• Journal of Information Processing Systems
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• v.17 no.4
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• pp.721-736
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• 2021

For the mobile edge computing (MEC) system supporting dense network, a joint allocation algorithm of computing and communication resources based on reinforcement learning is proposed. The energy consumption of task execution is defined as the maximum energy consumption of each user's task execution in the system. Considering the constraints of task unloading, power allocation, transmission rate and calculation resource allocation, the problem of joint task unloading and resource allocation is modeled as a problem of maximum task execution energy consumption minimization. As a mixed integer nonlinear programming problem, it is difficult to be directly solve by traditional optimization methods. This paper uses reinforcement learning algorithm to solve this problem. Then, the Markov decision-making process and the theoretical basis of reinforcement learning are introduced to provide a theoretical basis for the algorithm simulation experiment. Based on the algorithm of reinforcement learning and joint allocation of communication resources, the joint optimization of data task unloading and power control strategy is carried out for each terminal device, and the local computing model and task unloading model are built. The simulation results show that the total task computation cost of the proposed algorithm is 5%-10% less than that of the two comparison algorithms under the same task input. At the same time, the total task computation cost of the proposed algorithm is more than 5% less than that of the two new comparison algorithms.

• IE interfaces
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• v.2 no.2
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• pp.25-32
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• 1989

We study the joint cost allocation based on the book profit producing power of the output through the A-S price mechanism. We show what part of the A-S book profit is allocated to the joint cost and what part is allocated to the variable total book profit of the short-run book profit function. Also we compare some other classical joint cost allocation methods with this A-S price method.

• KSII Transactions on Internet and Information Systems (TIIS)
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• v.7 no.12
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• pp.3037-3054
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• 2013

Interference between primary user (PU) and secondary user (SU) transceivers should be mitigated in order to implement underlay spectrum sharing in cognitive radio networks (CRN). Considering this scenario, an improved joint subcarrier and bit allocation scheme for cognitive user with primary users' cooperation (PU Coop) in CRN is proposed. In this scheme, the optimization problem is formulated to minimize the average interference power level at the PU receiver via PU Coop, which guarantees a higher primary signal to interference plus noise ratio (SINR) while maintaining the secondary user total rate constraint. The joint optimal scheme is separated into subcarrier allocation and bit assignment in each subcarrier via arith-metric geo-metric (AM-GM) inequality with asymptotical optimization solution. Moreover, the joint subcarrier and bit optimization scheme, which is evaluated by the available SU subcarriers and the allocated bits, is analyzed in the proposed PU Coop model. The performance of cognitive spectral efficiency and the average interference power level are investigated. Numerical analysis indicates that the SU's spectral efficiency increases significantly compared with the PU non-cooperation scenario. Moreover, the interference power level decreases dramatically for the proposed scheme compared with the traditional Hughes-Hartogs bit allocation scheme.

• Journal of Communications and Networks
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• v.14 no.3
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• pp.257-266
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• 2012

A decode-and-forward two-way relay system benefits from orthogonal frequency division multiplexing (OFDM) and relay transmission. In this paper, we consider a decode-and-forward two-way relay system over OFDMwith two strategies: A joint subcarrier matching algorithm and a power allocation algorithm operating with a total power constraint for all subcarriers. The two strategies are studied based on average capacity using numerical analysis by uniformly allocating power constraints for each subcarrier matching group. An optimal subcarrier matching algorithm is proposed to match subcarriers in order of channel power gain for both transmission sides. Power allocation is defined based on equally distributing the capacity of each hop in each matching group. Afterward, a modified water-filling algorithm is also considered to allocate the power among all matching groups in order to increase the overall capacity of the network. Finally, Monte Carlo simulations are completed to confirm the numerical results and show the advantages of the joint subcarrier matching, power allocation and water filling algorithms, respectively.

• KSII Transactions on Internet and Information Systems (TIIS)
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• v.14 no.7
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• pp.3134-3155
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• 2020

In this paper, we study the joint radio and computational resource allocation in the ultra-dense mobile-edge computing networks. In which, the scenario which including both computation offloading and communication service is discussed. That is, some mobile users ask for computation offloading, while the others ask for communication with the minimum communication rate requirements. We formulate the problem as a joint channel assignment, power control and computational resource allocation to minimize the offloading cost of computing offloading, with the precondition that the transmission rate of communication nodes are satisfied. Since the formulated problem is a mixed-integer nonlinear programming (MINLP), which is NP-hard. By leveraging the particular mathematical structure of the problem, i.e., the computational resource allocation variable is independent with other variables in the objective function and constraints, and then the original problem is decomposed into a computational resource allocation subproblem and a joint channel assignment and power allocation subproblem. Since the former is a convex programming, the KKT (Karush-Kuhn-Tucker) conditions can be used to find the closed optimal solution. For the latter, which is still NP-hard, is further decomposed into two subproblems, i.e., the power allocation and the channel assignment, to optimize alternatively. Finally, two heuristic algorithms are proposed, i.e., the Co-channel Equal Power allocation algorithm (CEP) and the Enhanced CEP (ECEP) algorithm to obtain the suboptimal solutions. Numerical results are presented at last to verify the performance of the proposed algorithms.

• Journal of Communications and Networks
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• v.18 no.3
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• pp.375-386
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• 2016

Most existing studies on broadcast services in orthogonal frequency division multiplexing (OFDM) systems have focused on how to allocate the transmission power to the subcarriers. However, because a broadcasting system must guarantee quality of service to all users, the performance of the broadcast service dominantly depends on the channel state of the user who has the lowest received signal-to-noise ratio among users. To reduce the effect of the worst user on the system performance, we propose a joint delivery scheme of unicast and broadcast transmissions in OFDM systems with broadcast and unicast best-effort users. In the proposed joint delivery scheme, the BS delivers the broadcast information using both the broadcast and unicast subcarriers at the same time in order to improve the performance of the broadcast service. The object of the proposed scheme is to minimize the outage probability of the broadcast service while maximizing the sum-rate of best-effort users. For the proposed joint delivery scheme, we develop an adaptive power and subcarrier allocation algorithm under the constraint of total transmission power. This paper shows that the optimal power allocation over each subcarrier in the proposed scheme has a multi-level water filling form. Because the power allocation and the subcarrier assignment problems should be jointly solved, we develop an iterative algorithm to find the optimal solution. Numerical results show that the proposed joint delivery scheme with adaptive power and subcarrier allocation outperforms the conventional scheme in terms of the outage probability of the broadcast service and the sum-rate of best-effort users.

• KSII Transactions on Internet and Information Systems (TIIS)
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• v.7 no.6
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• pp.1379-1397
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• 2013

We consider a system where a licensed radio spectrum is shared by multiple primary users(PUs) and secondary users(SUs). As the spectrum of interest is licensed to primary network, power and channel allocation must be carried out within the cognitive radio network so that no excessive interference is caused to PUs. For this system, we study the joint beamforming and power allocation problem via game theory in this paper. The problem is formulated as a non-cooperative beamforming and power allocation game, subject to the interference constraints of PUs as well as the peak transmission power constraints of SUs. We design a joint beamforming and power allocation algorithm for maximizing the total throughput of SUs, which is implemented by alternating iteration of minimum mean square error based decision feedback beamforming and a best response based iterative power allocation algorithm. Simulation results show that the algorithm has better performance than an existing algorithm and can converge to a locally optimal sum utility.

• KSII Transactions on Internet and Information Systems (TIIS)
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• v.10 no.3
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• pp.1016-1033
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• 2016

In this paper, we present a joint relay-and-antenna selection and power allocation strategy for multiple-input multi-output (MIMO) amplify-and-forward (AF) two-way relay networks (TWRNs). In our approach, we select the best transmit and receive antennas at the two sources, a best relay and a best transmit and receive antenna at the selected relay based on maximizing the minimum of the end-to-end received signal-to-noise-ratios (SNRs) under a total transmit power constraints. We obtained the closed-form solution for the optimal power allocation firstly. Then with the optimal allocation solution we found, we can reduce the joint relay-and-antenna selection to a simpler problem. Besides, the overall outage probability is investigated and a tight closed-form approximation is derived, which provides a method to evaluate the outage performance easily and fast. Simulation results are presented to verify the analysis.

• KSII Transactions on Internet and Information Systems (TIIS)
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• v.16 no.9
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• pp.3008-3028
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• 2022

In this paper, we investigate traffic scheduling for a delay-sensitive multi-hop relay network, and aim to minimize the priority-based end-to-end delay of different data packet via joint relay selection, subcarrier assignment, and power allocation. We first derive the priority-based end-to-end delay based on queueing theory, and then propose a two-step method to decompose the original optimization problem into two sub-problems. For the joint subcarrier assignment and power control problem, we utilize an efficient particle swarm optimization method to solve it. For the relay selection problem, we prove its convexity and use the standard Lagrange method to deal with it. The joint relay selection, subcarriers assignment and transmission power allocation problem for each hop can also be solved by an exhaustive search over a finite set defined by the relay sensor set and available subcarrier set. Simulation results show that both the proposed routing scheme and the resource allocation scheme can reduce the average end-to-end delay.

• KSII Transactions on Internet and Information Systems (TIIS)
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• v.3 no.4
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• pp.385-398
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• 2009

This paper aims to find a suitable solution to joint allocation of sub-channel and transmit power for multiple users in an IEEE 802.16e OFDMA/TDD cellular system. We propose the FASA (Fairness insured Aggressive Sub-channel Allocation) algorithm, which is a dynamic channel allocation algorithm that considers all of the users' channel state information conditionally in order to maximize throughput while taking into account fairness. A dynamic power allocation algorithm, i.e., an improved CHC algorithm, is also proposed in combination with the FASA algorithm. It collects the extra downlink transmit power and re-allocates it to other potential users. Simulation results show that the joint allocation scheme with the improved CHC power allocation algorithm provides an additional increase of sector throughput while simultaneously enhancing fairness. Four frames of time delay for CQI feedback and scheduling are considered. Furthermore, by addressing the difference between uplink and downlink scheduling in an IEEE 802.16e OFDMA TDD system, we can employ the uplink channel information directly via channel sounding, resulting in more accurate uplink dynamic resource allocation.