正交分頻多工是現今一種相當有潛力的無線網路傳輸技術, 搭配合作
式網路能藉由使用者之間互相協助傳遞訊息至目的端來形成一個分散式
天線陣列, 進而提供空間分集增益。 在網路資源有限的情況下, 感知無線
電技術能有效率的使用頻譜資源, 在不干擾主用戶訊號太多的狀況下, 透
過已知的通道訊息來做適當的資源分配, 將能有效提升系統的整體效能。
在本論文中, 我們考慮一個合作式感知無線電暨正交分頻多工解碼傳
遞的網路, 並在共同頻譜存取機制上做資源分配讓感知無線電使用者達到
最佳的傳輸率。 此最佳化問題包含了功率分配與子載波配對, 其中功率分
配的問題除了考慮在來源端與中繼端下有各自的最大功率限制外, 為了維
持主用戶的通訊品質, 也必須保證對主用戶的功率干擾在一個預先設定好
的門檻值下。 由於聯合功率分配與子載波配對是一個混合整數規劃問題,
因此我們提出以混合型基因演算法來解決此問題。 在染色體的初始化步驟,
我們提出了兩種產生染色體初始值的方案, 使得基因演算法在一開始能找
到較好的初始值, 避免落入局部最小區域。 第一個方案為先將問題利用對
偶方法來拆開, 已推導出的式子來產生染色體的初始值。 第二個方案為先
利用邊界方法來計算染色體的初始化門檻值, 而染色體則是以隨機的方式
產生, 若其計算值達到門檻則允入此染色體。 並針對感知無線電的特性在
交配運算中提出新的子載波配對法與功率分配法。 基於當子載波數目越大
時, 基因演算法較難收斂, 我們亦提出了一兩階段式低複雜度演算法, 其
在第一階段, 先固定功率並使用基因演算法來求得子載波配對, 而在第二
階段, 則將子載波配對代入問題, 並利用凸最佳化來求得最佳的來源端與
中繼端的功率分配。 而為參考我們亦忽略了某些限制以求得系統的效能上
限值。 相關模擬結果顯示在不同的限制考量下, 我們所提出之演算法相較
於前人的方法, 能以較低的複雜度獲得更好的系統效能。

In this thesis, we consider the resource allocation for cognitive
decode-and-forward (DF) relay networks, where the concurrent spec-
trum access model in cognitive radio (CR) systems is employed to
improve the spectrum efficiency. Our objective is to maximize the
sum rate of the CR users by appropriate power allocation and sub-
carrier pairing under the individual power constrains at the source
and the relay with the interference introduced to the primary user
(PU) being kept below a tolerable range. Such a joint consideration
leads to a mixed integer programming (MIP) problem, which calls
for enormous amount of complexity. To resolve this MIP problem
with reasonable cost, a heterogeneous genetic algorithm (HGA) is
addressed. In the HGA, each chromosome is divided into an inte-
ger string for subcarrier pairing, and a real number string for power
allocation. In addition, new crossover and mutation operations are
implemented for this new type of chromosomes. To facilitate the
searching for the optimal solutions, two initialization schemes are
also considered. Furthermore, in light of the low convergence of HGA
when the number of subcarriers is large, a two-stage low-complexity
implementation of the HGA is presented, which first uses the GA
to determine the proper subcarrier pairs with equal power allocation
and then devises the optimal power allocation at the source and the
relay via convex optimization in the second stage. To evaluate the
performance of the HGA, an upper bound which is determined by
loosening some constraints in the optimization problem considered,
is furnished as well. Conducted simulations show that the HGA and
its two-stage implementation provide superior performance, yet with
low complexity, compared with some representative previous works.

[1] J. Mitola, G.Q. Maquire Jr., “Cognitive radio: Making software radios more
personal,” IEEE Pers. Commun., vol. 6, no. 4, pp. 13-18, Aug. 1999.
[2] E. Hossain and V. K. Bhargava, Ed. Cognitive Wireless Communication Net-
works, Springer, 2007.
[3] T. Weiss, J. Hillenbrand, A. Krohn and F. K. Jondral, “Mutual interference in
OFDM-based spectrum pooling systems,” in Proc. IEEE Vehicular Technol.
Conf., vol. 4, pp. 1873-1877, May, 2004.
[4] T. Weiss and F. K. Jondral, “Spectrum pooling: an innovative strategy for
the enhancement of spectrum efficiency,” IEEE Commun. Magazine, vol. 42,
no. 3, pp. S8-14, Mar. 2004.
[5] Y.-C. Liang, K.-C. Chen, G.Y. Li and P. Mahonen, “Cognitive radio network-
ing and communications: an overview,” IEEE Trans. Vehicular Technology,
vol. 60, no. 7, pp. 3386-3407, Sep. 2011.
[6] P.-H. Lin, S.-C. Lin, H.-J. Su and Y.-W.P. Hong, “Improved transmission
strategies for cognitive radio under the coexistence constraint,” IEEE Trans.
Wireless Commun., vol. 11, no. 11, pp. 4058-4073, Nov. 2012.
[7] A. Sendonaris, E. Erkip, and B. Aazhang, “User cooperation diversity-part I:
system description” and “User cooperation diversity-part II: implementation
aspects and performance analysis,” IEEE Trans. Commun., vol. 51, no. 11,
pp. 1927-1938, 1939-1948, Nov. 2003.
[8] K. J. R. Liu, A. K, W. S. Sadek, and A. Kwasinski, Cooperative Communi-
cations and Networking. Cambridge University Press, 2008.
57
[9] Q. Zhang, J. Zhang, C. Shao, et al., “Power allocation for regenerative relay
channel with rayleigh fading,” in Proc. IEEE Veh. Technol. Conf. Spring,
pp.1167-1171, May. 2004.
[10] L. Vandendorpe, R. Duran, J. Louveaux, and A. Zaidi, “Power allocation for
OFDM transmission with DF relaying,” in Proc. IEEE Int. Conf. Commun.,
pp. 3795-3800, 2008.
[11] L. Vandendorpe, J. Louveaux, O. Oguz, and A. Zaidi, “Rate-optimized power
allocation for DF-relayed OFDM transmission under sum and individual
power constraints,” EURASIP J. Wireless Commun. Networks, Article ID
814278, 2009.
[12] Y. Wang, X. Qu, T. Wu, and B. Liu, “Power allocation and subcarrier pairing
algorithm for regenerative OFDM relay system,” in Proc. IEEE Veh. Technol.
Conf., pp. 2727-2731, Apr. 2007.
[13] Y. Li, W. Wang, J. Kong, W. Hong, X. Zhang, and M. Peng, “Power allocation
and subcarrier pairing in OFDM-based relaying networks,” in Proc. IEEE Int.
Conf. Commun., pp. 2602-2606, May. 2008.
[14] Y. Li, W. Wang, J. Kong, and M. Peng, “Subcarrier pairing for amplify- and-
forward and decode-and-forward OFDM relay links,” IEEE Commun. Lett.,
vol. 13, no. 4, pp. 209-211, Apr. 2009.
[15] C. N. Hsu, H. J. Su, and P. H. Lin, “Joint subcarrier pairing and power
allocation for OFDM transmission with decode-and-forward relaying,” IEEE
Trans. Signal Proce., vol. 59, no. 1, pp. 399-414, Jan. 2011.
[16] X. Li, Q. Zhang, G. Zhang and J. Qin, “Joint power allocation and subcarrier
pairing for cooperative OFDM AF multi-relay networks,” IEEE Commun.
Lett., vol. 17, no. 5, pp. 872-875, May 2013.
[17] T. Wang, Y. Fang and L. Vandendorpe, “Power minimization for OFDM
transmission with subcarrier-pair based opportunistic DF relaying,” IEEE
Commun. Lett., vol. 17, no. 3, pp. 471-474, Mar. 2013.
58
[18] G. Bansal, M. J. Hossain, and V. K. Bhargava, “Optimal and suboptimal
power allocation schemes for OFDM-based cognitive radio systems,” IEEE
Trans. Wireless Commun., vol. 7, no. 11, pp. 4710-4718, Nov. 2008.
[19] G. Bansal, M. J. Hossain, V. K. Bhargava, and T. Le-Ngoc, “Subcarrier
and power allocation for OFDMA-based cognitive radio systems with joint
overlay and underlay spectrum access mechanism,” IEEE Trans. Vehicular
Technology, vol. 62, no. 3, Mar. 2013.
[20] G. Zhao, C. Yang, G. Li, D. Li and A. Soong, “Power and channel allocation
for cooperative relay in cognitive radio networks,” IEEE Journal of Selected
Topics in Signal Proce., vol. 5, no. 1, pp. 151-159, 2011.
[21] M. Shaat and F. Bader, “Asymptotically optimal subcarrier matching and
power allocation for cognitive relays with power and interference constraints,”
in Proc. IEEE Wireless Commun. and Networking Conference, pp. 663-668,
Apr. 2012.
[22] G. A. S. Sidhu, F. Gao, W. Chen, and W. Wang, “Joint subcarrier pairing
and power loading in relay aided cognitive radio networks,” in Proc. IEEE
Wireless Commun. and Networking Conference, pp. 669-673, Apr. 2012.
[23] J.H. Holland, Genetic Algorithms. Sci. Am. 1992.
[24] T. Yalcinoz and H. Altum, “Power economic dispatch using a hybrid genetic
algorithm,” IEEE Power Eng. Rev., vol. 21, pp. 59-60, 2001.
[25] H.-Y. Lu and W.-H. Fang, “Joint transmit/receive antenna selection in MIMO
systems based on the priority-based genetic algorithm,” IEEE Antennas and
Wireless Propagation Letters, vol. 6, pp. 588-591, 2007.
[26] C.-T. Su and C.-S. Lee, “Network reconfiguration of distribution systems us-
ing improved mixed-integer hybrid differential evolution,” IEEE Trans. Power
Delivery, vol. 18, no. 3, pp. 1022-1027, July, 2003.
[27] I. G. Damousis, A. G. Bakirtzis and P. S. Dokopoulos, “A solution to the unit-
commitment problem using integer-coded genetic algorithm,” IEEE Trans.
Power Systems, vol. 19, no. 2, pp. 1165-1172, May, 2004.
59
[28] K. Tang, K. Man, S. Kwong, and Q. He, “Genetic algorithms and their ap-
plications,” IEEE Sig. Proc. Magazine, vol. 13, pp. 22-37, Nov. 1996.
[29] W. Yu and R. Lui, “Dual methods for nonconvex spectrum optimization of
multicarrier systems,” IEEE Trans. Commun., vol. 54, no. 7, pp. 1310-1322,
July, 2006.
[30] W. Yu and T. Lan, “Transmitter optimization for the multi-antenna downlink
with per-antenna power constraints,” IEEE Trans. Signal Proc., vol. 55, no.
6, pp. 2646-2660, Jun. 2007.
[31] S. Boyd and A. Mutapcic, “Subgradient methods,” Lecture Notes of EE364b,
Stanford University, Stanford, CA, Spring quarter 2007-2008.
[32] S. Boyd and L. Vandenberghe, Convex Optimization. Cambridge Cambridge
University Press, 2004.
[33] H. W. Kuhn, “The hungarian method for the assignment problem,” in 50
Years of Integer Programming 1958-2008. Springer Berlin Heidelberg, pp. 29-
47, 2010.
[34] M. Grant and S. Boyd, CVX: Matlab software for disciplined convex pro-
gramming. 2008