1,987 research outputs found
Maximizing energy-efficiency in multi-relay OFDMA cellular networks
No full textThis contribution presents a method of obtaining the optimal power and subcarrier allocations that maximize the energy-efficiency (EE) of a multi-user, multi-relay, orthogonal frequency division multiple access (OFDMA) cellular network. Initially, the objective function (OF) is formulated as the ratio of the spectral-efficiency (SE) over the power consumption of the network. This OF is shown to be quasi-concave, thus Dinkelbach's method can be employed for solving it as a series of parameterized concave problems. We characterize the performance of the aforementioned method by comparing the optimal solutions obtained to those found using an exhaustive search. Additionally, we explore the relationship between the achievable SE and EE in the cellular network upon increasing the number of active users. In general, increasing the number of users supported by the system benefits both the SE and EE, and higher SE values may be obtained at the cost of EE, when an increased power may be allocated
Spectrum/energy efficient resource allocation for multi-user multi-relay OFDMA cellular networks: a fractional programming approach
No full textThis thesis focuses on the energy efficiency (EE) of relay-aided cellular networks, which is motivated by the ever-increasing need to support higher and higher data rates, while reducing the energy costs. Relaying is a beneficial tool for either increasing the reliability or the coverage area of a wireless network as a result of the reduced communication distances, albeit this might increase the energy consumption in practice. Our approach in this thesis was to study and model progressively more complex and more realistic cellular networks. By utilizing novel transmission protocol designs, we were able to formulate their associated EE resource allocation optimization problems. Thus, efficient tools can be employed for solving these problems to maximize the EE
Multi-objective routing optimization using evolutionary algorithms
No full textWireless ad hoc networks suffer from several limitations, such as routing failures, potentially excessive bandwidth requirements, computational constraints and limited storage capability. Their routing strategy plays a significant role in determining the overall performance of the multi-hop network. However, in conventional network design only one of the desired routing-related objectives is optimized, while other objectives are typically assumed to be the constraints imposed on the problem. In this paper, we invoke the Non-dominated Sorting based Genetic Algorithm-II (NSGA-II) and the MultiObjective Differential Evolution (MODE) algorithm for finding optimal routes from a given source to a given destination in the face of conflicting design objectives, such as the dissipated energy and the end-to-end delay in a fully-connected arbitrary multi-hop network. Our simulation results show that both the NSGA-II and MODE algorithms are efficient in solving these routing problems and are capable of finding the Pareto-optimal solutions at lower complexity than the ’brute-force’ exhaustive search, when the number of nodes is higher than or equal to 10. Additionally, we demonstrate that at the same complexity, the MODE algorithm is capable of finding solutions closer to the Pareto front and typically, converges faster than the NSGA-II algorithm
Joint transmit and receive beamforming for multi-relay MIMO-OFDMA cellular networks
No full textA novel transmission protocol is conceived for a multi-user, multi-relay, multiple-input-multiple-output orthogonal frequency-division multiple-access (MIMO-OFDMA) cellular network based on joint transmit and receive beamforming. More specifically, the network's MIMO channels are mathematically decomposed into several effective multiple-input-single-output (MISO) channels, which are spatially multiplexed for transmission. For the sake of improving the attainable capacity, these MISO channels are grouped using a pair of novel grouping algorithms, which are then evaluated in terms of their performance versus complexity trade-of
On-Demand Decode and Forward Cooperative MAC for VoIP in Wireless Mesh Networks
No full textThe employment of wireless mesh networking in real- life scenarios has attracted substantial research interest in recent years and in this context VoIP has become a ubiquitous application. However, it has been demonstrated that VoIP transmissions over a multihop network may still remain inadequate in terms of their high packet-loss ratio and network-induced delays. To alleviate these limitations, we propose a novel distributed MAC-layer cooperation protocol, which is based on the decode-and-forward regime, whilst relying on the lowest possible control packet overhead. Furthermore, we employ several improvements across the protocol stack, including an improved PHY-layer, based on three-stage turbo-style differential detection, packet aggregation in the MAC-layer, as well as on adjusting the retransmission limit of each packet in order to reduce the delay imposed when employing cooperation. We characterize our improved system in a Wireless Mesh Network (WMN) scenario using the OMNeT++ network simulator and compare it to an 802.11g-based benchmarker. As a benefit of these techniques, we have observed up to 10-fold reduction in the energy consumption per bit, despite increasing the number of simultaneous calls supported by up to 9, when the number of hops between the sources and destination is 6
Spectral and energy spectral efficiency optimization of joint transmit and receive beamforming based multi-relay MIMO-OFDMA cellular networks
No full textWe first conceive a novel transmission protocol for a multi-relay multiple-input--multiple-output orthogonal frequency-division multiple-access (MIMO-OFDMA) cellular network based on joint transmit and receive beamforming. We then address the associated network-wide spectral efficiency (SE) and energy spectral efficiency (ESE) optimization problems. More specifically, the network's MIMO channels are mathematically decomposed into several effective multiple-input--single-output (MISO) channels, which are essentially spatially multiplexed for transmission. Hence, these effective MISO channels are referred to as spatial multiplexing components (SMCs). For the sake of improving the SE/ESE performance attained, the SMCs are grouped using a pair of proposed grouping algorithms. The first is optimal in the sense that it exhaustively evaluates all the possible combinations of SMCs satisfying both the semi-orthogonality criterion and other relevant system constraints, whereas the second is a lower-complexity alternative. Corresponding to each of the two grouping algorithms, the pair of SE and ESE maximization problems are formulated, thus the optimal SMC groups and optimal power control variables can be obtained for each subcarrier block. These optimization problems are proven to be concave, and the dual decomposition approach is employed for obtaining their solutions. Relying on these optimization solutions, the impact of various system parameters on both the attainable SE and ESE is characterized. In particular, we demonstrate that under certain conditions the lower-complexity SMC grouping algorithm achieves 90% of the SE/ESE attained by the exhaustive-search based optimal grouping algorithm, while imposing as little as 3.5% of the latter scheme's computational complexity
Achieving maximum energy-efficiency in multi-relay OFDMA cellular networks: a fractional programming approach
No full textIn this paper, we consider the joint power and subcarrier allocation problem in the context of maximizing the Energy-Efficiency (EE) of a multi-user, multi-relay Orthogonal Frequency Division Multiple Access (OFDMA) cellular network. Our objective function is formulated as the ratio of the Sum-Rate (SR) over the total power dissipation. We prove that the fractional programming problem considered is quasi-concave and then employ Dinkelbach's method for finding the optimal solution at a low complexity. This method allows us to solve the above-mentioned master problem by solving a series of parameterized concave secondary problems. These secondary problems are solved using a dual decomposition approach, which allows us to further decompose each secondary problem into a number of similar subproblems. We characterize the impact of various system parameters on the attainable EE and Spectral-Efficiency (SE) of the system when employing both EE Maximization (EEM) and SE Maximization(SEM) algorithms. In particular, we observe that increasing the number of relays provides diminishing returns in EE gain, whilst increasing both the number of available subcarriers and the number of active User Equipment~(UE) increases both the EE and SE of the system as a benefit of the increased frequency and multi-user diversity, respectively. Finally, we demonstrate that as expected, increasing the available power tends to improve the SE when using our SEM algorithm. By contrast, given a sufficiently high available power, our EEM algorithm attains the maximum achievable EE and a suboptimal SE
Distributed energy spectral efficiency optimization for partial/full interference alignment in multi-user multi-relay multi-cell MIMO systems
No full textThe energy spectral efficiency maximization (ESEM) problem of a multi-user, multi-relay, multi-cell system is considered, where all the network nodes are equipped with multiple antenna aided transceivers. In order to deal with the potentially excessive interference originating from a plethora of geographically distributed transmission sources, a pair of transmission protocols based on interference alignment (IA) are conceived, which may be distributively implemented in the network. The first, termed the full-IA protocol, avoids all intra-cell interference (ICI) and other-cell interference (OCI) by finding the perfect interferencenulling receive beamforming matrices (RxBFMs). The second protocol, termed as partial-IA, only attempts to null the ICI. Employing the RxBFMs computed by either of these protocols mathematically decomposes the channel into a multiplicity of non-interfering multiple-input–single-output (MISO) channels, which we term as spatial multiplexing components (SMCs). The problem of finding the optimal SMCs as well as their power control variables for the ESEM problem considered is formally defined and converted into a convex optimization form with the aid of carefully selected variable relaxations and transformations. Thus, the optimal SMCs and power control variables can be distributively computed using both the classic dual decomposition and subgradient methods. The performance of both protocols is characterized, and the ESEM algorithm conceived is compared to a baseline equal power allocation (EPA) algorithm. The results indicate that indeed, the ESEM algorithm performs better than the EPA algorithm in most cases. Furthermore, surprisingly the partial-IA protocol outperforms the full-IA protocol in all cases considered, which may be explained by the fact that the partial-IA protocol is less restrictive in terms of the number of available transmit dimensions at the transmitters. Given the typical cell sizes considered in this paper, the path-loss sufficiently attenuates the majority of the interference, and thus the full-IA protocol over-compensates, when trying to avoid all possible sources of interference. We have observed that, given a sufficiently high maximum power, the partial-IA protocol achieves an energy spectral efficiency (ESE) that is 2.42 times higher than that attained by the full-IA protocol
Some Weighted Hardy-type Inequalities of Vector-Valued Functions
No full textBy adopting the C-technique of Cheung and Pečrić, we establish some interesting weighted Hardy-type inequalities of vector-valued functions. These generalize and improve some existing results of Cheung, Cheung-Hanjš-Pēcarić, Hanjš-Love-Pečarić, Levinson, and Pachpatte. [ABSTRACT FROM AUTHOR
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