The problem of joint power and sub-channel allocation to maximize energy efficiency (EE) and spectral efficiency (SE) simultaneously in in-band full-duplex (IBFD) orthogonal frequency-division multiple access (OFDMA) network is addressed considering users' QoS in both uplink and downlink. The resulting optimization problem is a non-convex mixed integer non-linear program (MINLP) which is generally difficult to solve. In order to strike a balance between the EE and SE, we restate this problem as a multi-objective optimization problem (MOOP) which aims at maximizing system's throughput and minimizing system's power consumption, simultaneously. To this end, the ǫconstraint method is adopted to transform the MOOP into single objective optimization problem (SOOP). The underlying problem is solved via an efficient solution based on the majorization minimization (MM) approach. Furthermore, in order to handle binary subchannel allocation variable constraints, a penalty function is introduced. Simulation results unveil interesting tradeoffs between EE and SE.Index Terms-Full-duplex (FD) communication, energyefficiency (EE), spectral-efficiency (SE), mixed integer nonlinear program (MINLP), multi-objective optimization problem (MOOP), ǫ-method, majorization minimization (MM).
In this paper, we develop a framework to maximize the network energy efficiency (EE) by optimizing joint userbase station (BS) association, subchannel assignment, and power control considering an in-band full-duplex (IBFD)-enabled smallcell network. We maximize EE (ratio of network aggregate throughput and power consumption) while guaranteeing a minimum data rate requirement in both the uplink and downlink. The considered problem belongs to the category of mixed-integer non-linear programming problem (MINLP), and thus is NPhard. To cope up with this complexity and to derive a tradeoff between system throughput and energy utilization, we first restate the considered problem as a multi-objective optimization problem (MOOP) aiming at maximizing system's throughput and minimizing system's energy consumption, simultaneously. This MOOP is then tackled by using-constraint method. To do so, we first transform the binary subchannel and BS assignment variables into continuous ones without altering the feasible region of the problem and then approximate the nonconvex rate functions through majorization-minimization (MM) approach. Simulation results are presented to demonstrate the effectiveness of our proposed algorithm in improving network's EE compared to the existing literature. Furthermore, simulation results unveil that by employing the IBFD capability in OFDMA networks, our proposed resource allocation algorithm achieves a 69% improvement in the EE as compared to the half-duplex system for practical values of residual self-interference.
Here, we consider the problem of jointly optimizing users' offloading decisions, communication and computing resource allocation in a sliced multi-cell mobile edge computing (MEC) network. We minimize the weighted sum of the gap between the observed delay at each slice and its corresponding delay requirement, where weights set the priority of each slice. Fractional form of the objective function, discrete subchannel allocation, considered partial offloading, and the interference incorporated in the rate function, make the considered problem a complex mixed integer non-linear programming problem. Thus, we decompose the original problem into two sub-problems: (i) offloading decision-making and (ii) joint computation resource, subchannel, and power allocation. We solve the first sub-problem optimally and for the second sub-problem, leveraging on novel tools from fractional programming and Augmented Lagrangian method, we propose an efficient algorithm whose computational complexity is proved to be polynomial. Using alternating optimization, we solve these two sub-problems iteratively until convergence is obtained. Simulation results demonstrate the convergence of our proposed algorithm and its effectiveness compared to existing schemes.
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