Thesis Organization

The organization of this thesis is as follows. Chapter 2 compares the difference between IEEE 802.15.3c and IEEE 802.11ad. Then, we give the overview of IEEE 802.15.3c standard. Chapter 3 first makes overview of traditional frequency domain equalizer (FDE) and time domain equalizer (TDE), and then the proposed algorithms for FDE and TDE are described. The hardware architecture design and RTL simulation result are addressed in Chapter 4. Also, we will discuss the advantage and disadvantage between the proposed FDE and TDE. In Chapter 5, baseband receiver system and chip implementation of the proposed FDE and TDE are presented. Finally, Chapter 6 is the conclusion and the future work.

Chapter 2

Overview of Multi-Gbps Transmission Indoor Wireless Communication

Standards

This chapter introduces the standards for indoor wireless communication. Section 2.1 makes comparison of IEEE 802.15.3c and IEEE 802.11ad. The detail specifications of IEEE 802.15.3c will be described in Section 2.2 with special emphasis on equalization related parts and channel model.

2.1 Comparison of IEEE 802.15.3c and IEEE 802.11ad

Both standards have OFDM and single carrier (SC) mode and have quite the same system specifications. The comparisons of these two standards are listed in Table 2-1[3]. In the 802.11ad, a new mode, named low power SC PHY, is added. This mode aimed to have low data rate with low power consumption. The low power SC mode uses Reed-Solomon (RS) code instead of low-density parity-check (LDPC) for channel coding to reduce the computational complexity and power consumption.

Besides, the payload of this new mode is different with the other modes. The data block length is still 448 like the SC mode; however, it is divided into 7 sub-blocks which are composed of 56 data chips and 8 known data chips for GI. If single carrier frequency domain equalizer (SC-FDE) [6] is adopted in the receiver, a smaller sub-block length will reduce the power consumption. It is because frequency domain

equalizer needs less tap to equalize the received signals.

The following sections will focus on system specifications of IEEE 802.15.3c, since the final version of IEEE 802.11ad is not yet released.

Table 2-1 Comparison of 802.15.3c and 802.11ad

802.15.3c 802.11ad

Frequency Band

57-66 GHz

Sample (Chip) Rate

2640 MHz (OFDM) 1760 MHz (SC)

Preamble Structure

Almost the same

Payload Structure

OFDM : 512 + 64 (GI), 336 useful subcarriers SC: 448 + 64 (known GI)

low power SC : [56 + 8 (known GI)]*7+64

Number of Pilot

(OFDM)

Both are 16, but with different location

2.2 IEEE 802.15.3c Specifications

The IEEE 802.15.3c standard which is based on SC and OFDM transmission is developed for Wireless Personal Area Network (WPAN) to provide short range (<10 m) and very high speed (>2 Gbps) multimedia data services to personal computer and consumer appliances located in rooms, offices, and so on [7]. In the beginning of transmission, IEEE 802.15.3c uses Common Mode Signaling (CMS) and a preamble is attached in front of the data stream. The CMS is specified to enable interoperability among different PHY modes. After the CMS, the frame payload is transmitted in different PHY modes. The preamble is added to aid receiver algorithms related to AGC setting, antenna diversity selection, timing acquisition, frequency offset estimation, frame synchronization, and channel estimation. With insertion of CP, it can reduce the impact of ISI. With channel coding, the system can correct the transmission errors. Furthermore, the standard defines interleaving, scrambler, unequal channel coding, and several modulation scheme to achieve better performance.

2.2.1 Basic Specifications

In IEEE 802.15.3c, there are three transmission modes: Single Carrier (SC) mode, High Speed Interface (HSI) mode, and Audio/Visual (AV) mode. HSI and AV mode use OFDM transmission, and SC mode is single carrier transmission. The detail specifications of SC and HSI mode are listed in Table 2-2 and Table 2-3 respectively.

The RF band occupies 9 GHz bandwidth while the sampling rate is 1760 MHz as shown in Fig. 2-1. The standard indicates that the RF band is divided into four sub-bands such that the Nyquist bandwidth of each sub-band is exactly 1760MHz. In

addition, each sub-band has 432 MHz spacing to prevent the interference from each Table 2-2 SC mode specifications

Description Value

Chip rate (MHz) 1760

Chip duration (ns) ~0.568

Subblock length (samples) 512

Pilot length (samples) 0 8 64

Length of data chips per subblock (samples) methods. The measurement shall be performed in AWGN channel with a frame payload length of 2048

Table 2-3 HSI mode specifications

Description Value

Sampling rate (MHz) 2640

Sampling period (ns) ~0.38

Number of subcarriers/FFT size 512

Data/pilot/guard subcarriers 336/16/141

Guard interval length (samples) 64

Subcarrier frequency spacing (MHz) 5.156

Modulation schemes QPSK, 16-QAM, 64-QAM

FEC types

LDPC(672,336), LDPC(672,504), LDPC(672,420), LDPC(672,588) Transmit center frequency tolerance

(ppm)

±20

2.2.2 Equalization Related Specifications

In this standard, there are some well-known data streams that are assigned to specify different purposes, i.e. MAC layer control signal, the piconet coordinate signal, or the performance improving signal. This section will introduce the specific signaling that is directly related to the equalization.



Common Mode Signaling(CMS)

The CMS is a low data rate SC mode and specified to enable the switching among different PHY modes. It’s also used for transmission of the beacon frame, sync frame,

command frame, and training frame in the beamforming procedure. The order of sequences in time is SYNC, SFD and CES.

SYNC

Fig. 2-2 CMS frame format

As shown in Fig. 2-2, the CMS preamble is constructed by Golay complimentary sequences a₁₂₈ and b₁₂₈ of length 128, which are listed in Table 2-4. The SYNC field uses a repetition of codes for higher robustness frame detection. The main purpose of SFD is used to establish frame timing as well as the header rate. The CES field is used for channel estimation. The sequences a256 and b256 inside the CES steam also hold the property of Golay complimentary, and can be decomposed as

256 128 128

Sequence name Sequence value

a₁₂₈ 0536635005C963AFFAC99CAF05C963AF

b128 0A396C5F0AC66CA0F5C693A00AC66CA0



SC and HSI PHY preamble

A PHY preamble is added to aid the receiver algorithms such as AGC setting, timing acquisition, frame synchronization, and channel estimation. After the CMS, the system will switch to the designated mode and begin to transmit the data payload. In each beginning of transmission, the transmitter will send a PHY preamble to aid the receiver algorithms, just like the one in CMS. In SC mode, the preamble is transmitted at the rate of 1760 MHz. The SC PHY frame format and preamble structure are shown in Fig. 2-3 and Fig. 2-4.

Time

PHY preamble PHY Payload field Frame header

Fig. 2-3 SC PHY frame format

SC PHY preamble

Fig. 2-4 SC PHY preamble structure

Like CMS preamble structure, the PHY preamble consists of SYNC, SFD, and CES field. Each of the field functions like the one in CMS preamble: SYNC field for frame detection, SFD field for validating the beginning of the frame, and CES field for channel estimation.

The preamble of HSI mode is transmitted at the sampling rate of 2640 MHz. Two types of preamble are defined for this mode: the long preamble and optional short preamble. The former has the same structure as the CMS, and the latter has the same

structure as defined for the SC mode.



Data Payload and Pilot Channel Estimation Sequence(PCES)

In SC mode, the data stream is divided into data blocks with each data block has 64 sub-blocks, as shown in Fig. 2-5 and Fig. 2-6. Each data block is followed by a PCES. The PCES insertion is an optional feature that allows the system to re-acquire the channel information periodically. The PCES is the same as the CES field in the SC PHY preamble and is shown in (2.2).

SC 256 256 256 256 128

PCES = [a b a b b ]

(2.2)

Since PCES contains the information of the CES field, this cyclic prefixed signal can provide the channel information periodically. These pilot words which are c₀a_L and c1bL are used for timing tracking, compensation for clock drift, and compensation for frequency offset error. Furthermore, the pilot words act as the cyclic prefix and

Fig. 2-5 SC PHY payload structure

Block0

In HSI mode, every 96 (N_PCES) OFDM symbols will insert one PCES, as shown in Fig. 2-7. In one OFDM symbol, cyclic prefix of 64 samples is added to prevent ISI effect. The PCES is identical to the CES field prepended by a₁₂₈ in the PHY preamble which is shown in (2.3).

HSI 128 256 256 256 256 128

PCES = [a a b a b b ]

(2.3)

Fig. 2-7 HSI PHY payload structure

2.2.3 Channel Model

Under the 60 GHz RF band, there are some special properties when waves are transmitted in the air that is much different from those below 10GHz RF band channel.

Due to strong directivity, wave reflexes, diffracts, and scatters slightly. Also, the energy of the wave centralizes in a certain angles. Since the oxygen absorbs the wave in this RF band, the transmission distance is very short, less than 10 meters, which leads to negligible multipath effect. Based on these properties, IEEE 802.15.3c standard is pronounced for the indoor, over Gbps data rate wireless transmission using 60 GHz RF band. In general, for such a high data rate, the channel would be influenced a lot by line-of-sight (LOS)/non-light-of-sight (NLOS) channel, root-mean-square (RMS) delay spread, Doppler Effect, and negligible multipath effect when the wireless communication system operates under the 60 GHz RF band. These

properties are listed below:



High Path Loss

While the EM wave passes through the medium, the medium absorbs the energy and limits the distance that the EM wave can travel. The more energy it lost, the shorter it can travel. The ratio of energy loss is mainly depends on the characteristic of the medium and the EM wavelength. The wavelength of 60 GHz wave is close to the length of the oxygen chemical bond, so the wireless communication in 60 GHz RF band suffers tremendously high path loss. As the result, the transmission distance is limited to about 10 m in maximum. Moreover, the effect of the multi-path fading is reduced since the non-line-of-sight (NLOS) wave travels more distance and loses more energy than the line-of-sight (LOS) wave.



Strong Directivity

The strong directivity means that the EM wave energy almost centralizes in a small angle path. Based on physical principle of diffraction, the beam width is inversely proportional to the operating frequency [8]. This phenomenon shows that the antenna can only receive the signal from the transmitter antenna within a small angle range. In conclusion, the NLOS path has lower path gain relative to the LOS path, and the multi-path fading effect is small.

The channel model is based on the golden set released by IEEE 802.15.3c group [9] [10]. The golden channel with RMS delay spread 3.2ns is chosen as the simulation channel model. Fig. 2-8 and Fig. 2-10 are SC channel impulse response and channel frequency response with sampling rate 1.76GHz, respectively. Fig. 2-9 and Fig. 2-11 are HSI channel impulse response and channel frequency response with sampling rate

2.64GHz, respectively.

Fig. 2-8 SC channel impulse response

Fig. 2-9 HSI channel impulse response

Fig. 2-10 SC channel frequency response

Fig. 2-11 HSI channel frequency response

Chapter 3 SC/OFDM Dual-Mode Frequency and Time Domain Equalizer

This chapter will review frequency and time domain equalization with channel estimation in Section 3.1 and 3.2 respectively. Section 3.3 is the proposed frequency and time domain equalizer.

3.1 Review of Frequency Domain Equalization (FDE) [11]

A simple illustration of fully parallel FDE is shown in Fig. 3-1. The input passes through Serial-to-Parallel block and transforms to frequency domain by FFT. Then, the frequency domain data is multiplied with coefficients W and then transformed back to time domain by IFFT. Unlike TDE, the number of coefficients in FDE is fixed without regard to the length of the channel impulse response. The potential problem is when the length of the CIR is longer than the length of the CP. In that case, the circular convolution is ruined and FDE fails to equalize the channel effect. However, the channel model shows that the maximum length of CIR is far less than the length of CP, so this system does not have each problem.

FFT IFFT

Fig. 3-1 Structure of fully parallel FDE

The formula of circular convolution can be transformed into a simple multiplication in the frequency domain, and the capital letter means frequency domain signal:



= Η

R D

(3.1)

,where H is a diagonal matrix, R is a received signal vector, and D is transmitted data vector. To recover the transmitted data, we multiply the inverse of H on both sides of equation:

1 1



 



  

H R H H D D

(3.2)

, where the inverse of H is also a diagonal matrix. After CP removal, we can fully recover the transmitted signal D.

The above equations describe the ideal case: no AWGN and time-variant channel.

In reality, the white noise always exists due to the thermal noise, and the channel varies with time due to many effects, such as related movement, air flow, or moving object. Thus, the equation should be:

k

 J

k

( ) t  H

k



k



k

R D N

(3.3)

, where Jk

(t) means the time-variant effect matrix, N

k is a AWGN vector, and k is the index of the subchannels. If we simply multiply the inverse of Hk all the time, the time-variant effect will corrupt the data. Furthermore, to get the accurate inverse of Hk

is a difficult job under AWGN. To break through the predicament, the first thing is to overcome AWGN and get the inverse of Hk as accurate as possible. Then, an adaptive algorithm is performed to track the changes in the time-variant channel. In this way, the time-variant component J_k

(t) is no more an issue in the equalization.

3.1.1 Channel Estimation

,where U512 is the frequency domain constant value of u512, and k is the sub-carrier index.

This solution is known as zero-forcing (ZF) method. The benefit is the simple implementation, but this method suffers from a problem: noise enhancement. With AWGN, Eqn. (3.4) is revised as Eqn. (3.5).

The noise enhancement occurs when the channel gain Hk is so small that the noise

N

k is the dominant part in received signal. In that case, especially with large Nk, the

estimation result is far away from perfect estimation.

Since there are 2 U512 in CES, using Least-Square (LS) method is a better way than using ZF. The main point of LS is to minimize the sum of the squares of the error.

First of all, the equalization can be described as:

U

512,_k

Then, we need to minimize the sum of the squares, so let the partial derivative on

W

k be zero.

Finally, the solution of ˆ

W indicates the system will have minimum of S.

_k

2

2

Substituting R_k with U₅₁₂, the channel estimation result is:

512, 512, time-variant channel. However, we do not have any known message in the frequency domain when the system is SCBT. Thus, our FDE requires an adaptive algorithm against the time-variant channel.

There are many adaptive algorithms developed in the literals. These algorithms mainly focus on their computational complexity and convergence speed. The widely used algorithms are Minimum-Mean-Square-Error (MMSE), Recursive-Least-Square (RLS), and Least-Mean-Square (LMS) [12],[13]. Due to 2640MHz sampling rate, high computational complexity algorithms are not suitable for such high sampling rate system because of high hardware complexity and power consumption. Furthermore, using the information of SNR is not practical in the hardware design. Based on above the considerations, we will show that LMS is a good choice for the FDE.

Let’s consider the block diagram of the adaptive FDE shown in Fig. 3-2. R is the output from FFT, and the adaptive FDE do the equalization and update filter

coefficients W. The FDE output is transformed back to time domain and decision of data is made by the demapper. The error E is the difference between FDE output and the training sequence (or sliced output when the data is transmitted).

Update

Fig. 3-2 Illustration of adaptive FDE

The idea of LMS algorithm is to use the method of the steepest descent to find a set of W which minimizes the cost function. In our design, the FDE takes a subblock into the equalization, so the cost function should involve a block of errors, which is so called Block LMS (BLMS) [14]. However, since the equalization is independent of each subchannel, we can consider each cost function C_k in each subchannel independently instead of whole subblock.

{ 2}

k k

C  Ex E

(3.12)

The notation of Ex{.} is used to denote the expect value because we don’t want to be confused with the error E. Then, applying the steepest descent is to take the partial derivative with respect to the filter coefficients W.

* *

{ } 2 { }

C Ex Ex

   EE   EE

(3.13)

Since the equalization is independent of each subchannel, Eqn. (3.13) is equal to

zeros when the error E and coefficient W are in different subchannel. Then, substituting E with received signal R, we can rewrite Eqn. (3.13) as

*

, where k is the subchannel index. Now, these derivatives show the steepest ascent of the cost function. To find out the minimum of the cost function, we take a step size of

2



in the opposite direction of the derivatives.

, *

, where n indicates the subblock index or symbol index at SC or OFDM mode.

The expected value can be simplified, and the whole LMS algorithm can be expressed as:

LMS:

W

_{k n, 1}

 W

_{k n,}

  R E

_{k k}^* (3.16)

The derivations of MMSE and RLS can be found in [16], [17]:

MMSE:

, where n indicates the subblock index or symbol index at SC or OFDM mode,



_n² and



_s²are variance of noise and signal respectively, Y is equalized signal, U is the intermediate vector, and g_n is the gain vector.

Compared with MMSE [15]-[17] and RLS [18], [19], the LMS algorithm has less computational complexity than RLS since there is only one multiplication for updating at each sub-channel. In hardware design, more operations on updating will cause a longer feedback latency. The latency will impact the performance since the coefficient of equalizer can not be updated immediately. In high sampling rate system, high computational operations will required more pipelined stages, thus the latency is much longer. Furthermore, the low computational complexity leads to low power consumption. The low power issue is more important in the modern SOC design. In that case, LMS also has the advantage of low power consumption property. On the other hand, MMSE also has less computational complexity than RLS, but it requires the information of SNR, which is hard to be evaluated since there are Doppler and channel Effect on the received signal. Although there are some algorithms [15]-[17]

trying to do SNR evaluation, the result is still not reliable in the practical system.

Based on these considerations, LMS is suitable for FDE in high sampling rate design and can also achieve the required bit error rate (BER) with LS channel estimation that will be mentioned in Section 3.3.2.

3.2 Review of Time Domain Equalizer (TDE)

The basic structure of the TDE is the FIR filter, which performs the convolution between data stream and the filter coefficients. A simple illustration of the FIR filter is shown in Fig. 3-3, which is known as Zero Forcing (ZF). A robust adaptive decision

3.1.2. The Least Mean-Square (LMS) equalizer coefficients updating method is chosen to minimize the mean-square error, instead of ZF [20].

………

D D ……… D

＊ ＊ ＊

∑

………

input

＊

W0 W1 W2

w

n

output

Fig. 3-3 FIR filter structure

However, the computational complexity of the convolution in both ZF and LMS is proportional to the length of the filter taps, which is determined by the length of the CIR. From the channel model in Fig. 2-8 and Fig. 2-9, the filter coefficients must satisfy the mathematical property in Eqn. (3.19).

 

* 1 0 0 0

h w 

(3.19)

Although the parallel architecture can increase the throughput of TDE, the complexity grows linearly with the number of coefficients.

3.2.1 Multi-path Interference Cancellation

Multi-path Interference Cancellation (MPIC) [21] method is an efficient way for suppressing Inter-path Interference (IPI). MPIC is composed of two parts. The first part is multi-path interference replica, and the second part is multi-path interference cancellation [22].

The following lower-case variables are all in time domain. In Fig. 3-4, during data transmission, dominant data path could be interfered by other multi-path data and  is the multi-path delay. path gains. The rest channel path gains almost equal to zeros. Therefore, the received signal is expressed like

, where h means channel impulse response matrix, x is the transmitted data vector, and y is the received signal vector. Define main data path gain vector

1,1 2,2 ,

and second data path gain vector

1,1+ 2,2+ - ,

where N is the number of total sub-channels.

A modified MPIC [21] has two stages. In initial stage, we can obtain the channel impulse response after channel estimation. Assume multi-path gain is small, so we don’t consider the multi-path gain m[t]. Then, we have

A modified MPIC [21] has two stages. In initial stage, we can obtain the channel impulse response after channel estimation. Assume multi-path gain is small, so we don’t consider the multi-path gain m[t]. Then, we have

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