2,210 research outputs found
Uplink CoMP under a Constrained Backhaul and Imperfect Channel Knowledge
Coordinated Multi-Point (CoMP) is known to be a key technology for next
generation mobile communications systems, as it allows to overcome the burden
of inter-cell interference. Especially in the uplink, it is likely that
interference exploitation schemes will be used in the near future, as they can
be used with legacy terminals and require no or little changes in
standardization. Major drawbacks, however, are the extent of additional
backhaul infrastructure needed, and the sensitivity to imperfect channel
knowledge. This paper jointly addresses both issues in a new framework
incorporating a multitude of proposed theoretical uplink CoMP concepts, which
are then put into perspective with practical CoMP algorithms. This
comprehensive analysis provides new insight into the potential usage of uplink
CoMP in next generation wireless communications systems.Comment: Submitted to IEEE Transactions on Wireless Communications in February
201
Distributed Multicell Beamforming Design Approaching Pareto Boundary with Max-Min Fairness
This paper addresses coordinated downlink beamforming optimization in
multicell time-division duplex (TDD) systems where a small number of parameters
are exchanged between cells but with no data sharing. With the goal to reach
the point on the Pareto boundary with max-min rate fairness, we first develop a
two-step centralized optimization algorithm to design the joint beamforming
vectors. This algorithm can achieve a further sum-rate improvement over the
max-min optimal performance, and is shown to guarantee max-min Pareto
optimality for scenarios with two base stations (BSs) each serving a single
user. To realize a distributed solution with limited intercell communication,
we then propose an iterative algorithm by exploiting an approximate
uplink-downlink duality, in which only a small number of positive scalars are
shared between cells in each iteration. Simulation results show that the
proposed distributed solution achieves a fairness rate performance close to the
centralized algorithm while it has a better sum-rate performance, and
demonstrates a better tradeoff between sum-rate and fairness than the Nash
Bargaining solution especially at high signal-to-noise ratio.Comment: 8 figures. To Appear in IEEE Trans. Wireless Communications, 201
Downlink Training in Cell-Free Massive MIMO: A Blessing in Disguise
Cell-free Massive MIMO (multiple-input multiple-output) refers to a
distributed Massive MIMO system where all the access points (APs) cooperate to
coherently serve all the user equipments (UEs), suppress inter-cell
interference and mitigate the multiuser interference. Recent works demonstrated
that, unlike co-located Massive MIMO, the \textit{channel hardening} is, in
general, less pronounced in cell-free Massive MIMO, thus there is much to
benefit from estimating the downlink channel. In this study, we investigate the
gain introduced by the downlink beamforming training, extending the previously
proposed analysis to non-orthogonal uplink and downlink pilots. Assuming
single-antenna APs, conjugate beamforming and independent Rayleigh fading
channel, we derive a closed-form expression for the per-user achievable
downlink rate that addresses channel estimation errors and pilot contamination
both at the AP and UE side. The performance evaluation includes max-min
fairness power control, greedy pilot assignment methods, and a comparison
between achievable rates obtained from different capacity-bounding techniques.
Numerical results show that downlink beamforming training, although increases
pilot overhead and introduces additional pilot contamination, improves
significantly the achievable downlink rate. Even for large number of APs, it is
not fully efficient for the UE relying on the statistical channel state
information for data decoding.Comment: Published in IEEE Transactions on Wireless Communications on August
14, 2019. {\copyright} 2019 IEEE. Personal use of this material is permitted.
Permission from IEEE must be obtained for all other use
Joint Multi-Cell Resource Allocation Using Pure Binary-Integer Programming for LTE Uplink
Due to high system capacity requirement, 3GPP Long Term Evolution (LTE) is
likely to adopt frequency reuse factor 1 at the cost of suffering severe
inter-cell interference (ICI). One of combating ICI strategies is network
cooperation of resource allocation (RA). For LTE uplink RA, requiring all the
subcarriers to be allocated adjacently complicates the RA problem greatly. This
paper investigates the joint multi-cell RA problem for LTE uplink. We model the
uplink RA and ICI mitigation problem using pure binary-integer programming
(BIP), with integrative consideration of all users' channel state information
(CSI). The advantage of the pure BIP model is that it can be solved by
branch-and-bound search (BBS) algorithm or other BIP solving algorithms, rather
than resorting to exhaustive search. The system-level simulation results show
that it yields 14.83% and 22.13% gains over single-cell optimal RA in average
spectrum efficiency and 5th percentile of user throughput, respectively.Comment: Accepted to IEEE Vehicular Technology Conference (VTC Spring), Seoul,
Korea, May, 201
How to Solve the Fronthaul Traffic Congestion Problem in H-CRAN?
The design of efficient wireless fronthaul connections for future heterogeneous networks incorporating emerging paradigms such as heterogeneous cloud radio access network (H-CRAN) has become a challenging task that requires the most effective utilization of fronthaul network resources. In this paper, we propose and analyze possible solutions to facilitate the fronthaul traffic congestion in the scenario of Coordinated Multi-Point (CoMP) for 5G cellular traffic which is expected to reach ZetaByte by 2017. In particular, we propose to use distributed compression to reduce the fronthaul traffic for H-CRAN. Unlike the conventional approach where each coordinating point quantizes and forwards its own observation to the processing centre, these observations are compressed before forwarding. At the processing centre, the decompression of the observations and the decoding of the user messages are conducted in a joint manner. Our results reveal that, in both dense and ultra-dense urban small cell deployment scenarios, the usage of distributed compression can efficiently reduce the required fronthaul rate by more than 50% via joint operation
- …