70 research outputs found
Coordinated Multi-cell Beamforming for Massive MIMO: A Random Matrix Approach
We consider the problem of coordinated multi- cell downlink beamforming in
massive multiple input multiple output (MIMO) systems consisting of N cells, Nt
antennas per base station (BS) and K user terminals (UTs) per cell.
Specifically, we formulate a multi-cell beamforming algorithm for massive MIMO
systems which requires limited amount of information exchange between the BSs.
The design objective is to minimize the aggregate transmit power across all the
BSs subject to satisfying the user signal to interference noise ratio (SINR)
constraints. The algorithm requires the BSs to exchange parameters which can be
computed solely based on the channel statistics rather than the instantaneous
CSI. We make use of tools from random matrix theory to formulate the
decentralized algorithm. We also characterize a lower bound on the set of
target SINR values for which the decentralized multi-cell beamforming algorithm
is feasible. We further show that the performance of our algorithm
asymptotically matches the performance of the centralized algorithm with full
CSI sharing. While the original result focuses on minimizing the aggregate
transmit power across all the BSs, we formulate a heuristic extension of this
algorithm to incorporate a practical constraint in multi-cell systems, namely
the individual BS transmit power constraints. Finally, we investigate the
impact of imperfect CSI and pilot contamination effect on the performance of
the decentralized algorithm, and propose a heuristic extension of the algorithm
to accommodate these issues. Simulation results illustrate that our algorithm
closely satisfies the target SINR constraints and achieves minimum power in the
regime of massive MIMO systems. In addition, it also provides substantial power
savings as compared to zero-forcing beamforming when the number of antennas per
BS is of the same orders of magnitude as the number of UTs per cell
Coexistence of RF-powered IoT and a Primary Wireless Network with Secrecy Guard Zones
This paper studies the secrecy performance of a wireless network (primary
network) overlaid with an ambient RF energy harvesting IoT network (secondary
network). The nodes in the secondary network are assumed to be solely powered
by ambient RF energy harvested from the transmissions of the primary network.
We assume that the secondary nodes can eavesdrop on the primary transmissions
due to which the primary network uses secrecy guard zones. The primary
transmitter goes silent if any secondary receiver is detected within its guard
zone. Using tools from stochastic geometry, we derive the probability of
successful connection of the primary network as well as the probability of
secure communication. Two conditions must be jointly satisfied in order to
ensure successful connection: (i) the SINR at the primary receiver is above a
predefined threshold, and (ii) the primary transmitter is not silent. In order
to ensure secure communication, the SINR value at each of the secondary nodes
should be less than a predefined threshold. Clearly, when more secondary nodes
are deployed, more primary transmitters will remain silent for a given guard
zone radius, thus impacting the amount of energy harvested by the secondary
network. Our results concretely show the existence of an optimal deployment
density for the secondary network that maximizes the density of nodes that are
able to harvest sufficient amount of energy. Furthermore, we show the
dependence of this optimal deployment density on the guard zone radius of the
primary network. In addition, we show that the optimal guard zone radius
selected by the primary network is a function of the deployment density of the
secondary network. This interesting coupling between the two networks is
studied using tools from game theory. Overall, this work is one of the few
concrete works that symbiotically merge tools from stochastic geometry and game
theory
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