325 research outputs found
Spectral Efficiency of Multi-User Adaptive Cognitive Radio Networks
In this correspondence, the comprehensive problem of joint power, rate, and
subcarrier allocation have been investigated for enhancing the spectral
efficiency of multi-user orthogonal frequency-division multiple access (OFDMA)
cognitive radio (CR) networks subject to satisfying total average transmission
power and aggregate interference constraints. We propose novel optimal radio
resource allocation (RRA) algorithms under different scenarios with
deterministic and probabilistic interference violation limits based on a
perfect and imperfect availability of cross-link channel state information
(CSI). In particular, we propose a probabilistic approach to mitigate the total
imposed interference on the primary service under imperfect cross-link CSI. A
closed-form mathematical formulation of the cumulative density function (cdf)
for the received signal-to-interference-plus-noise ratio (SINR) is formulated
to evaluate the resultant average spectral efficiency (ASE). Dual decomposition
is utilized to obtain sub-optimal solutions for the non-convex optimization
problems. Through simulation results, we investigate the achievable performance
and the impact of parameters uncertainty on the overall system performance.
Furthermore, we present that the developed RRA algorithms can considerably
improve the cognitive performance whilst abide the imposed power constraints.
In particular, the performance under imperfect cross-link CSI knowledge for the
proposed `probabilistic case' is compared to the conventional scenarios to show
the potential gain in employing this scheme
Delay Performance of MISO Wireless Communications
Ultra-reliable, low latency communications (URLLC) are currently attracting
significant attention due to the emergence of mission-critical applications and
device-centric communication. URLLC will entail a fundamental paradigm shift
from throughput-oriented system design towards holistic designs for guaranteed
and reliable end-to-end latency. A deep understanding of the delay performance
of wireless networks is essential for efficient URLLC systems. In this paper,
we investigate the network layer performance of multiple-input, single-output
(MISO) systems under statistical delay constraints. We provide closed-form
expressions for MISO diversity-oriented service process and derive
probabilistic delay bounds using tools from stochastic network calculus. In
particular, we analyze transmit beamforming with perfect and imperfect channel
knowledge and compare it with orthogonal space-time codes and antenna
selection. The effect of transmit power, number of antennas, and finite
blocklength channel coding on the delay distribution is also investigated. Our
higher layer performance results reveal key insights of MISO channels and
provide useful guidelines for the design of ultra-reliable communication
systems that can guarantee the stringent URLLC latency requirements.Comment: This work has been submitted to the IEEE for possible publication.
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QoS-driven adaptive resource allocation for mobile wireless communications and networks
Quality-of-service (QoS) guarantees will play a critically important role in future
mobile wireless networks. In this dissertation, we study a set of QoS-driven resource
allocation problems for mobile wireless communications and networks.
In the first part of this dissertation, we investigate resource allocation schemes
for statistical QoS provisioning. The schemes aim at maximizing the system/network
throughput subject to a given queuing delay constraint. To achieve this goal, we
integrate the information theory with the concept of effective capacity and develop
a unified framework for resource allocation. Applying the above framework, we con-sider a number of system infrastructures, including single channel, parallel channel,
cellular, and cooperative relay systems and networks, respectively. In addition, we
also investigate the impact of imperfect channel-state information (CSI) on QoS pro-visioning. The resource allocation problems can be solved e±ciently by the convex
optimization approach, where closed-form allocation policies are obtained for different
application scenarios.
Our analyses reveal an important fact that there exists a fundamental tradeoff
between throughput and QoS provisioning. In particular, when the delay constraint
becomes loose, the optimal resource allocation policy converges to the water-filling
scheme, where ergodic capacity can be achieved. On the other hand, when the
QoS constraint gets stringent, the optimal policy converges to the channel inversion scheme under which the system operates at a constant rate and the zero-outage
capacity can be achieved.
In the second part of this dissertation, we study adaptive antenna selection for
multiple-input-multiple-output (MIMO) communication systems. System resources
such as subcarriers, antennas and power are allocated dynamically to minimize the
symbol-error rate (SER), which is the key QoS metric at the physical layer. We
propose a selection diversity scheme for MIMO multicarrier direct-sequence code-
division-multiple-access (MC DS-CDMA) systems and analyze the error performance
of the system when considering CSI feedback delay and feedback errors. Moreover,
we propose a joint antenna selection and power allocation scheme for space-time
block code (STBC) systems. The error performance is derived when taking the CSI
feedback delay into account. Our numerical results show that when feedback delay
comes into play, a tradeoff between performance and robustness can be achieved by
dynamically allocating power across transmit antennas
Energy-aware resource allocation in next generation wireless networks : application in large-scale MIMO Systems
In this thesis, we investigate the resource allocation problem for wireless networks that incorporate large-scale multiple-input multiple-output (MIMO) systems. These systems are considered as key technologies for future 5G wireless networks and are based on using few hundreds of antennas simultaneously to serve tens of users in the same time-frequency resource. The gains obtained by large-scale MIMO systems cannot be fully exploited without adequate resource allocation strategies. Hence, the aim of this thesis is to develop energy-aware resource allocation solutions for large-scale MIMO systems that take into consideration network power cost.
Firstly, this thesis investigates the downlink of a base station equipped with large-scale MIMO system while taking into account a non-negligible transmit circuit power consumption. This consumption involves that activating all RF chains does not always necessarily achieve the maximum sum-rate. Thus, we derive the optimal number of activated RF chains. In addition, efficient antenna selection, user scheduling and power allocation algorithms in term of instantaneous sum-rate are proposed and compared. Also, fairness is investigated by considering equal receive power among users.
Secondly, this thesis investigates a large-scale MIMO system that incorporates energy harvesting that is a promising key technology for greening future wireless networks since it reduces network operation costs and carbon footprints. Hence, we consider distributed large-scale MIMO systems made up of a set of remote radio heads (RRHs), each of which is powered by both an independent energy harvesting source and the grid. The grid energy source allows to compensate for the randomness and intermittency of the harvested energy. Optimal on-line and off-line energy management strategies are developed. In addition, on-line energy management algorithm based on energy prediction is devised. The feasibility problem is addressed by proposing an efficient link removal algorithm and for better energy efficiency, RRH on/off operation is investigated.
Thirdly, wireless backhauling was proposed as an alternative solution that enable low-cost connection between the small base stations and the macro base station in heterogeneous networks (HetNets). The coexistence of massive MIMO, HetNets and wireless backhauling is a promising research direction since massive MIMO is a suitable solution to enable wireless backhauling. Thus, we propose a new transmission technique that is able to efficiently manage the interference in heterogeneous networks with massive MIMO wireless backhaul. The optimal time splitting parameter and the allocated transmit power are derived. The proposed transmission technique is shown to be more efficient in terms of transmit power consumption than the conventional reverse time division duplex with bandwidth splitting.
In this thesis, we developed efficient resource allocation solutions related to system power for wireless networks that incorporate large-scale MIMO systems under different assumptions and network architectures. The results in this thesis can be expanded by investigating the research problems given at the end of the dissertation
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