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Space-time power scheduling of MIMO links? fairness and QoS considerations
Power control, beamforming and link scheduling are all important operations to improve the power-and-spectral efficiency of networks of multiple-input multiple-output (MIMO) wireless links. We call a joint optimization of the above operations the space-time power scheduling (STPS) scheme. The STPS scheme is formulated as joint optimization of the transmitter covariance matrices of all active MIMO links over all dimensions of space and time, which includes the dimension of frequency as a dual form of time. In this paper, we address the proportional fair (PF) and quality-of-service (QoS) issues of the STPS scheme, which are important for networks with asymmetric topology and/or asymmetric traffic demands. Both slow fading channels and fast fading channels are considered. We demonstrate that the PF-STPS scheme provides a very attractive tradeoff between sum capacity and rate distribution for asymmetric links. We also demonstrate that the QoS-STPS scheme has a much higher power-and-spectral efficiency than the previously existing QoS based scheme that do not exploit the temporal freedom. Efficient optimization algorithms for both PF-STPS and QoS-STPS are provided. The STPS scheme is a centralized cooperative scheme which requires a scheduler. For ad hoc networks, this scheduler can be elected adaptively among eligible nodes in the network
Autonomous Algorithms for Centralized and Distributed Interference Coordination: A Virtual Layer Based Approach
Interference mitigation techniques are essential for improving the
performance of interference limited wireless networks. In this paper, we
introduce novel interference mitigation schemes for wireless cellular networks
with space division multiple access (SDMA). The schemes are based on a virtual
layer that captures and simplifies the complicated interference situation in
the network and that is used for power control. We show how optimization in
this virtual layer generates gradually adapting power control settings that
lead to autonomous interference minimization. Thereby, the granularity of
control ranges from controlling frequency sub-band power via controlling the
power on a per-beam basis, to a granularity of only enforcing average power
constraints per beam. In conjunction with suitable short-term scheduling, our
algorithms gradually steer the network towards a higher utility. We use
extensive system-level simulations to compare three distributed algorithms and
evaluate their applicability for different user mobility assumptions. In
particular, it turns out that larger gains can be achieved by imposing average
power constraints and allowing opportunistic scheduling instantaneously, rather
than controlling the power in a strict way. Furthermore, we introduce a
centralized algorithm, which directly solves the underlying optimization and
shows fast convergence, as a performance benchmark for the distributed
solutions. Moreover, we investigate the deviation from global optimality by
comparing to a branch-and-bound-based solution.Comment: revised versio
Power control for predictable communication reliability in wireless cyber-physical systems
Wireless networks are being applied in various cyber-physical systems and posed to support
mission-critical cyber-physical systems applications. When those applications require reliable and
low-latency wireless communication, ensuring predictable per-packet communication reliability is a
basis. Due to co-channel interference and wireless channel dynamics (e.g. multi-path fading), however,
wireless communication is inherently dynamic and subject to complex uncertainties. Power
control and MAC-layer scheduling are two enablers. In this dissertation, cross-layer optimization
of joint power control and scheduling for ensuring predictable reliability has been studied. With an
emphasis on distributed approaches, we propose a general framework and additionally a distributed
algorithm in static networks to address small channel variations and satisfy the requirements on
receiver-side signal-to-interference-plus-noise-ratio (SINR). Moreover, toward addressing reliability
in the settings of large-scale channel dynamics, we conduct an analysis of the strategy of joint
scheduling and power control and demonstrate the challenges.
First, a general framework for distributed power control is considered. Given a set of links
subject to co-channel interference and channel dynamics, the goal is to adjust each link\u27s transmission
power on-the-fly so that all the links\u27 instantaneous packet delivery ratio requirements
can be satised. By adopting the SINR high-delity model, this problem can be formulated as
a Linear Programming problem. Furthermore, Perron-Frobenius theory indicates the characteristic
of infeasibility, which means that not all links can nd a transmission power to meet all the
SINR requirements. This nding provides a theoretical foundation for the Physical-Ratio-K (PRK)
model. We build our framework based on the PRK model and NAMA scheduling. In the proposed
framework, we dene the optimal K as a measurement for feasibility. Transmission power and
scheduling will be adjusted by K and achieve near-optimal performance in terms of reliability and
concurrency.
Second, we propose a distributed power control and scheduling algorithm for mission-critical
Internet-of-Things (IoT) communications. Existing solutions are mostly based on heuristic algorithms
or asymptotic analysis of network performance, and there lack eld-deployable algorithms
for ensuring predictable communication reliability. When IoT systems are mostly static or low mobility,
we model the wireless channel with small channel variations. For this setting, our approach
adopts the framework mentioned above and employs feedback control for online K adaptation and
transmission power update. At each time instant, each sender will run NAMA scheduling to determine
if it can obtain channel access or not. When each sender gets the channel access and sends a
packet, its receiver will measure the current SINR and calculate the scheduling K and transmission
power for the next time slot according to current K, transmission power and SINR. This adaptive
distributed approach has demonstrated a signicant improvement compared to state-of-the-art
technique. The proposed algorithm is expected to serve as a foundation for distributed scheduling
and power control as the penetration of IoT applications expands to levels at which both the
network capacity and communication reliability become critical.
Finally, we address the challenges of power control and scheduling in the presence of large-scale
channel dynamics. Distributed approaches generally require time to converge, and this becomes a
major issue in large-scale dynamics where channel may change faster than the convergence time
of algorithms. We dene the cumulative interference factor as a measurement of impact of a single
link\u27s interference. We examine the characteristic of the interference matrix and propose that
scheduling with close-by links silent will be still an ecient way of constructing a set of links
whose required reliability is feasible with proper transmission power control even in the situation of
large-scale channel dynamics. Given that scheduling alone is unable to ensure predictable communication
reliability while ensuring high throughput and addressing fast-varying channel dynamics,
we demonstrate how power control can help improve both reliability at each time instant and
throughput in the long-term. Collectively, these ndings provide insight into the cross-layer design
of joint scheduling and power control for ensuring predictable per-packet reliability in the presence
of wireless network dynamics and uncertainties
A Tutorial on Clique Problems in Communications and Signal Processing
Since its first use by Euler on the problem of the seven bridges of
K\"onigsberg, graph theory has shown excellent abilities in solving and
unveiling the properties of multiple discrete optimization problems. The study
of the structure of some integer programs reveals equivalence with graph theory
problems making a large body of the literature readily available for solving
and characterizing the complexity of these problems. This tutorial presents a
framework for utilizing a particular graph theory problem, known as the clique
problem, for solving communications and signal processing problems. In
particular, the paper aims to illustrate the structural properties of integer
programs that can be formulated as clique problems through multiple examples in
communications and signal processing. To that end, the first part of the
tutorial provides various optimal and heuristic solutions for the maximum
clique, maximum weight clique, and -clique problems. The tutorial, further,
illustrates the use of the clique formulation through numerous contemporary
examples in communications and signal processing, mainly in maximum access for
non-orthogonal multiple access networks, throughput maximization using index
and instantly decodable network coding, collision-free radio frequency
identification networks, and resource allocation in cloud-radio access
networks. Finally, the tutorial sheds light on the recent advances of such
applications, and provides technical insights on ways of dealing with mixed
discrete-continuous optimization problems
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