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Mobile edge computing (MEC) and millimeter wave (mmWave) communications arecapable of significantly reducing the network's delay and enhancing itscapacity. In this paper we investigate a mmWave and device-to-device (D2D)assisted MEC system, in which user A carries out some computational tasks andshares the results with user B with the aid of a base station (BS). We proposea novel two-timescale joint hybrid beamforming and task allocation algorithm toreduce the system latency whilst cut down the required signaling overhead.Specifically, the high-dimensional analog beamforming matrices are updated in aframe-based manner based on the channel state information (CSI) samples, whereeach frame consists of a number of time slots, while the low-dimensionaldigital beamforming matrices and the offloading ratio are optimized morefrequently relied on the low-dimensional effective channel matrices in eachtime slot. A stochastic successive convex approximation (SSCA) based algorithmis developed to design the long-term analog beamforming matrices. As for theshort-term variables, the digital beamforming matrices are optimized relying onthe innovative penalty-concave convex procedure (penalty-CCCP) for handling themmWave non-linear transmit power constraint, and the offloading ratio can beobtained via the derived closed-form solution. Simulation results verify theeffectiveness of the proposed algorithm by comparing the benchmarks.

Latency Minimization for mmWave D2D Mobile Edge Computing Systems: Joint Task Allocation and Hybrid Beamforming Design