發(fā)布時(shí)間：2018-07-03 20:26

  本文選題：能量效率 + 資源分配�。� 參考：《東南大學(xué)》2016年博士論文

【摘要】：無(wú)線通信技術(shù)的發(fā)展和網(wǎng)絡(luò)規(guī)模的擴(kuò)大,導(dǎo)致了能量消耗爆炸式的增長(zhǎng),現(xiàn)有移動(dòng)通信行業(yè)已經(jīng)成為高能耗的產(chǎn)業(yè)。隨著高速率無(wú)線通信業(yè)務(wù)需求的不斷增長(zhǎng),通信系統(tǒng)的能耗會(huì)進(jìn)一步增加。綠色節(jié)能的通信網(wǎng)絡(luò)已經(jīng)成為不可避免的發(fā)展趨勢(shì)。另外,移動(dòng)通信系統(tǒng)中的無(wú)線資源是有限的,并且日益緊缺,例如：功率資源、時(shí)間資源、頻率資源、空間資源等。如何通過(guò)合理的資源配置獲得更高的系統(tǒng)性能,一直是無(wú)線通信技術(shù)研究的重要內(nèi)容。目前,高容量和高頻譜效率不再是無(wú)線資源管理追求的單一目標(biāo)。本論文主要從能量效率最優(yōu)的角度,對(duì)無(wú)線資源管理問(wèn)題進(jìn)行了研究。通過(guò)對(duì)無(wú)線資源進(jìn)行有效地配置,獲得最優(yōu)的系統(tǒng)能量效率,同時(shí)滿足無(wú)線網(wǎng)絡(luò)的各種需求。首先,研究了多天線多小區(qū)協(xié)作系統(tǒng)高能效的下行傳輸方法。在保證用戶最小速率要求的前提下,以系統(tǒng)能量效率為目標(biāo),進(jìn)行聯(lián)合的波束成型和功率分配的設(shè)計(jì)。將問(wèn)題描述為一個(gè)非線性的分?jǐn)?shù)規(guī)劃,并且包含非凸的約束條件。由于小區(qū)間干擾的存在和目標(biāo)函數(shù)的分?jǐn)?shù)結(jié)構(gòu),考慮采用多小區(qū)協(xié)作迫零的波束成型方案,消除干擾,從而簡(jiǎn)化目標(biāo)函數(shù)和約束條件的非凸形式。采用參數(shù)化的轉(zhuǎn)換對(duì)分?jǐn)?shù)規(guī)劃問(wèn)題進(jìn)行非分?jǐn)?shù)的處理,并采用迭代算法計(jì)算輔助參數(shù)�？紤]到協(xié)作迫零波束成型進(jìn)行零空間的分解計(jì)算的復(fù)雜性,采用一種波束跟蹤的方法進(jìn)行簡(jiǎn)化處理。在給定波束成型矩陣后,求解了能效最優(yōu)的功率分配問(wèn)題。仿真結(jié)果表明,該方案的能量效率性能優(yōu)于傳統(tǒng)的最大化系統(tǒng)容量的傳輸方案,并且對(duì)于慢變信道,該方案能夠以更低的復(fù)雜度獲得與傳統(tǒng)空間分解方法接近的能量效率性能。其次,研究了多小區(qū)正交頻分多址接入(OFDMA, Orthogonal Frequency Division Multiple Access)系統(tǒng)高能效的資源分配方法。該方案基于非完美的信道狀態(tài)信息進(jìn)行資源分配的設(shè)計(jì),并通過(guò)基站間的協(xié)作來(lái)處理同信道干擾。提出一種聯(lián)合的用戶調(diào)度、速率分配和功率分配算法,最大化系統(tǒng)能量效率。由于目標(biāo)函數(shù)和約束條件的概率化限制,通過(guò)對(duì)約束條件的轉(zhuǎn)化和近似,推導(dǎo)了速率變量與其他優(yōu)化變量的函數(shù)關(guān)系�？紤]到優(yōu)化問(wèn)題的混合非凸性質(zhì),即功率變量的連續(xù)性和用戶調(diào)度變量的離散性,采用一種交替計(jì)算的迭代算法,并證明了算法的收斂性。然后分別計(jì)算了能效最優(yōu)的用戶調(diào)度和能效最優(yōu)的功率分配。對(duì)功率分配子問(wèn)題進(jìn)行了下界縮放和參數(shù)化轉(zhuǎn)換,并且當(dāng)?shù)�代過(guò)程收斂時(shí)下界是緊的。通過(guò)最優(yōu)的能量效率的折衷函數(shù)曲線,分析了算法中最優(yōu)能量效率的一些有用性質(zhì)。仿真結(jié)果表明,雖然最優(yōu)問(wèn)題是非凸的,提出的方案可以得到與最優(yōu)能效的上界接近的能效性能。與傳統(tǒng)資源分配方案(如譜效最大化方案、不考慮信道誤差的方案和等功率輪詢調(diào)度方案等)相比,該方案可以獲得更高的能效性能。同時(shí),當(dāng)存在同信道干擾時(shí),提出的算法能夠以很低的吞吐量損失,獲得較大的能效增益。再次,研究了異構(gòu)網(wǎng)絡(luò)高能效的資源分配方法。該資源分配方案以權(quán)重能效為目標(biāo),即不同類型的基站具有不同的能效權(quán)重。將優(yōu)化問(wèn)題描述為一個(gè)非線性的分?jǐn)?shù)求和優(yōu)化問(wèn)題,并且同時(shí)考慮每個(gè)用戶的最小速率要求。為了處理優(yōu)化問(wèn)題的混合非凸形式,采用一種兩步方案,即分開(kāi)處理子信道分配和功率分配問(wèn)題。初始化一個(gè)功率分配,提出一種啟發(fā)式的子信道分配方案。首先找到距離速率要求最遠(yuǎn)的用戶,再將該用戶具有最高信干噪比的子信道分配給它。當(dāng)所有用戶的速率需求滿足后,每個(gè)子信道上以最大的信干噪比的方式進(jìn)行用戶選擇。對(duì)于給定的子信道分配,通過(guò)一階近似將目標(biāo)函數(shù)進(jìn)行線性化,然后通過(guò)參數(shù)化方法對(duì)分?jǐn)?shù)求和算法進(jìn)行處理,并通過(guò)一個(gè)兩層迭代算法獲得功率分配。仿真結(jié)果表明,提出的算法需要很少的迭代次數(shù)就可以達(dá)到收斂,并且可以獲得比全局能效最大化算法和譜效最大化算法更高的權(quán)重能效性能。另外,所提出的算法在宏基站和微微基站之間存在能量效率的折衷關(guān)系。然后,研究了中繼輔助的OFDMA系統(tǒng)高能效資源分配問(wèn)題�？紤]多中繼多用戶的蜂窩系統(tǒng),采用解碼轉(zhuǎn)發(fā)中繼協(xié)議。在保證系統(tǒng)最小速率的前提下,該資源分配算法以能量效率最大化為目標(biāo)。通過(guò)基站到中繼和中繼到用戶兩個(gè)時(shí)隙的聯(lián)合速率分配、功率分配和子載波分配,獲得最大的系統(tǒng)能量效率,保證最小速率和中斷概率等服務(wù)質(zhì)量要求。為了獲得全局最優(yōu)解,對(duì)優(yōu)化問(wèn)題的子載波分配變量進(jìn)行了連續(xù)化松弛,使得所有優(yōu)化變量(速率變量、功率變量、載波分配變量)統(tǒng)一為連續(xù)變量�？梢宰C明,最終的載波分配可以獲得和沒(méi)有松弛時(shí)相同的性能,即保證方案的全局最優(yōu)性。對(duì)優(yōu)化問(wèn)題進(jìn)行了參數(shù)化的處理,并通過(guò)二分法進(jìn)行了求解。由于在解碼轉(zhuǎn)發(fā)協(xié)議中,鏈路的容量為兩時(shí)隙的鏈路容量的最小值,所以每次迭代中需要求解的問(wèn)題是一個(gè)最大化最小問(wèn)題。通過(guò)引入輔助變量將該問(wèn)題轉(zhuǎn)換為上鏡圖形式,并在對(duì)偶問(wèn)題求解時(shí)通過(guò)最優(yōu)性判斷消除了新引入的輔助變量,獲得全局最優(yōu)的資源分配解。另外,采用了比例公平的機(jī)制進(jìn)行了能效方案的設(shè)計(jì),平衡系統(tǒng)的能量效率和用戶的公平性。仿真結(jié)果表明,與傳統(tǒng)方案相比,提出的方案可以獲得更高的能效性能。同時(shí),系統(tǒng)能效與譜效、系統(tǒng)能效與用戶公平性、系統(tǒng)譜效與用戶公平性存在一定的折衷關(guān)系。最后,研究了中繼輔助的蜂窩系統(tǒng)低復(fù)雜度的高能效功率分配和速率自適應(yīng)方案�？紤]多中繼多用戶的蜂窩系統(tǒng),采用前向放大中繼協(xié)議。該資源分配以能量效率為目標(biāo),同時(shí)考慮系統(tǒng)的最小速率限制和最大發(fā)射功率限制等約束條件。通過(guò)兩個(gè)時(shí)隙聯(lián)合的功率和速率的分配設(shè)計(jì),獲得最大的系統(tǒng)能量效率和低復(fù)雜度最優(yōu)的資源分配方案�？紤]到問(wèn)題的非凸性,利用高信噪比近似和參數(shù)化轉(zhuǎn)換的方法,對(duì)優(yōu)化問(wèn)題進(jìn)行了求解。一般地,分?jǐn)?shù)規(guī)劃問(wèn)題需要迭代求解,并且在迭代過(guò)程中,需要多次求解多約束的優(yōu)化問(wèn)題。根據(jù)最優(yōu)解的可行域范圍,分別研究了該優(yōu)化問(wèn)題的最優(yōu)解可能出現(xiàn)的幾種形式,并分別進(jìn)行了求解。然后利用二分法的上下界特殊的更新形式,以及最優(yōu)能效與功率和速率的折衷關(guān)系,在每次迭代中判決最優(yōu)解是否滿足條件,從而進(jìn)行判決循環(huán)跳出。該方案可以減少迭代次數(shù),以及降低每次迭代優(yōu)化問(wèn)題的計(jì)算復(fù)雜度。仿真結(jié)果表明,該方案可以在不損失能效性能的前提下,降低算法的復(fù)雜度,同時(shí)保證獲得全局最優(yōu)的能量效率性能。尤其是當(dāng)最大發(fā)射功率約束或最小速率約束要求嚴(yán)格時(shí),提出的算法僅需要很少的迭代次數(shù)。另外,與譜效優(yōu)先的算法和不考慮信道非完美性的方法相比,提出的方案可以獲得比更高的能效性能。
[Abstract]:The development of wireless communication technology and the expansion of the network scale have led to the explosive growth of energy consumption. The existing mobile communications industry has become a high-energy consumption industry. With the increasing demand for high-speed wireless communication services, the energy consumption of communication systems will be further increased. The green and energy-saving communication network has become an inevitable issue. In addition, wireless resources in mobile communication systems are limited and are increasingly scarce, such as power resources, time resources, frequency resources, space resources, and so on. How to obtain higher system performance through reasonable allocation of resources has always been an important content of wireless communication technology research. At present, high capacity and high frequency spectrum efficiency are no longer available. This paper is the single goal of wireless resource management. This paper mainly studies the problem of wireless resource management from the perspective of the optimal energy efficiency. Through the effective configuration of wireless resources, the optimal system energy efficiency is obtained and the various needs of the wireless network are met. First, the multi antenna and multi cell collaboration system is studied. Under the premise of ensuring the minimum rate of users, the design of joint beamforming and power allocation is designed with the goal of system energy efficiency. The problem is described as a nonlinear fractional programming with a non convex constraint condition. Due to the existence of small interval interference and the fractional junction of the target function In order to eliminate interference and simplify the non convex form of the objective function and constraint condition, the method of parameterized conversion is used to deal with the fractional programming problem, and the auxiliary parameters are calculated by the iterative algorithm. A beam tracking method is used to simplify the processing. The power allocation problem with the best energy efficiency is solved after a given beamforming matrix. The simulation results show that the energy efficiency performance of the scheme is better than that of the traditional transmission scheme maximizing the system capacity. And for the slow changing channel, the scheme can be reduced to a lower complex. The heterozygosity obtains the energy efficiency performance that is close to the traditional spatial decomposition method. Secondly, the high efficiency resource allocation method of the OFDMA (Orthogonal Frequency Division Multiple Access) system is studied. The scheme is designed based on the imperfect channel state information, and through the base station. A joint user scheduling, rate allocation and power allocation algorithm is proposed to maximize the energy efficiency of the system. Due to the probability limitation of the target function and constraint conditions, the function relationship between the rate variable and the other optimal variables is derived by the transformation and approximation of the constraint conditions. The hybrid non convex property of the problem, that is, the continuity of power variables and the discreteness of the user scheduling variables, the convergence of the algorithm is proved by an iterative algorithm, and then the energy efficiency optimal user scheduling and energy efficiency optimal power allocation are calculated. The power gamete problem is reduced and parameterized. And when the iterative process converges, the lower bound is tight. Some useful properties of the optimal energy efficiency in the algorithm are analyzed by the optimal energy efficiency tradeoff function curve. The simulation results show that, although the optimal problem is non convex, the proposed scheme can get the energy efficiency close to the optimal energy efficiency. The scheme, such as the scheme of spectral efficiency maximization, the scheme of the channel error and the equal power polling scheduling, can obtain higher energy efficiency performance. At the same time, the proposed algorithm can gain more energy efficiency with low throughput loss when there is the same channel interference. Again, the high energy efficiency of the heterogeneous network is studied. Resource allocation method. The resource allocation scheme aims at weighting energy efficiency, that is, different types of base stations have different energy efficiency weights. The optimization problem is described as a nonlinear fractional sum optimization problem, and the minimum rate requirements for each user are considered at the same time. In order to deal with the mixed non convex form of the optimization problem, a kind of two is used. The step scheme is to separate the sub channel allocation and power allocation problem separately. Initialize a power allocation and propose a heuristic subchannel allocation scheme. First, the user with the farthest distance rate is found, and then the subchannel with the maximum signal to noise ratio is assigned to it. Each subchannel when the rate needs of all users are satisfied. The channel is selected in the way of the maximum signal to noise ratio. For given subchannel allocation, the target function is linearized by the first order approximation, then the fractional sum algorithm is processed by the parameterization method, and the power distribution is obtained by a two layer iterative algorithm. The simulation results show that the proposed algorithm needs little. The number of iterations can be convergent and the weight efficiency performance can be higher than the global energy efficiency maximization algorithm and the spectral efficiency maximization algorithm. In addition, the proposed algorithm has the tradeoff between the energy efficiency between the Acer station and the micro base station. Then, the high efficiency resource allocation problem of the secondary assisted OFDMA system is studied. Based on the minimum rate of the system, the resource allocation algorithm aims at maximizing the energy efficiency. The maximum system energy is obtained from the base station, the relay and the relay to the user's two time slots, the power allocation and the subcarrier allocation. In order to obtain the global optimal solution, in order to obtain the global optimal solution, the subcarrier allocation variable of the optimization problem is continuous relaxation, so that all the optimization variables (rate variables, power variables, carrier allocation variables) are unified as continuous variables. It can be proved that the final carrier allocation can be obtained and failed. The same performance, that is, to ensure the global optimality of the scheme, is to ensure the global optimality of the scheme. The optimization problem is parameterized and solved by a dichotomy. Since the capacity of the link is minimum of the link capacity of the two slot in the decoding and forwarding protocol, the problem that needs to be solved in each iteration is a maximum minimum problem. By introducing the auxiliary variable, the problem is converted to the form of the upper mirror, and the newly introduced auxiliary variable is eliminated by the optimality judgment in the solution of the dual problem, and the global optimal resource allocation solution is obtained. In addition, the energy efficiency scheme is designed to balance the energy efficiency of the system and the fairness of the user by using the mechanism of proportional fairness. The simulation results show that compared with the traditional scheme, the proposed scheme can obtain higher energy efficiency. At the same time, there is a tradeoff between system energy efficiency and spectral efficiency, system energy efficiency and user fairness, system spectral efficiency and user fairness. Finally, the high energy efficient power allocation and rate self distribution of the low complexity of the relay assisted bee nest system are studied. Adaptive scheme. Considering the multi relay and multiuser cellular system, the forward amplification relay protocol is adopted. The resource allocation is aimed at energy efficiency, and the minimum rate constraints and maximum transmission power constraints are considered. The maximum system energy efficiency is obtained through the allocation of the power and rate of two slots combined with the power and rate. In consideration of the non convexity of the problem, the optimization problem is solved by using the method of high signal-to-noise ratio approximation and parameterized conversion. Generally, the fractional programming problem needs to be solved iteratively. In the iterative process, the optimization problem of multiple constraints needs to be solved many times. According to the feasible domain model of the optimal solution. Several forms of the optimal solution of the optimization problem are studied and solved respectively. Then, the special updating form of the upper and lower bounds of the dichotomy, and the tradeoff between the optimal energy efficiency and the power and rate are used, and the decision of the optimal solution satisfies the condition in each iteration, thus the decision cycle is jumped out. It can reduce the number of iterations and reduce the computational complexity of each iterative optimization problem. The simulation results show that the proposed scheme can reduce the complexity of the algorithm without loss of energy efficiency and guarantee the energy efficiency performance of the global optimal. Especially when the maximum emission power constraint or minimum rate constraint is strict. The proposed algorithm requires only a small number of iterations. In addition, the proposed scheme can obtain higher energy efficiency compared with the spectral efficiency priority algorithm and the method that does not consider the channel imperfections.
【學(xué)位授予單位】：東南大學(xué)
【學(xué)位級(jí)別】：博士
【學(xué)位授予年份】：2016
【分類號(hào)】：TN92

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