摘要
预编码技术,与检测技术相伴而生,在发送端已知信道状态信息(CSI)的前提下,作为发送端的预处理技术,可以在发送端消除多用户／多天线之间的多址干扰(MAI)以及由于无线信道的频率选择性衰落带来的符号间干扰(ISI),从而有效地降低了接收机算法的复杂性,对于下行信道,用户作为终端的情况下,由于终端尺寸大小,功耗因素以及价格等各方面的限制,把复杂的接收机算法平行的放在基站侧实现,从而降低接收机信号处理的复杂度,显得更加有意义。
与点到点的通信模式不同,在中继系统中,加入了中继节点后,通信过程分为两个阶段,即源节点与中继节点通信的阶段和中继节点与目的节点通信两个阶段,如果考虑源节点与目的节点之间有直达路径,则在通信过程中还有协作分集的功能。本文系统研究了中继通信系统中和直达路径两种情况下的预编码和目的节点均衡算法的联合优化,具体内容包括：
1)针对“有”和“无”直达路径两种情形,在论文的第三章给出了中继通信系统在多天线配置情况下,理想信道状态下的预编码算法。假定在接收端具有完美的信道估计,并且目的节点反馈给中继节点以及中继节点反馈回源节点的信道没有任何误码出现,因而是一种理想的情况。本文给出了没有直达路径情况下的两种闭式解。其中一种闭式解根据目的节点信号检测的MMSE准则,在放松约束条件的情况下给出了预编码性能的下界,尽管如此,预编码性能上仍要好于ZF/MMSE线性预编码算法；另一种方法基于AX+XB=C矩阵方程求解,给出了最优的预编码矩阵设计,与类似的方法相比,在中断概率和遍历容量上都具有明显的增益提升
另外在源节点和目的节点有直达路径的情况下,给出了两种优化方法,第一种方法利用目的节点在通信的两个阶段得到的信号进行联合均衡处理,通过联合最大比合并算法,利用迭代方法给出了最优的误码性能；另外一种方法先对每个阶段接收的信号分别进行均衡处理,然后通过调整两个支路的权重给出合并方法,与第一种方法不同的是,这里只是针对权重进行标量调整,而第一种方法由于两条路径联合优化,彼此的均衡矩阵相互包含,既有幅度调整,又有相位调整,通过分析与仿真详细地比较了二者的性能,同时也比较了“有”和“无”直达路径下,协作分集增益的比较。
2)本文在第四章研究了中继通信系统在信道状态信息不理想情况下的鲁棒预编码设计方法。首先给出了中继非理想信道的建模方法,然后在源节点和目的节点没有直达路径情况下,给出了源节点、中继节点和目的节点的预编码和检测算法的联合设计方法,并详细比较了鲁棒设计和非鲁棒设计的性能差别；对于源节点和中继节点有直达路径的情况,分成了两种类型：第一种类型考虑了源节点预编码和目的节点均衡算法的联合优化,此时优化问题可以归结为Schur-Concave优化问题,给出了相应的闭式解,第二种情况研究了中继节点预编码和目的节点均衡算法的联合优化,讨论了优化问题的上下界,以内点法为优化算法,利用matlab优化工具箱给出了优化问题的解。
3)第五章给出了认知无线网络下相应的波束赋形(看作一种广义预编码)算法,分成两种情况进行讨论。首先讨论了主基站和认知基站在多天线配置下的波束赋形和功率控制的联合优化设计,针对主用户和认知用户的不同优先级,给出了两者不同的权重设计方法,即：为了充分保证主用户的性能,主用户权重因子设置为1,而对于认知用户,通过自适应的权重设计因子适当降低认知用户的信泄噪比门限,从而为不同QoS性能需求的认知用户的接入提供了信泄噪比参考门限。第二种情况,给出了基站为多天线配置,主用户和认知用户在单天线配置下非理想信道的波束赋形设计方法,这里主用户和认知用户的信道都为非理想信道,为了分析简单起见,考虑了认知无线网络只有一个主用户和一个认知用户的情形,对应的优化问题是一个分式优化问题,通过适当变换,等价成分子与分母的独立优化,最后优化问题可以归结为凸优化问题,利用凸优化工具Sedumi给出优化问题的解,通过与相关文献的比较分析中看出该方法的优势所在。
Precoding technology, accompanied by detection cr equalization, under the assumption of channel state information is known at transmitter, acts as the pre-processing technique at transmitter side, which can mitigate the interference caused by the multi-access interference owing to multi user or multi-antenna and the inter symbol interference caused by frequency selective fading owing to multi path effect in wireless channel, accordingly, which reduced the complexity at receiver side. And for downlink channel, user equipment(UE) act as the receive terminal, owing to its limited size, power consumption and prices etc, parallelly, the equalization algorithm at UE can be arranged at Base Station(BS) side as pre-process technique, which is very important especially for reducing the complexity at UE.
Different from point to piont communication mode, in relay systems, when relay node added, the whole communication process is divided into2phases, the first phase is the communication between source and relay node, and the second phase is the communication between relay and destination node, if there exists direct path between source and destination node, the relay communication systems will have cooperrative diversity gain, precoding and equalization joint optimization algorithms are deeply researched in this dissertation when "with" or "without" direct path in relay systems, the content includes:
According to there exists direct path or not in three node relay systems, in chapter3, the precoding related algorithms are given in relay systems under the ideal channel state information when communication nodes equipped with multi antennas. In this part, assumed the channel estimation is perfect at receiver, moreover, there is no error appeared in the second phase's CSI feedback from destination to relay node, as well as the first phase's CSI feedback from relay to source node, therefore, this is the ideal case in fact, and two closed form resolutions are given in this chapter, the first one is based on MMSE criterion to the estimated signal at destination node, the lower bound performance is illutrated when the constraint condition is relaxed, even so, the performance is still better than the other linear resolution based on ZF/MMSE criterion because the joint optimization between the transfer matrix at relay node and scalar factor at destination node, the second method is based on the solvement of matrix equation with type "AX+XB=C", the optimization solution is given and compared with other related solutions, the outage probability and ergodic capacity are all impoved obviously to some extent.
When there exists direct path between source and relay node, two solutions are given in the following parts in chapter3, the first one is based on the joint equalization to the signal received at destimation node of the two paths, that is the direct path together with "source-relay-destination" path, also using the iterative solution,through the joint maximum rate combination(MRC) and get the optimal performance, the second solution is as following:the equalization solution is applied to each path first, then adjust the weight factor to these two pathes, the combining method is used at last, which is different from the first method, only scalar weight is adjusted here, owing to the joint optimization to the two pathes, the equalization matrix in these two phases is contained in each other, that is not only phase but also amplitude scalar weight are adjusted, the performance is compared between these two solutions, and the cooperative diversity gain is also given between the two cases ("with" or "without" direct path in the three nodes relay systems).
In chapter4, the robust precoding algorithm is given under non-ideal CSI in relay systems. Firstly, the non-ideal channel modeling method is introduced, and then the robust joint precoding and equalization optimization algorithm is supplied under the case without direct path between source and destination node, and detailedly the difference is analysed between robust and non-robust solution; take the case with direct path into account between source and destination node, it is divided into two sub-cases, the first one considers the joint optimization between source precoding and destination equalization, which can be ascribed as Schur-Concave optimization problem, and the closed resolution is given in this part; the second sub-case is related to the joint optimization between relay precoding and destination equalization, then upper and lower bound is shown, adopt the inner-point optimization method, and the resolution is given based on Matlab optimization toolbox.
In chapter5, the general precoding (such as smart antenna beamforming) algorithm in cognitive radio network is consided, and this chapter is divided into two problems. Firstly, beamforming and power control joint optimization is discussed in cognitive network, and according to the different priority between primary and cognitive users, two weight generation methods are supplied, in order to ensure the performance of primary users, the weight factor is set as1, and to cognitive user, the adaptive weight factor is adopted in order to reduce the access signal-to-leakage and noise ratio(SLNR) threshold, from this scheme, the reference SLNR threshold is supplied to cognitive users. In the second part, the beamforming designing algorithm is given under non-ideal CSI cognitive radio networks, the base station is configured with multi-antenna here, while for primary and cognitive users, there is only one antenna configured, and briefly, this case only considered one primary user and one cognitive user, and this problem is ascribed as fraction optimization problem, through proper decomposition, it can be regard as the numerator and denominator seperate optimization problem. And finally it is can be ascribed to convex optimization problem, and we can get the resolution based on Sedumi (the convex optimization toolbox), this solution is better than other solution in related references, as can be seen clearly through the comparative simulation analysis.
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[1]Enlight 256 white papers, http://www.lenslet.com/technology.asp