Fairness Compared with Single Carrier Transmissions

6 7 8 9 10 11 12

Number of secondary users

Spectral efficiency (bps/Hz)

Proposed algorithm ZF with optimal scheduling SVD with optimal scheduling

Figure 5.4: Sum rate for various numbers of secondary users, where the number of transmit antennas is three , and I_max = 10 dBm.

Table 5.2.

5.2.2 Fairness Compared with Single Carrier Transmissions

Fig. 5.6 shows the fairness index versus the number of subcarrier. Although the fairness is poor in single channel transmission, the fairness index improve significantly as the number of subcarriers increase. The fairness index is 0.93 for 8 subcarriers.

Thus, the secondary BS can still serve users fairly on multicarrier transmissions even if the proposed algorithm is in order to maximize system sum rate.

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Table 5.2: Simulation Parameters for Multicarrier Transmissions

Position of primary BS (0 m, 300 m)

Position of primary user (0 m, 200 m) Position of secondary BS (400 m, 0 m)

Position of secondary users

Fixed location at cell edge with equal separation.

Cell radius 300 m

Antenna spacing equal spacing with ^λ₂ Transmit power of primary user 20 dBm

Transmit power of secondary BS 20 dBm in each subcarrier Noise power −110 dBm in each subcarrier.

Pathloss exponent 4

Standard deviation of shadowing 8 dB

Channel type i.i.d. Rayleigh fading in each sub-carrier.

Primary BS Primary user Secondary BS Secondary user

(400m,0m) Secondary BS Primary BS

(0m,300m)

D (0m,200m)

Figure 5.5: Distribution of primary and secondary systems.

1 2 3 4 5 6 7 8

0.74 0.76 0.78 0.8 0.82 0.84 0.86 0.88 0.9 0.92 0.94

Number of sub−channel

Fairness index

Figure 5.6: Fairness index for different number of transmit antennas.

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CHAPTER 6

Conclusions

In this thesis, we developed a joint power allocation, transmitted beamforming and user scheduling design for the hierarchical cognitive radio networks in both frequency selective and frequency non-selective fading channel. SDR is applied to transfer the MINLP joint design problem to a convex problem. The numerical results show the performance for different numbers of antennas, secondary users and maximal allow-able interference power to the primary system. The improvement of the sum rate saturates when a large number of transmit antennas, even the scheduling is consid-ered jointly. The proposed algorithm is very flexible for different interference power constraints. The sum rate of the secondary system improve significantly as the inter-ference power constraint becomes less strict, which is contributed by joint beamform-ing and schedulbeamform-ing design. The user diversity of the proposed algorithm is the same as the exhausted scheduling. Therefore, the scheduling of the proposed algorithm is approaching to the optimal value.

Our proposed algorithm can also be applied to the multicarrier transmissions.

The proposed algorithm is designed for the sum rate maximization. Although, the fairness of the proposed is not the main design goal, users can be served quite fairly as the proposed algorithm is applied to the multicarrier transmissions.

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Vita

Yu-Jung Liu

He was born in Taiwan, R. O. C. in 1987. He received his B.S. at the De-partment of Communication Engineering, National Chiao-Tung University in 2009.

From July 2009 to August 2011, he worked his Master degree in the Mobile Commu-nications and Cloud Computing Lab at the Institute of Communication Engineering at National Chiao-Tung University. His research interests are in the field of wireless communications.

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