| 摘要: | Explosive growth in data rate put some serious challenges on wireless system designers and link budget planning. Low transmit power, system coverage and capacity, high data rates, spatial diversity and quality of services (QOS) are the key factors in future wireless communication system that made it attractive. Dual-hop relaying is the promising underlying technique for future wireless communications to address such dilemmas. The objective of this dissertation is to optimize the performance of dual-hop amplify-and-forward (AF) relay networks to enhance the energy and spectral efficiency.
Multiple-input multiple-output (MIMO) relays provide the high throughput of MIMO communication with the coverage extension capability of relay transmission. But one of the main limitations of MIMO relay network is effectively managing the intersymbol interference (ISI) and multiple antenna interference (MAI). In the first part of this dissertation, an equalize-and-forward (EF) relaying schemes are designed to efficiently mitigate the interference generated due to multipath channels, by jointly optimize equalizer weights and power allocation for dual-hop MIMO relay networks with full/partial channel state information (CSI) knowledge. We then extend the design to a more general case in which the direct link between the source and the destination is taken into account. Furthermore, two relay selection algorithms based on the allocated power and the mean square error (MSE) performance are investigated for the two scenarios which attain a performance that is comparable to that of cases with brute-force search or without relay selection.
In second part of this dissertation, we extend relay selection algorithms based on the allocated power to a perturbation-based power allocation and multi-relay selection for further enhancement of performance of MIMO relay networks. In this approach, the relays are partitioned into two groups according to Lagrangian multipliers of power constraints. The power allocation for the relays is perturbed by increasing the power for the potential relay’s group, while decreasing the power of the relays in the other group. An optimization framework is then formulated as a trade-off between the relay selection and the mean square error (MSE) performance degradation.
A pricing-based approach is proposed in third part of this dissertation to achieve energy-efficient power allocation in relay-assisted multiuser networks. We consider a network price to the power consumption as a penalty for the achievable sum rate, and study its impact on the tradeoff between the energy efficiency (EE) and the spectral efficiency (SE). Due to non-convex nature of the original problem, it is It is hard to directly solve it, and thus a concave lower bound on the pricing-based utility is applied to transform the problem into a convex one. Through dual decomposition, a q-price algorithm is proposed for iteratively tightening the lower bound and finding the optimal solution. In addition, an optimal price that enables green power allocation is defined and found from the viewpoint of maximizing EE. Moreover, we also analyze the optimal power allocation strategies of the pricing-based approach in a two-user case under different noise operating regimes, yielding on-off, water-filling, and channel-reversal approaches. We then prolong this idea for two-way relay networks as in fourth part of this dissertation and propose a novel energy-efficient power allocation schemes to improve the EE in multiuser multi-carrier two-way relay networks which are able to not only balance the EE of the two-way links but also ensure the quality-of-service (QoS).
The last part of this dissertation is dedicated to the design of a novel joint source and relay transmit power allocation and energy transfer schemes to maximize the network sum rate within a deadline subject to energy causality and battery storage constraints. The non-convex sum rate optimization problem is transformed into a solvable convex optimization problem using a successive convex approximation for low-complexity (SCALE) algorithm. The effect of node′s battery capacity and energy harvesting profiles on the network sum rate maximization are investigated.;Explosive growth in data rate put some serious challenges on wireless system designers and link budget planning. Low transmit power, system coverage and capacity, high data rates, spatial diversity and quality of services (QOS) are the key factors in future wireless communication system that made it attractive. Dual-hop relaying is the promising underlying technique for future wireless communications to address such dilemmas. The objective of this dissertation is to optimize the performance of dual-hop amplify-and-forward (AF) relay networks to enhance the energy and spectral efficiency.
Multiple-input multiple-output (MIMO) relays provide the high throughput of MIMO communication with the coverage extension capability of relay transmission. But one of the main limitations of MIMO relay network is effectively managing the intersymbol interference (ISI) and multiple antenna interference (MAI). In the first part of this dissertation, an equalize-and-forward (EF) relaying schemes are designed to efficiently mitigate the interference generated due to multipath channels, by jointly optimize equalizer weights and power allocation for dual-hop MIMO relay networks with full/partial channel state information (CSI) knowledge. We then extend the design to a more general case in which the direct link between the source and the destination is taken into account. Furthermore, two relay selection algorithms based on the allocated power and the mean square error (MSE) performance are investigated for the two scenarios which attain a performance that is comparable to that of cases with brute-force search or without relay selection.
In second part of this dissertation, we extend relay selection algorithms based on the allocated power to a perturbation-based power allocation and multi-relay selection for further enhancement of performance of MIMO relay networks. In this approach, the relays are partitioned into two groups according to Lagrangian multipliers of power constraints. The power allocation for the relays is perturbed by increasing the power for the potential relay’s group, while decreasing the power of the relays in the other group. An optimization framework is then formulated as a trade-off between the relay selection and the mean square error (MSE) performance degradation.
A pricing-based approach is proposed in third part of this dissertation to achieve energy-efficient power allocation in relay-assisted multiuser networks. We consider a network price to the power consumption as a penalty for the achievable sum rate, and study its impact on the tradeoff between the energy efficiency (EE) and the spectral efficiency (SE). Due to non-convex nature of the original problem, it is It is hard to directly solve it, and thus a concave lower bound on the pricing-based utility is applied to transform the problem into a convex one. Through dual decomposition, a q-price algorithm is proposed for iteratively tightening the lower bound and finding the optimal solution. In addition, an optimal price that enables green power allocation is defined and found from the viewpoint of maximizing EE. Moreover, we also analyze the optimal power allocation strategies of the pricing-based approach in a two-user case under different noise operating regimes, yielding on-off, water-filling, and channel-reversal approaches. We then prolong this idea for two-way relay networks as in fourth part of this dissertation and propose a novel energy-efficient power allocation schemes to improve the EE in multiuser multi-carrier two-way relay networks which are able to not only balance the EE of the two-way links but also ensure the quality-of-service (QoS).
The last part of this dissertation is dedicated to the design of a novel joint source and relay transmit power allocation and energy transfer schemes to maximize the network sum rate within a deadline subject to energy causality and battery storage constraints. The non-convex sum rate optimization problem is transformed into a solvable convex optimization problem using a successive convex approximation for low-complexity (SCALE) algorithm. The effect of node′s battery capacity and energy harvesting profiles on the network sum rate maximization are investigated. |
