Abdallah Bou Saleh

On the coverage extension and capacity enhancement of inband relay deployments in LTE-advanced networks

Decode-and-forward relaying is a promising enhancement to existing radio access networks and is currently being standardized in 3GPP to be part of the LTE-Advanced release 10. Two inband operation modes of relay nodes are to be supported, namely Type 1 and Type 1b. Relay nodes promise to offer considerable gain for system capacity or coverage depending on the deployment prioritization. However, the performance of relays, as any other radio access point, significantly depends on the propagation characteristics of the deployment environment. Hence, in this paper, we investigate the performance of Type 1 and Type 1b inband relaying within the LTE-Advanced framework in different propagation scenarios in terms of both coverage extension capabilities and capacity enhancements. A comparison between Type 1 and Type 1b relay nodes is as well presented to study the effect of the relaying overhead on the system performance in inband relay node deployments. System level simulations show that Type 1 and Type 1b inband relay deployments offer low to very high gains depending on the deployment environment. As well, it is shown that the effect of the relaying overhead is minimal on coverage extension whereas it is more evident on system throughput.

Comparison of Relay and Pico eNB Deployments in LTE-Advanced

Relaying is one of the most important novel elements in 3GPP LTE-Advanced study item. It promises to offer significant gain for system capacity or coverage depending on the deployment prioritization. In this paper, we investigate the feasibility of relay node deployments in terms of system throughput and cell coverage area extension as compared to pico node deployments and traditional homogeneous single-hop macro cells. Relay backhaul link overhead is taken into consideration as a limiting factor in a relay deployment; nevertheless, results show that its effect on coverage extension is in most cases negligible. While the effect of relay backhaul overhead is small in coverage limited scenarios, the limitations on throughput due to relaying are evident, in particular, for 500m ISD scenarios. This study also demonstrates that both relay and pico node deployments outperform clearly traditional macro cell deployment in terms of coverage and network capacity.

Performance of Amplify-and-Forward and Decode-and-Forward Relays in LTE-Advanced

Current broadband wireless networks are characterized by large cell sizes. Yet, even in advanced networks, users on the cell edge will face relatively low Signal-to- Interference-plus-Noise-Ratio (SINR). An attractive solution for this problem is provided by multi-hop technologies. In this paper, we consider the performance of full duplex Amplify-and- Forward (AF) and half duplex Decode-and-Forward (DF) Relay Nodes (RNs) from 3G LTE-Advanced perspective. The comparison between AF and DF relaying is important because both approaches are currently under consideration in LTE- Advanced study item in 3GPP. Performance evaluation considers AF RN loop back signal interference and concurrent DF RN transmissions on the access link. Results show that the concurrent transmissions improve the spectral efficiency for DF RN over performing AF RN.

Enhancing LTE-advanced relay deployments via Biasing in cell selection and handover decision

Decode-and-forward relaying is a promising enhancement to current radio access networks and is now being standardized in the 3GPP work item on LTE-Advanced. Relay enhanced networks are expected to fulfill the demanding coverage and capacity requirements in a cost-efficient way. However, due to the low transmit power characteristic of relay nodes, their coverage areas will be relatively small in the overlaying macro cell. Therefore, the performance of relay deployments may be limited by load imbalances, unless system parameters are properly selected. In this paper, we propose a practical solution for this problem by introducing a bias to cell selection and handover thresholds. By applying the bias, the relay cells can be extended and an appropriate load balance can be achieved. Via a comprehensive system level simulation campaign, it is shown that biasing results in significant user throughput improvements in both the uplink and the downlink.

Analysis of the Impact of Site Planning on the Performance of Relay Deployments

Network planning tools are routinely used by operators to improve the system performance and to provide a satisfactory service with minimal deployment expenditure. In this context, the deployment flexibility of relay nodes, which mainly stems from the wireless backhaul link, compact physical characteristics, and low power consumption, can be exploited to enhance the system performance. In this paper, we model and analyze two simple approaches that can be used for the planning of two-hop cellular relay networks. The aim of the so-called relay location selection and serving cell selection approaches is to enhance system performance by improving the quality of the wireless relay backhaul link. Location selection takes into account the shadowing properties at the different possible relay node deployment locations, considering only the link quality toward the serving base station. On the other hand, cell selection considers the case where a relay node performs cell reselection from a severely shadowed serving base station to a neighboring cell that is less shadowed. A simple model for evaluating and analyzing the impact of both network planning techniques on the system performance of relay deployments is given. In addition, we present the analytical framework through closed-form derivations of the signal-to-interference ratio (SIR) and end-to-end rate distributions. Performance evaluations that investigate the impact of site planning on the quality of the relay backhaul link, end-to-end rate, resource allocation on the two hops, upper bounds on planning gains, access-link limitations, and the deployment of multiple relay nodes are carried out. Results show significant improvements, which justify the need for relay site planning in relay enhanced networks.

Resource sharing in LTE-Advanced relay networks: uplink system performance analysis

Relay-enhanced networks are expected to fulfil the demanding coverage and capacity requirements in a cost-efficient way. Type 1 inband relaying has been standardised as an integral part of the Third Generation Partnership Project (3GPP) Long-Term Evolution Release 10 and beyond (LTE-Advanced). This type of relay nodes (RNs) supports a relaying mode where the RN to donor evolved node B (donor eNB, DeNB) link (relay link, a.k.a. backhaul link) transmission is time-division multiplexed with the RN-served user equipments (RUEs) to RN link (access link) transmission, whereas macrocell-served user equipments (MUEs) share the same resources with the RNs at DeNB. Hence, system performance depends strongly on the resource sharing strategy among and within the links. Further, the set of subframes assigned for the relay link transmission is semi-statically configured and thus a dynamic reconfiguration to adapt to fast-changing system conditions (e.g. RN cell load) is not viable. Besides, in order to fully exploit the benefits of relaying, the inter-cell interference, which is increased because of the presence of RNs, should be limited via a proper power control (PC) scheme on each link. Therefore, an optimisation of both the resource sharing and PC strategy is required to enhance the overall performance of relay networks. In order to tackle these issues, we employ a statistic-based over-provisioned backhaul subframe allocation to be utilised for flexible co-scheduling of RNs and MUEs at the DeNB. In addition, we propose a combination of RN scheduling based on the number of RUEs and user throughput throttling achieving max–min fairness. Performance analysis of various resource sharing techniques along with PC optimisation is then carried out within the LTE-Advanced uplink framework in urban and suburban scenarios. Comprehensive results show that the proposed schemes achieve significant throughput gains and high system fairness with substantially increased flexibility in resource allocation. Copyright

Performance Analysis of Relay Site Planning Over Composite Fading/Shadowing Channels With Cochannel Interference

The performance of relay deployments depends significantly on the capacity of the wireless relay link between a relay node (RN) and its serving base station (BS). Exploiting the deployment flexibility of RNs, relay site planning (RSP) can be utilized to overcome the limitations of the relay link. In particular, RSP is carried out by selecting RN deployment locations from a discrete set of alternatives considering the signal-to-interference-plus-noise ratio (SINR) on the relay link as the selection criterion. In this paper, we present an analytical framework for RSP that can be used for planning and dimensioning of two-hop cellular relay networks operating over composite fading/shadowing channels in the presence of cochannel interference. Nakagami–lognormal distribution is used to model the relay link, whereas the access link between a mobile terminal (MT) and its serving RN is modeled by Rician–lognormal distribution. As these composite models do not have closed-form distribution functions, we utilize mixture gamma (MG) distribution to accurately approximate them. Further, the total cochannel interference in the considered multicellular system is approximated using the moment-generating function (MGF) matching method. Accordingly, we present closed-form expressions for the distributions of relay-link SINR, link rates, and end-to-end rate. In addition, RSP is shown to effectively decrease the amount of fading (AoF) and, thus, mitigate the detrimental effects of composite fading/shadowing. Thorough results reveal significant performance improvements, which justify the use of RSP in cellular relay networks.

Flexible Backhaul Resource Sharing and Uplink Power Control Optimization in LTE-Advanced Relay Networks

Type 1 inband relaying, as being standardized in 3GPP LTE-Advanced, supports a relaying mode where the backhaul link transmission is time-division multiplexed with the access link transmission, whereas macro users share the same resources with the relays. Hence, system performance depends strongly on the resource sharing and allocation strategy among and within the different links. Moreover, the set of subframes assigned for the backhaul transmission is semi-statically configured and thus a dynamic reconfiguration to adapt to fast-changing system conditions, e.g. relay cell load, is not viable. In order to tackle the above issues, we propose a statistic-based over-provisioned backhaul subframe allocation to be utilized for flexible co scheduling of relays and macro users at the donor eNB. In addition, a power control optimization is investigated to enhance overall system performance and to reduce the donor eNB receiver dynamic range. Performance evaluation is carried out within the LTE Advanced uplink context in urban and suburban scenarios. Results as well show that the proposed scheme achieves significant throughput gains.

Evaluating the energy efficiency of LTE-Advanced relay and Picocell deployments

As wireless communications experience tremendous growth, energy consumption is becoming a crucial problem in the wireless industry. Energy saving mechanisms are important for supporting the mobility and extending the battery life of a user equipment, reducing the operation costs of a network operator, enhancing corporate image of operators and vendors alike, and providing a lower total energy consumption which reflects in a lower environmental impact. Herein, we evaluate the energy efficiency of different small node deployments, namely relay nodes and Picocells, within the 3GPP LTE-Advanced framework. The energy efficiency of small node deployments is considered on both the uplink and downlink of 3GPP-defined urban and suburban models. In specific, the work investigates the impact of deploying different numbers of small nodes on reducing area power consumption, or alternatively, on enhancing the throughput power consumption of access networks.

Automated uplink power control optimization in LTE-Advanced relay networks

Relaying is standardized in 3rd Generation Partnership Project (3GPP) Long-Term Evolution (LTE)-Advanced Release 10 as a promising cost-efficient enhancement to existing radio access networks. Relay deployments promise to alleviate the limitations of conventional macrocell-only networks such as poor indoor penetration and coverage holes. However, to fully exploit the benefits of relaying, power control (PC) in the uplink should be readdressed. In this context, PC optimization should jointly be performed on all links, i.e., on the donor-evolved Node B (DeNB)-relay node (RN), the DeNB-user equipment (UE) link, and the RN–UE link. This ensures proper management of interference in the network besides attaining a receiver dynamic range which ensures the orthogonality of the single-carrier frequency-division multiple access (SC-FDMA) system. In this article, we propose an automated PC optimization scheme which jointly tunes PC parameters in relay deployments. The automated PC optimization can be based on either Taguchi’s method or a meta-heuristic optimization technique such as simulated annealing. To attain a more homogeneous user experience, the automated PC optimization scheme applies novel performance metrics which can be adapted according to the operator’s requirements. Moreover, the performance of the proposed scheme is compared with a reference study that assumes a scenario-specific manual learn-by-experience optimization. The evaluation of the optimization methods within the LTE-Advanced uplink framework is carried out in 3GPP-defined urban and suburban propagation scenarios by applying the standardized LTE Release 8 PC scheme. Comprehensive results show that the proposed automated PC optimization can provide similar performance compared to the reference manual optimization without requiring direct human intervention during the optimization process. Furthermore, various trade-offs can easily be achieved; thanks to the new performance metrics.