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https://doi.org/10.22214/ijraset.2022.42172

May 2022

Spectral Efficiency Improvement Techniques in OFDM and UFMC Krishana Kishore Padarthi1, Venkata Tarun Mantripragada2, Nithish Panchakshari3, Dr. M. V. Nageswara Rao4 1, 2, 3, 4

Dept. of ECE, GMR Institute of Technology (Affiliated to JNTUK, KAKINADA) Rajam, India

Abstract: This article describes about to analyse the spectral efficiency of different modulation techniques in OFDM and UFMC. UFMC is a multi-carrier modulation technique in fifth generation network (5G). In this paper, we review the different modulation techniques in 5G technology and motivate the need of UFMC technique in 5G wireless communication. In 4G OFDM modulation technique, some drawbacks like side band leakages, high Peak to Average Power ratio (PAPR) and spectrum utilization degrades the performance of the system. Another multi carrier technique called Filter Bank Multi carrier (FBMC) which is better than OFDM, have some issues in practical aspects. So by considering the above parameters a move to another technique called Universal Filtered Multi Carrier (UFMC) is used because of good spectrum usage. This paper also explains about the UFMC system model. Based on the Simulation results in MATLAB, the spectrum utilization of UFMC system is much better than OFDM system. Keywords: UFMC, OFDM, 5G, IFFT, FFT, DAC, ADC and Spectrum efficiency. I. INTRODUCTION At present the main data transmission technology in wireless communication system is Orthogonal Frequency Division Multiplexing (OFDM). OFDM is used in LTE/LTE advanced (4G) and IEEE 802.11 (WI-FI) networks. OFDM is a modulation technique with strongly efficient in bandwidth usage. It is immune to multi path fading and Inter Symbol Interference (ISI). The recent advancements in Digital signal processing make the OFDM very popular. Above all advantages, OFDM is having some disadvantages like high Peak to Average Power Ratio (PAPR) and high Bit error rate (BER). The sensitivity of devices used in the OFDM transmitter side such as Digital- to-Analog Converter (DAC) The spectrum utilization of OFDM is not better when compare to other modulation techniques like UFMC. Spectrum efficiency plays a main role in rapid Mobile Broadband Networks (MBB). But in order to meet the higher requirements in 5G, this OFDM technology is not sufficient. So, the evolution of new technologies like Filter Bank Multi Carrier (FBMC) and Universal Filtered Multi Carrier (UFMC) are emerged. However, FBMC is not the right one because large filter length affects the symbol decoding time and having complex receiver structure in MIMO. Also FBMC is not suitable for burst transmissions or delay sensitive applications. A. OFDM System Orthogonal frequency division multiplexing (OFDM) is a frequency – division multiplexing (FDM) scheme used as a digital multicarrier modulation method. In OFDM the entire bandwidth is divided into several sub carriers ,and these sub carriers are transmitted parallely. An OFDM system consists of a transmitter and a receiver

Fig.1. Filtering methods in OFDM B. OFDM Bascis

Fig.2. OFDM Basics

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International Journal for Research in Applied Science & Engineering Technology (IJRASET) ISSN: 2321-9653; IC Value: 45.98; SJ Impact Factor: 7.538 Volume 10 Issue V May 2022- Available at www.ijraset.com In digital communications, information is expressed in the form of bits. The term symbol refers to a collection, in various sizes, of bits . OFDM data are generated by taking symbols in the spectral space using M-PSK, QAM, etc, and convert the spectra to time domain by taking the Inverse Discrete Fourier Transform (IDFT). Since Inverse Fast Fourier Transform (IFFT) is more cost effective to implement, it is usually used instead . Once the OFDM data are modulated to time signal, all carriers transmit in parallel to fully occupy the available frequency bandwidth C. Orthogonality Signals are orthogonal if they are mutually independent of each other. Two signals are said to be orthogonal when their dot product is equal to zero. Let’s take a sine wave of frequency m and multiply it by sinusoid of a frequency n, where both m and n are integers. The integral or the area under the product is given by: f(t)=sinmwt×sinnwt By simple trigonometric relationship,this is equal to a sum of two sinusoids of frequency (n-m) and (n+m) = 0.5(n-m) + 0.5(n+m).The baseband frequency of each subcarrier is chosen to be an integer multiple of the inverse of the symbol time, resulting in all subcarriers having an integer number of cycles per symbol. As a consequence the subcarriers are orthogonal to each other.

Figure 3 Overlapping of subcarriers in Frequency domain D. OFDM Transmitter and Receiver

Fig 4. OFDM Transmitter and Receiver OFDM transmitters generate both the carrier and the data signal simultaneously with purely digital circuits residing in the specialized DSP(Digital Signal Processor) microchips. The specific process of digital signal generation used in OFDM is based on the series of mathematical computations known as an Inverse Fourier Transform, and the process results in the formation of a complex modulated waveform at the output of the transmitter. The incoming serial data is first converted from serial to parallel and grouped into x bits each to form a complex number. The complex numbers are modulated in a base band fashion by the IFFT and converted back to serial data for transmission. A guard interval is inserted between symbols to avoid Inter Symbol Interference (ISI) caused by multipath distortion. The discrete symbols are converted to analog and lowpass filtered for RF up-conversion. f(n) = ∑

F(K) exp (

)

The receiver performs the reverse operation of the transmitter, mixing the RF signal to base band for processing, then using a Fast Fourier Transform (FFT) to analyze the signal in the frequency domain. The amplitude and phase of the sub carriers is then picked out and converted back to digital data. The IFFT and the FFT are complementary function and the most appropriate term depends on whether the signal is being received or generated. In cases where the signal is independent of this distinction then the term FFT and IFFT is used. F(K) = ∑

f(n) exp (

)

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International Journal for Research in Applied Science & Engineering Technology (IJRASET) ISSN: 2321-9653; IC Value: 45.98; SJ Impact Factor: 7.538 Volume 10 Issue V May 2022- Available at www.ijraset.com II. UFMC SYSTEM UFMC, a generalization of Filtered OFDM and FBMC multi-carrier modulation technique. Generally in filtered OFDM, entire band is filtered where as in FBMC individual sub carriers are filtered. But in UFMC group of sub carriers are filtered . This is the main difference in Filtered OFDM, FBMC and UFMC multi-carrier. Grouping of sub carriers helps in reducing the filter length in UFMC. IN UFMC, to retain the complex orthogonality, QAM is used which works with existing MIMO. The whole UFMC transmitter section is shown in figure 6. Here the full band of ‘N’ sub carriers are partitioned into several sub bands. Each sub band has a fixed number of sub carriers. In transmitter section no need of employing all sub bands for a transmission. To get rid of from the sub band carrier interfere, Inverse Fast Fourier Transform (IFFT) is used. At each N-point IFFT, sub bands are computed and zeros are allocated for unallocated carriers. IFFT converts frequency domain (Xi) to time domain (xi). After the N-point IFFT, the output can be written as Yi = IFFT {xi}

(1)

Now the time domain signals comes from the IFFT goes to Band filter of length ‘L’ block. Each sub band output is filtered by band filter of Length ‘L’ It is expressed as y = H. ~ Q. yi

(2)

Where H is called toeplitz matrix having dimensions (N+L-1) and ‘~Q’ is called as Inverse Fourier matrix. Actually band filters uses Chebyshev window/filtering operation. Here parameterized side lobes attenuation is used to filter the IFFT outputs. Now all the outputs from the band filters are summed at the end and passes through the channel. From the channel the data bits are transmitted to UFMC receiver. UFMC receiver do’s 2N-point Fast Fourier Transform (FFT). FFT converts the data of time domain to frequency domain. It is shown in the form of equation ~Y = FFT { [yT, 0, 0, ………, 0] }

(3)

To prevent Inter Symbol Interference (ISI), guard intervals of zeros are added between successive IFFT symbols. ISI is due to transmitter filter delay. To receive N length frequency domain signal ‘Y’, even sub carriers are discarded. Now the data goes for equalization process and the original data bits are retrieved by symbol demapping. It is shown in the UFMC receiver section figure 6

FIG 6 UFMC Transmitter

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Fro m Cha nnel

Zer o Pad din

2N Poin t FFT

Frequency Domain Equalizati on per sub band

Symbol Demappi ng

Recove red Data bits

FIG 7 UFMC Recievr: III. LITERATURE SURVEY Wetjie Tan and Atal&2018 [1] proposed “Spectral efficiency of massive MIMO system with multiple sub array antenna” the advantages of this paper is It achieves spectral efficiency more effectively and disadvantages of this paper is Failed to support scalability Jingon joung and Atal& 2014 [2] proposed “Spectral efficiency and energy efficiency of OFDM “the advantages of this paper is It achieves Energy efficiency , Spectral efficiency and dis advantage of the paper is It cannot be achieved for MIMO Hyinso and Atal&2016 [3] proposed “Resource block management for uplink UFMC system “ the advantages of this paper is It allocates the frequency to user efficiently and the disadvantages of the paper is Produces more side band leakage Waleed Shahjehan and Atal & 2017 [4] proposed “Universal Filtered Multicarrier for 5G “ the advantages of this paper is Bit error rate (BER) is low and dis advantages is Receiver complexity is more In OFDM the entire bandwidth is divided into number of sub-carriers and these sub carriers are transmitted in parallel to increase symbol duration to achieve high data rates and to reduce ISI and it is shown in the figure 7. An OFDM signal is the sum of all sub carriers signal which are modulated at the sub channels with equal bandwidth.

Fig.7. Filtering methods in OFDM, FBMC and UFMC techniques. High PAPR value and high BER are the major disadvantages in OFDM. The sensitivity of devices used in the OFDM transmitter side like DAC and HPA are very harsh to the signal processing loop which affects the performance of the system The spectrum utilization of OFDM is not good in OFDM because many side lobes might pick up interfering signals, which in turn results in the increase of noise level at the receiver. Both OFDM and FBMC supports MIMO technology, but OFDM has some drawbacks [7]. In OFDM ‘Cyclic Prefix’ is must to avoid Inter Symbol Interference (ISI) and to convert the channel to a number of sub-carrier channels. But in FBMC, Cyclic Prefix is not used, but it has the capability to convert the channel to a set of sub-carrier channels and to remove ISI. Both OFDM and FBMC supports MIMO system. The primary reason of using OFDM in MIMO system is to remove interference and the main aim of FBMC is to overcome some of the shortcomings in OFDM. FBMC is the betterment of OFDM. So, FBMC is equals to the derivative of OFDM. Both plays a prominent role in the area of wireless communication modulation techniques. But by using filter banks it has possible to get our desired results than OFDM. In Massive MIMO FBMC concept, the complexity of the system and delay can be reduced by reducing the sub-carriers. In Massive MIMO FBMC system, Analysis can be done at Receiver side and Synthesis at Transceiver side. The sub-carriers spectral localization in OFDM are weak which might results in spectral leakages and also interference issues with unsynchronized signals.

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International Journal for Research in Applied Science & Engineering Technology (IJRASET) ISSN: 2321-9653; IC Value: 45.98; SJ Impact Factor: 7.538 Volume 10 Issue V May 2022- Available at www.ijraset.com A. Route Map In section 1 introduction about ofdm and ufmc and section 2 literature survey and section 3 system model and section 4 improvement techniques for spectral efficiency and section 5 total spectral efficiency and section 6 simulation results and section 7 conclusion. B. System Model We consider a MU-MIMO system which consists of one BS and K active users. The BS is equipped with M antennas, while each user has a single-antenna. In general, each user can be equipped with multiple antennas. However, for simplicity of the analysis, we limit ourselves to systems with single-antenna users. See Figure 5.1.1. We assume that all K users share the same time-frequency resource. Furthermore, we assume that the BS and the users have perfect CSI. The channels are acquired at the BS and the users during the training phase. The specific training schemes depend on the system protocols (frequency-division duplex (FDD) or timedivision duplex (TDD)). Let H ∈ CM×K be the channel matrix between the K users and the BS antenna array, where the kth column of H, denoted by hk, represents the M × 1 channel vector between the kth user and the BS. In general, the propagation channel is modeled via large-scale fading and small-scale fading. But in this chapter, we ignore large-scale fading, and further assume that the elements of H are i.i.d. Gaussian distributed with zero mean and unit variance.

Fig 8 Multi user MIMO Here, K single-antenna users are served by the M-antenna BS in the same time-frequency resource. C. Uplink Transmission Uplink (or reverse link) transmission is the scenario where the K users transmit signals to the BS. Let sk, where E {|sk|2} = 1, be the signal transmitted from the kth user. Since K users share the same time-frequency resource, the M ×1 received signal vector at the BS is the combination of all signals transmitted from all K users: ∑ = ℎ +n (3.2) =√ + +n (3.3) where pu is the average signal-to-noise ratio (SNR), n CM×1 is the additive noise vector, and s = [s1 ... sK]T . We assume that the elements of n are i.i.d. Gaussian random variables (RVs) with zero mean and unit variance, and independent of H. From the received signal vector yul together with knowledge of the CSI, the BS will coherently detect the signals transmitted from the K users. The channel model is the multiple-access channel which has the sum-capacity [16] = log 2 ( + ). (3.4) , The aforementioned sum-capacity can be achieved by using the successive interference cancellation (SIC) technique. With SIC, after one user is detected, its signal is subtracted from the received signal before the next user is detected D. Downlink Transmission Downlink (or forward link) is the scenario where the BS transmits signals to all K users. Let x ∈ CM×1 , where E {||x||2} = 1, be the signal vector transmitted from the BS antenna array. Then, the received signal at the kth user is given by ℎ + (3.5) , =

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International Journal for Research in Applied Science & Engineering Technology (IJRASET) ISSN: 2321-9653; IC Value: 45.98; SJ Impact Factor: 7.538 Volume 10 Issue V May 2022- Available at www.ijraset.com where pd is the average SNR and zk is the additive noise at the kth user. We assume that zk is Gaussian distributed with zero mean and unit variance. Collectively, the received signal vector of the K users can be written as = +z, (3.6) where ydl = [ydl,1 ydl,2 . . . ydl,K]T and z = [z1 z2 . . . zK]T .The channel model is the broadcast channel whose sum-capacity is known to be ∗ = log 2 ( + ) (3.7) Dq is the diagonal matrix whose kth diagonal element is qk. The sum-capacity can be achieved by using the dirty-paper coding (DPC). IV. IMPROVEMENTS TECHNIQUES FOR SPECTRAL EFFICIENCY A. Maximum-Ratio Combining Receiver With MRC, the BS aims to maximize the received signal-to-noise ratio (SNR) of each stream, ignoring the effect of multiuser interference. The kth column of the MRC receiver matrix A is: ( ) , = ∈ ( ) and equality holds when ak = const · hk, the MRC receiver is: amrc,k = const · hk. Plugging amrc,k into , the received SINR of the kth stream for MRC is given by ,

∥

= ∑

∥ |

∥

B. Zero-Forcing Receiver By contrast to MRC, zero-forcing (ZF) receivers take the Interuser Interference into account, but neglect the effect of noise. With ZF, the multiuser interference is completely nulled out by projecting each stream onto the orthogonal complement of the interuser interference. More precisely, the kth column of the ZF receiver matrix satisfies:

The ZF receiver matrix, which satisfies for all k, is the pseudo-inverse of the channel matrix H. With ZF, we have ⋀ =( )−1 =√puS+( )−1 n. This scheme requires that M ≥ K (so that the matrix HHH is invertible). We can see that each stream (element) of y˜ul is free of multiuser interference. The kth stream of y˜ul is used to detect sk: ⋀ ,

=√puSk+

⋀

where n˜k denotes the kth element of (HHH)-1 HHn. Thus, the received SINR of the kth stream is given by

C. Minimum Mean-Square Error Receiver The linear minimum mean-square error (MMSE) receiver aims to minimize the mean-square error between the estimate AH yul and the transmitted signal s. More precisely,

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where cov(v1,v2) =E {v1vH2 } , where v1 and v2 are two random column vectors with zero-mean elements. It is known that the MMSE receiver maximizes the received SINR. Therefore, among the MMSE, ZF, and MRC receivers, MMSE is the best. At high SNR (high pu), ZF approaches MMSE, while at low SNR, MRC performs as well as MMSE. Furthermore, the received SINR for the MMSE receiver is given by

V. SIMULATION RESULTS By comparing snr and ber parameters in ofdm the snr is increasing while the ber is decreasing . i.e the snr is inversely proportional to ber. SNR vs BER analysis of OFDM for M-ary PSK BER SNR(dB) BPSK QPSK 8PSK 16PSK 0

0.4

0.2

0.32

0.39

0.32

0.15

0.29

0.37

0.3

0.08

0.23

0.3

0.28

0.033

0.17

0.29

0.24

0.009

0.1

0.23

0.18

0.05

0.19

0.018

0.04

0.0015 0

0.13

0.075

0.0042

0.075

0.04

0.0004

0.0019

SNR vs BER analysis of OFDM for M-ary QAM BER SNR(dB) 8QAM 16QAM 32QAM 0

0.16

0.25

0.3

2 4

0.1 0.06

0.2 0.15

0.24 0.19

0.022

0.1

0.15

0.006

0.06

0.1

0.03

0.06

0.008 0.0016 0

0.03 0.009 0.001

10 12 14 16

0.0007 0 0 0

415

Fig 10 performance analysis of ofdm in M-ary psk By comparing snr and ber paramnets in ufmc the snr is increasing and ber is decreasing .

Fig 11 snr vs ber In ufmc Based on simulation results. We can know that the capacity will increase as the transmitted power increases. We will analyze the relationship between capacity and the number of antennas which is shown in figure 5.2.1.The simulation is done with SNR equal to 5dB, while channel is assumed as Rayleigh fading and both transmit side and receive side have perfect channel state information. The spectral efficiency of all of these three precoding schemes increases as the number of transmitted antennas increases. P-ZF has the best performance, while ZF is not far behind. In particular, when the number of transmitted antennas goes to infinite, performance of ZF approaches to that of MMSE.

Fig 11 spectral efficiency vs number of antennas

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International Journal for Research in Applied Science & Engineering Technology (IJRASET) ISSN: 2321-9653; IC Value: 45.98; SJ Impact Factor: 7.538 Volume 10 Issue V May 2022- Available at www.ijraset.com The capacity of MRC is relatively lower than others. Moreover, when the number of antennas is smaller than 250, the slopes of these three curves are relatively large. However, the spectral efficiency increases slightly when the number is larger than 250. From this figure 7.2.1, we can see that the spectral efficiency cannot increase infinitely with the limitation of power consumption. A large amount of antennas contributes to high power consumption as well as cost for hardware devices. When the number of antennas goes infinity, the interference among users will dominant the system performance. Under this condition, the more users in cells, the stronger inter-cell inference will produce. The system cannot linear increase as the number of antennas increases. These two simulation figures illustrate how the capacity is influenced by transmitted power and number of antennas. VI. CONCLUSION We have investigated the performance of OFDM system and MATLAB Simulation was employed to investigate performance trends. Thus, Orthogonal Frequency Division Multiplexing is a form of multi-carrier modulation technique with high spectral efficiency, robustness to channel fading, immunity to impulse interference, uniform average spectral density, less non linear distortion and we obtain a better quality signal at the receiver. The system performance of wireless communication system has been improved significantly in terms of capacity, latency, Reliability. We firstly analyzed the history of wireless communication system. Introduced the importance of MIMO and massive MIMO , system model and problem formulation. By combining results of Massive MIMO, we present that massive MIMO remarkably improves the spectral efficiency and energy efficiency. We mainly focused on spectral efficiency for various BS antenna configurations in MIMO and massive MIMO systems. There are still some aspects that should be done in the future. REFERENCES [1] [2] [3] [4]

[5] [6] [7] [8] [9]

Kabir and Waziha, “Title of the Paper”, IEEE 2008 China-Japan Joint Microwave Conference (CJMW S2008)Shanghai,China, pp.1781-84, Sept. 2008. (doi:10.1109/CJMW.2008.477241) Hwang Taewon,Chenyang Yang, Gang Wu ,Shaoqian Li and Ye Li, G. (2009). OFDM and Its Wireless Applications, 58(4), 1673–1694. doi:10.1109/tvt.2008.2004555 Jun Zhang, Zhong (2010). [IEEE 2010 International Conference on Communications, Circuits and Systems (ICCCAS) - Chengdu, China (2010.07.282010.07.30)] Simulation and analysis of OFDM system based on simulink, (0),28–31. doi: 10.1109/ icccas .2010.5582049 Noor, Litifa, Anpalagan, Alagan, Kandeepan and Sithamparanathan (2007). [IEEE 2007 9th International Symposium on Signal Processing and Its Applications (ISSPA) - Sharjah, United Arab Emirates (2007.2.12-2007.2.15)] - SNR and BER derivation and analysis of downlink OFDM systems with noisy fading Doppler channels,1–4. doi:10.1109/isspa.2007.4555298 Xiurong Bao, (2012). [IEEE 2012 International Conference on Computer Science and Information Processing (CSIP) - Xian, Shaanxi, China (2012.08.242012.08.26)] - Matlab simulation and performance analysis of OFDM system,1423–1426. doi:10.1109/CSIP.2012.6309131 Stankovic.V and Haardt.M, _x0010_Generalized design of multiuser MIMO precoding matrices, IEEE Trans. Wireless Commun., vol. 7, pp. 953961, Mar. 2008. Caire.G and Shamai.S, _x0010_On the achievable throughput of a multi-antenna Gaussian broadcast channel, IEEE Trans. Inf. Theory, vol. 49, no. 7, pp. 16911706, Jul. 2003. Yoo.T and Goldsmith.A, _x0010_On the optimality of multi antenna broadcast scheduling using zero-forcing beam forming, IEEE J. Sel. Areas Commun., vol. 24, no. 3, pp. 528541, Mar. 2006. Jindal.N and Goldsmith.A, _x0010_Dirty-paper coding vs. TDMA for MIMO broadcast channels, IEEE Trans. Inf. Theory, vol. 51, no. 5, pp. 17831794, May 2005.

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