251 research outputs found
Sub-channel Assignment, Power Allocation and User Scheduling for Non-Orthogonal Multiple Access Networks
In this paper, we study the resource allocation and user scheduling problem
for a downlink nonorthogonal multiple access network where the base station
allocates spectrum and power resources to a set of users. We aim to jointly
optimize the sub-channel assignment and power allocation to maximize the
weighted total sum-rate while taking into account user fairness. We formulate
the sub-channel allocation problem as equivalent to a many-to-many two-sided
user-subchannel matching game in which the set of users and sub-channels are
considered as two sets of players pursuing their own interests. We then propose
a matching algorithm which converges to a two-side exchange stable matching
after a limited number of iterations. A joint solution is thus provided to
solve the sub-channel assignment and power allocation problems iteratively.
Simulation results show that the proposed algorithm greatly outperforms the
orthogonal multiple access scheme and a previous non-orthogonal multiple access
scheme.Comment: Accepted as a regular paper by IEEE Transactions on Wireless
Communication
Efficient radio resource management for future generation heterogeneous wireless networks
The heterogeneous deployment of small cells (e.g., femtocells) in the coverage area of the traditional macrocells is a cost-efficient solution to provide network capacity, indoor coverage and green communications towards sustainable environments in the future fifth generation (5G) wireless networks. However, the unplanned and ultra-dense deployment of femtocells with their uncoordinated operations will result in technical challenges such as severe interference, a significant increase in total energy consumption, unfairness in radio resource sharing and inadequate quality of service provisioning. Therefore, there is a need to develop efficient radio resource management algorithms that will address the above-mentioned technical challenges. The aim of this thesis is to develop and evaluate new efficient radio resource management algorithms that will be implemented in cognitive radio enabled femtocells to guarantee the economical sustainability of broadband wireless communications and users' quality of service in terms of throughput and fairness. Cognitive Radio (CR) technology with the Dynamic Spectrum Access (DSA) and stochastic process are the key technologies utilized in this research to increase the spectrum efficiency and energy efficiency at limited interference. This thesis essentially investigates three research issues relating to the efficient radio resource management: Firstly, a self-organizing radio resource management algorithm for radio resource allocation and interference management is proposed. The algorithm considers the effect of imperfect spectrum sensing in detecting the available transmission opportunities to maximize the throughput of femtocell users while keeping interference below pre-determined thresholds and ensuring fairness in radio resource sharing among users. Secondly, the effect of maximizing the energy efficiency and the spectrum efficiency individually on radio resource management is investigated. Then, an energy-efficient radio resource management algorithm and a spectrum-efficient radio resource management algorithm are proposed for green communication, to improve the probabilities of spectrum access and further increase the network capacity for sustainable environments. Also, a joint maximization of the energy efficiency and spectrum efficiency of the overall networks is considered since joint optimization of energy efficiency and spectrum efficiency is one of the goals of 5G wireless networks. Unfortunately, maximizing the energy efficiency results in low performance of the spectrum efficiency and vice versa. Therefore, there is an investigation on how to balance the trade-off that arises when maximizing both the energy efficiency and the spectrum efficiency simultaneously. Hence, a joint energy efficiency and spectrum efficiency trade-off algorithm is proposed for radio resource allocation in ultra-dense heterogeneous networks based on orthogonal frequency division multiple access. Lastly, a joint radio resource allocation with adaptive modulation and coding scheme is proposed to minimize the total transmit power across femtocells by considering the location and the service requirements of each user in the network. The performance of the proposed algorithms is evaluated by simulation and numerical analysis to demonstrate the impact of ultra-dense deployment of femtocells on the macrocell networks. The results show that the proposed algorithms offer improved performance in terms of throughput, fairness, power control, spectrum efficiency and energy efficiency. Also, the proposed algorithms display excellent performance in dynamic wireless environments
Energy-Efficient Heterogeneous Cellular Networks with Spectrum Underlay and Overlay Access
In this paper, we provide joint subcarrier assignment and power allocation
schemes for quality-of-service (QoS)-constrained energy-efficiency (EE)
optimization in the downlink of an orthogonal frequency division multiple
access (OFDMA)-based two-tier heterogeneous cellular network (HCN). Considering
underlay transmission, where spectrum-efficiency (SE) is fully exploited, the
EE solution involves tackling a complex mixed-combinatorial and non-convex
optimization problem. With appropriate decomposition of the original problem
and leveraging on the quasi-concavity of the EE function, we propose a
dual-layer resource allocation approach and provide a complete solution using
difference-of-two-concave-functions approximation, successive convex
approximation, and gradient-search methods. On the other hand, the inherent
inter-tier interference from spectrum underlay access may degrade EE
particularly under dense small-cell deployment and large bandwidth utilization.
We therefore develop a novel resource allocation approach based on the concepts
of spectrum overlay access and resource efficiency (RE) (normalized EE-SE
trade-off). Specifically, the optimization procedure is separated in this case
such that the macro-cell optimal RE and corresponding bandwidth is first
determined, then the EE of small-cells utilizing the remaining spectrum is
maximized. Simulation results confirm the theoretical findings and demonstrate
that the proposed resource allocation schemes can approach the optimal EE with
each strategy being superior under certain system settings
Resource allocation for transmit hybrid beamforming in decoupled millimeter wave multiuser-MIMO downlink
This paper presents a study on joint radio resource allocation and hybrid precoding in multicarrier massive multiple-input multiple-output communications for 5G cellular networks. In this paper, we present the resource allocation algorithm to maximize the proportional fairness (PF) spectral efficiency under the per subchannel power and the beamforming rank constraints. Two heuristic algorithms are designed. The proportional fairness hybrid beamforming algorithm provides the transmit precoder with a proportional fair spectral efficiency among users for the desired number of radio-frequency (RF) chains. Then, we transform the number of RF chains or rank constrained optimization problem into convex semidefinite programming (SDP) problem, which can be solved by standard techniques. Inspired by the formulated convex SDP problem, a low-complexity, two-step, PF-relaxed optimization algorithm has been provided for the formulated convex optimization problem. Simulation results show that the proposed suboptimal solution to the relaxed optimization problem is near-optimal for the signal-to-noise ratio SNR <= 10 dB and has a performance gap not greater than 2.33 b/s/Hz within the SNR range 0-25 dB. It also outperforms the maximum throughput and PF-based hybrid beamforming schemes for sum spectral efficiency, individual spectral efficiency, and fairness index
Partially-Distributed Resource Allocation in Small-Cell Networks
We propose a four-stage hierarchical resource allocation scheme for the
downlink of a large-scale small-cell network in the context of orthogonal
frequency-division multiple access (OFDMA). Since interference limits the
capabilities of such networks, resource allocation and interference management
are crucial. However, obtaining the globally optimum resource allocation is
exponentially complex and mathematically intractable. Here, we develop a
partially decentralized algorithm to obtain an effective solution. The three
major advantages of our work are: 1) as opposed to a fixed resource allocation,
we consider load demand at each access point (AP) when allocating spectrum; 2)
to prevent overloaded APs, our scheme is dynamic in the sense that as the users
move from one AP to the other, so do the allocated resources, if necessary, and
such considerations generally result in huge computational complexity, which
brings us to the third advantage: 3) we tackle complexity by introducing a
hierarchical scheme comprising four phases: user association, load estimation,
interference management via graph coloring, and scheduling. We provide
mathematical analysis for the first three steps modeling the user and AP
locations as Poisson point processes. Finally, we provide results of numerical
simulations to illustrate the efficacy of our scheme.Comment: Accepted on May 15, 2014 for publication in the IEEE Transactions on
Wireless Communication
Joint User-Association and Resource-Allocation in Virtualized Wireless Networks
In this paper, we consider a down-link transmission of multicell virtualized
wireless networks (VWNs) where users of different service providers (slices)
within a specific region are served by a set of base stations (BSs) through
orthogonal frequency division multiple access (OFDMA). In particular, we
develop a joint BS assignment, sub-carrier and power allocation algorithm to
maximize the network throughput, while satisfying the minimum required rate of
each slice. Under the assumption that each user at each transmission instance
can connect to no more than one BS, we introduce the user-association factor
(UAF) to represent the joint sub-carrier and BS assignment as the optimization
variable vector in the mathematical problem formulation. Sub-carrier reuse is
allowed in different cells, but not within one cell. As the proposed
optimization problem is inherently non-convex and NP-hard, by applying the
successive convex approximation (SCA) and complementary geometric programming
(CGP), we develop an efficient two-step iterative approach with low
computational complexity to solve the proposed problem. For a given
power-allocation, Step 1 derives the optimum userassociation and subsequently,
for an obtained user-association, Step 2 find the optimum power-allocation.
Simulation results demonstrate that the proposed iterative algorithm
outperforms the traditional approach in which each user is assigned to the BS
with the largest average value of signal strength, and then, joint sub-carrier
and power allocation is obtained for the assigned users of each cell.
Especially, for the cell-edge users, simulation results reveal a coverage
improvement up to 57% and 71% for uniform and non-uniform users distribution,
respectively leading to more reliable transmission and higher spectrum
efficiency for VWN
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