307 research outputs found
Wireless Power Transfer in Massive MIMO Aided HetNets with User Association
This paper explores the potential of wireless power transfer (WPT) in massive
multiple input multiple output (MIMO) aided heterogeneous networks (HetNets),
where massive MIMO is applied in the macrocells, and users aim to harvest as
much energy as possible and reduce the uplink path loss for enhancing their
information transfer. By addressing the impact of massive MIMO on the user
association, we compare and analyze two user association schemes. We adopt the
linear maximal ratio transmission beam-forming for massive MIMO power transfer
to recharge users. By deriving new statistical properties, we obtain the exact
and asymptotic expressions for the average harvested energy. Then we derive the
average uplink achievable rate under the harvested energy constraint.Comment: 36 pages, 11 figures, to appear in IEEE Transactions on
Communication
User Association in 5G Networks: A Survey and an Outlook
26 pages; accepted to appear in IEEE Communications Surveys and Tutorial
Energy and spectral efficiency tradeoff with user association and power coordination in massive MIMO enabled HetNets
In this letter, we investigate the tradeoff between energy efficiency (EE) and spectral efficiency (SE) while ensuring proportional rate fairness in massive multiple-input multiple-output enabled heterogenous networks, where user association and power coordination are jointly considered. It is first formulated as a multi-objective optimization problem, and then transformed into a single-objective optimization problem. To solve this mixed-integer non-convex problem, an effective algorithm is developed, where the original problem is separated into lower level power coordination problem and master user association problem. Simulation results verify that our proposed algorithm can significantly improve the performance of EE-SE tradeoff and obtain higher rate fairness compared with other algorithms
Wireless Power Transfer in Massive MIMO-Aided HetNets With User Association
This paper explores the potential of wireless power transfer (WPT) in massive multiple-input multiple-output (MIMO)-aided heterogeneous networks (HetNets), where massive MIMO is applied in the macrocells, and users aim to harvest as much energy as possible and reduce the uplink path loss for enhancing their information transfer. By addressing the impact of massive MIMO on the user association, we compare and analyze user association schemes: 1) downlink received signal power (DRSP)-based approach for maximizing the harvested energy and 2) uplink received signal power (URSP)-based approach for minimizing the uplink path loss. We adopt the linear maximal-ratio transmission beamforming for massive MIMO power transfer to recharge users. By deriving new statistical properties, we obtain the exact and asymptotic expressions for the average harvested energy. Then, we derive the average uplink
User Association and Load Balancing for Massive MIMO through Deep Learning
This work investigates the use of deep learning to perform user cell
association for sum-rate maximization in Massive MIMO networks. It is shown how
a deep neural network can be trained to approach the optimal association rule
with a much more limited computational complexity, thus enabling to update the
association rule in real-time, on the basis of the mobility patterns of users.
In particular, the proposed neural network design requires as input only the
users' geographical positions. Numerical results show that it guarantees the
same performance of traditional optimization-oriented methods
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