  We consider computation offloading for Internet-of-things (IoT) applications
in multiple-input-multiple-output (MIMO) cloud-radio-access-network (C-RAN).
Due to the limited battery life and computational capability in the IoT devices
(IoTDs), the computational tasks of the IoTDs are offloaded to a MIMO C-RAN,
where a MIMO radio resource head (RRH) is connected to a baseband unit (BBU)
through a capacity-limited fronthaul link, facilitated by the spatial filtering
and uniform scalar quantization. We formulate a computation offloading
optimization problem to minimize the total transmit power of the IoTDs while
satisfying the latency requirement of the computational tasks, and find that
the problem is non-convex. To obtain a feasible solution, firstly the spatial
filtering matrix is locally optimized at the MIMO RRH. Subsequently, we
leverage the alternating optimization framework for joint optimization on the
residual variables at the BBU, where the baseband combiner is obtained in a
closed-form, the resource allocation sub-problem is solved through successive
inner convexification, and the number of quantization bits is obtained by a
line-search method. As a low-complexity approach, we deploy a supervised deep
learning method, which is trained with the solutions to our optimization
algorithm. Numerical results validate the effectiveness of the proposed
algorithm and the deep learning method.
