Relaxed-Bound K-Best Sphere Detection for Differential Unitary Space-Time Modulation
文献类型：会议论文
| 作者 | Du, Yipeng ; Han, Shuangshuang; Liu, Jian; Zhang, Yinghua
|
| 出版日期 | 2017 |
| 会议日期 | 2017-10-11 |
| 会议地点 | Nanjing, China |
| 英文摘要 | Large scale multiple input multiple output (MIMO), commonly referred to as massive MIMO, is one of the most important physical layer disruptions in network evolution that would lead to significant increase in spectral and energy efficiency envisioned for 5G networks. However, the computational complexity of massive MIMO system becomes the key problem for its practical implementation. As known, Differential Unitary Space-Time Modulation (DUSTM) has been proposed as an efficient modulation choice for such MIMO transmissions where channel state information (CSI) is not required at the receiver. However, traditional DUSTM implementation relies mainly on Maximum Likelihood Detection (MLD) achieving optimal performance, which is not easily compatible with massive MIMO systems due to its exponentially increasing complexity with increasing number of transmitting and receiving antennas. Therefore, in this paper, in order to overcome the aforementioned disadvantages, we propose a new detection algorithm named RelaxedBound K-Best Sphere Detection (RBKSD). Compared to the traditional K-Best Sphere Detection (KSD) algorithm, the main idea of our algorithm is to relax the bound K of the conventional KSD to be K + Γ, thereby achieving quasioptimal performance with significantly reduced computational complexity. Simulation results confirm that the proposed RBKSD algorithm shows superior performance and reduced complexity compared to other notable schemes such as MLD, traditional sphere detection algorithm and the KSD. |
| 源URL | [http://ir.ia.ac.cn/handle/173211/15303]  |
| 专题 | 自动化研究所_复杂系统管理与控制国家重点实验室_先进控制与自动化团队 |
| 推荐引用方式 GB/T 7714 | Du, Yipeng,Han, Shuangshuang,Liu, Jian,et al. Relaxed-Bound K-Best Sphere Detection for Differential Unitary Space-Time Modulation[C]. 见:. Nanjing, China. 2017-10-11. |
入库方式： OAI收割
来源：自动化研究所
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