a shadowing-aware automatic gain control scheme for ofdm wireless communication system

The SIJ Transactions on Computer Networks & Communication Engineering (CNCE), Vol. 2, No. 3, May 2014

ISSN: 2321-2403 © 2014 | Published by The Standard International Journals (The SIJ) 43

Abstract—In Orthogonal Frequency-Division Multiplexing (OFDM) wireless communication system, the

power of received signal might suffer giant variation from the non-ideal channel effect, such as path loss and

shadowing effect. Then, it might lead to serious degradations of system performance in OFDM wireless

system. Automatic Gain Control (AGC) must periodic control the Variable Gain Amplifier (VGA) for whole

packet to ensure such received power stably. Typically, shadowing effect is not considered in designs. In this

work, we create an AGC scheme to balance the convergence rate and the gain stability under shadowing effect

in OFDM wireless communication system. The path loss and dynamic shadowing effect could be solved by a

new AGC algorithm. With a mechanism of reference value alignment and a precise gain calculation in the

PLCP and data symbol duration, ADC can prevent from quantization mismatch and saturation error. And the

proposed AGC algorithm uses the signal power normalizes to improve the signal convergence speed and

stables the signal amplitude. In simulations, this solution can achieve 10% PER with a 3-dB SNR loss.

Keywords—Automatic Gain Control; Channel; Path Loss; Shadowing Effect; Wireless Communication.

Abbreviations—Automatic Gain Control (AGC); Bit Error Ratio (BER); Full Scale Range (FSR); Orthogonal

Frequency-Division Multiplexing (OFDM); Peak Average Power Ratio (PAR); Signal to Nosie Ratio (SNR);

Variable Gain Amplifier (VGA).

I. INTRODUCTION

FDM (Orthogonal Frequency-Division Multiplexing)

is a method of encoding digital data on multiple

carrier frequencies for transmission large amounts of

digital data over radio wave. This concept of using parallel

data transmission and frequency division multiplexing was

drawn firstly in 1960s due to the high channel efficiency and

low multipath distortion that make high data rate possible.

OFDM has developed into a popular scheme for wideband

digital communication whether wireless or over copper wires

used in applications such as digital television, audio

broadcasting, DSL broadband internet access, wireless

networks and 4G mobile communications. However, OFDM

systems are sensitive to imperfect synchronization and non-

ideal front-end effects, leading to serious degradations of

system performance.

AGC (Automatic Gain Control) is essential to OFDM

systems because of recurrent gain adjustment for VGA

(Variable Gain Amplifier) to ensure received power stable.

There have been several research contributions that provide

AGC algorithms [Fort & Eberle, 2003; Jimenez et al., 2004].

However, they only consider power loss in large-scale

wireless channel environment, and the dynamic shadowing

effect [Charalambous & Menemenlis, 2002] would cause a

significant loss of SNR if the AGC have no scheme to defend

it. In this thesis we propose an AGC scheme to defend the

dynamic shadowing effect and our algorithm can achieve an

acceptable degradation compared with ideal AGC under

shadowing.

The motivation of this research are: a) OFDM systems

are sensitive to imperfect synchronization and non-ideal

front-end effects, leading to serious degradations of system

performance, b) shadowing effect is not considered in typical

design. And the objectives of this research are to balance the

convergence rate and the gain stability under shadowing

effect in OFDM wireless communication system. Finally, the

contributions of this manuscript are: a) the path loss and

dynamic shadowing effect could be solved by a new AGC

scheme and algorithm, b) the proposed AGC algorithm

improves the signal convergence speed and stables the signal

amplitude.

O

*PhD Student, Department of Computer Science and Information Engineering, National Chiao Tung University, Hsinchu, TAIWAN.

E-Mail: skyfeeling.cs96{at}g2{dot}nctu{dot}edu{dot}tw

**Associate Professor, Department of Computer Science and Information Engineering, National Chiao Tung University, Hsinchu, TAIWAN.

E-Mail: tyhsu{at}cs{dot}nctu{dot}edu{dot}tw

Jian-Ya Chu* & Terng-Yin Hsu**

A Shadowing-aware Automatic Gain

Control Scheme for OFDM Wireless

Communication System



The complete AGC architecture is present in Section II.

Section III explains the design and proposed algorithm.

Simulation results are described in Section IV. Finally,

conclusions are given in Section V.

II. AGC ARCHITECTURE

The propose AGC architecture is shown in figure 1. The

VGA is a linear-in-dB analog device. It amplifying signal

amplitude depends on the gain value controlled by AGC. The

ADC is an analog-to-digital converter and it quantifies the

VGA output into the digital data. In the AGC function block,

it divided into several sub-blocks. The signal estimation

average block calculates and accumulates the signal power

from DAC. And then the gain of VGA can be calculated from

the average power value and reference value. Reference value

is the information of ADC signal size. Hence there is a

function to check the reference value in appropriate range. It

prevents ADC output from saturation error and quantization

mismatch by aligning ADC output level with reference value.

In the pilot average block, it tracks shadowing effect by using

average of pilot to calculate the gain and estimates precise

gain with pre-data symbol pilot. The state controller block

controls update timing of gain. According to the data input,

state controller choose signal power gain calculation or pilot

gain calculation. Our proposed AGC focus on shadowing

effect prevention. Hence, we assume the synchronization

timing and synchronization function are ideal in the

simulation platform.

Figure 1: The Proposed AGC Architecture

III. PROPOSED AGC ALGORITHM

The AGC flow chart is shown in figure 2. Every step has

control signal to make sure the timing of next step correct in

different duration and state.

Figure 2: The AGC Flow Chart

3.1. Set a Minimal Gain to Detect Signal

The signal transmitted through the channel would become

weak and unstable. Hence, there should be an amplifier

system receiving the signal and quantifying it into digital.

And a minimal gain value is set to detect the noise and signal

in the system. AGC must ensure the signal is not clipped in

the ADC for getting a stable signal and know the gain value

enough to detect noise and signal. It consider Noise figure on

RF front-end and it usually reserved for 6dB margin [Jimenez

et al., 2004]. According to a formula, SNR=6.07N+1.076, it

can get N closing to 1 and converts about 2 bits of the ADC.

Hence, AGC adjust gain of VGA, observes whether the

output of ADC swing about 1~2 bits vibration and keeps the

gain for VGA. There is a minimal gain estimation formula in

equation (1). G (t+1) and G (t) are next gain and current gain

respectively, 𝑃𝑛𝑜𝑖𝑠𝑒 _𝑟𝑒𝑓 is ADC noise reference value about

1~2bits and 𝑆𝑠𝑖𝑔𝑛𝑎𝑙 _𝑝𝑤𝑟 is average power of ADC output

signal. If the 𝑆𝑠𝑖𝑔𝑛𝑎𝑙 _𝑝𝑤𝑟 is less than 𝑃𝑛𝑜𝑖𝑠𝑒 _𝑟𝑒𝑓 , AGC

increases the gain, otherwise decrease.

refnoise

pwrsignal

P

StGtG

_

_11 (1)

The purpose of setting minimum gain is signal detection.

In this thesis we aim to address the impact of shadowing

effect and assume the detection function is ideal. We use

double sliding window detector and is shown in figure 3.

When the correlation energy is more than the threshold, the

system starts the AGC to adjust gain by AGC algorithm.

Figure 3: Double Sliding Window Detector



3.2. Reference Value Alignment and Gain Adjustment

When the signal is detected, the system enables AGC to start

gain calculation to amplify signal waveform of VGA. There

is a gain calculation equation (2.). It calculates the signal

power at 16 sample time once from the set of FIFO data and

gets a ratio by reference value. The ratio multiplied by

current gain value and adjustment factor (α) to be next gain

volume.

16

1

2

16n

avg

nFIFOFIFO

avg

refavg

FIFO

PFIFOtGtGtG 1 (2)

Considering the gain speed, 16 sample times once is

suitable. If the time of gain calculation once is too long, AGC

might not trace signal effectively. And the shadowing effect

still affects the signal. If the time of gain calculation once is

too short, the signal would have a small volatility and cause

worse performance.

The signal level size depends on the reference value and

allows the AGC to know whether the gain increase or

decrease. If the reference value is too large, there might be a

saturation error. If the value is too low and gets a weak

signal, it degrades signal performance by quantization

mismatch. Average power of ideal short training symbol is

usually used by reference value. However, ADC specification

and other causes on hardware system would affect the value

setting. In our simulation platform, there is no other hardware

effect. Therefore we only consider the signal level on ADC

dynamic range, and it can handle a consistent range. Hence,

the system SNR requirement determines the bit number of

ADC and is considered about the impact such as the Noise

Figure, BER (Bit error ratio) SNR, and PAR (Peak Average

Power Ratio) SNR and design margin. Besides the design

margin, an optimal signal output of ADC must be greater

than the impact to achieve the best signal quality. Therefore,

it uses a 6bit ADC in simulation platform, and its dynamic

range is 37.88dB. The platform assumes a 16QAM

communication system that has 6dB noise figure, 19dB BER

and 5.6dB PAR [Bernard Sklar, 2001]. It can rough estimate

these effects accounted for 80% of ADC total dynamic range,

and we can pre-assume that it is the optimal output of signal

on ADC.

Base on above assumptions, there is a simple calculation

for initial reference value. It uses the FSR (Full Scale Range)

of ADC to divide with a constant and then multiply 80% of

calculation value. Therefore, it assumes the FSR of ADC and

constant are 0.121v and 16. It can calculate the initial

reference value, (0.121/16)*0.8=0.0053. But the value is only

being an initial reference value in the AGC initial state. It

cannot make sure that the signal does not saturate.

In this thesis we propose an algorithm to align the

reference value. We set an upper and lower limitation to

check the maximum data in the FIFO at once of 16 sample

times. If the maximum data is more than the upper bound, the

system decreases the reference value by attenuation factor. If

the maximum data is lower than lower bound, the system

increases the reference value. The formula is in equation (3).

0:15max FIFOabsMAX

boundlowerMAXifL

tP

boundupperMAXifL

tP

tP

ref

ref

ref

11

11

1 (3)

The MAX is maximum data in the FIFO. 𝑃𝑟𝑒𝑓 (𝑡 + 1)

and 𝑃𝑟𝑒𝑓 (𝑡) are next reference value and current reference

value. The upper limitation is 80% of ADC FSR. The lower

is 50% of ADC FSR, and L is the attenuation factor. There is

a conception diagram in figure 4.

Figure 4: Conception of Reference Value Calculation

3.3. The Mechanism of AGC Updates Gain

Mechanism of gain adjustment on different state is shown in

figure 5. In the PLCP preamble and header duration the AGC

adjust gain once at every 16 sample time, and the system

aligns reference value before the end of 7th short training

preamble. In the PLCP headers duration, the system stop

reference value alignment, and AGC uses final reference

value to adjust gain for VGA. In the data symbol duration, it

uses a weight that is average of 4 pilots to obtain a precise

gain at every 80 samples once. Hence, there is a state

controller to control gain update for VGA. It controls the gain

algorithm in the different state.

Figure 5: Adjustment Gain in Different State



3.4. Gain Calculation by Pilot

The pilot is used in channel estimation and synchronization.

The polarity of pilot is controlled by a serial subcarriers

inserted in data symbol. As shown in figure 6, the received

signal has a sine wave on the envelope of total packet

because of shadowing effect, and it can also be observed

from polarity of pilot. We calculate the average of the

absolute value of four pilots in each data symbol. It can find a

situation similar to as described above in the figure 7.

Figure 6: Received Signal

Figure 7: The Average of 4 Pilots on Total Data Symbol

It is clear that shadowing effect would affect the data

symbol. Base on this average value, it takes advantage on this

change by predicting a precise gain value for the next data

symbol, and it prevents the AGC from giving not enough

gain to recovery the fluctuation intense signal. Hence, there is

a formula to calculate gain in the data symbol duration that is

in equation (4). G(t+1) and G(t) are next gain and current

gain respectively. J(t) refers to current average of pilot P(t) to

calculate a ratio, and the ratio multiplied by the current gain.

The L is arrangement factor. In the data symbol duration, the

signal is not regular. So the signal power cannot calculate

precise gain for VGA. But the pilot value is always 1, and it

can estimate the signal status.

LtJtGtGtG 1

4

14

1

n

npilottP

1

11

tPif

tPif

tP

tPabstJ (4)

In equation (2) the system adjusts the reference value to

calculate gain for AGC. It is according to the level of the

ADC output before the end of the PCLP preamble completed.

However, during the data symbol the signal may be saturated.

Since the data symbol of the output level is greater than the

PLCP preamble and headers in the ideal signal. There must

have a mechanism to check the ADC output level to prevent

the saturation error on ADC output and make sure the output

level is lower than the 90% of ADC FSR. In equation (5) it is

similar to equation (2). Its purpose is if the signal is greater

than the 90% ADC FSR, the system would decrease Pref to

keep output level under definition.

16:1FIFOabsxmatPoin

levelfullADCoftpoinIfL

tPtP refref %901

11

(5)

IV. SIMULATION RESULT

The simulation parameters are shown in Table 1.

Table 1: Simulation Parameter

Parameter Value

Modulation 16 QAM

Coding Rate 3/4

PSDU Length 1024 Bytes(0.25ms)

Packet No 2000

FFT size 64

width of ADC 6 bit

Path Loss -20dB to -80dB

Shadowing vibrate ±3dB

Channel Model Model B

RMS delay spread 15 ns

4.1. AGC Algorithm Result

In figure 8, it expresses AGC gain performance under

channel effect from VGA input to ADC output. The propose

AGC calculates the gain in different state. It controls gain to

stable the signal level and it controls an appropriate level for

ADC to avoid saturation signal degrade the system

performance. The first picture of figure 8 shows the path loss

with 3𝜋 period shadowing effect. It is our target to eliminate

this impact and the second picture shows the signal that

suffers these interferences such as AWGN, path loss and

shadowing effect. The third picture that are gain and packet

detection curves from the propose AGC algorithm. When the

system detect signal that is existing. The system starts the

AGC to calculate gain for VGA. While the AGC finish its

work and it can find the gain curve that looks like the

shadowing effect curve on the contrary. That means the AGC

can track shadowing effect on the signal and recover signal

successfully. So the fourth picture shows the AGC stable

signal and keeps signal level under FSR of ADC.



Figure 8: Simulation Result for Proposed AGC Algorithm

4.2. BER and PER Performance

In table 2, it is channel condition on simulation platform. The

“No-AGC” is ideal AGC and it doesn’t have any AGC

algorithm and it doesn’t run through the ADC. So the ideal

AGC does not have any quantization noise. The simulation

condition of ideal AGC only has AWGN for simulation-1 and

AWGN with multipath for simulation-2 and they get a ideal

curve respectively. The multipath uses mode-B of TGn

channel [TGn Channel Models, 2004] and it have 2 delay tap

and 15ns RMS delay spread. The purpose of ideal curve is to

know what difference in BER and PER between ideal and

propose or conventional AGC. The conventional AGC is

general algorithm [Tsung-Hsien Wang, 2010]. The other

parameters list in table 1.

Table 2: Channel Condition for Simulation Platform

No-AGC (Ideal-

AGC)

Propose AGC / Conventional

AGC

Simulation

-1

AWGN only

( No ADC

Quantization noise )

AWGN only

AWGN + Shadowing effect

Simulation

-2

AWGN + Multipath

( No ADC

Quantization noise)

AWGN + Multipath

AWGN + Multipath +

Shadowing effect

In figure 9 and figure 10, that is the result of BER and

PER especially on the simulation-1 condition. The

performance of propose AGC is better than conventional

AGC in the AWGN only environment. The difference in

BER and PAR between propose AGC and conventional AGC

are 0.5dB and 0.2dB. That is because of the propose AGC use

signal power normalize the AGC not only has a small α value

but also it doesn’t affect the convergence rate of the signal.

Hence the signal has a good convergence performance and

has little variation in the steady state by the propose AGC

gain adjustment. That can prove the propose AGC algorithm

is helpful to obtain a good signal quality in the data symbol

duration. On other hand the propose AGC is better than

conventional AGC in the shadowing effect environment and

it is more than 0.2dB difference at 3π shadowing and more

than 3dB difference at 6π shadowing. Because using of signal

power normalize and the mechanism of reference value

alignment the propose AGC reduces the signal has little

frequency oscillation on signal envelope than the

conventional AGC and the signal saturation probability is

also significantly reduced. That is helpful for the receiver

decoding and enhance the receiver performance. Although it

is out of 3dB range at 6π shadowing but it can still prove the

propose AGC algorithm is useful than the conventional AGC

on signal envelope recovery.

Figure 9: Simulation-1 BER

Figure 10: Simulation-1 PER

In figure 11 and figure 12, they are the result of BER and

PER especially on the simulation-2. In the performance of

propose AGC the difference in BER and PRE between ideal

AGC and propose AGC are 0.7dB and 1dB. That is Because

of multipath the SNR performance is bad than simulation-1

and it also affect the AGC performance. The multipath impair

the phase and amplitude of signal and the receiver need to

have a precise long preamble to calculate impulse response

for channel estimation to cancel the interference. But the



differences are under 1dB so the performance is acceptable.

The conventional AGC is still bad than propose AGC and it

is predictable from the result of simulation-1. The using of

signal power normalize and several AGC algorithm scheme

they still are helpful in the multipath environment. But the

ADC quantization noise and the same long preamble AGC

algorithm make they have the same performance in PER at no

shadowing environment. But the propose AGC is still better

than conventional AGC at 3π shadowing in the shadowing

effect environment. It can prove the propose AGC algorithm

is helpful on signal envelope recovery.

Figure 11: Simulation-2 BER

Figure 12: Simulation-2 PER

The plan test the propose AGC ability at the 2~8π

shadowing effect. The results of BER and PER are shown in

figure 13 and figure 14 respectively. The performance of

propose AGC has 0.5dB degradation between every different

shadowing period. Because in the worse shadowing

environment the propose AGC hard to compensate the signal

envelope variation, and the variation of signal envelop is very

severe, and the difference of signal power is very large at

every calculation interval of signal power. To analyze the

signal after the propose AGC adjustment, it can find there

still have a low frequency oscillation on the signal envelope

in the worse shadowing effect. This phenomenon makes the

receiver hard to decode the signal and degrade the

performance at every shadowing period. So the AGC need

precise gain compensation and also requires appropriate

adjustment of gain interval to compensate the signal envelope

variation. In the PLCP duration the adjustment gain interval

is 8us, but in the data symbol duration the interval is 40us. It

is need to be considered in the gain adjustment of data

symbol duration. At the result of 4π shadowing effect the

difference in BER and PER between ideal and the result are

3dB and 2.7dB acceptable in communication method. At 5π

shadowing or more shadowing period their BER and PER are

more than 3dB. Hence, it can know the ability of proposed

AGC is 2~ 4π.

Figure 13: Propose AGC BER Simulation Result

Figure 14: Propose AGC PER Simulation Result

V. CONCLUSION

In this thesis it introduces automatic gain control algorithm

for OFDM system. The path loss and dynamic shadowing

effect could be solved by a new AGC algorithm. It also

proposes a mechanism of reference value alignment to

prevent saturation and quantization noise on ADC output. It

provides a precise gain calculation in the PLCP and data

symbol duration and the performance is better than

conventional AGC at the 3π shadowing period. The proposed

AGC algorithm uses the signal power normalizes to improve

the signal convergence speed and stables the signal

amplitude. The advantage is it does not require large α value

to quickly achieve the initial reference value and the variation

of signal is very slightly in the steady state. In the shadowing

ability test the proposed AGC has good performance than

conventional AGC. It has a good ability in the

0~3πshadowing effect and it is acceptable in the maximum

shadowing effect is 4π.



REFERENCES

[1] Bernard Sklar (2001), “Digital Communication: Fundamentals

and Applications”, Prentice Hall; 2 Edition, ISBN-

10:0130847887 ISBN-13:987-0130847881.

[2] C.D. Charalambous & N. Menemenlis (2002), “Dynamical

Spatial Log-Normal Shadowing Models for Mobile

Communications”, Proceedings of the 27th General Assembly

of the International Union of Radio Science.

[3] A. Fort & W. Eberle (2003), “Synchronization and AGC

Proposal for IEEE 802.11a Burst OFDM Systems”, IEEE

Global Telecommunications Conference (GLOBECOM '03),

Pp. 1335–1338.

[4] V.P.G. Jimenez, M.J.F.-G. Garcia, F.J.G. Serrano & A.G.

Armada (2004), “Design and Implementation of

Synchronization and AGC for OFDM-based WLAN

Receivers”, IEEE Trans. Consum. Electron, Vol. 50, No 4, Pp

1016–1025.

[5] TGn Channel Models (2004), “IEEE Std. 802.11 – 03//940r4”,

Available: https://mentor.ieee.org/802.11/dcn/09/11-09-0308-

00-00ac-tgac-channel-model-addendum-document.doc.

[6] Tsung-Hsien Wang (2010), “Study on the Automatic Gain

Control Techniques for the IEEE 802.11a System”, National

Chung Cheng University.

Jian-Ya Chu has his B.S. degree in

Computer Science from National Chiao Tung

University, Taiwan, in 2011 and is studying

PhD in Computer Science of National Chiao

Tung University, Taiwan. Jian-Ya Chu’s

research areas include MIMO detection and

AGC in wireless communication.

Terng-Yin Hsu (M’07) received the B.S. and

M.S. degrees from Feng Chia University,

Taichung, Taiwan, in 1993 and 1995,

respectively, and the Ph.D. degree from

National Chiao-Tung University, Hsinchu,

Taiwan, in 1999, all in electronic engineering.

In 2003, he joined the Department of

Computer Science, National Chiao-Tung

University, where he is currently an Associate Professor. His current

research interests include VLSI architectures, wireless

communications, multi-spec transmissions, high-speed networking,

analog-like digital circuits, system-on-chip (SoC) design

technology, and related application-specific ICs (ASIC) designs.

a shadowing-aware automatic gain control scheme for ofdm wireless communication system

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