這篇論文介紹了一個在「多重擷取多載波通訊系統」中，可以節省傳送能量，也就是使傳送能量達到最小的方法。這篇論文中的方法，靈感來自於蟻群優化演算法，又稱為螞蟻演算法。那麼蟻群優化演算法是怎麼來的呢？蟻群優化演算法是由觀察蟻群分工合作的行為而來的。這個演算法包含了兩個要素：一個是「具啟發性的資訊」，另外一個則是「費洛蒙」。蟻群優化演算法在「非多項式問題(NP-hard problem)」中可以幫忙求出，一個趨近於最佳解的值。「蟻群優化演算法」屬於「演化式演算法」的一種，而「演化式演算法」又包含在「演化式計算」以內，他們有一層一層的關係。「演化式計算」是一種用「最佳化方法」來逼近全部解中最佳解的方法。蟻群優化演算法在資源分配的問題當中，利用群眾的力量，找到很多可能的解，來滿足問題的限制，以及逼近最佳解。我們將蟻群優化演算法和放寬傳輸速率的限制這兩者結合在一起，來趨近最佳解。 本篇論文中的模擬數據的結果顯示，我們提出的這個方法可以用來解決節省能源的問題，並且趨近於節約能源的最佳解。雖然他的複雜度相較於其他的方法，高了許多，不過我們將試著將載波的數目以及擷取的數目增大到原先的數百甚至數千倍，也許能在複雜度的表現上面，能夠勝出其他的方法。

[1] C. Y. Wong and R. S. Cheng, “Multiuser ofdm with adaptive subcarrier, bit, and power allocation,” vol. 17, no. 10, pp. 1747–1758, Oct. 1999.
[2] W. Rhee and J. M. cio, “Increase in capacity of multiuser ofdm system using dy- namic subchannel allocation,” in IEEE Vehicular Technology Society, Packard Building, Standford University, CA 94305-9515, May 2000, pp. 1085–1089.
[3] Y.-W. Huang and J.-M. Wu, “Energy ecient resource allocation with qos constraints for multiuser ofdma systems,” NTHU master thesis, 2011.
[4] Z. Shen, J. G. Andrews, and B. L. Evans, “Adaptive resource allocation in multiuser ofdm systems with proportional rate constraints,” IEEE transactions on wireless com- munications, vol. 4, no. 6, pp. 2726–2737, Nov. 2005.
[5] M. Dorigo, V. Maneizzo, and A. Colorni, “Ant system: optimization by a colony of cooperating agents,” IEEE transactions on systems, man, and cybernetics-Part B: cy- bernetics, vol. 26, no. 1, pp. 1–13, Feb. 1996.
[6] D. Liu, H. Zhang, W. Zheng, and X. Wen, “The sub-channel allocation algorithm in femtocell networks based on ant colony optimization.” Military Communications Conference, 2012.
[7] D. Wang, X. Xu, X. Chen, X. Tao, Y. Yin, and H. Hass, “Discrete power allocation via ant colony optimization for multi-cell ofdm systems.” IEEE Vehicular Technology Conference, Sept. 2012.
[8] H. Ahmadi, Y. H. Chew, and C. C. Chai, “Multicell multiuser ofdma dynamic resource allocation using ant colony optimization.” IEEE Vehicular Technology Conference, May 2011.
[9] X. Zhang, W. Ye, S. Feng, and H. Zhuang, “Adaptive resource allocation for ofdma system based on ant colony algorithm,” in International Conference on Industrial and Systems Engineering (ICISE), Dec. 2009, pp. 2526–2529.
[10] Y. Zhao, X. Xu, Z. Hao, X. Tao, and P. Zhang, “Resource allocation in multiuser ofdm system based on ant colony optimization.” Wireless Communications and Networking Conference, April 2010.
[11] W. Yu, W. Rhee, S. Boyd, and J. Cio, “Iterative water-filling for gaussian vector multiple-access channels,” IEEE Transactions Information Theory, vol. 50, pp. 145– 152, Jan. 2004.
[12] D. Tse and P. Viswanath, Fundamentals of wireless communication. Cambridge Uni- versity Press, 2005.
[13] D. Simon, Evolutionary Optimization Algorithms: Biologically-Inspired and Population- Based Approaches to Computer Intelligence, Cleveland State University, Department of Electrical and Computer Engineering.
[14] Y. fei Zhu and X.-M. Tang, “Overview of swarm intelligence,” in International Confer- ence on Computer Application and System Modeling (ICCASM), vol. 9, Oct. 2010, pp. 400–403.
[15] Charging a capacitor. [Online]. Available: http://hyperphysics.phy/- astr.gsu.edu/hbase/electric/capchg.html
[16] T. Stu ̈tzle and M. Dorigo, “A short convergence proof for a class of a short conver- gence proof for a class of ant colony optimization algorithms,” IEEE transactions on evolutionary computation, vol. 6, no. 4, pp. 358–365, Aug. 2002.