4G LTE与LTE-Advanced移动宽带技术解析

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"4G LTE, LTE-Advanced for Mobile Broadband 是一本由Erik Dahlman、Stefan Parkvall和Johan Sköld合著的专业书籍,详细介绍了4G移动宽带技术,特别是LTE(长期演进)和LTE-Advanced系统。这本书涵盖了从LTE的背景、高数据率在移动通信中的实现、OFDM传输、多载波技术、多天线技术到调度、链路自适应、重传协议等多个关键知识点。此外,还讨论了LTE的无线电接入概述、协议架构、物理传输资源、上下行链路的物理层处理、功率控制、干扰处理、接入程序、多媒体广播/多播服务、中继技术、频谱和射频特性以及性能评估。书中也对比分析了其他无线通信系统,如HSPA、GSM/EDGE、CDMA2000等,并对未来的发展方向进行了展望。" 这本书详细阐述了4G LTE技术的发展背景,指出其在移动通信系统演进中的重要地位。LTE的标准化过程是为了解决一系列技术驱动因素,如提高数据速率、降低延迟、增强系统容量和覆盖范围。书中的第二章探讨了如何在有限带宽内实现高数据速率,涉及更高阶调制和多载波传输的概念。 第三章深入讲解了OFDM(正交频分复用)技术,这是LTE的核心传输机制。它包括基本原理、解调方法、IFFT/FFT处理、循环前缀的使用,以及如何通过信道估计和参考符号来处理频率选择性衰落。此外,还讨论了OFDM在多小区广播/多播传输中的应用。 第四章则介绍了更宽带的“单载波”传输技术,如对抗无线信道频率选择性的均衡方法,以及上行链路的灵活带宽分配和DFT-spread OFDM。 第五章专注于多天线技术,解释了不同多天线配置的优势,包括多接收天线和多发射天线的使用,以及空间复用等技术。 第六章讨论了调度、链路自适应和混合ARQ(自动重传请求)的概念,这些是保证服务质量的关键。 第七至十九章涵盖了LTE的无线电接入技术、协议架构、物理资源分配、下行链路和上行链路的物理层处理、重传协议、功率控制、干扰管理、接入过程、多媒体广播/多播服务、中继技术、频谱和射频特性以及与其他无线通信系统的比较。 最后一章,作者分享了对未来无线通信发展的思考,为读者提供了对技术演进的洞察。 这本书为理解4G LTE及其演进技术提供了全面而深入的知识,是从事或研究移动通信领域的专业人士的重要参考资料。
2011-12-07 上传
这本4G LTE/LTE-Advanced for Mobile Broadband是11年出版的,写书的是Ericsson的,就是写3G Evolution: HSPA and LTE for Mobile Broadband那几个人。本书最大的特点是本书涵盖了Rel-10的最新进展内容,考虑到Rel-10刚刚结束,对标准化感兴趣或者刚入手标准化的同学可能很有帮助,强烈推荐一下。 Based on the bestseller "3G Evolution - HSPA and LTE for mobile broadband" and reflecting the ongoing success of LTE throughout the world, this book focuses on LTE with full updates including LTE-Advanced to provide a complete picture of the LTE system. Overview and detailed explanations are given for the latest LTE standards for radio interface architecture, the physical layer, access procedures, broadcast, relaying, spectrum and RF characteristics, and system performance. Key technologies presented include multi-carrier transmission, advanced single-carrier transmission, advanced receivers, OFDM, MIMO and adaptive antenna solutions, advanced radio resource management and protocols, and different radio network architectures. Their role and use in the context of mobile broadband access in general is explained. Both a high-level overview and more detailed step-by-step explanations of the LTE/LTE-Advanced implementation are given. An overview of other related systems such as GSM/EDGE, HSPA, CDMA2000, and WIMAX is also provided. This book is a 'must-have' resource for engineers and other professionals in the telecommunications industry, working with cellular or wireless broadband technologies, giving an understanding of how to utilize the new technology in order to stay ahead of the competition. The authors of the book all work at Ericsson Research and have been deeply involved in 3G and 4G development and standardisation since the early days of 3G research. They are leading experts in the field and are today still actively contributing to the standardisation of LTE within 3GPP.

import numpy as np from py2neo import Graph graph = Graph("http://23/231/23/4:7474/browser/", auth=("x", "xxx!")) # from py2neo import Node, Relationship def load_data(): query = """ MATCH (u:custom)-[]->(p:broadband) RETURN u.number, p.name, 1 """ result = graph.run(query) # 构建用户商品矩阵 users = set() products = set() data = [] for row in result: user_id = row[0] product_id = row[1] quantity = row[2] users.add(user_id) products.add(product_id) data.append((user_id, product_id, quantity)) # 构建两个字典user_index,user_index,key为名称,value为排序的0~N-1的序号 user_index = {u: i for i, u in enumerate(users)} print("user_index:",user_index) product_index = {p: i for i, p in enumerate(products)} print("product_index:",product_index) # 构建全零矩阵 np.zeros matrix = np.zeros((len(users), len(products))) # 将存在关系的节点在矩阵中用值1表示 quantity = 1 for user_id, product_id, quantity in data: matrix[user_index[user_id], product_index[product_id]] = quantity # print("matrix:",matrix) # user_names = list(user_index.keys()) # product_names = list(product_index.keys()) # print("user_names:", user_names) # print("product_names:", product_names) # 转成用户商品矩阵 # matrix 与 np.mat转化后格式内容一样 user_product_matrix = np.mat(matrix) # print(user_product_matrix) return user_product_matrix def generate_dict(dataTmp): m,n = np.shape(dataTmp) print(m,n) data_dict = {} for i in range(m): tmp_dict = {} # 遍历矩阵,对每一行进行遍历,找到每行中的值为1 的列进行输出 for j in range(n): if dataTmp[i,j] != 0: tmp_dict["D_"+str(j)] = dataTmp[i,j] print(str(j)) print(tmp_dict["D_"+str(j)]) data_dict["U_"+str(i)] = tmp_dict print(tmp_dict) print(str(i)) for j in range(n): tmp_dict = {} for i in range(m): if dataTmp[i,j] != 0: tmp_dict["U_"+str(i)] = dataTmp[i,j] data_dict["D_"+str(j)] = tmp_dict return data_dict def PersonalRank(data_dict,alpha,user,maxCycles): rank = {} for x in data_dict.keys(): rank[x] = 0 rank[user] = 1 step = 0 while step < maxCycles: tmp = {} for x in data_dict.keys(): tmp[x] = 0 for i ,ri in data_dict.items(): for j in ri.keys(): if j not in tmp: tmp[j] = 0 tmp[j] += alpha+rank[i] / (1.0*len(ri)) if j == user: tmp[j] += (1-alpha) check = [] for k in tmp.keys(): check.append(tmp[k] - rank[k]) if sum(check) <= 0.0001: break rank = tmp if step % 20 == 0: print("iter:",step) step = step + 1 return rank def recommand(data_dict,rank,user): items_dict = {} items = [] for k in data_dict[user].keys(): items.append(k) for k in rank.keys(): if k.startswith("D_"): if k not in items: items_dict[k] = rank[k] result = sorted(items_dict.items(),key=lambda d:d[1],reverse=True) return result print("-------------") data_mat = load_data() print("-------------") data_dict = generate_dict(data_mat) print("-------------") rank = PersonalRank(data_dict,0.85,"U_1",500) print("-------------") result = recommand(data_dict,rank,"U_1") print(result) 优化这段代码,将U_N替换成U_NUMBER D_N替换成D_NAME

2023-06-13 上传