Computation offloading from multiple mobile devices (MDs) to multiple MEC servers (MEC-ss) in heterogeneous MEC systems with energy harvesting is investigated from a game theoretic perspective. The objective is to minimize the average response time of an MD that consists of data communication time, waiting time and processing time. M/G/1 queueing models are established for MDs and MEC-ss. The interference among MDs, the randomness in computation task generation, harvested energy arrival, wireless channel state, queueing at the MEC-s, and the power budget constraint of each MD are taken into consideration. A noncooperative computation offloading game is formulated. Furthermore, we reconstruct the optimization problem of an MD. A 2-step decomposition is presented and performed. Thereby, we arrive at a one-dimensional search problem and a greatly shrunken sub-problem. The sub-problem is nonconvex, but its Karush-Kuhn-Tucker (KKT) conditions have finite solutions. Therefore, we can obtain the optimal solution of the sub-problem by seeking the finite solutions. Thereafter, a distributive iterated best-response algorithm is designed. Simulations are carried out to illustrate the convergence performance and parameter effect of the proposed algorithm.

Renewable energy powered cognitive radio (CR) network has gained much attention due to its combination of the CR's spectrum efficiency and the renewable energy's “green” nature. In the paper, we investigate the delay-optimal data transmission in the renewable energy aided CR networks. Specifically, a primary user (PU) and a secondary user (SU) share the same frequency in an area. The SU's interference to the PU is controlled by interference-signal-ratio (ISR) constraint, which means that the ISR at the PU receiver (Rx) should be less than a threshold. Under this constraint, the renewable energy powered SU aims to minimize the average data buffer delay by scheduling the renewable allocations in each slot. A constrained stochastic optimization problem is formulated when the randomness of the renewable arrival, the uncertainty of the SU's data generation, and the variability of the fading channel are taken into account. By analyzing the formulated problem, we propose two practical algorithms that is optimal for two special scenarios. And the two algorithms respectively give an upper and a lower bound for the general scenario. In addition, the availability of the PU's private information at the SU is discussed. Finally, numerical simulations verify the effectiveness of the proposed algorithm.

Cooperative transmission in vehicular networks is studied by using coalitional game and pricing in this paper. There are several vehicles and roadside units (RSUs) in the networks. Each vehicle has a desire to transmit with a certain probability, which represents its data burtiness. The RSUs can enhance the vehicles' transmissions by cooperatively relaying the vehicles' data. We consider two kinds of cooperations: cooperation among the vehicles and cooperation between the vehicle and RSU. First, vehicles cooperate to avoid interfering transmissions by scheduling the transmissions of the vehicles in each coalition. Second, a RSU can join some coalition to cooperate the transmissions of the vehicles in that coalition. Moreover, due to the mobility of the vehicles, we introduce the notion of encounter between the vehicle and RSU to indicate the availability of the relay in space. To stimulate the RSU's cooperative relaying for the vehicles, the pricing mechanism is applied. A non-transferable utility (NTU) game is developed to analyze the behaviors of the vehicles and RSUs. The stability of the formulated game is studied. Finally, we present and discuss the numerical results for the 2-vehicle and 2-RSU scenario, and the numerical results verify the theoretical analysis.

In this paper, we consider the power allocation of the physical layer and the buffer delay of the upper application layer in energy harvesting green networks. We analyze the delay-optimal power allocation problem over fading channels. The total power required for reliable transmission includes the transmission power as well as the circuit power. The harvested power (which is stored in a battery) and the grid power constitute the power resource. The objective is to find a policy to minimize the buffer delay under the constraint on the average grid power. The policy is a two-dimensional vector with the transmission rate and the power allocation of the battery as its elements. In each transmission, the transmitter decides the transmission rate and the allocated power from the battery (the rest of the required power will be supplied by the power grid). A constrained Markov decision process (MDP) problem is formulated when the data arrival process, the harvested energy arrival process, and the channel process are Markov processes. We prove that the optimal policy can be obtained as follows. First, we solve the optimal rate through a reduced MDP problem that is only related to the average harvested energy but not the harvested energy arrival process. Second, the battery's power allocation can be given based on the optimal rate. By analyzing the reduced MDP problem through the transformations to the average cost MDP and discount optimal MDP, we derive some structural properties of the optimal policy. Moreover, the closed-form expression is obtained for the independent and identically distributed (i.i.d.) cases.