 Open Access
 Total Downloads : 165
 Authors : Shruti Tiwari, R. K. Chidar
 Paper ID : IJERTV6IS090123
 Volume & Issue : Volume 06, Issue 09 (September 2017)
 DOI : http://dx.doi.org/10.17577/IJERTV6IS090123
 Published (First Online): 25092017
 ISSN (Online) : 22780181
 Publisher Name : IJERT
 License: This work is licensed under a Creative Commons Attribution 4.0 International License
Massive MIMO: Maximal User Allocation with Uniform Power for Enhancement of Spectral Efficiency
Shruti Tiwari, ME Scholar Electronics and Communication UIT RGPV
Bhopal (M.P.) India
R. K. Chidar, HOD
Electronics and Communication UIT RGPV
Bhopal (M.P.) India
Abstract New network generation needs to cope with demand of high area throughput to manage with the growing wireless data traffic. Previous network generations resulted improvement in area throughput by cell densification and allocation of more bandwidth. Further cell densification is complex and it appears that a saturation point is reached. Valuable frequency bands below 6 GHz provide good network coverage and service quality. Higher bands work well for short range lineof sight conditions. So we cannot expect major bandwidth improvements. Earlier network generations has not seen any improvements in spectral efficiency (bit/s/Hz/cell). This paper describes the physical layer technology for Massive MIMO to improve the spectral efficiency for future 5G networks.
Keywords MUMIMO, spectral efficiency, multi cell, throughput, reuse factor, physical layer, power allocation

INTRODUCTION
Wireless data traffic has doubled every two years since the beginning of wireless communications. Different technologies have dominated in previous network generations. Current exponential growth in cellular networks is driven by wireless data traffic. It seems that this trend will not break. Even the well reputed Cisco Visual Networking and Ericsson Mobilitys Report have indicated faster growth rate than the expected rate by Martin Cooper.
Evolving 5G technologies need to focus on improving the area throughput to keep up the growth rate in wireless data traffic. The area throughput for wireless network is measured in bit/s/Km2 and is modelled as
Area Throughput (bit/s/Km2) = Bandwidth (Hz) * Cell density (cells/ Km2) * Spectral Efficiency (bit/s/Hz/cell)
Three main components that can be improve to have high area throughput are

Allocation of more bandwidth for 5G services.

Cell densification by adding more independent cells.

Improving data efficiency per cells for a given amount of bandwidth.
Previous network generations have not seen any improvement for spectral efficiency (SE). This factor in future might become the primary way to achieve high area throughput for 5G networks.

MultiUser Multiple Input Multiple Output (MUMIMO)
Spectral efficiency of a single input single output (SISO) channel is upper bounded by the Shannon capacity given as log2 (1 SNR)bit / s / Hz for additive white Gaussian noise
(AWGN) channels. Capacity is a logarithmic function of SNR denoted by SNR . Thus to increase the capacity, increase in
SNR is required, which corresponds an increase in transmitted signal power. For example, a system operating at 2 bit/s/Hz needs to double its spectral efficiency to 4 bit/s/Hz. This corresponds improving SNR by a factor of 5, from 3 to

This example clearly indicates that the logarithm of the SE forces us to increase transmitted power exponentially to achieve a linear increase in SE for SISO channels. This method is highly inefficient and nonscalable. It gets worsen due to interfering transmission from other cells as other users scale their transmit power.
Traditionally, the time/frequency resources have been divided into resource blocks with only one user terminal active per block. Thus the terminal receives single data stream with an SE as log2 (1 SNR)bit / s / Hz . Multiple parallel transmission increases SE efficiently. Suppose there are G parallel transmissions, then the sum SE becomes
G log2 (1 SNR)bit / s / Hz with G as a multiplicative pre log factor. Parallel transmission can be realized as two distinct cases

Pointtopoint MIMO [2], where Base station (BS) with multiple antennas (M) communicates with single user terminal having multiple antennas.

Multiuser MIMO [3], where BS with multiple antennas communicates with multiple users (K) with single antennas.

In pointtopoint MIMO, compact user terminal limits the number of antennas that can be installed in a compact user terminal. While MUMIMO can have any number of spatially separated user terminals equipped with single antenna. Figure 1 shows a schematic illustration for MU MIMO system.
zlk of UE k in cell l is therefore an ergodic random
2
variable with cell specific distribution. The model is used to study the average performance of random interfering user equipments. The time and frequency resources are divided into frames of duration Tc seconds and Wc Hz, as shown in figure 1. Assuming frame dimensions such that Tc is less than or equal to coherence time of all UEs and Wc
is less than or equal to coherence bandwidth for all user
equipments which results all channels static within the frame:
hjlk
N denotes the channel response between BS j and UE
k in cell l . These channel responses are drawn as realisations from zero mean circular symmetric Complex Gaussian distributions:
jlk l ,k M
h CN 0, l I , (2)
where IM
is the M M
identity matrix. This is a theoretical
model for non Line of Sight propagation [2]. Function
l l ,k
In the downlink, the BS multiplexes one data stream per
gives the variation of the channel attenuation from BS j to any
user while in the uplink, receives one stream per user. BS uses its antennas to focus each signal towards its desired receiver
UE position z. The value of
l l ,k
very slowly over time and
and separates multiple signals received from multiuser,
maximum number of data streams that can be transmitted simultaneously in a cell separable in spatial domain is given as min (M,K) and this is referred as multiplexing gain of MU MIMO system.


Massive MIMO
Massive MIMO technique is based on equipping base stations (BSs) with hundreds or thousands of antennas. This
frequency, thus we assume that it is known at the BS j for all l
and K and that each UE knows its value to its serving BS.
We consider the time division duplex protocol, as shown in figure 2, where B 1 out of S symbols in each frame are reserved for UL pilot signalling. Due to the channel reciprocity in time division duplex system there is no DL pilot signalling and no feedback CSI. The remaining S B symbols are
can provide unprecedented array gains and allows multiple allocated for payload data and are split between UL and DL
user MIMO communication to tens or hundreds of user
equipments per cell. With massive MIMO things that were
transmission. Let
(ul )
and (dl )
denote the fixed fractions
random before starts to look deterministic as a result the effect
allocated for UL and DL, respectively. These fractions can be
of small scale fading can be averaged out. Another advantage
selected arbitrary, subject to the constraint
(ul ) (dl ) 1
of massive MIMO, that it enables us to reduce transmitted power. On the uplink, reduction in transmit power results in more battery backup. Whereas on the downlink, reduction in emitted RF power results in low consumption of electricity.
In this paper, we provide some basic guidelines for designing of Massive MIMO network and showcase the SEs that the technology can deliver to 5G networks. We analyze the S expressions valid for both uplink (UL) and downlink (DL) transmission with random user location and power control that yields uniform user equipment (UE) performance. We consider conventional linear processing schemes maximum ratio (MR) combining/ transmission and zero forcing (ZF) to suppress inter cell interference.
and that integers.
(ul ) (S B) and
(dl ) (S B)
are positive


SYSTEM MODEL
Consider the system model were payload data is transmitted with universal time and frequency reuse. We consider multi cell massive MIMO with L cells. Each cell consist of M
Fig. 2: The transmission is divided into frames of S TcWc symbols, whereof B symbols are dedicated to pilot transmission. The remaining S B symbols are used for
antennas at the base station, single antenna user equipment at
payload data, where
(ul )
and
(dl )
are respectively the
the time out of Kmax user equipments. The subset of active user equipments changes over time, thus the name UE
fractions of UL and DL transmission.
k {1,……………, K} in the cell l L is given to different
UEs at the different times. The geographical position

Uplink
The received UL signal
y j
M at BS j in a frame
factor
p
f K to be an integer, where p is the pilot sequence
is modelled similar to [3], as
length. This results division of L cells into group of f disjoints
K cells. Then,
f 1is called as universal pilot reuse while
y j
lL k 1
plk hjlk xlk n j
(3)
f 1is called nonuniversal pilot reuse.
Where
xlk is the transmitted symbol by UE k in cell l.
Fig. 3 illustrate such reuse pattern. Different colours use different sub sets of pilot sequences. Same coloured cells use
This signal is normalized as E{ xlk  } 1, while the
2
exactly the same pilot sequence and results in pilot
contamination to each other and not to cells of different
M
corresponding UL transmit power is defined by
plk 0 . The
colour.
additive noise is modeled as nj
is the noise variance.
CN(0, 2 I ) , where 2

Downlink
The received DL signal frame is modelled [3] as
K
z jk
M at UE k in cell j in a
Fig 3. Illustration of potential symmetric reuse patterns created by three different pilot reuse factors, f, in a cellular network with hexagonal cells.
z hT w s n
(4)
jk ljk lm lm jk lL m1
Where (.)T denotes transpose, slm is the symbol transmitted
IV. FLOWCHARTS Flowchart for generating hexagonal network:
for UE k in the cell l,
wlm
is the corresponding precoding
START
Take two Inputs : Points and Radius.
vector. The additive noise is modeled as
njk
CN (0, 2 ) , with same variance as the UL.


DESIGN GUIDELINES
Extract Distances and Angles.
We provide basic design guidelines for Massive MIMO which can deliver to 5G networks. We consider cellular network topology with hexagonal cells, where each cell looks as shown in figure1. BS is placed in the centre of each cell and
Symmetry allows to rotate all angles to lie in area 0,pi/3 .
users are distributed over the cell randomly. When many such
cells are placed together, cellular network has the shape as figure 3.
Extract the Cartesian coordinates for rotated points.

Power Allcation
We assume a power allocation policy
pl ,k
l l ,k
l 1,…..L
k 1,…….K
(5)
Where
0is a design parameter.
l
p
l ,k l ,k
determines
average received signal power per antenna. Thus
p
l
l ,k l ,k
Check if the points are in hexagon in an area limited by three lines.
2
determines the SNR achieved at each BS.
2
Output : Okay (Matrix with Booleans telling if points are in hexagon) .
BS BS
This policy is called channel inversion power allocation.

NonUniversal pilot allocation
SE of a particular cell j is influenced by the pilot signaling carried in other cells. Degradation in channel state information (CSI) estimation increases due to interfering cells using same
pilot sequence as cell j . Channel attenuation of the
interference increases with distance which suggests placing interfering cells far away from cell j . We assume pilot reuse
Flowchart for generating UE Locations Flowchart for solving problem of uneven signal strength to users
START
Check which UE locations are inside hexagon and compute (1) and (2) for worst case and best case interference.
Case 2 : Compute Waterlevel if only a subset of users served by BS j are allocated non zero power.
Iterate the generation of UE locations until all of them are inside hexagonal cell.
Case 1 : Compute waterlevel if all of the users served by BS j are allocated non zero power.
Generate UE locations randomly with uniform distribution inside the cells.
Iteration over Base Stations to perform power allocation.
Define Matrix for storing UE locations.
Preallocation of Matrix for power allocation Coefficients.
Set no. Of UE locations in Monte Carlo Simulations.
START
Inputs : kappa (pathloss exponent) Forbidden Region MonteCarlo UEs
Inputs : rhos – ktÃ—kr matrix with effective channel gains.
q – ktÃ—1 vector with total power at BS j .
Check if the difference between allocated power and available power is minimized by allocating power to all users or only subset.
Compute power allocation with optimal waterlevel.
Output : Power allocation – ktÃ—kr matrix with power allocation coefficients.

SIMULATION RESULTS
We considered single cell scenario and linear precoding schemes such as (a) maximum ratio (MR) and (b) zeroforcing (ZF) to investigate how large gains can be achieved.
Simulations are performed on MATLAB with
K 10 user
terminals served simultaneously by a BS with M antennas. For simplicity, average SNR per user is assumed as 5 dB with perfect CSI available everywhere and channels are modelled as uncorrelated Rayleigh fading.
Fig. 4 shows that the multiplexing gain min(M , K) is outperforming when we consider M K as capacity increases with linear ZF processing and reaches upper curve, which represents bound when interference is neglected. So Massive MIMO with linear process such as ZF can serve all the K users as if each user is alone in the cell.
Fig 4. Average spectral efficiency in a multiuser MIMO system with K = 10 users and varying number of BS antennas. Each user has an average SNR of 5dB and the channels are Rayleigh fading.
Fig. 5 compares SE obtained for perfect CSI and CSI estimated with pilot signal with p . Simulation results obtained represents SE as function of M and compares Time
Division Duplexing (TDD) mode p K 10 with
Fig 5. Average downlink spectral efciency, as a function of the number of BS antennas, with different processing schemes and different types of CSI available at the BS. (a)Downlink simulation with maximum ratio precoding.
(b) Downlink simulation with zeroforcing precoding.
Fig. 6 shows the average SE for different number of users in a multicell scenario. Simulation result indicates same
performance for two SNR levels 0 dB and
BS
Frequency Division Duplexing (FDD) mode with 2
p 10, p M or p min(M ,50) . Result indicates
20 dB. Array gains of Massive MIMO makes SE
that the performance loss for ZF precoding to MR precoding is more when compared to MR. TDDsystem benefits on adding more antennas while FDD system benefits on adding more antennas only if the pilot sequences are made longer.
2
BS
interference limited and not noise limited, hence it works equally for high and low SNRs. Result also indicates different pilot reuse factors at different user load. Moreover, difference in SE for ZF and MR is relatively small.
Fig 6 Average spectral efficiency, as a function of the number of users, with different processing schemes and pilot reuse factors. Two different SNR levels are considered.
Fig 7. Average spectral efficiency, as a function of the number of BS antennas, with ZF processing, a pilot reuse factor f =3, and an SNR of 0 dB. The number of users is optimized for each antenna number to yield the highest SE, and the corresponding number of users is also shown.
Fig. 7 show optimization of number of active user for each M to achieve highest SE. The Massive MIMO network considered, achieves 52 bit/s/Hz/cell for M=100 antennas and
114 bit/s/Hz/cell for M=400 antennas. On dividing upper curve with bottom curve, we get SE per user which lies in the modest range of 1 to 2.5 bit/s/Hz. Such SEs can be achieved easily by using conventional modulations such as 16QAM.

CONCLUSION
Linear processing such as ZF or MR provides sum spectral efficiency close to upper bound where interference is neglected between users when M K . Massive MIMO FDD systems are feasible with large antennas for slowly varying channels but it requires large pilot overhead. TDD systems are more scalable as pilot sequences only need to be of length K irrespective to M. High array gain makes Massive MIMO interference limited system and not noise limited. It performances equally well for high as well as low SNRs. Pilot reuse factor is an important design parameter whose choices depend on user load, environment and number of BS antennas.
REFERENCES

Ericsson Mobility ReportMobile World CONGRESS EditionFeb 2016.

An Overview of MASSIVE MIMO :Benefits and Challenges.(LU LU,Geoffrey Ye Li,A.Lee Swindlehurst)

Massive MIMO for Spectral Efficiency:How Many Users and Pilots Should be Allocated? (Emil Bjornson, Erik G. Larsson, Merouane Debbah)

X.Gao, O.Edfors, F.Rusek, and F.Tufvesson,Massive MIMO performance evaluation based on measured propagation data,IEEE.Trans.Wireless