247667 Uttar Pradesh India

This brief presents the hardware implementation of deep neural network-based speech enhancement algorithm (DNN-SEA) with a precise sigmoid activation function. Further, an adaptive step-size-based slope and intercept method (AS-SIM) has been developed to approximate the sigmoid function that uses the maximum allowable error (epsilon) as an input parameter. The performance of the DNN-SEA is measured in terms of speech quality and intelligibility using a perceptual evaluation of speech quality (PESQ) and short-time objective intelligibility (STOI) parameters, respectively. Hardware implementation is performed by Synopsys Design Compiler with a TSMC 90-nm library. The performance comdparison has been conducted in terms of area, gate count, delay, and power consumption. Results confirm that the proposed AS-SIM approximation improves performance in terms of PESQ and STOI value and significantly reduces the area requirement and power consumption.

关键词： approximation algorithms Hardware Speech enhancement Signal processing algorithms Noise measurement Neurons Training Speech enhancement sigmoid activation piecewise linear function deep neural network perceptual evaluation of speech quality short-time objective intelligibility

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Achievable Sum Rate Optimization on NOMA-Aided Cell-Free Massive MIMO With Finite Blocklength Coding

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IEEE Transactions on Vehicular Technology 2025年

作者： Chong, Baolin Lu, Hancheng Chen, Yuang Qin, Langtian Guo, Fengqian University of Science and Technology of China Cas Key Laboratory of Wireless-Optical Communications Hefei230027 China Deep Space Exploration Laboratory Hefei230088 China

Non-orthogonal multiple access (NOMA)-aided cell-free massive multiple-input multiple-output (CFmMIMO) has been considered a promising technology to fulfill the strict quality of service requirements for ultra-reliable low-latency communications (URLLC). However, finite blocklength coding (FBC) in URLLC makes it challenging to achieve optimal performance in the NOMA-aided CFmMIMO system. In this paper, we investigate the performance of the NOMA-aided CFmMIMO system with FBC in terms of achievable sum rate (ASR). Firstly, we derive a lower bound on the ergodic data rate. Then, we formulate an ASR maximization problem by jointly considering power allocation and user equipment (UE) clustering. To tackle such an intractable problem, we decompose it into two sub-problems, i.e., the power allocation problem and the UE clustering problem. A successive convex approximation algorithm is proposed to solve the power allocation problem by transforming it into a series of geometric programming problems. Meanwhile, two algorithms based on graph theory are proposed to solve the UE clustering problem by identifying negative loops. Finally, alternative optimization is performed to find the maximum ASR of the NOMA-aided CFmMIMO system with FBC. The simulation results demonstrate that the proposed algorithms significantly outperform the benchmarks in terms of ASR under various scenarios. © 1967-2012 IEEE.

关键词： approximation algorithms

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A 2-D Autofocus Algorithm for Long Synthetic Aperture Time SAR in Low-Contrast Scenarios Based on Deep Neural Network

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IEEE TRANSACTIONS ON GEOSCIENCE AND REMOTE SENSING 2025年 63卷

作者： Xie, Yi Chen, Longyong Tang, Haibo Zhang, Fubo Jiang, Tao Li, Zhenhua Chinese Acad Sci Aerosp Informat Res Inst Natl Key Lab Microwave Imaging Beijing 100190 Peoples R China Univ Chinese Acad Sci Sch Elect Elect & Commun Engn Beijing 100049 Peoples R China Naval Univ Engn Sch Elect Engn Wuhan 430033 Peoples R China

As synthetic aperture radar (SAR) technology continues to evolve with a focus on miniaturization, reduced weight, and lower costs, its range of applications has broadened to encompass lightweight and slow-moving platforms, such as small autonomous aerial vehicles (AAVs) and airships. However, this advancement introduces a new challenge for SAR systems in the form of excessively long synthetic aperture time (LSAT). LSAT-SAR faces significant challenges caused by its longer integration time (10-1000 times more than conventional airborne SAR's synthetic aperture time), including more stringent error tolerance of navigation system and more demanding trajectory control requirements. These issues often result in severe 2-D defocusing. Existing high-precision navigation systems and traditional autofocus algorithm, though adequate for conventional airborne SAR, often fail to meet the stringent requirements of LSAT-SAR, especially in low-contrast scenarios, necessitating more efficient and more robust compensation methods. To address these challenges, we propose a 2-D autofocus algorithm for LSAT-SAR using deep neural networks (DNNs). The proposed approach treats the 2-D focusing problem as a series of coupled 1-D curve estimation tasks, employing a weighted entropy loss function. The solution is optimized in an unsupervised manner using a DNN. Finally, the additional refinement is performed through a specialized fine-tuning correction module. Experiments on real LSAT-SAR images with synthetic aperture times of 150-270 s show that the proposed method substantially exceeds the performance of traditional approaches in low-contrast scenarios. It demonstrates excellent robustness, offering valuable insights and technical guidance for LSAT-SAR autofocusing.

关键词： Synthetic aperture radar Apertures Accuracy Artificial neural networks Motion compensation Trajectory Error analysis Entropy Costs approximation algorithms 2-D autofocus deep neural network (DNN) long synthetic aperture time (LSAT) low contrast

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A 16-Channel 60μW Neural Synchrony Processor for Multi-Mode Phase-Locked Neurostimulation 43

A 16-Channel 60μW Neural Synchrony Processor for Multi-Mode...

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43rd Annual IEEE Custom Integrated Circuits Conference, CICC 2022

作者： Shin, Uisub Ding, Cong Somappa, Laxmeesha Woods, Virginia Widge, Alik S. Shoaran, Mahsa EPFL Lausanne Switzerland Cornell University IthacaNY United States University of Minnesota MinneapolisMN United States

ISBN: (纸本)9781665407564

Measuring neural oscillatory synchrony facilitates our understanding of complex brain networks and the underlying pathological states. Altering the cross-regional synchrony-as a measure of brain network connectivity-via phase-locked deep brain stimulation (DBS) could provide a new therapeutic solution for various neurological [1] and psychiatric disorders [2]. This feature is missing in current neuromodulation devices and requires an accurate, energy-efficient computation of oscillatory phase and cross-regional synchrony on chip. The conventional iterative vector processing approach via CORDIC [3] can accurately extract the instantaneous phase and phase locking value (PLV) at the cost of high power consumption (400μW). As a result, it cannot be applied to large-scale (>100-CH) neuronal networks. Moreover, the latency in the pipelined CORDIC processor may hinder timely phase-locked stimulation in the absence of an excessively high clock speed. Alternatively, the PLV extractors in [4], [5] utilized simple approximation algorithms such as 1-bit quantization and local minima detection. These methods, albeit efficient, compromise PLV accuracy and cannot extract the instantaneous phase of neuronal signals. To provide an efficient, flexible, and accurate phase-locked DBS platform, this paper integrates a 16-channel low-noise AFE, an energy-efficient multi-mode phase synchrony processor, and a 4-channel neurostimulator that is locked to specific neuronal oscillatory phases (i.e., fixed or random phase, PLV or PAC). An amplitude-locked control can be further enabled through envelope and multi-band spectral energy extraction for common use cases such as epilepsy. © 2022 IEEE.

关键词： approximation algorithms

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Fast Computation of Born-Approximated Multistatic Scattering

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IEEE TRANSACTIONS ON ULTRASONICS FERROELECTRICS AND FREQUENCY CONTROL 2021年第12期68卷 3637-3645页

作者： Elbaz, Eden Brick, Yaniv Ben Gurion Univ Negev Sch Elect & Comp Engn IL-8410501 Beer Sheva Israel

An algorithm for the fast computation of multistatic scattering patterns, produced by low-contrast inhomogeneous volumetric objects, which satisfy the Born approximation assumption, is presented. The algorithm relies on hierarchical interpolation and aggregation of optimally sampled phase-compensated partial contributions to the scattered field integral. The Born approximation enables the efficient simultaneous computation for ranges of incident and observation angles, as well as excitation frequencies. This leads to a reduction in the asymptotic computational complexity by several polynomial orders of the problem's acoustical size. The algorithm's error controllability and favorable computation time are demonstrated via representative examples.

关键词： Scattering approximation algorithms Nonhomogeneous media Interpolation Integral equations Frequency control Observers Fast methods multilevel algorithms wave scattering

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Joint Traffic-Aware Consolidated Middleboxes Selection and Routing in Distributed SDNs

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IEEE TRANSACTIONS ON NETWORK AND SERVICE MANAGEMENT 2021年第2期18卷 1415-1429页

作者： Liu, Guiyan Guo, Songtao Li, Baochun Chen, Chao Chongqing Univ Coll Comp Sci Chongqing 400044 Peoples R China Univ Toronto Dept Elect & Comp Engn Toronto ON M5S 3G4 Canada

Software middlebox-based services can be flexibly managed by software defined networking (SDN) and network function virtualization (NFV). Meanwhile, traffic routing can be simplified and the number of routing rules in the SDN-enabled switches can be reduced through the consolidated middlebox model. However, different network functions in middleboxes may alter the volume of processed traffic, so high congestion may occur in specific bottleneck links if middlebox selection and traffic routing are not well jointly planned. Besides, in a statically switch-controller configured SDN, traffic dynamics will not only affect the link load in the data plane, but also pose a challenge to controller load balancing. Therefore, it's necessary to achieve better quality-of-service (QoS) performance in both control and data planes. In this article, we first formulate this problem as a joint traffic-aware consolidated middleboxes selection and routing (JTMSR) problem and prove its NP-hardness. Then, we design a two-phase algorithm to achieve the controller and link load balancing where the first phase is to redirect selected flows by applying wildcard rules and the second phase is to find fine-grained routing path by a rounding-based algorithm with bounded approximation factor. Finally, compared with the existing algorithms through extensive simulations, it demonstrates that our method has near-optimal controller load balancing and link load balancing performance and can improve response time by 9.7% compared with static scheme.

关键词： Middleboxes Control systems Routing Optical switches Load management Process control approximation algorithms Software defined networking consolidated middlebox load balancing rounding scalability

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A Survey on approximation in Parameterized Complexity: Hardness and algorithms

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algorithms 2020年第6期13卷 146-146页

作者： Feldmann, Andreas Emil Karthik, C. S. Lee, Euiwoong Manurangsi, Pasin Charles Univ Prague Dept Appl Math KAM Prague 11800 Czech Republic Tel Aviv Univ Dept Comp Sci IL-6997801 Tel Aviv Israel NYU Courant Inst Math Sci New York NY 10012 USA Google Res Mountain View CA 94043 USA

Parameterization and approximation are two popular ways of coping with NP-hard problems. More recently, the two have also been combined to derive many interesting results. We survey developments in the area both from the algorithmic and hardness perspectives, with emphasis on new techniques and potential future research directions.

关键词： parameterized complexity approximation algorithms hardness of approximation

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Weighted Frequent Itemset Mining Using Weighted Subtrees: WST-WFIM

IEEE CANADIAN JOURNAL OF ELECTRICAL AND COMPUTER ENGINEERING

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IEEE CANADIAN JOURNAL OF ELECTRICAL AND COMPUTER ENGINEERING 2021年第2期44卷 206-215页

作者： Nalousi, Saeed Farhang, Yousef Sangar, Amin Babazadeh Islamic Azad Univ Urmia Branch Dept IT & Comp Engn Orumiyeh *** Iran Islamic Azad Univ Khoy Branch Dept IT & Comp Engn Khoy Iran

There is a wide range of association rules mining algorithms concerning the importance of weighted data to extract frequent rules. In some algorithms, each item has only one weight in the data set, while, in others, each item in each segment of the data set has only one specific weight. In this work, a new efficient algorithm named weighted frequent itemset mining using weighted subtrees (WST-WFIM) has been proposed to discover the average weight of frequent rules. This algorithm uses specific trees and some new data structures on the frequent pattern growth (FP-Growth) algorithm to calculate the average weight of discovered rules. It works on the data set that each item in each transaction has a specific weight and keeps them in the dedicated tree. Moreover, we have proposed the shared transactions' concept and WST-WFIM by using it and calculate the average weight for discovered frequent rules. The algorithm capability was evaluated by standard sparse and dense weighted data sets. The results show that, while the algorithm works on weighted transactions, in sparse data sets, relative runtime increases by a decrease in minimum support (MinSup) parameter in comparison to the FP-Growth, and memory usage is approximately the same. The variations of runtime and memory usage with the MinSup parameter are opposite for dense data sets.

关键词： Performance evaluation Drugs Runtime Itemsets Maintenance engineering approximation algorithms Data structures Data mining shared transactions weighted items weighted rule generation

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