AlphaSeq is a new paradigm to design sequencess with desired properties based on deep reinforcement learning (DRL). In this work, we propose a new metric function and a new reward function, to design an improved version of AlphaSeq. We show analytically and also through numerical simulations that the proposed algorithm can discover sequence sets with preferable properties faster than that of the previous algorithm.

In this paper, we propose an efficient method for computing the weight spectrum of LDPC convolutional codes based on circulant matrices of quasi-cyclic codes. In the proposed method, we reduce the memory size of their parity-check matrices with the same distance profile as the original codes, and apply a forward and backward tree search algorithm to the parity-check matrices of reduced memory. We show numerical results of computing the free distance and the low-part weight spectrum of LDPC convolutional codes of memory about 130.

We consider Monte Carlo tree search problem, a variant of Min-Max tree search problem where the score of each leaf is the expectation of some Bernoulli variables and not explicitly given but can be estimated through (random) playouts. The goal of this problem is, given a game tree and an oracle that returns an outcome of a playout, to find a child node of the root which attains an approximate min-max score. This problem arises in two player games such as computer Go. We propose a simple and efficient algorithm for Monte Carlo tree search problem.

Multiple access (MA) technology is of most importance for beyond long term evolution (LTE) system. Non-orthogonal multiple access (NOMA) utilizing power domain and advanced receiver has been considered as a candidate MA technology recently. In this paper, power assignment method, which plays a key role in performance of NOMA, is investigated. The power assignment on the basis of maximizing geometric mean user throughput requires exhaustive search and thus has an unacceptable computational complexity for practical systems. To solve this problem, a novel power assignment method is proposed by exploiting tree search and characteristic of serial interference cancellation (SIC) receiver. The proposed method achieves the same performance as the exhaustive search while greatly reduces the computational complexity. On the basis of the proposed power assignment method, the performance of NOMA is investigated by link-level and system-level simulations in order to provide insight into suitability of using NOMA for future MA. Simulation results verify effectiveness of the proposed power assignment method and show NOMA is a very promising MA technology for beyond LTE system.

This letter presents a technique to reduce the complexity of the soft-output multiple-input multiple-output symbol detection based on Dijkstra's algorithm. By observing that the greedy behavior of Dijkstra's algorithm can entail unnecessary tree-visits for the symbol detection, this letter proposes a technique to evict non-promising candidates early from the search space. The early eviction technique utilizes layer information to determine if a candidate is promising, which is simple but effective. When the SNR is 30dB for 6×6 64-QAM systems, the average number of tree-visits in the proposed method is reduced by 72.1% in comparison to that in the conventional Dijkstra's algorithm-based symbol detection without the early eviction.

An efficient pruning method is proposed for the infinity-norm sphere decoding based on Schnorr-Euchner enumeration in multiple-input multiple-output spatial multiplexing systems. The proposed method is based on the characteristics of the infinity norm, and utilizes the information of the layer at which the infinity-norm value is selected in order to decide unnecessary sub-trees that can be pruned without affecting error-rate performance. Compared to conventional pruning, the proposed pruning decreases the average number of tree-visits by up to 37.16% in 44 16-QAM systems and 33.75% in 66 64-QAM systems.

We propose new adaptive tree search algorithms for multiple-input multiple-output (MIMO) systems based on path metric comparison. With the fixed number of survivor paths, the correct path metric may be temporarily larger than the maximum path metric of the survivor paths under an ill-conditioned channel. There have been also adaptive path metric algorithms that control the number of survivor paths according to SNR. However, these algorithms cannot instantaneously adapt to the channel condition. The proposed algorithms accomplish dynamic adaptation based on the ratio of two minimum path metrics as the minimum is significantly smaller than the second minimum under good channel conditions and vice versa. The proposed algorithms are much less complex than the conventional noise variance-based adaptive tree search algorithms while keeping lower or similar error performance. We first employ the proposed adaptive tree search idea to K-best detection and then extend it QRD-M MIMO detection.