331 research outputs found
Improving Macrocell - Small Cell Coexistence through Adaptive Interference Draining
The deployment of underlay small base stations (SBSs) is expected to
significantly boost the spectrum efficiency and the coverage of next-generation
cellular networks. However, the coexistence of SBSs underlaid to an existing
macro-cellular network faces important challenges, notably in terms of spectrum
sharing and interference management. In this paper, we propose a novel
game-theoretic model that enables the SBSs to optimize their transmission rates
by making decisions on the resource occupation jointly in the frequency and
spatial domains. This procedure, known as interference draining, is performed
among cooperative SBSs and allows to drastically reduce the interference
experienced by both macro- and small cell users. At the macrocell side, we
consider a modified water-filling policy for the power allocation that allows
each macrocell user (MUE) to focus the transmissions on the degrees of freedom
over which the MUE experiences the best channel and interference conditions.
This approach not only represents an effective way to decrease the received
interference at the MUEs but also grants the SBSs tier additional transmission
opportunities and allows for a more agile interference management. Simulation
results show that the proposed approach yields significant gains at both
macrocell and small cell tiers, in terms of average achievable rate per user,
reaching up to 37%, relative to the non-cooperative case, for a network with
150 MUEs and 200 SBSs
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
State of the Art, Taxonomy, and Open Issues on Cognitive Radio Networks with NOMA
The explosive growth of mobile devices and the rapid increase of wideband
wireless services call for advanced communication techniques that can achieve
high spectral efficiency and meet the massive connectivity requirement.
Cognitive radio (CR) and non-orthogonal multiple access (NOMA) are envisioned
to be important solutions for the fifth generation wireless networks.
Integrating NOMA techniques into CR networks (CRNs) has the tremendous
potential to improve spectral efficiency and increase the system capacity.
However, there are many technical challenges due to the severe interference
caused by using NOMA. Many efforts have been made to facilitate the application
of NOMA into CRNs and to investigate the performance of CRNs with NOMA. This
article aims to survey the latest research results along this direction. A
taxonomy is devised to categorize the literature based on operation paradigms,
enabling techniques, design objectives and optimization characteristics.
Moreover, the key challenges are outlined to provide guidelines for the domain
researchers and designers to realize CRNs with NOMA. Finally, the open issues
are discussed.Comment: This paper has been accepted by IEEE Wireless Communications
Magazine. Pages 16, Figures
Performance evaluation of cross-layer energy-efficient transmit antenna selection for spatial multiplexing systems
Abstract Multiple-input multiple-output (MIMO) and cognitive radio (CR) are key techniques for present and future high-speed wireless technologies. On the other hand, there are rising energy costs and greenhouse emissions associated with the provision of high-speed wireless communications. Consequently, the design of high-speed energy efficient systems is paramount for next-generation wireless systems. This thesis studies energy-efficient antenna selection for spatial multiplexing multiple-antenna systems from a cross-layer perspective, contrary to the norm, where physical-layer energy efficiency metrics are optimized. The enhanced system performance achieved by cross-layer designs in wireless networks motivates this research. The aim of the thesis is to propose and analyze novel cross-layer energy-efficient transmit antenna selection schemes that enhance energy efficiency and system performance - with regard to throughput, transmission latency, packet error rate and receiver buffer requirements. Firstly, this thesis derives the analytical expression for data link throughput for point-to-point spatial multiplexing multiple-antenna systems - which include MIMO and underlay CR MIMO systems - equipped with linear receivers with N-process stop-and-wait (N-SAW) as the automatic repeat request (ARQ) protocol. The performance of cross-layer transmit antenna selection, which maximizes the derived throughput metric, is then analyzed. The impact of packet size, number of SAW processes and the stalling of packets inside the receiver reordering buffer is considered in the investigation. The results show that the cross-layer approach, which takes into account system characteristics at both the data link and physical layers, has superior performance in comparison with the conventional physical-layer approach, which optimizes capacity. Secondly, this thesis proposes a cross-layer energy efficiency metric, based on the derived system throughput. Energy-efficient transmit antenna selection for spatial multiplexing MIMO systems, which maximizes the proposed cross-layer energy efficiency metric, by jointly optimizing the transmit antenna subset and transmit power, subject to spectral efficiency and transmit power constraints, is then introduced and analyzed. Additionally, adaptive modulation is incorporated into the proposed cross-layer scheme to enhance system performance. Cross-layer energyefficient transmit antenna selection for underlay CR MIMO systems, where interference constraints now come into play, is then considered. Lastly, this thesis develops novel reduced complexity versions of the proposed cross-layer energyefficient transmit antenna selection schemes - along with detailed complexity analysis - which shows that the proposed cross-layer approach attains significant energy efficiency and performance gains at affordable computational complexity
A Two-Phase Power Allocation Scheme for CRNs Employing NOMA
In this paper, we consider the power allocation (PA) problem in cognitive
radio networks (CRNs) employing nonorthogonal multiple access (NOMA) technique.
Specifically, we aim to maximize the number of admitted secondary users (SUs)
and their throughput, without violating the interference tolerance threshold of
the primary users (PUs). This problem is divided into a two-phase PA process:
a) maximizing the number of admitted SUs; b) maximizing the minimum throughput
among the admitted SUs. To address the first phase, we apply a sequential and
iterative PA algorithm, which fully exploits the characteristics of the
NOMA-based system. Following this, the second phase is shown to be quasiconvex
and is optimally solved via the bisection method. Furthermore, we prove the
existence of a unique solution for the second phase and propose another PA
algorithm, which is also optimal and significantly reduces the complexity in
contrast with the bisection method. Simulation results verify the effectiveness
of the proposed two-phase PA scheme
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