1,378 research outputs found
Area Spectral Efficiency Analysis and Energy Consumption Minimization in Multi-Antenna Poisson Distributed Networks
This paper aims at answering two fundamental questions: how area spectral
efficiency (ASE) behaves with different system parameters; how to design an
energy-efficient network. Based on stochastic geometry, we obtain the
expression and a tight lower-bound for ASE of Poisson distributed networks
considering multi-user MIMO (MU-MIMO) transmission. With the help of the
lower-bound, some interesting results are observed. These results are validated
via numerical results for the original expression. We find that ASE can be
viewed as a concave function with respect to the number of antennas and active
users. For the purpose of maximizing ASE, we demonstrate that the optimal
number of active users is a fixed portion of the number of antennas. With
optimal number of active users, we observe that ASE increases linearly with the
number of antennas. Another work of this paper is joint optimization of the
base station (BS) density, the number of antennas and active users to minimize
the network energy consumption. It is discovered that the optimal combination
of the number of antennas and active users is the solution that maximizes the
energy-efficiency. Besides the optimal algorithm, we propose a suboptimal
algorithm to reduce the computational complexity, which can achieve near
optimal performance.Comment: Submitted to IEEE Transactions on Wireless Communications, Major
Revisio
User Association in 5G Networks: A Survey and an Outlook
26 pages; accepted to appear in IEEE Communications Surveys and Tutorial
Edge Computing-Enabled Cell-Free Massive MIMO Systems
Mobile edge computing (MEC) has been introduced to provide additional
computing capabilities at network edges in order to improve performance of
latency critical applications. In this paper, we consider the cell-free (CF)
massive MIMO framework with implementing MEC functionalities. We consider
multiple types of users with different average time requirements for
computing/processing the tasks, and consider access points (APs) with MEC
servers and a central server (CS) with the cloud computing capability. After
deriving successful communication and computing probabilities using stochastic
geometry and queueing theory, we present the successful edge computing
probability (SECP) for a target computation latency. Through numerical results,
we also analyze the impact of the AP coverage and the offloading probability to
the CS on the SECP. It is observed that the optimal probability of offloading
to the CS in terms of the SECP decreases with the AP coverage. Finally, we
numerically characterize the minimum required energy consumption for
guaranteeing a desired level of SECP. It is observed that for any desired level
of SECP, it is more energy efficient to have larger number of APs as compared
to having more number of antennas at each AP with smaller AP density.Comment: Submitted to IEEE Transactions on Wireless Communication
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