MATLAB: Adding Noise with Regard to Signal to Noise Ratio (SNR) or EbNo?
These stuffs $E_s/N_0, E_b/N_0 \textrm{ and SNR}$ are convertible.

\begin{align}
E_s/N_0 &amp;= E_b/N_0 + 10\log_{10}(k) \\
E_s/N_0 &amp;= 10\log_{10}(T_{sym}/T_{samp}) + \mathrm{SNR}
\end{align}

where $k$ is the number of information bits per symbol, $T_{sym}$ is the signal's symbol period and $T_{samp}$ is the signal's sampling period. More details can be found at AWGN channel MATLAB.

And by description of awgn() function:


  y = awgn(x,snr) adds white Gaussian noise to the vector signal x. The
  scalar snr specifies the signal-to-noise ratio per sample, in dB. If x
  is complex, awgn adds complex noise.


In your case, no oversampling thus awgn() uses its snr parameter to generate $E_s/N_0 =$ snr.

You are free to plot your BER curves over $E_s/N_0$ or $E_b/N_0$ because they are equivalent. You just need to convert them back and forth via the modulation order $k$.

However $E_b/N_0$ is usually prefered because (EbN0 wikipedia)


  $E_b/N_0$ directly indicates the power efficiency of the system without
  regard to modulation type, error correction coding or signal bandwidth
  (including any use of spread spectrum). This also avoids any confusion
  as to which of several definitions of "bandwidth" to apply to the
  signal.