摘要翻译：
针对K用户多输入多输出(MIMO)干扰信道(IC)提出了一种基于粒子群算法(PSO)和人工蜂群算法(ABC)的干扰对准(IA)方案。干扰处理是无线通信中的一个重要问题，干扰对准(IA)提供了一个有希望的解决方案。然而，IA仍然缺乏明确和直接的设计程序。实际上，大多数IA算法都是以最小化某一干扰泄漏(IL)为目标的，这就导致了复杂的优化任务，涉及大量决策变量，同时还存在IA解的收敛性问题。本文将粒子群优化算法和ABC算法结合起来进行遗传算法优化，更适合于大规模优化。并对这四种算法进行了比较。合作社提出的办法似乎很有希望。
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英文标题：
《A Novel Mataheuristic based Interference Alignment for K-User
  Interference Channel : A Comparative Study》
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作者：
Lysa Ait Messaoud, Fatiha Merazka, and Daniel Massicotte
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最新提交年份：
2017
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分类信息：

一级分类：Electrical Engineering and Systems Science        电气工程与系统科学
二级分类：Signal Processing        信号处理
分类描述：Theory, algorithms, performance analysis and applications of signal and data analysis, including physical modeling, processing, detection and parameter estimation, learning, mining, retrieval, and information extraction. The term "signal" includes speech, audio, sonar, radar, geophysical, physiological, (bio-) medical, image, video, and multimodal natural and man-made signals, including communication signals and data. Topics of interest include: statistical signal processing, spectral estimation and system identification; filter design, adaptive filtering / stochastic learning; (compressive) sampling, sensing, and transform-domain methods including fast algorithms; signal processing for machine learning and machine learning for signal processing applications; in-network and graph signal processing; convex and nonconvex optimization methods for signal processing applications; radar, sonar, and sensor array beamforming and direction finding; communications signal processing; low power, multi-core and system-on-chip signal processing; sensing, communication, analysis and optimization for cyber-physical systems such as power grids and the Internet of Things.
信号和数据分析的理论、算法、性能分析和应用，包括物理建模、处理、检测和参数估计、学习、挖掘、检索和信息提取。“信号”一词包括语音、音频、声纳、雷达、地球物理、生理、（生物）医学、图像、视频和多模态自然和人为信号，包括通信信号和数据。感兴趣的主题包括：统计信号处理、谱估计和系统辨识；滤波器设计；自适应滤波/随机学习；（压缩）采样、传感和变换域方法，包括快速算法；用于机器学习的信号处理和用于信号处理应用的机器学习；网络与图形信号处理；信号处理中的凸和非凸优化方法；雷达、声纳和传感器阵列波束形成和测向；通信信号处理；低功耗、多核、片上系统信号处理；信息物理系统的传感、通信、分析和优化，如电网和物联网。
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英文摘要：
  This paper presents a new Interference Alignment (IA) scheme for K-User Multiple Input Multiple Output (MIMO) Interference Channel (IC) based on two metaheuristics, namely Particle Swarm Optimization (PSO) and Artificial Bee Colony (ABC) Algorithm. Tackling interference is an essential issue in wireless communications to which Interference Alignment (IA) provides a promising solution. However, IA still lacks of explicit and straightforward design procedures. In fact, most of IA procedures aim to minimize a certain Interference Leakage (IL) which measures the effect of the interference on the network, this results in complex optimization tasks involving a large amount of decision variables, together with a problem of convergence of the IA solutions. In this paper the IA optimization is performed using PSO, ABC and their cooperative counterparts, more suitable for large scale optimization. A comparison between the four algorithms is also carried out. The cooperative proposed approaches seem to be promising.
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PDF链接：
https://arxiv.org/pdf/1710.00864