With the rapid increase in communication technologies, shortage of spectrum will be a major issue faced in the coming years. Cognitive radio is a promising solution to this problem and works on the principle of sharing between cellular subscribers and ad-hoc Device to Device (D2D) users. Existing 5G spectrum sharing techniques work as per a fixed rule and are pre-established. Also, recent game theoretic approaches for spectrum sharing uses unrealistic assumptions with less likely practical implications. Here, a novel spectrum sharing technique is proposed using 5G enabled bidirectional cognitive deep learning nodes (BCDLN) along with dynamic spectrum sharing long short-term memory (DSLSTM). A joint spectrum allocation and management is carried out with wireless cyclic prefix orthogonal frequency division multiple access (CP-OFDMA). The BCDLN self-learning nodes with decision making capability route information to several destinations at a constant spectrum sharing target, and cooperate via DSLSTM. BCDLN based on time balanced and unbalanced channel knowledge is also examined. With the proposed framework, expressions are derived for the spectrum allocated to multiple sources to obtain their spectrum targets as a variant of the participation node spectrum sharing ratio (PNSSR). The impression of noise when all nodes broadcast with equal spectrum allocation is also investigated.

中文翻译：

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一种在 5G 网络中使用动态长短期记忆和 CP-OFDMA 的新型频谱共享方案

随着通信技术的快速发展，频谱短缺将是未来几年面临的主要问题。认知无线电是解决这个问题的一个很有前途的解决方案，它的工作原理是在蜂窝用户和 ad-hoc 设备到设备 (D2D) 用户之间共享。现有的 5G 频谱共享技术按照固定规则工作并且是预先建立的。此外，最近用于频谱共享的博弈论方法使用了不太可能具有实际意义的不切实际的假设。在这里，提出了一种新的频谱共享技术，使用支持 5G 的双向认知深度学习节点 (BCDLN) 以及动态频谱共享长短期记忆 (DSLSTM)。使用无线循环前缀正交频分多址（CP-OFDMA）进行联合频谱分配和管理。具有决策能力的 BCDLN 自学习节点将信息路由到一个恒定频谱共享目标的多个目的地，并通过 DSLSTM 进行协作。还检查了基于时间平衡和非平衡信道知识的 BCDLN。使用所提出的框架，推导出分配给多个源的频谱的表达式，以获得它们的频谱目标，作为参与节点频谱共享比率（PNSSR）的变体。还研究了所有节点以相等的频谱分配进行广播时的噪声印象。为分配给多个源的频谱导出表达式以获得它们的频谱目标，作为参与节点频谱共享比率（PNSSR）的变体。还研究了所有节点以相等的频谱分配进行广播时的噪声印象。为分配给多个源的频谱导出表达式以获得它们的频谱目标，作为参与节点频谱共享比率（PNSSR）的变体。还研究了所有节点以相等的频谱分配进行广播时的噪声印象。