Degrees of Freedom in Wireless Interference Networks with Cooperative Transmission and Backhaul Load Constraints
Degrees of freedom (DoF) gains are studied in wireless networks with cooperative transmission under a backhaul load constraint that limits the average number of messages that can be delivered from a centralized controller to base station transmitters. The backhaul load is defined as the sum of all t...
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Zusammenfassung: | Degrees of freedom (DoF) gains are studied in wireless networks with
cooperative transmission under a backhaul load constraint that limits the
average number of messages that can be delivered from a centralized controller
to base station transmitters. The backhaul load is defined as the sum of all
the messages available at all the transmitters per channel use, normalized by
the number of users. For Wyner's linear interference network, where each
transmitter is connected to the receiver having the same index as well as one
succeeding receiver, the per user DoF is characterized and the optimal scheme
is presented. Furthermore, it is shown that the optimal assignment of messages
to transmitters is asymmetric and satisfies a local cooperation constraint, and
that the optimal coding scheme relies only on one-shot cooperative zero-forcing
transmit beamforming. Using insights from the analysis of Wyner's linear
interference network, the results are extended to the more practical hexagonal
sectored cellular network, and coding schemes based on cooperative zero-forcing
are shown to deliver significant DoF gains. It is established that by allowing
for cooperative transmission and a flexible message assignment that is
constrained only by an average backhaul load, one can deliver the rate gains
promised by information-theoretic upper bounds with practical one-shot schemes
that incur little or no additional load on the backhaul. Finally, useful upper
bounds on the per user DoF for schemes based on cooperative zero-forcing are
presented for lower values of the average backhaul load constraint, and an
optimization framework is formulated for the general converse problem. |
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DOI: | 10.48550/arxiv.1610.09453 |