射频无线能量传输（Wireless Energy Transfer，WET）技术以射频信号为能量载体，通过射频/直流转换将接收到的射频信号转换为能量。该技术可以为一些低功耗的无线设备提供持续的无线供电，从而解决了此类设备由于能量不足而导致的生命周期瓶颈问题。本文主要进行信息与能量联合传输的方法研究。目前此课题的两个主流研究方向为无线携能通信（Simultaneous Wireless Information and Power Transfer，SWIPT）和无线供能通信（Wireless Powered Communications，WPC）。SWIPT技术利用射频信号的双重用途（既可作为信息的载体，又可作为能量的载体），在接收端将接收信号分为两部分，一部分用作信息解调，而另一部分用作能量采集。WPC技术使用采集后传输（Harvest-then-Transmit） 协议：用户设备（User Equipment，UE）接收到由基站端传输的能量之后存储在电池当中，并使用电池中的能量进行上行信息传输。 本文分别针对SWIPT和WPC两个主流研究方向，通过研究资源分配算法，联合优化无线信息与能量传输。本文所关注的主要问题有：第一，SWIPT技术将接收信号分成两部分，分别进行信息解调和能量接收，导致频谱效率和接收到的能量不能同时达到最优。因此，需要通过资源分配方法，根据具体服务质量需求，进行频谱效率和接收能量两方面性能的权衡。 考虑到未来移动网络的主要趋势是小区密集化，本文将SWIPT技术应用于协作多点（Coordinated Multi-point，CoMP）的下行链路传输（简称为SWIPT-CoMP 下行传输）当中，以达到信号增强和干扰消除的目的。第二，在SWIPT-CoMP传输中，容量有限的回程链路将会制约系统的性能。能量采集与信号所携带的数据无关。然而，传统的功率分裂技术无法在发送端分离信息信号和能量信号，能量数据必须通过回程链路传输，这样就造成了回程链路容量资源的浪费。第三，WPC技术研究中，接收端有限电池容量和射频/直流能量接收灵敏度（即射频/直流电路最小输入功率）等约束影响着算法设计的复杂度和系统性能，但是尚未有文献针对以上两个实用的约束展开深入研究。根据上述三个问题，本文的主要内容和贡献总结如下。 1. 本文将针对SWIPT-CoMP下行链路传输提出满足用户公平性的无线信息与能量联合传输算法。通过最优收发双端的波束成形矩阵和接收端功率分裂因子，以达到满足用户公平性要求的最优频谱效率。最优问题建模为一个最大最小（Max-Min）频谱效率问题。即在满足每用户的能量接收约束和每基站发送功率约束的条件下，最大化所有用户中的最小频谱效率。本文通过引入松弛变量、利用接收端最小均方误差信号检测改写频谱效率表达式、利用连续凸近似解决能量接收约束的反凸问题等方法将原始非凸问题转换为凸问题，并最终使用交替凸优化的方法求解此问题。原始问题最终转换成为二阶锥规划（Second Order Cone Programming，SOCP）问题。相对于传统的半定规划（Semi-definite Programming，SDP），SOCP避免了SDP中波束成形矩阵的协方差矩阵秩为1的约束，并且具有较快的运算速度。 2. 针对回程链路容量有限的SWIPT-CoMP下行传输，本文提出了一个基于双极化天线的双端信号分离算法。该算法利用极化分集（Polarization Diversity），将信息解调信号和能量接收信号 在射频域上分离开来。相对于传统的接收端功率分裂方法，该算法具有如下优点。 首先，发送端信息信号和能量信号分离使得能量数据不再需要从中心处理单元（Central Unit，CU）发送给各个射频拉远单元（Remote Radio Unit，RRU），避免能量信号占用回程链路资源的情况。这是因为能量接收与信号具体携带的数据无关，能量数据仅需在各个RRU上产生即可。其次，在接收端的射频域信号分离使得绝大多数信息信号仅用作信息采集，从而提高了回程链路资源利用率。事实上，仅有极少一部分由于交叉极化所引起的信息信号泄露到另一极化方向并被用作能量采集。再次，双端信号分离算法避免了为求解功率分裂因子而产生的迭代运算。此外，为了高效利用回程链路资源，本文采用了速率分裂的方法，对信息信号使用灵活的CoMP的传输模型，即联合处理（Joint Processing，JP）和协作波束成形（Coordinated Beamforming，CB）模型。此方法将信息数据分为公有和私有两部分，其中公有部分需要由CU发送给所有RRU并使用CoMP-JP模式传输，而私有部分仅从CU传输给该数据用户的主服务RRU并且使用CoMP-CB模式传输。 最优问题建模为最大化系统频谱效率，同时满足每用户能量接收约束、每基站（Base Station，BS）发送功率约束、和每条回程链路容量约束。 3. 针对WPC，本文主要研究了基于有限电池容量约束和能量接收灵敏度约束的多输入多输出系统上行传输。其中，有限电池容量约束要求本文研究多时隙优化，并且使得各个时隙之间相互不独立。相对于传统单时隙优化，多时隙优化虽然更为复杂但是具有更好的性能。 此外，本文将WPC的下行WET阶段建模为一个参数为${p}$的伯努利过程。即，当基站进行下行WET之前，首先估计接收信号功率。当且仅当预估接收功率大于能量接收灵敏度时，基站才进行下行WET，从而减小基站能量浪费。为最优接收能量，基站使用能量波束成形技术，并由此计算了下行WET的概率${p}$。最优问题建模为最大化多个时隙内的平均频谱效率，同时满足电池容量约束（即存入电池的能量不能大于电池的容量）和能量因果约束（即上行无线信息传输仅能使用当前时隙和之前时隙接收到的能量）。为了求解这一非凸问题，本文提出了一种联合最优功率分配和时间分配算法。在功率分配时，首先为当前时隙信息传输分配已有电池能量的${p}$部分，再使用注水算法分配每个天线上的功率。 同时，本文证明了时间分配为一个凸优化问题，可使用一维搜索进行求解。相对于已有算法，联合功率和时间分配算法具有复杂度低，频谱效率高的优点。此外，本文还将该算法扩展到多用户场景。

Since radio frequency (RF) signals can be transformed into energy through RF to direct current (RF/DC) circuits, RF wireless energy transfer (WET) can continuously and wirelessly ge low-energy-cost wireless devices, tackling the lifetime bottleneck of energy limited devices caused by energy shortage. This paper focuses on the research on jointly wireless information and energy transfer methods, which can be classified into two promising researching fields, i.e., simultaneous wireless information and power transfer (SWIPT), and wireless powered communications (WPC). Based on dual purposes of RF signals, SWIPT splits the received signal into two parts at the receiver. One part is used to information decoding (ID) and the other part is used to energy harvesting (EH) WPC adopts the harvest-then-transmit protocol, where the user equipment (UE) stores the received energy into their batteries, and then allocates these energy for the uplink information transmission.

This paper puts emphasis on the joint design of wireless information and energy transfer through resource allocation, for both SWIPT and WPC. We focus on the following problems in this paper. Firstly, in SWIPT, spectral efficiency and received energy cannot achieve optimal value at the same time, because the received signal has to be split to ID and EH, respectively. The trade-off between ID and EH should be carefully designed through resource allocation, based on specific quality of service requirements of communication scenarios. Considering that one of the key features of the next generation of mobile networks is network densification, we introduce SWIPT in the coordinated multi-point (CoMP) downlink transmission (which is called SWIPT-CoMP downlink transmission), to enhance received signals and to reduce interference. Secondly, the limited backaul capacity can directly affect the performance of SWIPT-CoMP systems. EH is independent with EH data. The traditional power splitting method cannot split EH and ID signals at the transmitter, and therefore EH data has to be transmitted through backhaul, wasting the backaul capacity. Thirdly, for WPC systems, limited battery capacity and energy sensitivity of (i.e., the minimum input signal power) RF/DC circuits will impact complexity of algorithm design and the system performance. However, up to now, no literature has taken these two constaints into consideration. Based on these three problems, the main content and contributions are summarized as follows.

1. This paper proposes a fair power splitting algorithm for SWIPT-CoMP downlink transmissions. Based on beamforming design at the transmitter and the receiver, and the optimal power splitting factor of the receiver, optimal spectral efficiency is achieved. We formulate this problem as a max-min problem, which maximizes the minimum spectral efficiency among all the users with satisfying the harvested energy constraint and the per-base station (BS) transmission power constraint. This problem is a non-convex problem,

which can be solved by a combination of mathematical methods, including introducing the slack variable, transforming spectral efficiency expression based on the minimum mean square error (MMSE) signal detector, addressing the reverse convex received energy constraint by successive convex approximation (SCA), and finally solving the original problem through alternating convex optimization (ACO). Therefore, the original optimization problem is recast as a second order cone programming (SOCP) problem. Compared with traditional semi-definite programming (SDP), SOCP has the high convergent speed, and avoids the rank one problem of SDP, where the rank of beamformer's covariance matrix should be one.

2. This paper proposes a dual polarized-antenna based double side signal splitting method for SWIPT-CoMP transmission with limited backhaul capacity. This method takes the use of polarization diversity to split ID and EH signals at the RF domain. Compared with the conventional power splitting method, the advantages of the proposed algorithm are multiple folds. First, the splitting of ID and EH signals at the RF domain makes that there is no needs to transmit energy data from the central unit (CU) to remote radio units through backhaul links, because EH is independent with energy data of the received signal and EH data can be created at RRUs. This practice can avoid consumption of energy harvesting signals on backhaul capacity resources. Second, based on signal splitting at the RF domain of the receiver, few ID signals leak to the EH due to the cross polarization, improving the utilization of backhaul capacity. Third, the proposed method reduces computational complexity because solving the power splitting factor by the iteration method is avoided. This paper also adopts the rate splitting method to adaptively use CoMP transmission model (i.e., CoMP joint processing (JP) and CoMP coordinated beamforming (CB)), which further splits user data into common and private parts. The common part adopts CoMP-JP transmission model, which needs transmitting common data from CU to all RRUs. The private part adopts CoMP-CB transmission model, where the private data is transmitted to the serving RRU of the data's user. The problem is formulated to maximize sum spectral efficiency while satisfying per-user harvested energy constraints, per-base station transmission power constraints, and per backhaul link capacity constraints.

3. For the RF wireless powered multiple input multiple output (MIMO) uplink transmission, we put emphasis on limited capacity batteries and energy receiving sensitivity (i.e., minimum required input power of the RF/DC circuit). We focus on optimal problem among multiple slots due to the limited capacity battery constraint which leads to independent between each slot. Multiple slot optimization has a better performance but suffers from more complex problem than existing single slot optimization. This paper forms a Bernoulli process with parameter \emph{p} for the downlink WET. Before downlink WET, the BS estimates the received power at the receiver and performs WET only if the estimated power is larger than the energy receiving sensitivity, reducing the energy waste of the BS. We adopt energy beamforming during WET, based on which the WET probability ${p}$ is calculated. The optimal problem is formulated as maximizing the average spectral efficiency among multiple slots while satisfying the energy battery capacity constraint (i.e., the maximum energy can be stored in the battery), and the energy causality constraint (i.e., the UL transmission can only use the energy harvested at the current and previous slots). To solve this non-convex problem,

this paper provides joint time and power allocation algorithm. During the power allocation, ${p}$ fraction of the available energy in the battery is allocated for the current slot uplink transmission, then water filling algorithm is adopted to allocate power for each antenna.

Meanwhile, the time allocation is demonstrated as a convex problem and can be solved by one-dimensional search. Compared with existing methods, the proposed algorithm has the advantages of low computational complexity and high spectral efficiency performance. Furthermore, this paper has expanded the proposed algorithm into the multiple user scenario.