414 research outputs found
Analyzing the Reduced Required BS Density due to CoMP in Cellular Networks
In this paper we investigate the benefit of base station (BS) cooperation in
the uplink of coordinated multi-point (CoMP) networks. Our figure of merit is
the required BS density required to meet a chosen rate coverage. Our model
assumes a 2-D network of BSs on a regular hexagonal lattice in which path loss,
lognormal shadowing and Rayleigh fading affect the signal received from users.
Accurate closed-form expressions are first presented for the sum-rate coverage
probability and ergodic sum-rate at each point of the cooperation region. Then,
for a chosen quality of user rate, the required density of BS is derived based
on the minimum value of rate coverage probability in the cooperation region.
The approach guarantees that the achievable rate in the entire coverage region
is above a target rate with chosen probability. The formulation allows
comparison between different orders of BS cooperation, quantifying the reduced
required BS density from higher orders of cooperation.Comment: Accepted for presentation in IEEE Globecom Conf., to be held in
Atlanta, USA, Dec. 2013. arXiv admin note: text overlap with arXiv:1302.159
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
Joint Scheduling and ARQ for MU-MIMO Downlink in the Presence of Inter-Cell Interference
User scheduling and multiuser multi-antenna (MU-MIMO) transmission are at the
core of high rate data-oriented downlink schemes of the next-generation of
cellular systems (e.g., LTE-Advanced). Scheduling selects groups of users
according to their channels vector directions and SINR levels. However, when
scheduling is applied independently in each cell, the inter-cell interference
(ICI) power at each user receiver is not known in advance since it changes at
each new scheduling slot depending on the scheduling decisions of all
interfering base stations. In order to cope with this uncertainty, we consider
the joint operation of scheduling, MU-MIMO beamforming and Automatic Repeat
reQuest (ARQ). We develop a game-theoretic framework for this problem and build
on stochastic optimization techniques in order to find optimal scheduling and
ARQ schemes. Particularizing our framework to the case of "outage service
rates", we obtain a scheme based on adaptive variable-rate coding at the
physical layer, combined with ARQ at the Logical Link Control (ARQ-LLC). Then,
we present a novel scheme based on incremental redundancy Hybrid ARQ (HARQ)
that is able to achieve a throughput performance arbitrarily close to the
"genie-aided service rates", with no need for a genie that provides
non-causally the ICI power levels. The novel HARQ scheme is both easier to
implement and superior in performance with respect to the conventional
combination of adaptive variable-rate coding and ARQ-LLC.Comment: Submitted to IEEE Transactions on Communications, v2: small
correction
Downlink Noncoherent Cooperation without Transmitter Phase Alignment
Multicell joint processing can mitigate inter-cell interference and thereby
increase the spectral efficiency of cellular systems. Most previous work has
assumed phase-aligned (coherent) transmissions from different base transceiver
stations (BTSs), which is difficult to achieve in practice. In this work, a
noncoherent cooperative transmission scheme for the downlink is studied, which
does not require phase alignment. The focus is on jointly serving two users in
adjacent cells sharing the same resource block. The two BTSs partially share
their messages through a backhaul link, and each BTS transmits a superposition
of two codewords, one for each receiver. Each receiver decodes its own message,
and treats the signals for the other receiver as background noise. With
narrowband transmissions the achievable rate region and maximum achievable
weighted sum rate are characterized by optimizing the power allocation (and the
beamforming vectors in the case of multiple transmit antennas) at each BTS
between its two codewords. For a wideband (multicarrier) system, a dual
formulation of the optimal power allocation problem across sub-carriers is
presented, which can be efficiently solved by numerical methods. Results show
that the proposed cooperation scheme can improve the sum rate substantially in
the low to moderate signal-to-noise ratio (SNR) range.Comment: 30 pages, 6 figures, submitted to IEEE Transactions on Wireless
Communication
Globally Optimal Energy-Efficient Power Control and Receiver Design in Wireless Networks
The characterization of the global maximum of energy efficiency (EE) problems
in wireless networks is a challenging problem due to the non-convex nature of
investigated problems in interference channels. The aim of this work is to
develop a new and general framework to achieve globally optimal solutions.
First, the hidden monotonic structure of the most common EE maximization
problems is exploited jointly with fractional programming theory to obtain
globally optimal solutions with exponential complexity in the number of network
links. To overcome this issue, we also propose a framework to compute
suboptimal power control strategies characterized by affordable complexity.
This is achieved by merging fractional programming and sequential optimization.
The proposed monotonic framework is used to shed light on the ultimate
performance of wireless networks in terms of EE and also to benchmark the
performance of the lower-complexity framework based on sequential programming.
Numerical evidence is provided to show that the sequential fractional
programming framework achieves global optimality in several practical
communication scenarios.Comment: Accepted for publication in the IEEE Transactions on Signal
Processin
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