未来宽带无线通信系统资源分配技术研究
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摘要
在无线通信系统中,由于无线资源的紧缺,如何有效地调度和分配资源以满足通信业务需求成为下一代无线通信系统的核心问题之一。无线资源的分配和调度是一种进一步扩大信道容量、改善通信质量关键技术,对于优化和提高通信系统性能具有优越和不可替代的意义。本论文以未来无线通信的两项关键技术——协作传输和认知无线电为课题思路,以提高网络容量,增加网络资源利用率,考虑系统公平性及满足不同用户的QoS业务需求等为目标,以最优化理论、博弈论以及市场理论等为理论雏形,重点、深入地展开未来宽带无线通信系统资源分配技术的研究。
本论文蒙多项国家863、国家自然科学基金及国际科技合作等项目的支持,围绕协作通信系统和认知无线电系统切景分析,深入地研究未来宽带无线通信中的资源分配技术,积极探索新的解决方案,取得了如下研究成果：
(1)针对OFDM两跳中继协作系统,提出了一种适合多业务QoS的资源分配算法。综合考虑多种业务模型的QoS要求及差异业务调度公平性,设计一种自适应累积效用函数(AU-函数,Aggregated Utility Function)的优化目标；通过联合中继选择及子载波、功率分配,从而在保证业务QoS、用户公平性的基础上提高系统性能。仿真结果表明,所提算法在系统吞吐量、时延、丢包率、缓存状态等方面性能得到了改善,且AU函数累积的形式有利于针对不同的QoS指标进行优化。
(2)针对多用户协作中继网络,设计用户协作传输模型,并提出一种分布式的分层博弈(HG, Hierachical Game)算法分析用户协作上行传输的资源分配算法。首先,通过用户配对将多用户网络分解成多个两用户协作传输网络,采用HG算法来解决协作资源分配问题。分层博弈模型包括两层博弈：在用户层面,采用两用户判决的纳什博弈来分布式地解决协作策略选择问题,即“是否协作”；在基站层面,使用合作博弈联合解决资源分配问题,即“怎样协作”。在所提HG算法中,相互协作是互惠的且总的效用可转移,证明了存在一个唯一的纳什均衡解。另外,进一步讨论了奖惩因子,证明如果用户更关心下一个时隙的传输并且进行适当的功率控制的话,用户会选择协作策略。因此,如果通过价格激励措施(如功率,带宽等),用户的自私行为可以得到更好的引导。仿真结果表明所提算法能够通过鼓励用户相互协作提高传输效率,并且在系统容量和改善不良单用户传输方面有很好的性能表现。
(3)为了提高协作系统的吞吐量,提出一种基于网络编码(NC, Network Coding)的OFDMA全双工协作中继传输方案,分析该方案下的两个经典的性能评价指标——系统容量和中断概率,并给出了闭式的表达式。相比于直接传输(DT, Direct Transmission)和普通的协作传输(CT, Cooperation Transmission)模式,基于网络编码的协作传输(CT NC,Netwrok Coding based Cooperative Transmission)可以取得比CT模式高11.8%的链路容量增益,比DT模式提高32.5%(SNR=20左右),同时中断概率比CT降低50%,比DT降低达100%(SNR=5左右)。可见,由于网络编码增益,CT_NC在系统容量和稳健性比DT和CT都有所提高。
同时,进一步提出一种双层纳什议价均衡(DL_NBS, Double Layer_Nash Bargaining Solution)算法以解决基于网络编码的全双工协作中继传输方案的资源分配问题,考虑协作用户吞吐量均衡,分别通过子载波议价均衡和功率议价均衡来协调用户对之间的子载波和功率分配。仿真结果表明,所提DL NBS博弈资源分配方案与传统资源分配算法相比,不仅更适用于分布式用户协作场景,且能取得公平性和有效性的折中。
(4)针对频谱共享和竞争问题,引进OODA认知环构建一种新型认知无线电网络频谱共享行为分析模型。并在此基础上,借鉴经济学理论,提出了一种基于效用函数设计的双重拍卖理论的频谱竞争方案,基于主、次网络用户在频谱管理问题的行为特征不同,考虑到无线信道的时变性,传输预测及频谱交易历史等问题,在判决过程适当中引入一定的预测和学习因素,分别设计主、次用户的决策效用函数,巧妙地解决了主次网络之间的频谱共享问题。基于主用户和次用户之间的频谱供求关系,考虑了两种场景：供过于求,即主用户较多(MPLS, More Primary Users Less Secondary User)和供不应求,即次用户较多(LPMS, Less Primary Users More Secondary User)情况下的主次用户交互具体过程。同时,在解决了主次网络之间的频谱共享问题之后,提出了四种拍卖出价指标解决次网络内部的频谱竞争问题。仿真结果表明,在两种供求关系下,所提算法能有效地解决主次网络之间的频谱共享问题,四种出价拍卖策略各有侧重的解决了次网络内部的频谱竞争问题,并较之传统集中式认知接入方式性能取得可比拟的频谱效率,且降低了网络部署成本,便于利用分布式自主交易的方式来达到频谱共享接入的目的。
此外,考虑认知无线电网络的动态智能特性,建模多个自治次用户策略性地接入可用机会频谱的认知无线电网络,提出一种基于增强型学习策略的双重拍卖机制来处理主次用户频谱接入竞争问题。考虑到用户的自私性、机会频谱接入的有限性和时变性,环境变化及多用户不同的传输需求,基于增强型学习策略分别设计次用户的出价机制和主用户保护价格机制。该机制可以影响主次用户之间频谱接入,所有用户的当前收益及回报。基于观察已分配历史资源情况、行为及状态现状及预测未来收益,增强型学习方法可以逐步改进次用户的出价策略和主用户的保护价格策略,达到最佳的信道接入策略,从而有效地来竞争频谱机会。仿真结果表明,所提基于增强型学习策略的双重拍卖算法能大大改善用户的竞价策略,用户丢包、出价效率、传输速率等性能都得到极大的提高。
综上所述,本论文针对无线资源分配,针对未来无线通信中协作通信和认知无线电两项关键技术,在物理层基于OFDM传输方式,并结合最优化理论、博弈论等数学理论,以提高网络容量、网络资源利用率及满足用户公平性、不同QoS业务需求等为目标,考虑跨层设计思想,深入地研究了信道、功率、传输时隙、频率等多重资源分配。为协作通信及认知无线电技术在未来无线移动通信中的应用和优化从理论上进行了探索性的研究。
With the increasing requirements of service and rate, how to make use of the limited wireless resource effectively to enlarge channel capacity and improve system quality has become a critical question to solve for the future wireless broadband communications. In this paper, we focus on two promising wireless communication technologies, i.e. cooperative transmission and cognitive radio, aiming at improving system capacity, increasing the network resource utilization, guarantee QoS requirement of different users and etc. to study on the resource allocation of them. Meanwhile, The optimization theory, game theory, and other marketing methods are employed as academic rudiment to make wide research deeply and intensively.
Supported by several project of National Natural Science Foundation of China, National High-Tech Research and Development Plan of China, National International Science and Technology Cooperation Project under Granted of Sweden and Canada, we aim at the resource allocation research in the future broadband wireless communications in the dissertation, by deep analysis of cooperative transmission and cognitive radio technologies, exploiting new solving methods to gurateen the QoS of users and provide high frequency efficiency through optimizing the resource from the aspect of system-level and user level. The main contributions of the dissertation are as follows:
(1) We investigate a multi-user multi-service scheduling scheme in the OFDMA based two-hop cooperative relay network, which aims at the cooperative resource allocation in this paper. We propose an adaptive Aggregated Utility Function (AU-Function) as the optimization objective, which simultaneously takes various multi-service QoS requirement and fairness among different services into consideration. We format the utility function into a several QoS parameters captured form, including rate, delay, jitter and Packet Loss Ratio (PLR) of services, which combines the QoS requirement well. Then, the complex resource allocation is decoupled into a joint relay-subcarrier selection and power allocation problem. Simulation results confirm that the proposed algorithm achieves an efficient balance among rate, delay, PLR, etc., and show that the users'QoS can be evaluated adaptively by the full dimensional utility consideration.
(2) Cooperation allows wireless network users to benefit from various gains such as an increase in the achieved rate or an improvement in the bit error rate. In the paper, we propose a distributed Hierarchical Game (HG) theoretic framework over multi-user cooperative communication networks to stimulate cooperation and improve the network performance. First, we study a two-user decision making game in the OFDMA based subscriber cooperative relaying network, in which subscribers transmit their own data in the first phase, while helping to retransmit their partner's or choosing to freeride in the second phase. Instead of consulting to a global optimal solution, we decouple the cooperation resource allocation into two level subproblems:a user level Nash game for distributed cooperation decision and a Base Station (BS) level coalition game for centralized resource allocation. In the proposed HG algorithm, where mutual cooperation is preferred and total payoff is transferable, we prove it converges to a unique optimal equilibrium and resolve the subcarrier assignment and power allocation among the couples. Besides, we discuss the existence of the publishing and rewarding coefficients in order to encourage cooperation. Then, we extend the HG to multi-user cases by coupling among subscribers according to the location information. The simulation results show that the proposed scheme with the distributed HG game achieves a well tradeoff between fairness and efficiency by improving the transmission efficiency of adverse users and outperforms those employing centralized schemes.
(3) In this paper, we analyze two classical performance metrics for user cooperation with network coding scheme. Two closed formulation of capacity and outage probability for OFDMA based two users cooperation are presented. Compared with the DT and conventional CT, simulation results show the advantages of CT_NC. It provides 11.8% higher link capacity than CT cases averagely; meanwhile, it obtains 50% lower outage probability. Therefore, CT_NC is much more powerful in both the system throughput and robustness due to the network coding diversity than the direct transmission scheme and conventional user cooperative protocol.
Besides, a network coding based two-subscriber-cooperation scheme in a full-duplex transmission mode of OFDMA system is proposed to further improve the network throughput. Besides, a double level Nash Bargaining Solution (DL_NBS) game is adopted to resolve the inter-user resource bargaining problem. In the inter-user pair, the pairwise capacity based NBS is utilized to allocate subcarrier and distribute power between the cooperative subscribers. The simulation results show that the proposed CoNC achieves system capacity 49.1% higher than the normal cooperative transmission, and 46.4% higher than the direct transmission. Meanwhile, compared with the traditional resource allocation algorithms, the two-level NBS solution achieves a well tradeoff between fairness and efficiency, as well as perfectly suits the distributed subscriber cooperation scenario.
(4) Cognitive networks are designed based on the concept of dynamic and intellectual network management, characterizing the feature of self-sensing, self-configuration, self-learning, self-consciousness, and such. In this paper, focusing on the spectrum sharing and competition, we propose a novel behavior modeling methodology basing on a paradigmatic cognitive radio network. We first discuss the preponderance and challenges of cognitive network, and explore the special behavior features basing on a modeling prototype for spectrum competition in a multi-radio cognitive network. Meanwhile, a rounded OODA cognitive behavior is explicitly given, within which the decision process is mathematically illustrated. We present a behavior model from the economical theoretical perspective, and introduce a double auction decision making algorithm which resolves the spectrum sharing between the primary network and secondary networks subtly. Two different utility functions for primary users and secondary users are designed basing on a supply-and-demand relationship between them. Also, we adopt expectation and learning process in the decide module, which takes consideration of the variance of channels, transmission forecasting, afore trading histories and etc. Numerical results with four bidding strategies are presented to reinforce the effectiveness of the two proposed utility evaluation based decision modules under two scenarios. Besides, we prove the proposed behavior model based spectrum access method maintains comparable frequency efficiency with traditional centralized CR access approaches.
In order to fully utilize scarce spectrum resources dynamically and intelligently, we model a cognitive radio network with various selfish autonomous secondary users who strategically interact to acquire dynamically available spectrum opportunities. Our main focus is on developing solutions for cognitive users to make full utilization of the spectrum with each other, given the selfish nature of users in the wireless network and complete for the limited and time-varying spectrum opportunities. To analyze the interactions among users given the environment disturbance, various transmission requirements, we propose a double auction framework to tackle the competition among users for spectrum opportunities over time. The double bidding actions affect the resource allocation and, hence, the rewards and future strategies of all users. Based on the observed resource allocations and corresponding rewards, reinforcement Learning methodology is deployed by wireless users to improve their bidding policy, building on which we formulate bidding prices for the secondary users and asking prices for the primary users. Simulation results show that the proposed Q-learning based double auction algorithm can significantly improve users'own bidding strategies and, hence, their performance in terms of packet loss, bidding efficiency and transmission rate is improved progressively.
Summarily, the dissertation do hard work on wireless resource allocation problem in cooperative transmission and cognitive radio network to improve system capacity, increase the network resource utilization, guarantee QoS requirement of different users and etc. The OFDM transmission is included in the physical layer, many mathematical methord are employed, and the cross-layer theory are taken as a methodology, this dissertation studies on allocation of the multiple network resource, i.e. channel, power, time, frequency etc. Therefore, it provides a deep and wide research theoretically for the implement and optimization of cooperation transmission and cognitive rado technologies in the future wireless communication.
本论文蒙多项国家863、国家自然科学基金及国际科技合作等项目的支持,围绕协作通信系统和认知无线电系统切景分析,深入地研究未来宽带无线通信中的资源分配技术,积极探索新的解决方案,取得了如下研究成果：
(1)针对OFDM两跳中继协作系统,提出了一种适合多业务QoS的资源分配算法。综合考虑多种业务模型的QoS要求及差异业务调度公平性,设计一种自适应累积效用函数(AU-函数,Aggregated Utility Function)的优化目标；通过联合中继选择及子载波、功率分配,从而在保证业务QoS、用户公平性的基础上提高系统性能。仿真结果表明,所提算法在系统吞吐量、时延、丢包率、缓存状态等方面性能得到了改善,且AU函数累积的形式有利于针对不同的QoS指标进行优化。
(2)针对多用户协作中继网络,设计用户协作传输模型,并提出一种分布式的分层博弈(HG, Hierachical Game)算法分析用户协作上行传输的资源分配算法。首先,通过用户配对将多用户网络分解成多个两用户协作传输网络,采用HG算法来解决协作资源分配问题。分层博弈模型包括两层博弈：在用户层面,采用两用户判决的纳什博弈来分布式地解决协作策略选择问题,即“是否协作”；在基站层面,使用合作博弈联合解决资源分配问题,即“怎样协作”。在所提HG算法中,相互协作是互惠的且总的效用可转移,证明了存在一个唯一的纳什均衡解。另外,进一步讨论了奖惩因子,证明如果用户更关心下一个时隙的传输并且进行适当的功率控制的话,用户会选择协作策略。因此,如果通过价格激励措施(如功率,带宽等),用户的自私行为可以得到更好的引导。仿真结果表明所提算法能够通过鼓励用户相互协作提高传输效率,并且在系统容量和改善不良单用户传输方面有很好的性能表现。
(3)为了提高协作系统的吞吐量,提出一种基于网络编码(NC, Network Coding)的OFDMA全双工协作中继传输方案,分析该方案下的两个经典的性能评价指标——系统容量和中断概率,并给出了闭式的表达式。相比于直接传输(DT, Direct Transmission)和普通的协作传输(CT, Cooperation Transmission)模式,基于网络编码的协作传输(CT NC,Netwrok Coding based Cooperative Transmission)可以取得比CT模式高11.8%的链路容量增益,比DT模式提高32.5%(SNR=20左右),同时中断概率比CT降低50%,比DT降低达100%(SNR=5左右)。可见,由于网络编码增益,CT_NC在系统容量和稳健性比DT和CT都有所提高。
同时,进一步提出一种双层纳什议价均衡(DL_NBS, Double Layer_Nash Bargaining Solution)算法以解决基于网络编码的全双工协作中继传输方案的资源分配问题,考虑协作用户吞吐量均衡,分别通过子载波议价均衡和功率议价均衡来协调用户对之间的子载波和功率分配。仿真结果表明,所提DL NBS博弈资源分配方案与传统资源分配算法相比,不仅更适用于分布式用户协作场景,且能取得公平性和有效性的折中。
(4)针对频谱共享和竞争问题,引进OODA认知环构建一种新型认知无线电网络频谱共享行为分析模型。并在此基础上,借鉴经济学理论,提出了一种基于效用函数设计的双重拍卖理论的频谱竞争方案,基于主、次网络用户在频谱管理问题的行为特征不同,考虑到无线信道的时变性,传输预测及频谱交易历史等问题,在判决过程适当中引入一定的预测和学习因素,分别设计主、次用户的决策效用函数,巧妙地解决了主次网络之间的频谱共享问题。基于主用户和次用户之间的频谱供求关系,考虑了两种场景：供过于求,即主用户较多(MPLS, More Primary Users Less Secondary User)和供不应求,即次用户较多(LPMS, Less Primary Users More Secondary User)情况下的主次用户交互具体过程。同时,在解决了主次网络之间的频谱共享问题之后,提出了四种拍卖出价指标解决次网络内部的频谱竞争问题。仿真结果表明,在两种供求关系下,所提算法能有效地解决主次网络之间的频谱共享问题,四种出价拍卖策略各有侧重的解决了次网络内部的频谱竞争问题,并较之传统集中式认知接入方式性能取得可比拟的频谱效率,且降低了网络部署成本,便于利用分布式自主交易的方式来达到频谱共享接入的目的。
此外,考虑认知无线电网络的动态智能特性,建模多个自治次用户策略性地接入可用机会频谱的认知无线电网络,提出一种基于增强型学习策略的双重拍卖机制来处理主次用户频谱接入竞争问题。考虑到用户的自私性、机会频谱接入的有限性和时变性,环境变化及多用户不同的传输需求,基于增强型学习策略分别设计次用户的出价机制和主用户保护价格机制。该机制可以影响主次用户之间频谱接入,所有用户的当前收益及回报。基于观察已分配历史资源情况、行为及状态现状及预测未来收益,增强型学习方法可以逐步改进次用户的出价策略和主用户的保护价格策略,达到最佳的信道接入策略,从而有效地来竞争频谱机会。仿真结果表明,所提基于增强型学习策略的双重拍卖算法能大大改善用户的竞价策略,用户丢包、出价效率、传输速率等性能都得到极大的提高。
综上所述,本论文针对无线资源分配,针对未来无线通信中协作通信和认知无线电两项关键技术,在物理层基于OFDM传输方式,并结合最优化理论、博弈论等数学理论,以提高网络容量、网络资源利用率及满足用户公平性、不同QoS业务需求等为目标,考虑跨层设计思想,深入地研究了信道、功率、传输时隙、频率等多重资源分配。为协作通信及认知无线电技术在未来无线移动通信中的应用和优化从理论上进行了探索性的研究。
With the increasing requirements of service and rate, how to make use of the limited wireless resource effectively to enlarge channel capacity and improve system quality has become a critical question to solve for the future wireless broadband communications. In this paper, we focus on two promising wireless communication technologies, i.e. cooperative transmission and cognitive radio, aiming at improving system capacity, increasing the network resource utilization, guarantee QoS requirement of different users and etc. to study on the resource allocation of them. Meanwhile, The optimization theory, game theory, and other marketing methods are employed as academic rudiment to make wide research deeply and intensively.
Supported by several project of National Natural Science Foundation of China, National High-Tech Research and Development Plan of China, National International Science and Technology Cooperation Project under Granted of Sweden and Canada, we aim at the resource allocation research in the future broadband wireless communications in the dissertation, by deep analysis of cooperative transmission and cognitive radio technologies, exploiting new solving methods to gurateen the QoS of users and provide high frequency efficiency through optimizing the resource from the aspect of system-level and user level. The main contributions of the dissertation are as follows:
(1) We investigate a multi-user multi-service scheduling scheme in the OFDMA based two-hop cooperative relay network, which aims at the cooperative resource allocation in this paper. We propose an adaptive Aggregated Utility Function (AU-Function) as the optimization objective, which simultaneously takes various multi-service QoS requirement and fairness among different services into consideration. We format the utility function into a several QoS parameters captured form, including rate, delay, jitter and Packet Loss Ratio (PLR) of services, which combines the QoS requirement well. Then, the complex resource allocation is decoupled into a joint relay-subcarrier selection and power allocation problem. Simulation results confirm that the proposed algorithm achieves an efficient balance among rate, delay, PLR, etc., and show that the users'QoS can be evaluated adaptively by the full dimensional utility consideration.
(2) Cooperation allows wireless network users to benefit from various gains such as an increase in the achieved rate or an improvement in the bit error rate. In the paper, we propose a distributed Hierarchical Game (HG) theoretic framework over multi-user cooperative communication networks to stimulate cooperation and improve the network performance. First, we study a two-user decision making game in the OFDMA based subscriber cooperative relaying network, in which subscribers transmit their own data in the first phase, while helping to retransmit their partner's or choosing to freeride in the second phase. Instead of consulting to a global optimal solution, we decouple the cooperation resource allocation into two level subproblems:a user level Nash game for distributed cooperation decision and a Base Station (BS) level coalition game for centralized resource allocation. In the proposed HG algorithm, where mutual cooperation is preferred and total payoff is transferable, we prove it converges to a unique optimal equilibrium and resolve the subcarrier assignment and power allocation among the couples. Besides, we discuss the existence of the publishing and rewarding coefficients in order to encourage cooperation. Then, we extend the HG to multi-user cases by coupling among subscribers according to the location information. The simulation results show that the proposed scheme with the distributed HG game achieves a well tradeoff between fairness and efficiency by improving the transmission efficiency of adverse users and outperforms those employing centralized schemes.
(3) In this paper, we analyze two classical performance metrics for user cooperation with network coding scheme. Two closed formulation of capacity and outage probability for OFDMA based two users cooperation are presented. Compared with the DT and conventional CT, simulation results show the advantages of CT_NC. It provides 11.8% higher link capacity than CT cases averagely; meanwhile, it obtains 50% lower outage probability. Therefore, CT_NC is much more powerful in both the system throughput and robustness due to the network coding diversity than the direct transmission scheme and conventional user cooperative protocol.
Besides, a network coding based two-subscriber-cooperation scheme in a full-duplex transmission mode of OFDMA system is proposed to further improve the network throughput. Besides, a double level Nash Bargaining Solution (DL_NBS) game is adopted to resolve the inter-user resource bargaining problem. In the inter-user pair, the pairwise capacity based NBS is utilized to allocate subcarrier and distribute power between the cooperative subscribers. The simulation results show that the proposed CoNC achieves system capacity 49.1% higher than the normal cooperative transmission, and 46.4% higher than the direct transmission. Meanwhile, compared with the traditional resource allocation algorithms, the two-level NBS solution achieves a well tradeoff between fairness and efficiency, as well as perfectly suits the distributed subscriber cooperation scenario.
(4) Cognitive networks are designed based on the concept of dynamic and intellectual network management, characterizing the feature of self-sensing, self-configuration, self-learning, self-consciousness, and such. In this paper, focusing on the spectrum sharing and competition, we propose a novel behavior modeling methodology basing on a paradigmatic cognitive radio network. We first discuss the preponderance and challenges of cognitive network, and explore the special behavior features basing on a modeling prototype for spectrum competition in a multi-radio cognitive network. Meanwhile, a rounded OODA cognitive behavior is explicitly given, within which the decision process is mathematically illustrated. We present a behavior model from the economical theoretical perspective, and introduce a double auction decision making algorithm which resolves the spectrum sharing between the primary network and secondary networks subtly. Two different utility functions for primary users and secondary users are designed basing on a supply-and-demand relationship between them. Also, we adopt expectation and learning process in the decide module, which takes consideration of the variance of channels, transmission forecasting, afore trading histories and etc. Numerical results with four bidding strategies are presented to reinforce the effectiveness of the two proposed utility evaluation based decision modules under two scenarios. Besides, we prove the proposed behavior model based spectrum access method maintains comparable frequency efficiency with traditional centralized CR access approaches.
In order to fully utilize scarce spectrum resources dynamically and intelligently, we model a cognitive radio network with various selfish autonomous secondary users who strategically interact to acquire dynamically available spectrum opportunities. Our main focus is on developing solutions for cognitive users to make full utilization of the spectrum with each other, given the selfish nature of users in the wireless network and complete for the limited and time-varying spectrum opportunities. To analyze the interactions among users given the environment disturbance, various transmission requirements, we propose a double auction framework to tackle the competition among users for spectrum opportunities over time. The double bidding actions affect the resource allocation and, hence, the rewards and future strategies of all users. Based on the observed resource allocations and corresponding rewards, reinforcement Learning methodology is deployed by wireless users to improve their bidding policy, building on which we formulate bidding prices for the secondary users and asking prices for the primary users. Simulation results show that the proposed Q-learning based double auction algorithm can significantly improve users'own bidding strategies and, hence, their performance in terms of packet loss, bidding efficiency and transmission rate is improved progressively.
Summarily, the dissertation do hard work on wireless resource allocation problem in cooperative transmission and cognitive radio network to improve system capacity, increase the network resource utilization, guarantee QoS requirement of different users and etc. The OFDM transmission is included in the physical layer, many mathematical methord are employed, and the cross-layer theory are taken as a methodology, this dissertation studies on allocation of the multiple network resource, i.e. channel, power, time, frequency etc. Therefore, it provides a deep and wide research theoretically for the implement and optimization of cooperation transmission and cognitive rado technologies in the future wireless communication.
引文
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