Mobile edge computing (MEC) is an emerging computing paradigm for enabling low-latency, high-bandwidth and agile mobile services by deploying computing platform at the edge of network. In order to improve the cloud-edge-end processing efficiency of the tasks within the limited computation and communication capabilities, in this article, we investigate the collaborative computation offloading, computation and communication resource allocation scheme, and develop a collaborative computing framework that the tasks of mobile devices (MDs) can be partially processed at the terminals, edge nodes (EN) and cloud center (CC). Then, we propose the pipeline-based offloading scheme, where both MDs and ENs can offload computation-intensive tasks to a particular EN and CC, according to their computation and communication capacities, respectively. Based on the proposed pipeline offloading strategy, a sum latency of all MDs minimization problem is formulated with the consideration of the offloading strategy, computation resource, delivery rate and power allocation, which is a non-convex problem and difficult to deal with. To solve the optimization problem, by using the classic successive convex approximation (SCA) approach, we transform the non-convex optimization problem into the convex one. Finally, simulation results indicate that the proposed collaboration offloading scheme with the pipeline strategy is efficient and outperforms other offloading schemes.

Huge demands for ubiquitous high data rate wireless communications have caused a sharp increase in energy consumption and greenhouse gas emission. In order to realize a sustainable development, it is critical to incorporate green communication technique into smart city developments. D2D communication has been recognized as one of the key technologies to improve data rate and reduce power consumption. In this paper, with the target of achieving green communications through D2D, we investigate the joint optimization of uplink subcarrier assignment and power allocation in D2D underlying cellular networks. Specifically, the problem formulation is to minimize the energy cost of all users in the system while guaranteeing the required data rate of both the D2D user equipments and cellular user equipments. Such an optimization problem is in general a mixed-integer nonlinear programming problem (MINLP) that is generally NP-hard. To make this problem tractable, we decompose it into the subcarrier assignment and power allocation subproblems. In particular, we design a heuristic algorithm to assign subcarrier by assuming that the transmit power is evenly allocated over all subcarriers. After that, we solve the power allocation subproblem by exploiting the difference between concave function (D.C.) structure of the constraints and transform it into a convex optimization problem. Simulation results demonstrate the remarkable improvement in terms of power consumption by using our algorithms.

The fundamental intelligent reflecting surface (IRS) deployment problem is studied for IRS-aided downlink multi-user communication system, where IRSs are arranged to be deployed in a specific area for enhancing the desired signal and suppressing interference. Specifically, we aim to maximize the minimum achievable rate over all locations in a specific area by jointly optimizing the transmit beamforming at the access point (AP) as well as the placement and reflective beamforming at the IRS. The formulated problem is non-convex and thus difficult to be solved directly. To draw essential insights, we first consider the single-user case and the optimal solution is derived in closed-form. The result shows that the optimal locations are in the connecting line between the AP and user, and the IRSs can be optimally deployed along the connecting line. Besides, a hybrid offline and online design scheme is developed for the multi-user case, where an area discretization strategy and deep neural network (DNN)-based curve fitting technique are proposed for optimizing the IRS locations in the offline manner. Then, an online iterative algorithm is presented to solve the transmit and received beamformig vectors, respectively. Numerical results show that the performance gain is increased by optimizing the IRS locations.

For the large scale wireless networks, restricted access window (RAW) mechanism is a promising technique for realizing large-scale sensor access with the limited collision probability. In this paper, we are committed to designing the traffic distribution based sensor grouping scheme to balance the energy efficiency (EE) of different groups in the large scale access networks. Specifically, by adopting the Markov chain model, we formulate the optimization problem of max-min EE by taking into account traffic demands with even distribution of different all groups, but the formulated problem is an integer nonlinear programming (INLP) problem. In order to solve the INLP problem, we propose an optimal traffic grouping algorithm (OTGA) by utilizing the branch-and-bound method (BBM) to accommodate for the congestion level among groups. Though the traffic demands of each group can be obtained from the traffic grouping scheme, different combination of heterogeneous sensors can generate the same traffic demands, which make it difficult to find the optimal solution of sensor grouping from the proposed traffic grouping scheme. Furthermore, a heuristic traffic-sensor mapping algorithm (HTMA) is presented to make the traffic demands of each group appropriate. Thus, the proposed scheme can achieve a sub-optimal performance with the individual EE. The numerical results are provided to verify the effectiveness of the proposed schemes.

This letter investigates the joint uplink and downlink resource allocation problem, where the non-orthogonal multiple access (NOMA) technology is applied to enhance device-to-device (D2D) communications. In order to maximize the sum data rate of NOMA-enabled D2D groups while guaranteeing the minimum data rate requirements of both cellular users (CUs) and D2D groups, we first design a subcarrier assignment algorithm to assign the downlink and uplink subcarriers for D2D groups, and then transform the problem into a convex optimization to obtain the optimal power allocation. Simulation results demonstrate the remarkable improvement in terms of data rate of D2D groups by using our algorithms.