10 research outputs found
Post-disaster 4G/5G Network Rehabilitation using Drones: Solving Battery and Backhaul Issues
Drone-based communications is a novel and attractive area of research in
cellular networks. It provides several degrees of freedom in time (available on
demand), space (mobile) and it can be used for multiple purposes (self-healing,
offloading, coverage extension or disaster recovery). This is why the wide
deployment of drone-based communications has the potential to be integrated in
the 5G standard. In this paper, we utilize a grid of drones to provide cellular
coverage to disaster-struck regions where the terrestrial infrastructure is
totally damaged due to earthquake, flood, etc. We propose solutions for the
most challenging issues facing drone networks which are limited battery energy
and limited backhauling. Our proposed solution based mainly on using three
types of drones; tethered backhaul drone (provides high capacity backhauling),
untethered powering drone (provides on the fly battery charging) and untethered
communication drone (provides cellular connectivity). Hence, an optimization
problem is formulated to minimize the energy consumption of drones in addition
to determining the placement of these drones and guaranteeing a minimum rate
for the users. The simulation results show that we can provide unlimited
cellular service to the disaster-affected region under certain conditions with
a guaranteed minimum rate for each user.Comment: 2018 IEEE Global Communications Conference: Workshops: 9th
International Workshop on Wireless Networking and Control for Unmanned
Autonomous Vehicle
Real-time dynamic spectrum management for multi-user multi-carrier communication systems
Dynamic spectrum management is recognized as a key technique to tackle
interference in multi-user multi-carrier communication systems and networks.
However existing dynamic spectrum management algorithms may not be suitable
when the available computation time and compute power are limited, i.e., when a
very fast responsiveness is required. In this paper, we present a new paradigm,
theory and algorithm for real-time dynamic spectrum management (RT-DSM) under
tight real-time constraints. Specifically, a RT-DSM algorithm can be stopped at
any point in time while guaranteeing a feasible and improved solution. This is
enabled by the introduction of a novel difference-of-variables (DoV)
transformation and problem reformulation, for which a primal coordinate ascent
approach is proposed with exact line search via a logarithmicly scaled grid
search. The concrete proposed algorithm is referred to as iterative power
difference balancing (IPDB). Simulations for different realistic wireline and
wireless interference limited systems demonstrate its good performance, low
complexity and wide applicability under different configurations.Comment: 14 pages, 9 figures. This work has been submitted to the IEEE for
possible publicatio
Weighted Sum Rate Maximization for Downlink OFDMA with Subcarrier-pair based Opportunistic DF Relaying
This paper addresses a weighted sum rate (WSR) maximization problem for
downlink OFDMA aided by a decode-and-forward (DF) relay under a total power
constraint. A novel subcarrier-pair based opportunistic DF relaying protocol is
proposed. Specifically, user message bits are transmitted in two time slots. A
subcarrier in the first slot can be paired with a subcarrier in the second slot
for the DF relay-aided transmission to a user. In particular, the source and
the relay can transmit simultaneously to implement beamforming at the
subcarrier in the second slot. Each unpaired subcarrier in either the first or
second slot is used for the source's direct transmission to a user. A benchmark
protocol, same as the proposed one except that the transmit beamforming is not
used for the relay-aided transmission, is also considered. For each protocol, a
polynomial-complexity algorithm is developed to find at least an approximately
optimum resource allocation (RA), by using continuous relaxation, the dual
method, and Hungarian algorithm. Instrumental to the algorithm design is an
elegant definition of optimization variables, motivated by the idea of
regarding the unpaired subcarriers as virtual subcarrier pairs in the direct
transmission mode. The effectiveness of the RA algorithm and the impact of
relay position and total power on the protocols' performance are illustrated by
numerical experiments. The proposed protocol always leads to a maximum WSR
equal to or greater than that for the benchmark one, and the performance gain
of using the proposed one is significant especially when the relay is in close
proximity to the source and the total power is low. Theoretical analysis is
presented to interpret these observations.Comment: 8 figures, accepted and to be published in IEEE Transactions on
Signal Processing. arXiv admin note: text overlap with arXiv:1301.293
JOINT CARRIER ALLOCATION AND PRECODING OPTIMIZATION FOR INTERFERENCE-LIMITED GEO SATELLITE
The rise of flexible payloads on satellites opens a door for controlling satellite resources according to the user demand, user location, and satellite position. In addition to resource management, applying precoding on flexible payloads is essential to obtain high spectral efficiency. However, these cannot be achieved using a conventional resource allocation algorithm that does not consider the user demand. In this paper, we propose a demand-aware algorithm based on multiobjective optimization to jointly design the carrier allocation and precoding for better spectral efficiency and demand matching with proper management of the satellite resources. The optimization problem is non-convex, and we solve it using convex relaxation and successive convex approximation. Then, we evaluate the performance of the proposed algorithm through numerical results. It is shown that the proposed method outperforms the benchmark schemes in terms of resource utilization and demand satisfaction
Spectrum optimization in multi-user multi-carrier systems with iterative convex and nonconvex approximation methods
Several practical multi-user multi-carrier communication systems are
characterized by a multi-carrier interference channel system model where the
interference is treated as noise. For these systems, spectrum optimization is a
promising means to mitigate interference. This however corresponds to a
challenging nonconvex optimization problem. Existing iterative convex
approximation (ICA) methods consist in solving a series of improving convex
approximations and are typically implemented in a per-user iterative approach.
However they do not take this typical iterative implementation into account in
their design. This paper proposes a novel class of iterative approximation
methods that focuses explicitly on the per-user iterative implementation, which
allows to relax the problem significantly, dropping joint convexity and even
convexity requirements for the approximations. A systematic design framework is
proposed to construct instances of this novel class, where several new
iterative approximation methods are developed with improved per-user convex and
nonconvex approximations that are both tighter and simpler to solve (in
closed-form). As a result, these novel methods display a much faster
convergence speed and require a significantly lower computational cost.
Furthermore, a majority of the proposed methods can tackle the issue of getting
stuck in bad locally optimal solutions, and hence improve solution quality
compared to existing ICA methods.Comment: 33 pages, 7 figures. This work has been submitted for possible
publicatio
Successive convex approximation based methods for dynamic spectrum management
This paper contains two parts. The first part presents a novel framework for the successive convex approximation (SCA) method to solve a general optimization problem, as well as its properties. This framework starts with making change of variables (COV), motivated by the fact that it might be easier to construct convex approximations for the problem after making the COV. Furthermore, a general method is proposed to construct a convex upper bound approximation (CUBA) for a nonconvex function that satisfies tightness and differentiation conditions. Moreover, a way is introduced to generalize that CUBA by incorporating a convex function. These methods lead to plenty of degrees of freedom for using the SCA method to solve a problem. The second part revisits state-of-the-art dynamic spectrum management (DSM) algorithms, namely the successive convex approximations for low-complexity (SCALE) algorithm, the convex approximation for distributed spectrum balancing (CA-DSB) algorithm and the difference-of-convex-functions algorithm based DSM (DCA-DSM) method, to show how they can be derived from the SCA and CUBA construction methods. Numerical experiments are shown to compare them. © 2012 IEEE