應用於毫米波頻段上之大規模機器型態通訊，將作為第五代行動通訊系統主要的應用場景之一。在以波束為空中接取介面的新型態通訊架構下，其中最主要的挑戰為：如何利用有限的波束用以服務大規模的使用者。由於在毫米波頻段上通道具有稀疏性，若是多個使用者具有最大功率的波束相同，意即一個波束需要服務多個使用者時，會造成非常嚴重的使用者間干擾，也就是波束內干擾。若是透過全數位預編碼的方式來消除波束內干擾，我們需確保傳送端射頻鏈結的數量要等於天線的數量。在巨量天線系統下，此情況將造成非常嚴重的功率消耗。因此，我們藉由波束選擇技術，同時透過非正交多址接入技術，適當分配傳送功率給不同波束與使用者，即可透過較少數量的射頻鏈結來達到系統效能最佳化的目的。考量到波束選擇結合功率分配之問題為一個複雜度極高的NP困難問題，非常難以解決，我們進一步將問題拆分成兩個子問題，分別是波束選擇部份以及功率分配的部份。針對這兩個問題，我們透過低複雜度的確定性演算法，同時選用不同的次經驗法則演算法，包含基因演算法、粒子群聚法以及模擬退火法，可有效地找到近似最佳解。
　　透過模擬結果發現，相較於凸優化鬆馳功率分配法與窮舉波束選擇法，我們所提出的確定性演算法為次優演算法，但複雜度非常低。另一方面，藉由我們提出的次經驗法則演算法可以得到比凸優化鬆馳功率分配法以及窮舉波束選擇法更好的效能。我們所提出的演算法，可以有效地降低波束介面上的硬體負荷，同時可以藉較低的複雜度達到趨近於最優的效能。此篇論文所提出之演算法，將能有效地應用於第五代通訊系統中大規模機器型態通訊的使用場景之下。

Massive machine type communications (mMTC) will be a killer use case of fifth-generation (5G) wireless systems using millimeter-wave (mmWave) band. Since 5G will be characterized by the beam-based air interface, a critical challenge is to serve massive users using available beams. Due to the sparsity of mmWave channels, several users might receive the strongest power from the same beam and then introduce severe intra-beam interference (intra-BI). While intra-BI can be mitigated through full digital precoding, the required number of RF chains is identical to the number of antennas, resulting in high power consumption using full digital precoding. By selecting a set of beams and applying non-orthogonal multiple access (NOMA) to properly allocate transmit power to each beam and user, the sum rate can be maximized using a smaller number of RF chains. Since the problem of joint beam selection and power allocation is NP-hard and thus difficult to solve, we decompose the problem into two sub-problems, i.e., beam selection and power allocation. Both sub-problems are solved by deterministic algorithms with much lower complexity than exhaustive search. Besides, we exploit some celebrated meta-heuristic approaches, including generic algorithm (GA), particle swarm optimization (PSO), and simulated annealing (SA), to efficiently search for good solutions. Simulation results show that the performance of the proposed deterministic algorithms are comparable to that of a sub-optimal algorithm based on convex-relaxation power allocation (CRPA) and exhaustive beam selection (EBS) but with much lower complexity. On the other hand, meta-heuristic methods can also delivery promising and sometimes better results than CRPA and exhaustive beam selection. The proposed approaches can relax the hardware burden of beam-based air interface and achieve close-to-optimal performance with low complexity and thus can be useful to support massive connectivity in 5G.

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