Real World

Basics of Synchronization

Known training sequence (a preamble) is prepended, or training can also be inserted periodically within the message

In every digital communication system, the Tx has the easier role of signal generation while the Rx has the tougher job of figuring out the intended message. Just like solving a puzzle told by someone. Estimating and compensating for the frequency, phase and timing offsets between Tx and Rx oscillators is one such challenge. The solution can be designed depending on many factors such as some part of data is known (called a ‘training sequence’) or not, the synchronizer needs to be one-shot or continuously updating, and so on.

Known Data Availability

Depending on the availability of known data, synchronization theory in digital systems are largely based on the following three approaches.

  • Data-aided: To help the Rx in many systems, the Tx inserts symbols already agreed upon with the Rx within the message such that the Rx can acquire unknown parameters through knowledge of this `data’. This is shown in Figure below. Performing synchronization using this training is known as data-aided synchronization. Most widespread wireless communication systems in today’s world such as LTE and WiFi implement algorithms based upon this approach.

    Known training sequence (a preamble) is prepended, or training can also be inserted periodically within the message

    One problem with data-aided synchronization strategy is the waste of resources. The power and time spent on transmitting training sequence could have been used for sending more data: the spectral efficiency of the system is reduced by a non-negligible factor. Assuming a training length of N_{\textmd{Train}} and message length of N_{\textmd{Data}}, the spectral efficiency decreases by a factor of

    (1)   \begin{equation*}             \frac{N_{{\textmd{Data}}}}{N_\textmd{Train}+N_{{\textmd{Data}}}}         \end{equation*}

  • Decision-directed: To avoid this penalty, alternative techniques need to be adopted. Extending the above idea, once the Rx starts demodulating the signal and making decisions, it can use those decisions as known data in order to successfully track the changes in nuisance parameters, such as slowly changing carrier phase offset. This technique is known as decision-directed synchronization. It is evident that decision-directed approach can work well only when the detector decisions are correct such as in high SNR case. Otherwise, a wrong decision leads to a poor estimate, a poor estimate leads to a wrong decision in the next cycle, and the chain continues in the form of error propagation.
  • Non-data-aided or Blind: In other situations, however, neither a preamble nor the decisions can be used. Here, some particular characteristics of the incoming signal can be employed to estimate the unknown parameters. This is known as non-data-aided or blind synchronization technique. Adopting a non-data-aided synchronization approach can retain the spectral efficiency but its convergence is slow because a large amount of data needs to be processed to average out the effects of noise and find a reliable estimate.

The benefits, drawbacks and conditions for these synchronization approaches are summarized in Table below.

Benefits and drawbacks of data-aided, decision-directed and non-data-aided synchronization approaches

Feedback or Feed-forward

Irrespective of the data knowledge, synchronization blocks can be implemented in one of the following two manners:

  • Feed-Forward, Open Loop, or Batch Processing: There are many applications in data communications where the transmission occurs in a start and stop manner with periods of inactivity in between. This is known as burst mode communication. Here, the Tx forms a complete packet by inserting a sequence of known symbols — also called a preamble or a training sequence — before the actual message symbols as shown in the above Figure. In burst-mode, samples of the received signal are processed to establish a direct one-shot estimate of the target parameter through batch processing. Signal processing to establish the expression for the estimate is based on an algorithm derived from the mathematical structure of the Rx signal. Once this parameter is determined, it is corrected from the Rx signal without feedback to any previous block. In case of phase synchronization for example, the phase estimate can be used to de-rotate all data in that burst.
  • Feedback, Closed Loop or Recursive: Many other communication links work in continuous mode where the signal is transmitted either at all times or for a long duration. Here, fast acquisition is not as important and the objective is to lock onto the target parameter within a reasonable time after the arrival of the received signal. So an estimate of the error signal (for example e_{\theta}= \theta_{\Delta} - \hat \theta_{\Delta}) is derived which forms the basis of a corrective signal that is fed back to a compensation unit. A Phase Locked Loop (PLL) can be employed for this purpose with some modifications discussed later. Feedback acquisition can work blindly, in a decision-directed manner or can also take help from training inserted periodically within the message as shown in Figure above. This category of processing has an inherent ability to automatically track slowly varying parameter changes.

In summary, there are 3 \times 2=6 possible ways to implement a synchronizer depending on the knowledge of data and the loop being closed or open. Different algorithms can be designed for many of these topologies but not all, examples of which we will see in many other articles.

What is Carrier Phase Offset and How It Affects the Symbol Detection

Eye diagrams for I arm of a 4-QAM signal for 15, 30 and 45 degrees phase offsets and a Raised Cosine filter with excess bandwidth 0.5. A similar eye diagram exists for Q arm as well

In case of Quadrature Amplitude Modulation (QAM) and other passband modulation schemes, Rx has no information about carrier phase of the Tx oscillator. To see the effect of the carrier phase offset, consider that a transmitted passband signal consists of two PAM waveforms in I and Q arms denoted by v_I(t) and v_Q(t) respectively and combined as

(1)   \begin{equation*}         s(t) = v_I(t) \sqrt{2} \cos 2\pi F_C t  - v_Q(t) \sqrt{2}\sin  2\pi F_C t     \end{equation*}

Here, F_C is the carrier frequency and v_I(t) and v_Q(t) are the continuous versions of sampled signals v_I(nT_S) and v_Q(nT_S) given by

(2)   \begin{equation*}       \begin{aligned}         v_I(nT_S)\: &= \sum _{i} a_I[i] p(nT_S-iT_M) \\         v_Q(nT_S) &= \sum _{i} a_Q[i] p(nT_S-iT_M)       \end{aligned}     \end{equation*}

In the above equation, a_I[m] and a_Q[m] are the inphase and quadrature components of the m^{th} symbol, p(nT_S) are the samples of a square-root Nyquist pulse with support -LG \le n \le LG and T_S and T_M are the sample time and symbol time, respectively.

In the absence of noise, the received signal for a passband waveform is the same as the transmitted signal except a carrier phase mismatch, i.e., we ignore every other distortion in the received signal except the phase offset \theta_\dd.

After bandlimiting the incoming signal through a bandpass filter, it is sampled by the ADC operating at F_S=1/T_S samples/second to produce

    \begin{align*}         r(nT_S) &= v_I(nT_S) \sqrt{2}\cos \left(2\pi F_C nT_S + \theta_{\Delta}\right) - v_Q(nT_S)\sqrt{2} \sin\left( 2\pi F_C nT_S + \theta_{\Delta}\right) \\                 &= v_I(nT_S) \sqrt{2}\cos \left(2\pi \frac{k_C}{N}n + \theta_{\Delta}\right) - v_Q(nT_S) \sqrt{2}\sin \left(2\pi \frac{k_C}{N}n + \theta_{\Delta}\right)     \end{align*}

where the relation F/F_S = k/N is used and k_C corresponds to F_C, and the angle \theta_{\Delta} is the phase difference between the incoming carrier and the local oscillator at the Rx.

To produce a complex baseband signal from the received signal, the samples of this waveform are input to a mixer which multiplies them with discrete-time quadrature sinusoids \sqrt{2}\cos 2\pi (k_C/N)n in the I arm and -\sqrt{2} \cdot \sin 2\pi (k_C/N)n for Q arm. We continue the derivation for I part and the same for Q is very similar and the reader can solve it as an exercise. Using the identities \cos(A)\cos(B) = 0.5 \left( \cos(A+B)\right.\} + \left.\cos(A-B) \right) and \sin(A)\cos(B) = 0.5 \left( \sin(A+B)\right.\} + \left.\sin(A-B) \right),

    \begin{equation*}         \begin{aligned}             x_I(nT_S) &= r(nT_S) \cdot \sqrt{2}\cos 2\pi \frac{k_C}{N}n \: \\                   &= v_I(nT_S)\left\{\cos\theta_{\Delta}  + \underbrace{\cos \left(2\pi \frac{2k_C}{N}n + \theta_{\Delta}\right)}_{\text{Double frequency term}} \right\} - \\                    & \qquad  \qquad \quad v_Q(nT_S) \left\{\sin \theta_{\Delta} + \underbrace{\sin \left(2\pi \frac{2k_C}{N}n + \theta_{\Delta} \right)}_{\text{Double frequency term}} \right\}         \end{aligned}     \end{equation*}

The matched filter output is written as

    \begin{equation*}         \begin{aligned}             z_I(nT_S) &= x_I(nT_S) * p(-nT_S) \\                       &= \left(v_I(nT_S) \cos \theta_{\Delta} - v_Q(nT_S) \sin\theta_{\Delta} + \right. \\                       &\hspace{.6in}\left.\text{Double frequency terms}\right)* p(-nT_S)         \end{aligned}     \end{equation*}

The double frequency terms in the above equation are filtered out by the matched filter h(nT_S) = p(-nT_S), which also acts a lowpass filter due to its spectrum limitation in the range -0.5 R_M \le F \le +0.5R_M, where R_M is the symbol rate. Writing the definitions of v_I(nT_S) and v_Q(nT_S) from Eq (2),

    \begin{equation*}         \begin{aligned}             z_I(nT_S) = \sum_i \Big\{ a_I[i]\cos \theta_{\Delta} - a_Q[i]\sin\theta_{\Delta} \Big\} r_p(iT_M - nT_S)         \end{aligned}     \end{equation*}

where r_p(nT_S) comes into play from the definition of auto-correlation function. To generate symbol decisions, T_M-spaced samples of the matched filter output are required at n = mL = mT_M/T_S. Downsampling the matched filter output generates

    \begin{equation*}         \begin{aligned}             z_I(mT_M) &= z_I(nT_S) \bigg| _{n = mL = mT_M/T_S} \\                       &= \sum \limits _i \Big\{ a_I[i]\cos \theta_{\Delta} - a_Q[i]\sin \theta_{\Delta}\Big\} r_p(iT_M - mT_M)         \end{aligned}     \end{equation*}

For a square-root Nyquist that satisfies no-ISI criterion, r_p(iT_M - mT_M) is zero except for i = m. Thus,

    \begin{equation*}       \begin{aligned}         z_I(mT_M) = a_I[m] \cos \theta_{\Delta} - a_Q[m]\sin\theta_{\Delta}       \end{aligned}     \end{equation*}

A similar derivation for Q arm yields the final expression for the symbol-spaced samples in the presence of phase offset \theta_{\Delta}.

(3)   \begin{equation*}       \begin{aligned}         z_I(mT_M) = a_I[m] \cos \theta_{\Delta} - a_Q[m]\sin\theta_{\Delta}       \\         z_Q(mT_M) = a_I[m] \sin \theta_{\Delta} + a_Q[m]\cos\theta_{\Delta}       \end{aligned}     \end{equation*}

From the phase rotation rule of complex numbers, we know that this expression is nothing but counterclockwise rotation by an angle \theta_{\Delta}. In polar form, this expression can be written as

(4)   \begin{equation*}         \begin{aligned}           |z(mT_M)| &= \sqrt{a_I^2[m] + a_Q^2[m]} \\           \measuredangle z(mT_M) &= \measuredangle \Big(a_Q[m],a_I[m]\Big) + \theta_{\Delta}         \end{aligned}     \end{equation*}

In conclusion, a mismatch of \theta_{\Delta} between incoming carrier and Rx oscillator rotates the desired outputs a_I[m] and a_Q[m] on the constellation plane by an angle \theta_{\Delta}. This is drawn in the scatter plot of Figure below for a 4-QAM constellation. Keep in mind that the blue circles are not one but several symbols mapped over one another due to the similar phase shift.

Scatter plot for a 4-QAM constellation in the presence of carrier phase offset and no noise


In Eq (3), start with \theta_{\Delta}=0 and observe that the I and Q outputs are a_I[m] and a_Q[m], respectively. This implies that signals in I and Q arms are completely independent of each other. Gradually increasing \theta_{\Delta} has two effects:

  • Since \cos \theta_{\Delta} < \cos 0 = 1, amplitude of a_I[m] in z_I(mT_M) reduces. The same phenomenon happens with a_Q[m] in z_Q(mT_M).
  • Since \sin \theta_{\Delta} > \sin 0 = 0, interference of a_Q[m] in z_I(mT_M) increases as well as that of a_I[m] in z_Q(mT_M).

This interference between I and Q components is known as cross-talk. Cross-talk increases with \theta_{\Delta} until for a 90^\circ difference, a_I[m] appears at Q output and -a_Q[m] at I output.

The effect of this cross-talk on a Raised Cosine shaped 4-QAM waveform with excess bandwidth \alpha=0.5 is shown in Figure below for a phase difference \theta_{\Delta} = 30^\circ. Observe the first sample: it is (-1,+1) in quadrant II. After phase rotation, I part moved towards left thus increasing its amplitude and Q moved downwards reducing its amplitude. This is evident through the first samples in the Figure. A similar argument holds for all other symbols.

Effect of 30 degrees phase rotation on a time domain 4-QAM waveform for a Raised Cosine filter with excess bandwidth 0.5. Observe how the samples at optimal locations move away from the ideal symbol amplitudes

Can phase rotation be beneficial?

Something really interesting has happened in Figure above. Notice that although the amplitude has decreased for some symbols, it has risen for some other symbols as well. This is the outcome of a circular rotation. While it is good to have some symbols with a little extra protection against noise, remember that it has come at a cost of reduced amplitudes for other symbols, making them much vulnerable to noise and other impairments. The overall effect is negative, just like strengthening your right arm in exchange of significantly weakening the left is dangerous for your body.

What was discussed above can be extended to the whole symbol stream. The cumulative effect of a phase offset is straightforward to see in a scatter plot. There will be clouds of samples from downsampled matched filter output around the original constellation.

Since the scatter plot is different than a raw time domain waveform, we employ the eye diagram to examine the effects of carrier phase offset (say, on an oscilloscope). First, start with a BPSK modulation scheme and remember that there is no Q channel in this case and consequently no cross-talk. However, the effect of phase rotation is a reduction in signal amplitude which can be observed by plugging a_Q = 0 in Eq (3) and only focusing on I arm.

(5)   \begin{equation*}         \begin{aligned}             z_I(mT_M) = a_I[m] \cos \theta_{\Delta}         \end{aligned}     \end{equation*}

Since \cos \theta_\Delta always lies between -1 and 1, the amplitude of the I signal gets reduced accordingly with the rest of the energy rising in the Q arm. From Eq (3), this signal is written as

    \begin{equation*}         \begin{aligned}             z_Q(mT_M) = a_I[m] \sin \theta_{\Delta}         \end{aligned}     \end{equation*}

With a phase offset of 45^\circ, the I branch loses half of its energy with the remaining half going in the Q arm. This is drawn in Figure below. In fact for a 90^\circ phase rotation, the I contribution actually reaches zero and all the energy of the signal appears across the Q branch. Due to this reason, we will see later that the Q arm is still employed for BPSK signals — not for data detection but helping in the phase synchronization procedure.

Eye diagrams of a BPSK signal for 0 and 45 degrees phase rotations and a Raised Cosine filter with excess bandwidth 0.5. Observe a reduction in I amplitude in proportion to the energy rising in the Q arm

Next, we turn our focus towards QAM and observe the amplitude change and cross-talk between I and Q branches for three different phase offsets, 15^{\circ}, 30^{\circ} and 45^{\circ}. Figure~?? illustrates the I channel for these phase offsets in a noiseless case and a 4-QAM signal. A similar diagram holds for Q arm as well and not drawn here. The optimal sampling instants are still visible due to zero noise but \bbf{the eye diagram looks more like a 4-PAM signal than that of a single 4-QAM signal due to the cross-talk from Q arm}. It is also evident that I and Q affect each other in equal proportions.

Eye diagrams for I arm of a 4-QAM signal for 15, 30 and 45 degrees phase offsets and a Raised Cosine filter with excess bandwidth 0.5. A similar eye diagram exists for Q arm as well

The reason there are only two eyes for 45^\circ phase offset is that \cos \theta_\Delta and \sin \theta_\Delta in Eq (3) become equal and hence many symbols a_I and a_Q cancel each in both I and Q arms of the output. In terms of the scatter plot, a rotation of 45^\circ shifts the constellation points onto the real and imaginary axes, so for the I plot shown here, the output at the sampling instant coincides only with a positive or negative symbol value.