To support massive connectivity and boost spectral efficiency for internet of things (IoT), a downlink scheme combining virtual multiple-input multiple-output (MIMO) and nonorthogonal multiple access (NOMA) is proposed. All the single-antenna IoT devices in each cluster cooperate with each other to establish a virtual MIMO entity, and multiple independent data streams are requested by each cluster. NOMA is employed to superimpose all the requested data streams, and each cluster leverages zero-forcing detection to de-multiplex the input data streams. Only statistical channel state information (CSI) is available at base station to avoid the waste of the energy and bandwidth on frequent CSI estimations. The outage probability and goodput of the virtual MIMO-NOMA system are thoroughly investigated by considering Kronecker model, which embraces both the transmit and receive correlations. Furthermore, the asymptotic results facilitate not only the exploration of physical insights but also the goodput maximization. In particular, the asymptotic outage expressions provide quantitative impacts of various system parameters and enable the investigation of diversity-multiplexing tradeoff (DMT). Moreover, power allocation coefficients and/or transmission rates can be properly chosen to achieve the maximal goodput. By favor of Karush-Kuhn-Tucker conditions, the goodput maximization problems can be solved in closed-form, with which the joint power and rate selection is realized by using alternately iterating optimization.Besides, the optimization algorithms tend to allocate more power to clusters under unfavorable channel conditions and support clusters with higher transmission rate under benign channel conditions.
翻译:为了支持大型连通性和提高互联网(IoT)的光谱效率,提议了一个将虚拟多投入多输出(MIMO)和非正统多存(NOMA)相结合的下行连接机制。每个组的所有单安诺纳 IoT 设备都相互合作,以建立一个虚拟的 MIMO 实体,每个组要求多个独立的数据流。NOMA 用于叠加所有请求的数据流,每个组利用零硬度检测,使输入数据流脱倍翻。只有统计频道国家信息(CSI)才能在基站提供,以避免在频繁的 CSI 估测中浪费精度和带宽度。虚拟 MIMO-NOMA 系统中的所有单安纳 IoT 设备都通过考虑 Kronecker 模型(既包含传输内容又接收关联性数据流) 来进行彻底调查。 此外, 软性结果不仅有助于探索物理洞察,而且有助于实现对投入数据流的最大化。 特别是, 软性表达式表达方式提供了各种系统参数的量化影响,并且能够通过正正解的汇率来调查多样化-多变式传输比率。