Low complexity channel tracking algorithms for coordinated and uncoordinated pilot access over high-rate internet of things

Given that high-rate internet-of-things (IoT) applications are supported by ubiquitous massive MIMO services, this paper introduces novel algorithms with significantly reduced complexity to estimate and track the channels of active and inactive devices, which can resolve intra-cell pilot collisions and utilize their information. Then, we propose an optimum coordinated access (O-CA) tracker whose complexity grows squarely with the number of devices instead of cubically. In addition, the error-boundedness and steady-state characteristics of O-CA are demonstrated. Although uncoordinated pilot access eliminates control signaling associated with active IoT devices, it needs the base station (BS) to identify the devices’ pilot transmission patterns (DPTPs). A low-complexity optimal uncoordinated access (O-UA) tracker is intended to contain DPTPs that expand exponentially, which is impractical. To this end, we derive an analytical closed-form solution for a near-optimal uncoordinated access (NO-UA) tracker that minimizes an upper bound on the error between O-UA and the simplified DPTPs. Consequently, the NO-UA results in significantly lower computational costs and superior estimation performance compared to existing trackers. Moreover, we derive the optimal linear uncoordinated access (OL-UA) tracker and its stability. Specifically, two heuristic trackers are introduced to detect DPTPs and track the devices’ channels. The simulation results and complexity analysis demonstrate that the designed trackers significantly reduce complexity compared with conventional trackers.