MIMO To LS- MIMO: A Road To Realization Of 5G

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This paper describes the earlier history of MIMO-OFDM and the antenna beam ... is the 5G technology mobile network which is realized using the massive MIMO ...
MIMO To LS- MIMO: A Road To Realization Of 5G Naveena Koppati1, Pavani K2, Dinesh Sharma3, Purnima K Sharma4 1 &2 M.Tech. Scholar (ECE Dept.), 3 &4Associate Professor (ECE dept.) VRSEC, Vijayawada (A.P.), India

Email: [email protected] [email protected] Abstract: MIMO means multiple inputs multiple outputs. As it refers MIMO is a RF technology used in many new technologies these days to increase link capacity and spectral efficiency. MIMO is used in Wi-Fi, LTE, 4G, 5G and other wireless technologies. This paper describes the earlier history of MIMO-OFDM and the antenna beam forming development in MIMO and types of MIMO. Also this treatise describes several decoding algorithms. The MIMO combined with OFDM increases the channel capacity. But the main problem is in estimating the transmitted signal from the received signal. So the channel knowledge is to be known in estimating the channel capacity. The advancement in MIMO-OFDM is Massive MIMO which is beneficial in providing additional data capacity in the increased traffic environment is described. In this memoir various application scenarios of LS-MIMO which increases the capacity are discussed. Keywords: multiple input-multiple output (MIMO), orthogonal frequency division multiplexing (OFDM), 5th generation (5G), decoding, channel capacity, large-scale MIMO.

1. INTRODUCTION MIMO technology is used as a base technology for 4G, 5G and other upcoming areas. The wireless LANs, 3G, 4G mobile networks are the recent technologies which are standardized by the MIMO technology. The fifth generation is the 5G technology mobile network which is realized using the massive MIMO technology[1]. The 4G technology provides fast and reliable data communication. Even though 5G is not employed in our lives it has an important future scope in the progressing technology. It presents a high resolution and fast internet access with a magical peak level data speed in cell phones for uploading and downloading. It allows users for printing operations using the recordings stored in the cell phone and also provides virtual private network.

FIGURE 1: Areas of 5G

The major advantage of this 5G technology is lower battery consumption and maximum data coverage which are important requirements of users. It is used in artificial intelligence (AI) for the use of sensors in our everyday life which are mostly wearable without any effect to human health [2] [3]. The most relevant technologies for 5G are IEEE802.11 wireless local area network, wireless metropolitan area network, wireless ad-hoc personal area network. The mobile phones which are integrated with 5G utilize a higher bandwidth. The advanced technologies of 5G are represented in the figure 1.In earlier 1990s, the spatial diversity is used between two systems with two antennas. But it is often limited. So additional levels of processing are utilized for spatial diversity and spatial multiplexing. This is used to limit the degradation due to multipath propagation and to make utilize of additional signal paths as additional channels to carry additional data [4]. The principle of diversity is to provide different versions of the same signal to the receiver . Various diversity modes are time diversity, frequency diversity and spatial diversity using time slots, frequency channels and antennas in space for reliable communication. Hence the multipath signals which actually introduce interference serves as channel for transmitting multiple signals to improve SNR. The two important transmission types of MIMO are: spatial diversity and spatial multiplexing [5].

2. CHANNEL MODELLING IN MIMO OFDM First of all let’s know about what is a channel and channel modeling in any wireless communication. A channel is a medium which is used as a path to transmit the data from one device to the other. The channel modeling is measuring the effects upon the signal due to some physical processing. In any wireless communication due to the reflection, refraction and scattering mechanisms the signal may effected by multipath fading, shadowing and path loss. So to deal with these effects and to save the signal the channel modeling is required [6]. In radio communications the radio suffers with fading during the multipath propagation that is the multiple transmitted signals from different transmitting antennas are delayed due to the obstacles in the transmission path. The multipath propagation is affected due to the atmospheric duct, inospheric reflection and reflection from the water bodies and tress, buildings, mountains etc…due to this fading the signal suffers with ISI and it can combat by providing some coding in transmitter side and equalizers at the receiving side[7],[8]. According to the wireless communication the channel capacity is the main amount of data imported by the radio channel is limited by the physical boundaries defined by the Shannon’s law. The data is carried over a given bandwidth with in the presence of noise. It is expressed in the form of C=Wlog2 (1+S/N) (1) C-channel capacity in bits per second, W-bandwidth in Hertz’s, S/N-signal to noise ratio. Hence the modulation scheme also plays a vital part in increasing the channel capacity; it requires higher modulation schemes which are not so cheap. So for some improvements we go for MIMO techniques. MIMO utilizes a matrix mathematical approach by using several set of antennas. Each antenna transmitter has a variety of paths to transmit and receive enables different paths. These can be represented as h12, travelling from antenna 1 to receiver antenna 2 and so forth. In this way the matrix can be setup as: R1= h11t1+h12t2+h13t3 (2) R2= h21t1+h22t2+h23t3 (3) R3= h21t1+h22t2+h23t3 (4) Then the matrix can be written as: [R] = [H]*[T] . To regain the transmitted data some signal processing is required and the reconstruction is done through the following matrix is : [T] = [H]-1*[R] (5) The main challenging problem in MIMO OFDM is channel estimation. Channel estimation means predicting the original transmitted signal from the received signal. Due to the random nature of the channel it is impossible to forecast the channel state information. So we go for channel estimation techniques which increases the capacity of system. Due to the communication channel is random adaptive channel estimation is good for communication systems. 2.1 .Adaptive channel estimation schemes The channel is random in nature and it changes the information quickly according to the changing environment. So these adaptive channel estimation schemes are playing more important role in the research field. The adaptive channel estimation scheme uses two types of schemes in channel estimation [9]. They are: 2.1.1. LMS (least mean square) The LMS algorithm is designed in such a way that the estimator changes its parameter values in accordance with the environment changes. It minimizes the mean square error of the received signal and estimated one. West (m+1) = West (m) + η S (m) e*(m) (6) The West (m+1) is the weight vector to be computed η is the step size and η S (m) e*(m) is the correlation factor to adjust the taps of the weight vector. The block diagram of the adaptive filter is shown below as figure 3:

Figure 3: Block diagram of adaptive filter

2.1.2. RMS (recursive mean square) Input and desired output past samples are required in this method. At each iteration these values will available. LSM algorithm is less complex than RLS. But RLS has better tracking capability, performance and anti-glitch ability. It eliminates signal error using recursive method. W (m) = W (m-1) + K (m) εH (m) (7) ε (m) = prior estimation error ε (m) = H (m)- WH (m-1) S (m) (8)

3. DETECTION IN MIMO SYSTEM The MIMO systems have multiple interfering messages transmitted at a time and expected to decode the information at the receiver without any ISI. The equalizers play an important role in the detection process. The equalization technique is to flatten the frequency response of the channel by employing an adaptive equalization filter in receiver section. Due to channel impairments and distortions in the signal the frequency response is not flat and causes ISI in the channel which degrades the channel capacity. The equalizers are mainly classified into two. They are linear and non-linear equalizers. The multiple symbols can be detected separately or jointly. In joint detection each symbol must know about the other symbol characteristics. So it gives better performance than separate detection even though it has computational complexity. The input symbol vectors are related with space time coding and the MIMO system model is given as Y=Hc+N (9) Y=received signal in multiple time slots, C=space time code word, N=noise matrix. The value of the H can be found out from the channel estimation values and so this is called as coherent detection. If the H value is known with some delay then the detection is called as non-coherent. The differential encoding is applied to the non-coherent detection and as a result the detection is done through block by block basis and leads to computational complexity and moreover they exhibits degraded power efficiency. Various numbers of detectors are proposed as for the requirement of the multiple applications. These MIMO detectors are categorized as optimum/suboptimum detectors, liner/non-liner detectors, sequential/one shot, adaptive /non-adaptive, hard-decision/soft-decision, blind/un-blind, coded/un-coded detectors [10] .The summarization of detectors based on linear and non-linear is shown in figure4.

3.1. Linear MIMO detectors These MIMO detectors are belonged to earlier 1976.Van Etten proposed the ML sequence estimation receiver to combine both the ICI and ISI in the channel of the transmission system. The importance of this work is not so recognized to great extent until the research in CDMA systems and multiple antenna system is possessed to a high degree. Linear MIMO detectors are known for the reduced complexity which falls into the category of optimum MIMO detectors. Comparing to ML detectors which are non-linear detectors these suffer from performance degradation. The linear detectors are MF detectors, ZF detectors, MMSE detectors. The MF detector transformation matrix is linear and is given as TM=HH (10) d = HH HS+HHn (11)

FIGURE4: Different MIMO detectors

3.1.1. Matched filter detector The MF detector is also known as optimal linear filter. It maximizes the resulted output signal to noise ratio with the additive stochastic noise. It doesn’t employ joint detection and it mainly depends on single user detection and whenever the MIMO systems are limited with CCI then this MF detector shows very poor performance.

3.1.2. Zero-forcing detector The linear ZF detector transformation matrix is TZF = HT (12) We have d = S + (HT) n (13) Indicates as a result of ZF detector which means that the interference is eliminated among the multiple inputs.Foschini, Wolninasky, Golden and Valenzuela had studied it first on VBLAST systems which are based on SDM. Zero-forcing the name itself implies to reduce the ISI to zero. It flattens the frequency response of the channel by inverting it and later again inverts the received signal to restore it. It was first studied by Robert Lucky. The frequency response may be constructed as shown below Z (f) =1/F (f) (14) Where F (f) is the frequency response of the channel It may become as Z (f) F (f) = 1 (15) This shows that it is flatten. But the problem here is with noise amplification and hence not suitable for high degree of noisy systems. 3.1.3. Minimum mean square detector And the transformation matrix for linear MMSE detector is given as d = Ty . This MMSE transformation matrix can also be modelled based on the MMSE criterion that minimizes the mean square error between the original transmitted data and channels output. The MMSE equalizer is based on the mean square error algorithm. It does not reduce the ISI but provides a trade-off between the ISI and noise improvement. It is basically a measure of quality of the estimator. The MMSE equation is stated as W = (𝐻𝐻 +𝑁𝑜 I)-1𝐻𝐻 (16) MMSE detectors achieve a better performance than the ZF detectors in the elimination of MUI. It jointly minimizes the total error for the advantage of noise enhancement. It was first proposed by Shnidman in 1967.

3.2. Non-linear MIMO detectors These are non-linear detectors which can gives better performance than the linear detectors. This concept was studied by Bergmans and Cover in 1974 and later also studied by another researcher named Carleial in 1975 in their theoretic studies of broadcast channels and of interference channels with respect to CDMA and multi-antenna system. The variant non-linear detectors are like SIC detectors, PIC detectors, MIC detectors and DFD detectors. 





3.2.1. Interference cancellation aided MIMO detectors The successive interference cancellation detectors (SIC) are the MIMO detectors was first proposed by Viterbi in that which a single symbol Si is detected at a time. Then this particular symbol imposes interference on the other symbols {SK} k≠I which are to be detected are subtracted after recreating the modulated signal with respect to the interference symbol. In this concept before detecting the weakest symbols effect of the interfering symbols which are strong are to be cancelled. This SIC imposes more complexity by increasing the processing delay. This symbol detection should done in a specific order and so this ordering of the symbols done based on the various criteria which include decreasing SNR, greatest SNR, increasing mean square error(MSE),least MSE. This SIC detector works well if there will be a considerable difference between the received and transmitted symbols. But this concept was not appropriate for all practical situations. So SIC detector was not suitable for the systems suffering with near –far problem. The parallel interference cancellation MIMO detector all the symbols are detected in parallel fashion and the regeneration of the interference symbol is done using the estimated interfering symbol and also this estimated symbol is used for deriving the interfering symbols from the received signals. This process is repeated for several loops and so this is referred as same as the SIC detector. Compared to SIC detector it has lower processing delay and also robust to error stream propagation. But it has the near far resistance problem that is some of the users will be resulted with weaker signals due to power handling. So these types of detectors are mostly suitable for systems which are dealing with same power signals rather than different ones. The multistage interference cancellation detectors are first studied by Timor for the interference cancellation in the frequency hopped CDMA systems. To cancel the interference in the symbols the MIC detector circuit has a suboptimum detector in its first stage. And it has the inputs up to n to (n-1) stage for the interference cancellation purpose. MIC detector has the similar concept of PIC detector in interference

cancellation process but it was not developed on PIC detector basis. Mostly this type of detector was studied in asynchronous DS CDMA and synchronous DS CDMA systems.  The decision feedback detector is also based on the concept of SIC detector. It mainly concentrates on the receiver optimization techniques using feed forward and feed backward in its stages. The decision feedback equalizer which consists of feed forward filter and the feedback filter. Inter symbol interference is determined by the DFE .The detected symbols are passed through the feedback filter that approximates the combined discrete equivalent baseband channel. The output ISI is subtracted from the incoming symbols. The feedback filter of the DFE is not prone to noise enhancement. The reason is it estimates the channel frequency response than its inverse. The DFE’s are better performers than linear equalizers especially in the case of channels with deep spectral nulls. The detection process in the discrete feedback depends on the decision taken. If a wrong decision is considered then the errors go on repeating which would be drastic error prone results. 3.2.2. Tree-search based detectors The tree-search based detectors are the most familiar detectors in the time of multi antenna MIMO systems. These tree search based detectors are more flexible and are very attractive in the sense of optimum ML performance. These detectors also reduce the computational complexity. The extensions of the trellis decoding with T-algorithm and Malgorithm are used to convert between the trellis to tree structure. But this type of detector was not as attractive as the previously discussed detectors. There are number of categories in tree search based detectors such as depth-first, breadth-first and best first tree search based detectors. The major part of the research on tree search based detectors which forcing them was to achieve an accurate or near ML performance at a reduced complexity. 3.2.3. Lattice reduction aided MIMO detectors Lattice reduction aided MIMO detectors are another most familiar type of MIMO detectors which is mainly based on the geometry concept of lattice. The LR detector can be used with a combination of other linear detectors like ZF and MMSE detectors but this combination will result with a good performance with some complexity. The systems which use pre-coding most likely uses this LR algorithm. The recent advancements in LR algorithm are LLL based LR algorithm, VLSI implementation aided pre-coders for MIMO detectors with LR algorithm and element-based LR detectors which mainly contributes in the reduction of the noise covariance matrix and soft-decision MIMO detectors. 3.2.4. Probabilistic data association based detectors The probabilistic data association based detectors are first proposed by Bar-Shalon in 1975. These detectors are used in radars for target tracking and surveillance in a cluttered and spurious environment. The further improvement of the PDA filter is the joint PDA filter which is mostly applied in the cases of multiple targets which are present out of all the strongest targets. The major applications of PDA detectors are in radars, sonars, electro-optical sensor networks and in navigation systems. Not only in these areas but PDA also can be applied in the computer field of vision on the issue of target tracking problem. 3.2.5. Semi-definite programming relaxation based detectors The SDPR based detectors are applied for the optimum MIMO detection problem using the mathematical model of semi definite programming which is related to the convex optimization theory. This convex optimization mathematical theory reduces the convex sets. If any difficult problem which is solved is to be finding as in the form of convex sets then mathematically it is easy to solve that problem by using a number of powerful algorithms. The most attractive feature of the SDPR is they achieve high performance with a polynomial time worst computational complexity. SDPR was first proposed for BPSK modulation CDMA systems and later extended to QPSK systems. The both of the results showed an efficient diversity order and near-ML BER performance. The recent developments in the SDPR are polynomial inspired SDPR, bound constrained SDPR, virtually antipodal SDPR which gives high SER performance. SDPR also applied for high order modulation systems such M-ary QAM and higher order QAM but the results not so good as BPSK and QPSK. So further research is going on this concept.

3.3. Detection in rank-deficient and overloaded MIMO system Basically in MIMO detection it is preferable to choose a full rank channel matrix. In CDMA systems we can attain the full rank channel matrix by using spreading codes. The multi antenna SDM systems which are assumed to be ideally in a scattering environment may suffer with fading channels in between the transmitter and receiver. Then it requires a full rank channel matrix capacity enhancement and to reduce the sensitivity. But in some propagation concepts there will be the lack of full rank channel matrix. If the space between the transmitter and receiver is small or large than it required then the angular spread is also small and the correlation between the transmitter and receiver

antennas suffer with rank deficiency i.e., rank [H] < min (Nt,Nr). This effect is known as keyhole or pinhole effect which is regularly referred as diffraction phenomenon which reduces the channel capacity [11]. Another problematic condition for MIMO detection is overloaded channel which mean that the number of users is greater than the space. So for these situations the standard linear detectors like ZF or MMSE couldn’t exhibit good performance. So some “pseudo-inverse” based linear detection, group detection, correlated PDA detectors are prescribed for MIMO detection when the matrix rank deficient and overloaded systems. In this way each MIMO detectors exhibit different performance characteristics and computational complexity. So we cannot say any one detection algorithm is best one. By comparing and analyzing one among all then we go for the suitable detection algorithm for such a particular MIMO system.

4. ADVANCED MIMO: MASSIVE MIMO The main difference between MIMO and LS-MIMO is in its energy efficiency (EE), bandwidth efficiency (BE), multiplexing gains and diversity gains. As the global mobile traffic is increasing rapidly day by day these massive MIMOs got a high importance in the technology [12]. In the LS-MIMO the physical layer technology is used for transmit pre-coding (TPC) or the action of detection. The LS-MIMO depends on the TPCs or detectors because of good performance. These detectors operated in single cell and multi cell environments. In the single cell scenario the linear pre-coders and detectors play the similar performance due to throughput. In the LS-MIMO the major preferences is given to the Nonlinear transmit precoding and pilot contamination.

4.1. APPLICATION SCENARIOS The application scenarios states the developing key techniques. These are can be classified into two types. These are 1). The Homogeneous Network (Homo Net) with only the micro cell development and, 2). Heterogeneous Network (Het Net) with both the small cells and the micro cell. 4.1.1. Homogeneous network scenarios Case i: Multi-layer Sectorization: This is depending upon increasing number of User Terminals and also their Teletraffic in the urban Environments, and improved system capacity for supporting their customer Requirements. These techniques of sectoring also provide the services to divide a single cell into multiple sectors for increasing network capacity. And also, the Sectorization improves the area Bandwidth Efficiency capacity (BE). In LS-MIMO Multilayer Sectorization provides high selectivity angular beam forming horizontally to reduce the interference. On adjusting the 3D beam forming the coverage of each beam is adjusted. The network throughput and capacity will be increased by using the same frequency ratio resources by reusing all the sectors. Case ii: Adaptive Beam forming: While the operation is carrying out each element of AA remains unchanged and so it is called as Fixed BF’s . This adaptive beam forming is based on the received signals for the suppression of spatial interference. This process is done in either Time –Domain (TD) or Frequency –Domain (FD). 3D BF is more flexible when compared with 2D adaptive BF. Case iii: Large- Scale cooperation: In a collocated deployment scenario most of the existing contributions on LSMIMO has different benefits where large number of antennas are installed within a single cell. This imposes challenges both on hardware design and on field deployment. For improving the indoor coverage using a moderate number of antennas the distributed antenna systems (DASs) associated with spatially separated antennas are used. DAS is capable of increasing the networks BE. 4.2. Het Net scenarios Case i: Wireless backhaul: The Het Net which has small cells will consider the design architecture in terms of energy and Bandwidth efficiency (BE) area. Actually, the alternative preference of the wired backhaul is due to its ease development. In this scenario LS-MIMO provides a high Degrees of Freedom, which supports multiple wireless backhauls in the Het Net .The MeNB is capable to avoid the interference between the MUEs Wireless Backhaul through the pre-coding. Case ii. Hotspot coverage: Considers This Hotspot scenario mainly uses the indoor coverage of buildings. Due to this, the tale-traffic is generated at different heights of buildings. Massive antenna array is capable of adjusting both the azimuth and elevation angles of beam, as it can transmit the beam directly to user terminals at different floors in buildings and it also improves the system throughput. MeNB with massive antenna array provides the indoor coverage of the buildings. The SeNBs equipped with massive Antenna Array is suitable for in-building coverage.

Case iii: Dynamic cell: It is more useful to expand or reduce the radius which it selects for adaptively connected to the SeNB which belongs to the small cells and stated their received power. It is also used for balancing the traffic between the macro cell and small cell [13].

5. CONCLUSION In this paper a brief concept of MIMO-OFDM is presented. The earlier developments of MIMO technology and the effect of fading channels which degrades the channel capacity are mentioned. To overcome these channel related problems several detection algorithms and some common important equalizers are studied. The recent advance in MIMO technology is large scale MIMO or massive MIMO. Todays and future technologies are based on this massive MIMO technology. There are problems related to spectrum, power and cost which making us lagging about these future technologies. Researchers are still progress in this work to resolve these problems and to increase the capacity of channels to avail a large number of users and to achieve quality of service. And the most relevant 5G technology which is based on LS-MIMO is not yet implemented and it may be resolved around the year 2020.

REFERENCES 1.

2.

3.

4. 5. 6. 7. 8. 9. 10. 11. 12. 13.

5G –evolution and revolution an international journal by Meenal.G.Kacchavay and Ajay p. Thakare – an international journal of computer science and mobile computing IJCSMC , vol.3, issue.3,March 2014 page(10801087). Purnima K Sharma, Dinesh Sharma, R.K.Singh, “Evolution of Mobile Wireless Communication Networks (0G8G)”, was published in International Journal of Applied Engineering Research (IJAER) (Print ISSN 0973-4562, Online ISSN 1087--1090), Vol. 10 (6), Page no. 14765-14778, 2015 Purnima K Sharma and Dinesh Sharma, “Mobile Wireless Technology is Boon or Curse?” was published in Indian Journal of Science and Technology (ISSN (Print): 0974-6846 ISSN (Online) : 0974-5645), Vol. 9(40), DOI: 10.17485/ijst/2016/v9i40/84293, October 2016. D. Gesbert; M. Kountouris; R. W. Heath, Jr.; C.-B. Chae & T. Sälzer (Oct 2007). "Shifting the MIMO Paradigm: From Single User to Multiuser Communications". IEEE Signal Processing Magazine. Tse D. and Viswanath P. (2005) “Fundamentals of wireless Communications,” Cambridge, Salz, J. (1985). “Digital transmission over cross-coupled linear channels,” AT&T Technical Journal, vol. 64, no. 6, pp. 1147-1159 Winter, J. (1987) “On the Capacity of Radio Communication Systems with Diversity in a Rayleigh Fading Environment” 10.1109/JSAC.1987.1146600 ISSN: 0733-8716 IEEE. Winters J. H., (1998) “The Diversity Gain of Transmit Diversity in Wireless Systems with Rayleigh Fading”, IEEE transactions on vehicular technology, vol. 47, no. 1, February 1998. PP 119 Md. M. Rana, Md. K.Hosain ‘Adaptive Channel Estimation Techniques for MIMO-OFDM Systems”IJACSA vol.1, no 6, December 2010. Fifty years of MIMO detection-a road to large scale MIMOs by Shaoshi Yang and Lajos Hanzo 2015. H. Shin and J. H. Lee, “Capacity of multiple-antenna fading channels: spatial fading correlation, double scattering, and keyhole,” IEEE Transactionson Information Theory,, vol. 49, no. 10, pp. 2636–2647, Oct.2003. H. Q. Ngo, E. Larsson, and T. Marzetta, “Energy and spectral efficiency of very large multiuser MIMO systems,”IEEE Trans. Commun., vol. 61, no. 4, pp. 1436–1449, Jul. 2013. “survey of Large-scale MIMO systems” by Kan Zheng ,senior Member, IEEE, Long Zhao, Jie Mei, Bin Shao, Wei Xiang, Senior Member, IEEE, and Lajos, Hanzo, Fellow, IEEE. Citation Information:10.1109/COMST.2015.2425294,IEEE Communications Surveys &Tutorials.