OFDM基础中英文对照外文翻译文献

外⽂参考⽂献翻译-中⽂基于4G LTE技术的⾼速铁路移动通信系统KS Solanki教授，Kratika ChouhanUjjain⼯程学院，印度Madhya Pradesh的Ujjain摘要：随着时间发展，⾼速铁路（HSR）要求可靠的，安全的列车运⾏和乘客通信。

为了实现这个⽬标，HSR的系统需要更⾼的带宽和更短的响应时间，⽽且HSR的旧技术需要进⾏发展，开发新技术，改进现有的架构和控制成本。

为了满⾜这⼀要求，HSR采⽤了GSM的演进GSM-R技术，但它并不能满⾜客户的需求。

因此采⽤了新技术LTE-R，它提供了更⾼的带宽，并且在⾼速下提供了更⾼的客户满意度。

本⽂介绍了LTE-R，给出GSM-R与LTE-R之间的⽐较结果，并描述了在⾼速下哪种铁路移动通信系统更好。

关键词：⾼速铁路，LTE，GSM，通信和信令系统⼀介绍⾼速铁路需要提⾼对移动通信系统的要求。

随着这种改进，其⽹络架构和硬件设备必须适应⾼达500公⾥/⼩时的列车速度。

HSR还需要快速切换功能。

因此，为了解决这些问题，HSR 需要⼀种名为LTE-R的新技术，基于LTE-R的HSR提供⾼数据传输速率，更⾼带宽和低延迟。

LTE-R能够处理⽇益增长的业务量，确保乘客安全并提供实时多媒体信息。

随着列车速度的不断提⾼，可靠的宽带通信系统对于⾼铁移动通信⾄关重要。

HSR的应⽤服务质量（QOS）测量，包括如数据速率，误码率（BER）和传输延迟。

为了实现HSR的运营需求，需要⼀个能够与 LTE保持⼀致的能⼒的新系统，提供新的业务，但仍能够与GSM-R长时间共存。

HSR系统选择合适的⽆线通信系统时，需要考虑性能，服务，属性，频段和⼯业⽀持等问题。

4G LTE系统与第三代（3G）系统相⽐，它具有简单的扁平架构，⾼数据速率和低延迟。

在LTE的性能和成熟度⽔平上，LTE- railway（LTE-R）将可能成为下⼀代HSR通信系统。

⼆ LTE-R系统描述考虑LTE-R的频率和频谱使⽤，对为⾼速铁路（HSR）通信提供更⾼效的数据传输⾮常重要。

附录一、英文原文OFDM Channel Estimation in the Presence of Frequency Offset andPhase NoiseAbstract–In this paper, we consider OFDM channel estimation in the presence of frequency offset and phase noise. In the literatures, most channel estimation methods assume perfect frequency synchronization and the knowledge of channel statistics. Phase noise and residual frequency offset cause inter-carrier interference (ICI), which consequently impairs the accuracy of channel estimation. The lack of knowledge of channel statistics can make channel estimation much harder. To resolve these problems, we propose with the aid of cyclic prefix (CP) based frequency offset estimation statistics-independent channel estimation. We iteratively search for the most likely channel impulse response (CIR) length, and use it not only for the optimum compensation of frequency offset, but also for finding the optimum window to filter the least square (LS) channel estimate which further suppress the effects of ICI and noise. The proposed scheme is compared with conventional methods for both non-interpolation and interpolation cases2. Numerical results are presented to illustrate the effectiveness of the proposed scheme.I. INTRODUCTIONOrthogonal frequency division multiplexing (OFDM) is a bandwidth efficient transmission technique which provides high bandwidth efficiency and is quite effective in handling time dispersion of multipath fading channels. It has been chosen as the transmission method of many standards in wire and wireless communications, such as Digital Subscribe Line (DSL), European Digital Audio and Video Broadcasting (DAB/DVB), IEEE 802.11a and European HIPERLAN/2 for wireless local area network (WLAN) etc..Based on multi-carrier modulation [1], OFDM has symbol period long enough to eliminate inter-symbol interference (ISI) caused by time dispersive channels. Nevertheless, the multicarrier modulation is also sensitive to frequency offset and phase noise. Frequency offset and phase noise cause loss of orthogonality among subcarriers and consequently introduce inter-carrier interference (ICI). The effect of phase noise has been analyzed in many papers [2]-[4]. Many approaches have also been proposed to analyze, estimate and compensate frequency offset [2][5]-[10]. Though it is impossible to estimate random phase noise, frequency offset estimation can be achieved by using pilot signals [5][6]. As these methods cause loss of bandwidth efficiency, non-pilot-aided frequency offset estimation has be used [7]-[10]. The cyclic prefix (CP) based method, initially proposed in [9], is quite attractive among non-pilot-aided approaches due to its simplicity. Nevertheless, the accuracy of the CP-based method could not be guaranteed for multipath fading channels. Later, asproposed in [10], the method of [9] was improved by considering the channel impulse response (CIR) length. The proposed method in [10], however, is not feasible in cases when the CIR length is unknown.Furthermore, channel estimation is a very important issue for OFDM systems. Blind channel estimation is a desirable approach as it does not require pilot signals. It does require, however, a large amount of data and thus higher computational complexity. With perfect frequency synchronization (without residual frequency offset), different pilot-symbol-aided channel estimation methods can be applied in OFDM [11]-[14]. The maximum likelihood/least square (ML/LS) estimators of [11] and [12] can readily be implemented without knowing channel statistics. The minimum mean square error (MMSE) estimators in [12]-[14], however, are more robust against noise and perform better than the ML/LS estimators. Nevertheless, its dependence on channel statistics and the operating signal to noise ratio (SNR) makes it disadvantageous. Despite its robustness against mismatch [13][14], when there is no a priori knowledge of channel statistics and the operating SNR, the performance inevitably degrades. Without the assumption of perfect frequency synchronization, the performance may further degrade due to frequency offset and phase noise.In this paper, we consider statistics-independent channel estimation in the presence of frequency offset and phase noise. As a function of the CIR length, the LS channel estimate results, which is based on the CP-based frequency offset estimation and compensation, are used to search for the CIR length iteratively. The minimization of channel estimation errors leads to the most likely CIR length, which is then used to optimize frequency offset estimate, and filter the LS channel estimate reducing its sensitivity to noise and ICI. Thus better performance is achieved.The paper is organized as follows. The OFDM system model is introduced in Section II. Section III presents and analyzes the proposed frequency offset and channel estimation scheme. Section IV provides the numerical results to illustrate the effectiveness of the proposed scheme. The paper is concluded in Section V.II. OFDM SYSTEM MODELThe basic principle of OFDM is to divide each data symbol into N samples (subcarriers). The length N discrete Fourier transform (DFT) is applied to those samples and a cyclic prefix(CP) is added to eliminate ISI. Data is recovered at the receiver in reverse order. We define the length of CP as g N and the length of CIR as L , and further assume that CIR is finite and its length is less than that of CP, i.e., L < g N .At OFDM receiver, following the DFT and due to the presence of frequency offset and phase noise, the received k th sample of the m th symbol in frequency domain can be expressed by()()()()()()2F g m n m N N n j N m m m m n r k s n g n eπεφξ++⎛⎫+ ⎪ ⎪⎝⎭⎧⎫⎪⎪=⊗+⎨⎬⎪⎪⎩⎭(1)where sm (n), gm (n) and ()n n φ denote the transmitted signal, the CIR and phase noise, respectively. ξm (n ) indicates the AWGN noise. ε is the normalized frequency offset3. We assume ε ≤ 0.5 and the 3 dB line width of phase noise is much less than frequency offset. Equivalently, (1) can be given by()()()()()()()()1m m 0I 0I N m m m m m m l l k r k x k h k x l h l l k z k -=≠=+-+∑(2)where x m (k ), h m (k ) and z m (k ) are the corresponding frequency domainexpressions of s m (n ) , g m (n ) and ξm (n ) respectively. I m (i )is a function of ε and φm (n ) , given by:()()()()2120g n n j m N N N N j n i N m n e I i eN πεπεφ+⎡⎤-⎣⎦++⎡⎤⎣⎦==∑(3)where i = 0,..., N −1 . From (2) together with (3), frequency offset and phase noise cause the common phase error (CPE) and introduce inter-carrier interference (ICI) as well. For the m th symbol, Representing (2) by matrix yields=+r pxh z(4)The frequency offset and phase noise in P affects the accuracy of channel estimation. We cannot measure phase noise, but frequency offset can be estimated and compensated to reduce its effects on channel estimation. The effects of phase noise and residual frequency offset (due to estimation errors) can possibly be suppressed by filtering channel estimate. For perfect frequency and phase synchronization, P reduces to identity matrix and therefore the performance of channel estimation canbe guaranteed.III. FREQUENCY OFFSET AND CHANNELESTIMATORSIn the presence of frequency offset and phase noise, both offset and channel response should be estimated to guarantee good receiver performance. Phase noise variance is assumed to be much less than unity.We a new scheme with which, by iteratively searching for the most likely CIR length and using it for both frequency offset and channel estimation, performance is greatly improved in comparison with conventional approaches. The proposed scheme is shown in Fig.1.A. CP-based Frequency Offset EstimatorCP-based frequency offset estimator in [9] is quite simple and bandwidth efficient,but it does not consider the effects of multipath fading and estimation results may not be accurate as it is based on the CP of one symbol only. The method proposed in [10] improves that of [9] by considering CIR length and taking more symbols into consideration. However, when averaging frequency offset estimates obtained separately from each symbol, accumulated errors may be larger than expected.Moreover, its dependence on the CIR length is quite a problem when channel statistics is not available. A different method is thus proposed in this paper to solve these problems. Like [10], several symbols are used to estimate frequency offset, but it does not accumulate errors by using the following expression for estimation.()()()()11*011ˆ2g M m m g m k N p p angle r k r k N N M επ--==-+⎛⎫=+ ⎪⋅⎝⎭∑∑ (5) where p is the CIR length which is unknown, M is the number of symbols used for averaging. The unknown parameter p (as we will show later) can be set initially to one and be found by iteration. Therefore, we can still get the accurate estimate of (5) even without channel statistics.B. Channel EstimatorChannel estimation is quite crucial for OFDM systems. Also as stated earlier, LS method is advantageous over MMSE method due to its simplicity and independence of channel statistics. Hence assuming in this paper unknown channel statistics, we will focus on LS method. The estimate of (5) can be used to compensate for the frequency offset, after which, we will get from (4) thatp '=+r p xh z (6)where p P 'takes the same form of P except that I (i ) is replaced by()()n 12(m n)((p))/N 2m/N (n)01g N j N N p n I i e N πεεπφ-++-++⎡⎤⎣⎦='=∑ (6a)As shown in Fig. 1, channel estimate is easily obtained by using the LS method, which can be expressed by1ls p p -=p x r (7)The LS method is quite sensitive to interference and noise. Therefore without perfect frequency and phase synchronization, the effects of frequency offset and phase noise become worse. Furthermore, there would still be residual frequency offset even after compensation, which, together with phase noise, introduces CPE and ICI. Though CPE might partially be compensated by channel estimation itself, ICI will definitely affect the accuracy of such estimation. Therefore, some method must be introduced to reduce the sensitivity of channel estimation to interference and noise.As CIR has a finite length in time domain, the response beyond this CIR length is thus due to ICI and noise. Hence, a window function may be used to filter out these effects of ICI and noise on channel estimates. In time-domain, using a window function on (7) yieldsH s ls l p p p h WW B h = (8)()()()()()011T p p diag diag b bp bp N ==-⋯⎡⎤⎣⎦p B b is an N × N diagonal matrix defined by the window function[]()[]()[]10,2(i)0.420.5cos()0.08cos(),2021,1m i p i p i p b i p p p p i p N ππ⎧∈⎪⎪⎪--⎪=++∈⎨⎪⎪∈+-⎪⎪⎩ (9)Note that rectangular window is not used here as it introduces more high frequency components than is tolerated which causes a distortion of channel frequency response. Instead, due to its excellent descending properties, Blackman function is used in (9), as the intermediate part of the designed window.C. The Most Likely CIR Length and Final SolutionThe most likely CIR length can be found by minimizing the cost function2ls p -h h(10)To simplify the process, the window function is not used during the search, and we only have to find the proper p that produces the frequency offset estimate minimizing (10). Unfortunately, there are two unknown parameters, h and ε in (10), which makes such direct minimization difficult.However, we notice that, in the absence of AWGN noise, as p increases, frequency offset estimate of (5) becomes more accurate and the difference of channel estimates of (7) for adjacent p ’s values becomes smaller, and minimum when p isgreater than or equal to CIR length. Therefore, the minimization of (10) can be obtained by the first occurrence of the minimum of21l s l s p p --h h(11)In the presence of AWGN noise, we have to assert that the value p that minimizes (10) is the same as that of (11) before we can use (11). Statistically the minimum of (11) would occur when p is close to the CIR length when noise is not so high. (11) decreases when p increases from 1 to the CIR length since the CIR effects decreases. For increasing p, which is equivalent to using fewer samples (see (5)), the frequency offset estimation becomes less accurate and so does the channel estimate. Thus when p becomes greater than CIR length but less than CP, the difference of (11) is statistically higher when p is greater than the CIR length than when p is close to the CIR length. Hence, the minimum of (11) occurs with high probability at the point where p is equal to the CIR length. Hence, the most likely CIR length can be found by varying p between 1 and g N , and choosing the value which satisfies the following criteria22112ls ls ls ls p p p p ----≤-h h h h(12)2211ls ls ls ls p p p p -+-≤-h h h h(13)To examine the effectiveness of the criteria, we resort to computer simulation. The final channel estimate is expressed by1H ls p p P h WB W r -= (14)Where P represents the estimated value of p . Note that CIR length can found with only a single search as in most cases it does not change even in a time variant channel and the result might be used for quite a few OFDM symbols.D. Interpolated Pilot SymbolsThe aforementioned channel estimator is for non-interpolation case. However, interpolation case is often used where pilot signals are multiplexed into the transmitted data stream, i.e., pilot signals are inserted into data stream every f D samples.Without loss of generality, we assume that / f K = N D is integer, i.e., there are K pilot samples per symbol. In this case, the principle of the proposed scheme remains correct, except that the size of DFT matrix W and the window diagonal matrix p B become K × K diagonal matrices. The searching process for p remains the same, but the interpolation must be applied to the result of (8) to get the complete channel estimate.IV. NUMERICAL RESULTSThe proposed scheme was evaluated by simulation. Part of simulation parameters is based on IEEE 802.11a standard e.g., DFT length, CP length and sample period s T are 64, 16 and 0.05μ s , respectively. 3dB line width of phase noise equals 0.1% of subcarrier spacing. The actual frequency offset ε is set to 0.1382. Number of symbols used to estimate frequency offset M equals 8. Exponential Rayleigh fading channel is used with the exponential power delay profile specified by ()maxmax 1s LT rms e e ττττ-where rms τ , s T and L are the mean delay spread, sample period and CIR length respectively. rms τ is set to 0.05μ s , which equals s T . L is set to 6. There are 16 symbols per packet. The total energy of CIR has been normalized to one. Channel changes independently from symbol to symbol, but remains static within a symbol.16QAM is used to examine our scheme. The proposed scheme is compared with the frequency offset estimator of [10] plus the LMMSE channel estimator of [13] (which is termed conventional method) for both non-interpolation and interpolation cases. For fair comparison, the frequency offset estimator uses the first M symbols of each packet with unknown CIR length. Simulation results are shown in Fig. 2-5. As can be seen from Fig. 2, the proposed scheme performs quite well in estimating frequency offset. For SNR ≥ 5dB , the mean square error of the estimation is of the order of 10−3 or less. The accurate frequency offset estimate also reflects the fact that the proposed scheme is quite successful in searching for the CIR length. From Fig. 3, the most likely CIR length is 5 and an estimated length between 5 and 7 accounts for over 80percent of total possibilities, which indicates the effectiveness of CIR length searching method. Note that, since the AWGN noise affects the searching process and we use the exponential power delay profile with the maximum delay spread of 6 s T , the earch result in between 5-7 is quite reasonable.With the estimated CIR length obtained, the receiver performance is shown in Fig. 4-5, where conventional method is designated by LMMSE+FOE and the perfect case indicates OFDM signal reception with perfect frequency synchronization and the LMMSE channel estimator. Note that, in order to meet the Nyquist sampling theorem, f D must be less than N /(2L ) to guarantee the estimation accuracy for the interpolation case. The proposed scheme outperforms the conventional method and approaches the perfect situation for both non-interpolation and interpolation cases. It is shown that the proposed scheme successfully eliminates the effect of phase noise and frequency offset by approaching the perfect case as SNR increases, while conventional method exhibits an error floor.V. CONCLUSIONIn this paper, we proposed a new statistics-independent channel estimation scheme in the presence of frequency offset and phase noise. By searching for the most likely CIR length, we optimize the frequency offset estimation result. Based on thesearched CIR length, a time domain window is designed to suppress noise as well as ICI caused by phase noise and residual frequency offset. By this means, the excellent performance is achieved. This scheme approaches the performance of the LMMSE channel estimator in the absence of frequency offset and phase noise while outperforms the conventional methods for frequency offset estimation and channel estimation.二、英文翻译OFDM信道估计中的频率偏移和相位噪声摘要在本文中，我们主要考虑了OFDM信道估计中存在频率偏移和相位噪声。

OFDMA技术一、定义OFDM(Orthogonal Frequency Division Multiplexing)即正交频分复用技术，实际上OFDM是MCM Multi-CarrierModulation，多载波调制的一种。

其主要思想是：将信道分成若干正交子信道，将高速数据信号转换成并行的低速子数据流，调制到在每个子信道上进行传输。

正交信号可以通过在接收端采用相关技术来分开，这样可以减少子信道之间的相互干扰ICI 。

每个子信道上的信号带宽小于信道的相关带宽，因此每个子信道上的可以看成平坦性衰落，从而可以消除符号间干扰。

而且由于每个子信道的带宽仅仅是原信道带宽的一小部分，信道均衡变得相对容易。

二、原理DM —— OFDM(Orthogonal Frequency Division Multiplexing)即正交频分复用技术，实际上OFDM是MCM Multi-CarrierModulation，多载波调制的一种。

其主要思想是：将信道分成若干正交子信道，将高速数据信号转换成并行的低速子数据流，调制到在每个子信道上进行传输。

正交信号可以通过在接收端采用相关技术来分开，这样可以减少子信道之间的相互干扰ICI 。

正交频分复用OFDM（OrthogonalFrequencyDivisionMultiplex)是一种多载波调制方式，通过减小和消除码间串扰的影响来克服信道的频率选择性衰落。

它的基本原理是将信号分割为N个子信号，然后用N个子信号分别调制N个相互正交的子载波。

由于子载波的频谱相互重叠，因而可以得到较高的频谱效率。

当接收机检测到信号到达时，首先进行同步和信道估计。

当完成时间同步、小数倍频偏估计和纠正后，经过FFT变换，进行整数倍频偏估计和纠正，此时得到的数据是QAM或QPSK的已调数据。

对该数据进行相应的解调，就可得到比特流。

FDM/FDMA（频分复用/多址）技术其实是传统的技术，将较宽的频带分成若干较窄的子带（子载波）进行并行发送是最朴素的实现宽带传输的方法。

[DOC]-通信工程专业毕业论文外文资料翻译--正交频分复用技术简介-其他专业中文3500字译文:正交频分复用技术简介正交频分复用是一种多载波调制技术。

其主要思想是:将信道分成若干正交子信道，将高速数据信号转换成并行的低速子数据流，调制到在每个子信道上进行传输。

正交信号可以通过在接收端采用相关技术来分开，这样可以减少子信道之间的相互干扰。

每个子信道上的信号带宽小于信道的相关带宽，因此每个子信道上的可以看成平坦性衰落，从而可以消除码间串扰。

而且由于每个子信道的带宽仅仅是原信道带宽的一小部分，信道均衡变得相对容易。

由于这种技术具有在杂波干扰下传送信号的能力，因此常常会被利用在容易受外界干扰或者抵抗外界干扰能力较差的传输介质中。

目前正交频分复用技术已经被广泛应用于广播式的音频、视频领域和民用通信系统，主要的应用包括:非对称的数字用户环路、欧洲电信标准协会的数字音频广播、数字视频广播、高清晰度电视、无线局域网等。

正交频分复用并不是才发展起来的新技术，其应用已有40余年的历史，在上个世纪60年代就已经有人提出了使用平行数据传输和频分复用的概念。

70年代，韦斯坦和艾伯特等人应用离散傅里叶变换和离散傅里叶逆变换的方法研制了一个完整的多载波传输系统，叫做正交频分复用系统。

正交频分复用是一种特殊的多载波传输方案，它应用离散傅里叶变换和离散傅里叶逆变换的方法解决了产生多个互相正交的子载波以及从子载波中恢复原信号的问题。

这就解决了多载波传输系统发送和传送的难题。

应用快速傅里叶变换和快速傅里叶逆变换更是使多载波传输系统的复杂度大大降低。

从此正交频分复用技术开始走向实用。

但是应用正交频分复用系统仍然需要大量繁杂的数字信号处理过程，而当时还缺乏数字处理功能强大的元器件，发射机和接收机振荡器的稳定性以及射频功率放大器的线性要求等因素也是正交频分复用技术实现的制约条件。

因此正交频分复用技术迟迟没有得到迅速发展。

80年代，集成电路获得了突破性进展，大规模集成电路让快速傅里叶变换和快速傅里叶逆变换的实现不再是难以逾越的障碍，一些其它难以实现的困难也都得到了解决，自此正交频分复用走上了通信的舞台，逐步迈向高速数字移动通信的领域。

IX. Multiple Access Techniques: FDMA, TDMA and CDMA复用技术：频分复用、时分复用、码分复用复用方案用于使许多用户同时使用同一个固定的无线电频带。

在任何无线电系统中分配的带宽总是有限的。

移动无线电话系统的典型总带宽是50MHz，它被分成两半用以提供系统的前向和反向连接。

任何无线网络为了提高用户容量都需要共享频谱。

频分复用（FDMA）、时分复用（TDMA）、码分复用（CDMA）是无线系统中由众多用户共享可用带宽的三种主要方法。

这些方法又有许多扩展和混合技术，例如正交频分复用（OFDM），以及混合时分和频分复用系统。

不过要了解任何扩展技术首先要求对三种基本方法的理解。

频分复用在FDMA中，可用带宽被分为多个较窄的频带。

每一用户被分配一个独特的频带用于发送和接收。

在一次通话中其他用户不能使用同一频带。

每个用户分配到一个由基站到移动电话的前向信道以及一个返回基站的反向信道，每个信道都是一个单向连接。

在每个信道中发送信号是连续的，以便进行模拟通信。

FDMA信道的带宽一般较小（30kHz），每个信道只支持一个用户。

FDMA作为大多数多信道系统的一部分用于初步分割分配到的宽频带。

将可用带宽分配给几个信道的情况见图9.1和图9.2。

时分复用通过分配给每一个用户一个时隙以便在其中发送或接收，TDMA将可用频谱分成多个时隙。

图9.3显示如何以一种循环复用的方式把时隙分配给用户，每个用户每帧分得一个时隙。

TDMA以缓冲和爆发方式发送数据。

因此每个信道的发射是不连续的。

待发送的输入数据在前一帧期间被缓存，在分配给该信道的时隙中以较高速率爆发式发送出去。

TDMA不能直接传送模拟信号因为它需要使用缓冲，因而只能用于传输数字形式的数据。

由于通常发射频率很高，TDMA会受到多径效应的影响。

这导致多径信号引起码间干扰。

TDMA一般与FDMA结合使用，将可用的全部带宽划分为若干信道。

5 G 无线通信网络中英文对照外文翻译文献(文档含英文原文和中文翻译 )翻译：5G 无线通信网络的蜂窝结构和关键技术摘要第四代无线通信系统已经或者即将在许多国家部署。

然而，随着无线移动设备和服务的激增，仍然有一些挑战尤其是4G 所不能容纳的，例如像频谱危机和高能量消耗。

无线系统设计师们面临着满足新型无线应用对高数据速率和机动性要求的持续性增长的需求，因此他们已经开始研究被期望于 2020 年后就能部署的第五代无线系统。

在这篇文章里面，我们提出一个有内门和外门情景之分的潜在的蜂窝结构，并且讨论了多种可行性关于5G 无线通信系统的技术，比如大量的MIMO 技术，节能通信，认知的广播网络和可见光通信。

面临潜在技术的未知挑战也被讨论了。

介绍信息通信技术（ICT）创新合理的使用对世界经济的提高变得越来越重要。

无线通信网络在全球 ICT 战略中也许是最挑剔的元素，并且支撑着很多其他的行业，它是世界上成长最快最有活力的行业之一。

欧洲移动天文台（EMO）报道2010 年移动通信业总计税收1740 亿欧元,从而超过了航空航天业和制药业。

无线技术的发展大大提高了人们在商业运作和社交功能方面通信和生活的能力无线移动通信的显著成就表现在技术创新的快速步伐。

从 1991 年二代移动通信系统(2G)的初次登场到 2001 年三代系统（3G）的首次起飞，无线移动网络已经实现了从一个纯粹的技术系统到一个能承载大量多媒体内容网络的转变。

4G 无线系统被设计出来用来满足 IMT-A 技术使用 IP 面向所有服务的需求。

在 4G 系统中，先进的无线接口被用于正交频分复用技术（OFDM ），多输入多输出系统（MIMO）和链路自适应技术。

4G 无线网络可支持数据速率可达 1Gb/s 的低流度，比如流动局域无线访问，还有速率高达 100M /s 的高流速，例如像移动访问。

LTE 系统和它的延伸系统 LTE-A，作为实用的 4G 系统已经在全球于最近期或不久的将来部署。

LTE关键技术之OFDM分析-外文翻译201x届本科毕业设计（外文翻译）学院：专业：姓名：学号：指导教师：完成时间：二〇一四年三月LTE的多址接入技术LTE的多址接入OFDM传输正交频分复用（OFDM）是一种多载波传输技术，已被采纳为3gpplong长期演化（LTE）的下行链路传输方案，也可用于其他几个无线技术，例如：wimax 和DVB广播技术。

它的特点是在一个频域内分布着许多带有间隔的子载波△f=1/Tu其中，Tu是每个子载波的调制符号时间。

如图2-1所示，“OFDM子载波间隔”。

OFDM的传输是基于块的。

每个OFDM符号间隔之间，调制符号是并行发送的。

调制符号可以通过调制字母表得到，如QPSK，16QAM或64QAM，对于3GPP组织LTE，子载波间隔是相等的为15 kHz。

另一方面，子载波的数目取决于传输带宽，在一个10MHZ的频谱分配下，600个子载波可以有序传输。

当然，带宽减小了，子载波数目也相应减少，带宽增加了，子载波数目也相应增加。

图2-1 OFDM子载波间隔在OFDM传输时，物理资源经常被描述成一个时域—频域的网格坐标图。

在这个坐标图里一列对应一个OFDM子载波，一行对应一个OFDM子载波。

如图2-2所示，“OFDM时频网格”。

尽管子载波的频谱有重叠，但在理想情况下，是对OFDM子载波解调后不引起任何干扰的，这是因为对每一个子载波间隔的特殊选择，让它等于相应的解调符号率。

图2-2 OFDM时频网格以一定的频率f s= N ×△f进行采样的OFDM信号，是该size-N 的逆离散傅立叶变换（IDFT）的调制符号块a0, a1,...a N-1。

因此，OFDM调制可以通过IDFT处理再到数字-模拟的转换来实现。

（见图2-3，“OFDM调制”）。

在实际中，OFDM调制是以快速傅立叶反变换（IFFT）方式实现简单和快速的处理，通过选择IDFT size N 等于2m（m为整数）。

中英文对照外文翻译文献(文档含英文原文和中文翻译)原文：RESEARCH OF CELLULAR WIRELESS COMMUNATIONSYSTEMA wide variety of wireless communication systems have been developed to provide access to the communications infrastructure for mobile or fixed users in a myriad of operating environments. Most of today’s wireless systems are based on the cellular radio concept. Cellular communication systems allow a large number of mobile users to seamlessly and simultaneously communicate to wireless modems at fixed base stations using a limited amount of radio frequency (RF) spectrum. The RF transmissions received at the base stations from each mobile are translated to baseband, or to a wideband microwave link, and relayed to mobile switching centers (MSC), which connect the mobile transmissions with the Public Switched Telephone Network (PSTN). Similarly, communications from the PSTN are sent to the base station, where they are transmitted to the mobile. Cellular systems employ eitherfrequency division multiple access (FDMA), time division multiple access (TDMA), code division multiple access (CDMA), or spatial division multiple access (SDMA) .Wireless communication links experience hostile physical channel characteristics, such as time-varying multipath and shadowing due to large objects in the propagation path. In addition, the performance of wireless cellular systems tends to be limited by interference from other users, and for that reason, it is important to have accurate techniques for modeling interference. These complex channel conditions are difficult to describe with a simple analytical model, although several models do provide analytical tractability with reasonable agreement to measured channel data . However, even when the channel is modeled in an analytically elegant manner, in the vast majority of situations it is still difficult or impossible to construct analytical solutions for link performance when error control coding, equalization, diversity, and network models are factored into the link model. Simulation approaches, therefore, are usually required when analyzing the performance of cellular communication links.Like wireless links, the system performance of a cellular radio system is most effectively modeled using simulation, due to the difficulty in modeling a large number of random events over time and space. These random events, such as the location of users, the number of simultaneous users in the system, the propagation conditions, interference and power level settings of each user, and the traffic demands of each user, combine together to impact the overall performance seen by a typical user in the cellular system. The aforementioned variables are just a small sampling of the many key physical mechanisms that dictate the instantaneous performance of a particular user at any time within the system. The term cellular radio system, therefore, refers to the entire population of mobile users and base stations throughout the geographic service area, as opposed to a single link that connects a single mobile user to a single base station. To design for a particular system-level performance, such as the likelihood of a particular user having acceptable service throughout the system, it is necessary to consider the complexity of multiple users that are simultaneously using the system throughout the coverage area. Thus, simulation is needed to consider the multi-user effects upon any of the individual links between the mobile and the base station.The link performance is a small-scale phenomenon, which deals with the instantaneouschanges in the channel over a small local area, or small time duration, over which the average received power is assumed constant. Such assumptions are sensible in the design of error control codes, equalizers, and other components that serve to mitigate the transient effects created by the channel. However, in order to determine the overall system performance of a large number of users spread over a wide geographic area, it is necessary to incorporate large-scale effects such as the statistical behavior of interference and signal levels experienced by individual users over large distances, while ignoring the transient channel characteristics. One may think of link-level simulation as being a vernier adjustment on the performance of a communication system, and the system-level simulation as being a coarse, yet important, approximation of the overall level of quality that any user could expect at any time.Cellular systems achieve high capacity (e.g., serve a large number of users) by allowing the mobile stations to share, or reuse a communication channel in different regions of the geographic service area. Channel reuse leads to co-channel interference among users sharing the same channel, which is recognized as one of the major limiting factors of performance and capacity of a cellular system. An appropriate understanding of the effects of co-channel interference on the capacity and performance is therefore required when deploying cellular systems, or when analyzing and designing system methodologies that mitigate the undesired effects of co-channel interference. These effects are strongly dependent on system aspects of the communication system, such as the number of users sharing the channel and their locations. Other aspects, more related to the propagation channel, such as path loss, shadow fading (or shadowing), and antenna radiation patterns are also important in the context of system performance, since these effects also vary with the locations of particular users. In this chapter, we will discuss the application of system-level simulation in the analysis of the performance of a cellular communication system under the effects of co-channel interference. We will analyze a simple multiple-user cellular system, including the antenna and propagation effects of a typical system. Despite the simplicity of the example system considered in this chapter, the analysis presented can easily be extended to include other features of a cellular system.2 Cellular Radio SystemSystem-Level Description：Cellular systems provide wireless coverage over a geographic service area by dividing the geographic area into segments called cells as shown in Figure 2-1. The available frequency spectrum is also divided into a number of channels with a group of channels assigned to each cell. Base stations located in each cell are equipped with wireless modems that can communicate with mobile users. Radio frequency channels used in the transmission direction from the base station to the mobile are referred to as forward channels, while channels used in the direction from the mobile to the base station are referred to as reverse channels. The forward and reverse channels together identify a duplex cellular channel. When frequency division duplex (FDD) is used, the forward and reverse channels are split in frequency. Alternatively, when time division duplex (TDD) is used, the forward and reverse channels are on the same frequency, but use different time slots for transmission.Figure 2-1 Basic architecture of a cellular communications system High-capacity cellular systems employ frequency reuse among cells. This requires that co-channel cells (cells sharing the same frequency) are sufficiently far apart from each other to mitigate co-channel interference. Channel reuse is implemented by covering the geographic service area with clusters of N cells, as shown in Figure 2-2, where N is known as the cluster size.Figure 2-2 Cell clustering:Depiction of a three-cell reuse pattern The RF spectrum available for the geographic service area is assigned to each cluster, such that cells within a cluster do not share any channel . If M channels make up the entire spectrum available for the service area, and if the distribution of users is uniform over the service area, then each cell is assigned M/N channels. As the clusters are replicated over the service area, the reuse of channels leads to tiers of co-channel cells, and co-channel interference will result from the propagation of RF energy between co-channel base stations and mobile users. Co-channel interference in a cellular system occurs when, for example, a mobile simultaneously receives signals from the base station in its own cell, as well as from co-channel base stations in nearby cells from adjacent tiers. In this instance, one co-channel forward link (base station to mobile transmission) is the desired signal, and the other co-channel signals received by the mobile form the total co-channel interference at the receiver. The power level of the co-channel interference is closely related to the separation distances among co-channel cells. If we model the cells with a hexagonal shape, as in Figure 2-2, the minimum distance between the center of two co-channel cells, called the reuse distance ND, is（2-1）R3D N Nwhere R is the maximum radius of the cell (the hexagon is inscribed within the radius).Therefore, we can immediately see from Figure 2-2 that a small cluster size (small reuse distance ND), leads to high interference among co-channel cells.The level of co-channel interference received within a given cell is also dependent on the number of active co-channel cells at any instant of time. As mentioned before, co-channel cells are grouped into tiers with respect to a particular cell of interest. The number of co-channel cells in a given tier depends on the tier order and the geometry adopted to represent the shape of a cell (e.g., the coverage area of an individual base station). For the classic hexagonal shape, the closest co-channel cells are located in the first tier and there are six co-channel cells. The second tier consists of 12 co-channel cells, the third, 18, and so on. The total co-channel interference is, therefore, the sum of the co-channel interference signals transmitted from all co-channel cells of all tiers. However, co-channel cells belonging to the first tier have a stronger influence on the total interference, since they are closer to the cell where the interference is measured.Co-channel interference is recognized as one of the major factors that limits the capacity and link quality of a wireless communications system and plays an important role in the tradeoff between system capacity (large-scale system issue) and link quality (small-scale issue). For example, one approach for achieving high capacity (large number of users), without increasing the bandwidth of the RF spectrum allocated to the system, is to reduce the channel reuse distance by reducing the cluster size N of a cellular system . However, reduction in the cluster sizeincreases co-channel interference, which degrades the link quality.The level of interference within a cellular system at any time is random and must be simulated by modeling both the RF propagation environment between cells and the position location of the mobile users. In addition, the traffic statistics of each user and the type of channel allocation scheme at the base stations determine the instantaneous interference level and the capacity of the system.The effects of co-channel interference can be estimated by the signal-tointerference ratio (SIR) of the communication link, defined as the ratio of the power of the desired signal S, to the power of the total interference signal, I. Since both power levels S and I are random variables due to RF propagation effects, user mobility and traffic variation, the SIR is also a random variable. Consequently, the severity of the effects of co-channel interference onsystem performance is frequently analyzed in terms of the system outage probability, defined in this particular case as the probability that SIR is below a given threshold 0S IR . This isdx p ]SIR Pr[SIR P )x 0SIR 0SIR 0outpage （⎰=<= （2-2）Where is the probability density function (pdf) of the SIR. Note the distinction between the definition of a link outage probability, that classifies an outage based on a particular bit error rate (BER) or Eb/N0 threshold for acceptable voice performance, and the system outage probability that considers a particular SIR threshold for acceptable mobile performance of a typical user.Analytical approaches for estimating the outage probability in a cellular system, as discussed in before, require tractable models for the RF propagation effects, user mobility, and traffic variation, in order to obtain an expression for PSIR （x ）. Unfortunately, it is very difficult to use analytical models for these effects, due to their complex relationship to the received signal level. Therefore, the estimation of the outage probability in a cellular system usually relies on simulation, which offers flexibility in the analysis. In this chapter, we present a simple example of a simulation of a cellular communication system, with the emphasis on the system aspects of the communication system, including multi-user performance, traffic engineering, and channel reuse. In order to conduct a system-level simulation, a number of aspects of the individual communication links must be considered. These include the channel model, the antenna radiation pattern, and the relationship between Eb/N0 (e.g., the SIR) and the acceptable performance.SIR(x)p翻译：蜂窝无线通信系统的研究摘要蜂窝通信系统允许大量移动用户无缝地、同时地利用有限的射频（radio frequency，RF）频谱与固定基站中的无线调制解调器通信。

mimo-ofdm无线通信技术及matlab实现英文版Title: MIMO-OFDM Wireless Communication Technology and MATLAB ImplementationIntroduction:MIMO-OFDM (Multiple-Input Multiple-Output Orthogonal Frequency Division Multiplexing) is a wireless communication technology that combines multiple antennas and orthogonal frequency division multiplexing to achieve high data rates, increased spectral efficiency, and improved system performance. In this article, we will provide a detailed and accurate overview of MIMO-OFDM technology, along with its MATLAB implementation.1. MIMO-OFDM Technology:1.1 Multiple-Input Multiple-Output (MIMO):- Definition: MIMO refers to the use of multiple antennas at both the transmitter and receiver to improve communication performance.- Benefits of MIMO: MIMO technology enables spatial multiplexing, diversity, and beamforming, leading to increased capacity, improved reliability, and enhanced coverage.1.2 Orthogonal Frequency Division Multiplexing (OFDM): - Definition: OFDM is a modulation technique that divides the available frequency spectrum into multiple orthogonal subcarriers.- Benefits of OFDM: OFDM enables high spectral efficiency, resistance to multipath fading, and robustness against frequency-selective fading channels.1.3 MIMO-OFDM Combination:- MIMO-OFDM combines the benefits of MIMO and OFDMto achieve high data rates, improved link reliability, and increased system capacity.- MIMO-OFDM utilizes spatial multiplexing totransmit multiple data streams simultaneously acrossdifferent subcarriers.2. MATLAB Implementation of MIMO-OFDM:2.1 Channel Modeling:- MATLAB provides various channel models that can be used to simulate realistic wireless communication scenarios, such as Rayleigh and Rician fading channels.- These channel models can be used to evaluate the performance of MIMO-OFDM systems under different channel conditions.2.2 Transmitter Design:- MATLAB provides tools for designing the MIMO-OFDM transmitter, including the generation of OFDM symbols, mapping of data streams to subcarriers, and spatial multiplexing using multiple antennas.- The transmitter design also includes the implementation of coding and modulation schemes to enhance the reliability and efficiency of the communication system.2.3 Receiver Design:- The receiver design in MATLAB involves the synchronization and demodulation of the received MIMO-OFDMsignals.- MATLAB provides algorithms for channel estimation, equalization, and decoding to mitigate the effects of channel impairments and enhance the overall system performance.2.4 Performance Evaluation:- MATLAB allows for the evaluation of various performance metrics of the MIMO-OFDM system, such as bit error rate (BER), signal-to-noise ratio (SNR), and throughput.- These performance metrics help in assessing the system's performance under different scenarios and optimizing the system parameters.Conclusion:MIMO-OFDM wireless communication technology combines the advantages of MIMO and OFDM to achieve high data rates, improved link reliability, and increased system capacity. MATLAB provides a comprehensive platform for the design, simulation, and evaluation of MIMO-OFDM systems, enabling researchers and engineers to analyze and optimize the performance of wireless communication systems.。