## 6 Evaluation Results  
Simulations were carried out to investigate BER performance _vs._ bit-energy to noise-spectral density ratio \(E_{b}/N_{o}\) of 16-DQPSK, 16-QAM, 16-OQPSK, 16-FOFDM (without cyclic prefix), 16-WOFDM (4-level symmetric with Haar filter coefficients), BPSK-16 OFDM, and BPSK-16 WOFDM with four DSNs (as one transmitting Virtual-MIMO node) and one DGN with three DANs (as one receiving Virtual-MIMO node) using Matlab/Simulink. Therefore, there are eight nodes in total in the system each with a single antenna. Matlab/Simulink is used as the simulation platform as it is one of the most widely used tools for physical layer modeling of wireless systems with many digital communication blocks and analyzing tools available for evaluating system performance. In addition, C and high definition languages (HDL) can be generated directly from Matlab/Simulink code for real hardware implementation. The information source of each DSN generates data at a rate of 250 kbps according to IEEE 802.15.4-2009 standard for WSNs. The typical transmission range of IEEE 802.15.4 based radio transceivers is 10-20 m, with a nominal maximum range of about 100 m in clear line-of-scenarios. Accordingly, the distance \(d^{L}\) between transmitting and receiving clusters is set to 20 m in this paper. At each DSN, information bits are modulated into a symbol stream using 16-DQPSK, 16-QAM, 16-OQPSK, 16-FOFDM, 16-WOFDM, BPSK-16 FOFDM, and BPSK-16 WOFDM. As in [35], the channel response matrix \(H\) is assumed to be known at DGN to detect the received signals using QR decomposition detection algorithm. All performance graphs are plotted with their 95% confidence intervals. Figure 7 shows that FOFDM, WOFDM (including BPSK-FOFDM and BPSK-WOFDM) and OQPSK based systems have better BER performance than QAM and DQPSK because as the constellation set size (\(M=2^{b}\) where \(b\) is the number of bits) increases, \(M\)-ary signaling performance improves. BER performance between 16-FOFDM and 16-WOFDM, and that between BPSK-16 FOFDM and BPSK-16 WOFDM are found to be comparable due to their equivalent filter bank structures [9]. The pair BPSK-16 FOFDM and BPSK-16 WOFDM is also found to perform better than 16-FOFDM and 16 WOFDM. This is because by using BPSK before 16-WOFDM or 16-FOFDM, the Euclidean distance among the signal vectors in the signal space increases due to which the signal energy associated with  
Figure 7: BER performance over transmission distance \(d^{L}=20\) m.  
that distance also increases. Due to the same reason 16 QAM performs better than 16 DQPSK. BPSK-16 WOFDM with SISO architecture expectedly performs worse as compared to BPSK-16 WOFDM with virtual MIMO architecture. The RF (Analog) energy consumption per bit per node over a transmission distance \(d^{L}=1-100\) m is shown in Figure 8 for a system with four DSNs, three DANs and one DGN each with single antenna using 16-DQPSK, 16-QAM, 16-FOFDM, 16-WOFDM, 16-OQPSK, BPSK-16 FOFDM, and BPSK-16 WOFDM with a PAPR of 0, 2.55, 3.01, 3.01, 0, 0 and 0 dB respectively. The results are also compared with that of BPSK-16 WOFDM with SISO architecture. In all the simulations, it is assumed \(G_{t}=G_{r}=8\) dBi, \(f_{c}=2.5\) GHz, \(\overline{P}_{b}^{L}=\overline{P}_{b}^{L}=10^{-3}\), \(M_{l}=40\) dB, \(N_{f}=10\) dB, \(\varsigma=0.35\), \(\sigma^{2}=-174\) dBm/Hz, \(G_{ti}=G_{ri}=2\) dBi, \(d^{l}=1\)\(m\), \(P_{DAC}=15.5\) mW, \(P_{ADC}=9.8\) mW, \(P_{fll}=1.25\) mW, \(P_{LO}=50\) mW, \(P_{LNA}=20\) mW, \(P_{IFA}=3\) mW, \(P_{mix}=30.3\) mW, \(P_{Add}=10\) mW, and \(P_{PS}=5\) mW. The RF (Analog) energy per bit per node is calculated using Equations (9)-(15) for every 5 m of distance to evaluate the effect of distance on energy consumption. It is observed that 16-QAM and 16-DQPSK based systems are the least energy-efficient due to their poor BER performance and complex RF architecture, with the former being the more dominant factor. However, both techniques performed almost the same even though the BER performance of 16-DQPSK is poorer as compared to 16-QAM.