![]() A key characteristic of D2D networks is that their topologies are reconfigurable to cope with network demands. In this paper, we study the distributed computational capabilities of device-to-device (D2D) networks. Further, we find that the efficient alternate optimization scales well with the number of nodes, and thus can be a practical solution for D2D computing in large networks. Through numerical experiments, we find that our methodology yields substantial improvements in network overhead compared with local computation and partially optimized methods, which validates our joint optimization approach. As a component of these two methods, we develop a coordinated beamforming algorithm which we show obtains the optimal beamformer for a common receiver characteristic. We propose two methods to solve the resulting non-convex mixed integer program: semi-exhaustive search optimization, which represents a ``best-effort'' at obtaining the optimal solution, and efficient alternate optimization, which is more computationally efficient. Variables in our model include task assignment, CPU allocation, subchannel selection, and beamforming design for multiple input multiple output (MIMO) wireless devices. In this paper, we develop an optimization methodology that considers topology configuration jointly with device and network resource allocation to minimize total D2D overhead, which we quantify in terms of time and energy required for task processing. A key challenge in providing this capability is the requirement for judicious management of the heterogeneous communication and computation resources that exist at the edge to meet processing needs. Device-to-device (D2D) communications is expected to be a critical enabler of distributed computing in edge networks at scale. ![]()
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