In this paper we analyze the performance of a mobile edge computing (MEC) system in the presence of spatially correlated shadowing. To achieve this goal, obstacles like walls are modelled by a Manhattan Poisson line Process (MPLP). Simultaneous wireless information and power transfer (SWIPT) is used to send command messages as well as to deliver the power to low power devices (LPDs). The energy harvested by the LPDs in the downlink phase is then used to either locally execute the assigned computation task or to offload it to the MEC server. By leveraging stochastic geometry, we derive analytical expressions for the success probability of the system which is defined as the joint probability of SINR downlink coverage and of harvesting enough energy to execute the computation task. Moreover, the impact of different system parameters on the trade-off between the harvested energy and the computational task admission rate is analyzed.

In this paper, we have considered a simultaneous wireless information and power transfer (SWIPT)-enabled mobile edge computing (MEC) system where a low power device (LPD) has to decide either to perform local computation or offloading. A decision strategy has been proposed and the impact of this strategy on a triple trade-off between energy, local computation and offloading has been evaluated. Moreover, in the local computation case, due to the existence of an uploading phase in which results are sent to the MEC server, another communication-computation trade-off has been analysed. In order to provide a system-level perspective on the performance of such a SWIPT-MEC network, we have employed stochastic geometry to characterise these communication-computation trade-offs of the system and we have investigated joint success probability of local computation and offloading.

In this paper, we propose the use of simultaneous wireless information and power transfer (SWIPT) to control distributed computation process while delivering power to perform the computation tasks requested. A real-time mobile computing (MC) system is considered, meaning that the trade-off between the information rate and the energy harvested must be carefully chosen to guarantee that the central processing unit (CPU) may perform tasks of given complexity before receiving a new control signal. In order to provide a system-level perspective on the performance of such a SWIPT-MC network, we make use of stochastic geometry to characterise the rate-energy trade-off of the system. The resulting achievable performance region is then put in relation with the CPU energy consumption to investigate the operating conditions of real-time computing systems.

Simultaneous wireless information and power transfer (SWIPT) is characterized by the ambiguous role of multi-user interference. In short, the beneficial effect of multi-user interference on RF energy harvesting is obtained at the price of a reduced link capacity, thus originating nontrivial tradeoffs between the achievable information rate and the harvestable energy. Arguably, in indoor environments, this tradeoff might be affected by the propagation loss due to blockage objects like walls. Hence, a couple of fundamental questions arise. How much must the network elements be densified to counteract the blockage attenuation? Is blockage always detrimental on the achievable rate-energy tradeoff? In this paper, we analyze the performance of an indoor multiple-input multiple-output SWIPT-enabled network in the attempt to shed a light of those questions. The effects of the obstacles are examined with the help of a stochastic geometry approach in which the locations of energy sources (also referred to as power heads) are distributed by using a Poisson point process and walls are generated through a Manhattan Poisson line process. The stochastic behavior of the signal attenuation and the multi-user interference is studied to obtain the joint complementary cumulative distribution function of information rate and harvested power. Theoretical results are validated through Monte Carlo simulations. Eventually, the rate-energy tradeoff is presented as a function of the density of walls to emphasize the cross-dependences between the deployment of the network elements and the macro parameters characterizing the topology of the venue.