# Hybrid Radio Resource Management with Co-scheduling for Relay Extended OFDMA Networks

## Abstract

In orthogonal frequency division multiple access networks buffer aided non-transparent in-band half duplex decode and forward relay nodes aim to improve coverage and capacity under fairness considerations. Existing centralized radio resource management and inter cell interference coordination schemes achieve these goals at the cost of heavy signalling overhead. Especially for frequency division duplex downlink transmission this is an critical issue. Fully decentralized schemes often focus on different types of frequency reuse schemes with less amount of necessary feedback. Here, it is often overseen that in a practical deployment, the backhaul link quality is the bottleneck of the two hop transmission and needs to be taken into account. Moreover, it is often modelled way too optimistic and necessary co-scheduling with single hop UE further limits the possible data rate. In order to minimize the required overhead this work proposes a hybrid radio resource management (RRM) scheme. The RRM includes synchronous adapted two-hop proportional frequency selective resource scheduling as the decentralized part. Asynchronous subband power allocation scheme with very limited feedback is proposed to maximize the wireless backhaul link quality with no loss for single hop UE. Comprehensive system level simulation results show stable fairness and throughput when minimizing the required feedback and improvements for the backhaul links based on the centralized adapted power allocation including no losses in the overall system. In addition possible energy savings for the shared channel are presented when applying the proposed scheme.

## Keywords

Radio resource management Relay Meta-heuristic Co-scheduling CQI feedback LTE advanced pro 5G## 1 Introduction

During the last decade academia and the industry have paid a lot of attention on the improvement of the system capacity of mobile networks. One possibility to satisfy the ever growing data demand and increase capacity is to densify the network, using different kind of small cells also known as heterogeneous networks [1]. Besides pico and femto cells, relay node (RN)s have been introduced to mobile networks, to improve coverage and capacity. The difference with RNs is that they are backhauled by a wireless link and therefore they might be an attractive alternative to wired backhauled pico cells [2] for operators to deploy because of reasonable capital expenditure (CAPEX) and operational expenditures (OPEX). Different kinds of RNs have been designed and standardized in recent years [3, 4] in third generation partnership project (3GPP) and will be further investigated in an upcoming study item defined as integrated access and backhaul (IAB) [5]. In general, such node types introduce a challenging research topic, primarily how to handle and allocate the available radio resources in the most efficient way and thus, how to gain the most from additional nodes in the mobile network. Those relay node (RNs) operating in half duplex mode suffer from a loss in transmission time. Sophisticated means of resource allocation have to be designed to overcome this problem and therefore, to maximize the spectral efficiency of the two hop links. Therefore, different targets for the radio resource management approaches have been identified, such as the maximization of spectral efficiency, fairness, interference mitigation, energy awareness, queueing and quality of service (QoS). Furthermore, the main challenge in designing RRM algorithms for relay extended systems is that typically an optimal solution requires prohibitively exhaustive solution search, with complexity of \(O((MK)^N)\), where M, K and N represent the number of RNs, macro base station (MBS)s, and subchannels, respectively . Various RRM strategies related to the aforementioned targets for all kind of RN types have been intensively studied in the past years. Excellent surveys can be found in the literature summarizing the current state of the art and reveal unsolved challenges [6, 7, 8, 9].

Different types of relay nodes, such as amplify and forward, decode and forward or self-backhauling RNs require different design principles of RRM schemes. It is of paramount importance for the design of RRM algorithms, to consider the underlying system assumptions.

For instance, Lee et al. conclude in [9], after an extensive literature search that, to date it remains a challenging task to design resource allocation schemes for LTE/LTE-A multihop networks with low complexity, while simultaneously excelling in aspects such as interference mitigation, resource utilization and global fairness. A comprehensive classification of RRM schemes for heterogeneous networks is done by the authors. An excellent overview of research problems in heterogeneous networks is given. Most of the considered schemes comes with the burden of heavily increased channel state information (CSI) feedback exchange, which introduces increased control traffic exchange among MBSs and additional signalling on the RN wireless backhaul (BH) links for the RNs [9]. By now, existing work fails to consider all constraints of a practical RN extended system, where only low complexity schemes can be applied not exceeding necessary CSI feedback exchange, especially for FDD downlink transmissions.

For instance, the authors in [10] consider a joint BH and access optimization for a centralized radio access network (C-RAN) time division duplex (TDD) system. They assume to have a system-wide channel knowledge at the central C-RAN control entity. Therefore the MBSs need to send huge amount of collected CSI information to the C-RAN central entity. Besides that, it is assumed that the MBSs only serve RNs, while no MBS user equipment (UE)s are considered in an ultra dense network. Especially in the half duplex mode only UEs with bad supportable rate of the direct link might gain from the two hop links compared to single hop connections. In [11] a cell centralized scheme is proposed which adjusts the time ratio between BH and access links (AL)s to counteract on the different channel states of both. This improves fairness and system throughput. However, they only consider one macro cell and thus do not consider inter MBS interference. They assume full flexibility of switching between transmit and receive time slots which is impractical. Further, they do not adapt the transmission power for BH improvement. Liu et al. propose a joint resource and power allocation optimization for downlink in [12]. They assume that there is no direct transmission between MBS and MSs. Further, they additionally take into account only slowly varying radio channels to receive accurate feedback to be able to calculate a near optimal power and subcarrier allocation for each slot, which is an impractical solution. In [13] a simple resource allocation algorithm is suggested to maximize cell average throughput and cell edge performance. However, no fast fading and therefore perfect channel knowledge is considered. Furthermore, no fairness or energy efficient transmission is considered. Jeon et al. [14] propose a distributed non-cooperative game resource allocation strategy to provide high cell throughput and fulfil minimum data rate. Proportional fair and explicit RN wireless BH improvement is not considered. In [15] the authors analyse practical resource allocation strategies for non-transparent inband relays in an FDD system. The MBS is able to serve the MBS UE and RNs in the same time slots. By varying the number of BH subframes, they show the influence on fairness and downlink throughput. However, no additional interference mitigation or energy efficient strategy is applied. Also no further strategy is introduced to improve the BH spectral efficiency. Wang et al. [16] study fairness and throughput downlink performance based on a designed three step resource allocation scheme. They introduce buffers at the RNs to avoid limitations of the performance because of the quality imbalance of BH and ALs, when no buffers would be applied buffers and therefore, the same transmission rates of BH and RN ALs have to be reached to prevent traffic congestion at the RN. No additional technique in terms of energy efficient or interference mitigation strategy is considered. In [17] low complexity resource partitioning schemes with two-hop proportional fairness are considered. The results show improved RN UE cell edge performance as well as increased average MBS UE performance under fairness improvement. However, the BH and AL match rate is assumed and no additional buffer to further improve the system is considered. Furthermore only the average MBS UE throughput is presented, without information about the CDF of the macro UEs. No adapted power allocation is applied to reduce interference or improve energy consumption. The goal of this work is to develop an RRM scheme which improves the overall performance of a half duplex relay extended OFDMA network, under consideration of all practical limitations of an DL FDD network, by improving the RN BH spectral efficiency (SE) while protecting the performance of the UEs, directly served by MBSs. The considered relay type is a non-transparent relay in half duplex and non cooperative mode, which makes the RN a separated small cell besides the existing MBS cells in the network. The RN has its own scheduling functionality and appears at the donor MBS as an UE. Furthermore, the RN has the ability to queue received data from its donor MBS. The serving station has suboptimal frequency selective channel knowledge based on UE channel quality information (CQI) feedback as defined in [18]. A UE has exclusively orthogonal access to a subchannel within a cell (MBS/RN), which results in no intra cell interference, while full reuse of subchannels among cells is assumed. This introduces inter cell interference but provides full access to the radio resources for each cell.

The target of this contribution is to design a practical low complexity solution which aims to satisfy a combination of several research challenges identified during the past years. On the one hand, in dense RN FDD networks the BH link is often identified as the bottleneck for the downlink. Therefore the main question answered in this work is, if an adapted subband transmission power pattern can improve the BH link spectral efficiency without losing macro UE performance and introducing additional non practical high amount of periodic CSI feedback. On the other hand, fairness needs to be obtained as well as queue-awareness and energy-efficiency needs to be considered. Therefore, the proposed practical low complexity algorithm results in the design of a hierarchical RRM scheme, which consists of the asynchronous centralized part as well as the synchronous decentralized part. The algorithm aims to reduce the interference and used energy for the transmission in the downlink shared channel, while improving the wireless BH rates under fairness and overhead constraints, and therefore resulting in an improved UE throughput.

The paper is organized as follows. In Sect. 2 a practical hierachical RRM approach is defined, followed by the considered system model in Sect. 3. Simulation results are discussed in Sect. 3.2 and concluded in Sect. 4.

## 2 Radio Resource Management Approach

In Figs. 1 and 2 all possible link types which can occur are illustrated.

The proposed RRM scheme which aims to improve the system capacity is separated into two parts. The first step is based on a centralized asynchronous approach, which aims to adapt the transmission powers of all serving stations, MBS as well as RNs, in the network. Both types of TTIs are taken into account, while interdependencies are considered. The average received powers of serving and interfering links need to be measured by the UEs and RNs and fed back to a central optimization entity. As an additional required information, the optimizer needs to know; 1. if the measurement received was performed by an MBS, RN UE or an RN and 2. in which subframe type the measurement was carried out. Once the centralized unit has collected all feedbacks, the optimization can be initiated. The final outcome of this asynchronous procedure consists of a transmission power adaptation for each serving station as well as the total number of physical resource block (PRB)s, where the adapted transmission power patterns will be applied. The process might be repeated in a larger time scale (e.g. hundreds of ms) to prevent additional feedback overhead, caused by the necessary UE measurement reports for the calculation. Once the improved power pattern is derived, no further action is needed by the centralized entity. It is obvious that this might result in suboptimal solution due to the nature of channel variations. However it can be easily applied in real networks due to its low amount of feedback to the centralized unit.

The second part of the process is based on a synchronous adapted scheduling procedure, where in each TTI the available PRBs are allocated to UEs and RNs to serve. This is done based on an adapted two hop proportional fair metric, which takes into account CQI reporting of RN and UEs, past decisions as well as resource allocation control rules, how to use the optimized subbands. Besides subband CQI reporting which needs to be fed back by the RN to its donor MBS for all served UEs, also relaxed different type of reports are considered to ease the amount of signalling overhead, further defined in Sect. 2.2.

### 2.1 Centralized Asynchronous RRM

To find the theoretically optimal power distribution pattern would be quite challenging, due to the huge number of existing combinations. The combinatorial problem cannot be optimally solved during runtime of the system, even if only average received power values are considered. To this end, a heuristic needs to be applied to find a nearly optimal solution. As an example, it is assumed to have two MBSs with additional two RNs in each coverage area. To reduce the number of combinations a limited subset of transmission power reduction values can be defined, which will be applied for AL and BH subframes, respectively. If for instance a set of 10 possible power reduction values are assumed, it already results in a large number of possible combinations, with already 10^{2} possibilities when RNs are in receiving mode multiplied with 10^{4} possible options which already results in total in \(1 \cdot 10^6\) combinations, according to Sect. 1. If a more realistic scenario is assumed with, e.g. 21 MBS plus 42 RNs, it results in an impractical solution space which is non-deterministic polynomial (NP) hard to solve. A near optimal result also cannot be found by an exhaustive search (deterministic approach) and thus, a heuristic approach needs to be applied, which is defined in following. For the asynchronous solution search a genetic algorithm is precisely adapted and applied to the optimization problem. In Fig. 3 the flow chart of the defined genetic algorithm (GA) is shown. The procedure first starts with the initialization of the algorithm. Several parameters, such as the choice of fitness function, mutation rate, number of generations (optimization steps), etc. are set. As an example, a parameter set applied for the conducted simulations can be found in Table 1. After the initialization is done, a randomly chosen first generation is defined. This generation includes a number of individuals which consists of a set of possible transmission power adaptation value (PAV)s for each MBS in the BHSF, the ALSF as well as a PAV for each RN in the ALSF. Based on the measurement reports received from RNs and UEs, as explained in the previous section. Based on the collected information the fitness values (FV)s for all individuals are calculated, as described in the following.

#### 2.1.1 Calculation of Serving and Interfering Links with Power Adaptation Values

*n*for each individual

*i*in the considered subframe type

*s*. The \(\theta\) is defined as the MBS

*m*or the RN

*r*specific PAV, as a scalar of the vector \(P_{pav}\), which consists of the defined possible PAVs. Accordingly, Eqs. 2 and 3 define the sum of the total number of effective interfering links, either for interfering MBSs \({\widetilde{i}}_{MBS}\) or RNs \({\widetilde{i}}_{RN}\), respectively. The sum of the effective interference caused by the RNs (\({\widetilde{i}}_{RN}\)) only needs to be defined for the ALSF, due to reception mode of RNs in the BHSF.

#### 2.1.2 Calculation of Adapted Wideband SINR for Access and BH Subframe

*i*) the resulting effective wideband Signal to Interference plus noise ratio (SINR)s (\({\widetilde{g}}\)) needs to be calculated for all UEs

*n*, based on [20]. Equation 4 defines the wSINRs for all UEs in the ALSF (\(s = 1\)) including the interference (\(\widetilde{\text{i}}_{\text{RN,AL}}\)) caused by the RNs in sending mode. Those are either UEs served by the MBSs (\(N_{UE_{DL}}\)) or UEs attached to the RNs (\(N_{UE_{2H}}\)). The additive average gaussian white noise is considered as \(\sigma\).

#### 2.1.3 Calculation of the Average Spectral Efficiency for Direct and Two Hop Connections

*n*of the individual

*i*in the total set of the generation.

#### 2.1.4 Description of Fitness Value Calculation

*n*is weighted by a scalar \(\omega _{n}\), which represents the number of two hop UE to be served by an individual RN. The higher \(\omega _{n}\) will be, the more important \(\varDelta\)rate becomes for the considered RN BH, because the RN needs to serve more two hop UEs. Second, the sum of the deltas of the rates for all MBS UEs during the BHSF is taken into account. It is additionally weighted by the factor \(\frac{{N_{DL_{m}}}}{N_{2H_{m}}}\) which represents the importance of the sum. The more MBS UEs are attached to the MBS directly, the more important it will be also to serve these UEs and thus, the rates of them are more important. On the other hand if the RNs attached to the MBSs, \(N_{RN_{m}}\) needs to serve more UEs \(N_{2H_{m}}\), the sum will be less prioritized. The next sum represents the value of delta rates for the UEs attached directly to the MBS and possibly served during the ALSF. Here an additional weight takes into account the importance of the sum. If a high number of two hop UEs are needed to be served by the RN attached to the donor MBS the more important the sum of the delta rates in the ALSF becomes to counteract on losses for the directly attached UE in the BHSF. Last but not least, the sum of the RN ALSF is considered as well. This aims not to introduce additional interference caused by the RNs when the AL rate is low and therefore, more PRBs would be needed for transmissions. This becomes the more relevant, the more two hop UE need to be served by RNs. Because of that, the additional weight \(\frac{{N_{2H_{m}}}}{N_{DL_{m}}}\) gives the sum more importance when more two hop UEs have to be served. Furthermore, the term gets less important if a relatively higher number of MBS UEs are attached and need better conditions to compensate possible losses in the BHSF.

Besides that, prohibitive possible outages of UE which might be introduced by the adapted power pattern is taken into account. Therefore, the algorithm checks whether MBS UEs might end up in outage in both possible transmission subframe types. If this is the case, the UE specific \(\varDelta\)rate is set to − 100, which represents the negative impact on the single UE. Therefore, the total FV of the considered individual is decreased and down prioritized in the parent selection process. The same procedure is applied for RNs in the BHSF. Here, RNs which might result in outage will influence even more the FV of the individual, due to an additional weight, which represents the number of two hop UEs attached to the RN.

#### 2.1.5 Generation of the Offspring

*N*.

Once, the parents are determined an one point crossover mechanism is used to create the offspring, as depicted in Fig. 5. Here, two random integer values *x*, *y* are chosen, if the number of MBS does not equal the number of RNs in the system. Otherwise one integer would be sufficient. The first value \(x \; \in M_{MBS}\) is taken out of the total number of the MBS in the system, which equals the number of possible crossover points for the individual part of the MBSs. Second value \(y \; \in N_{RN}\) is used to find a crossover-points for the PAVs of the RNs. The couple of individuals reproduce themselves as depicted in Fig. 5. For the next couples the process is repeated and different crossover points are determined until the total set of offspring PAVs is created. Finally the offspring needs to be slightly changed to not stuck in a local optimum of the search space, when just reproducing the new set of PAVs based on the legacy. Therefore, a small number of values have to be randomly changed, which is defined as the mutation process. The final outcome of the genetic algorithm is very sensitive to the mutation rate. Therefore, a sensitivity analysis has been carried out to adjust the mutation rate to result in the best possible solution. Figure 6 gives the results of the performed sensitivity analysis. The illustrated adjustment of the mutation rate was done based on Eq. 12 with different percentage of mutation rate. For each generation only the maximum FVs are depicted. It can be observed, that the best found mutation rate was 0.05 (doted black curve), which means 5% of the total number of PAVs of the generation were mutated. All other tested mutation rates between 1 and 10% didn’t result in a higher maximum FV.

#### 2.1.6 Description of the Final Outcome of the Optimization

Furthermore, Fig. 8 gives an impression how a configuration of the optimized subbands could look like in time domain. As already explained in Sect. 2.1.4 the GA derives a percentage of subbands, based on the number of UEs and RNs which gain from the configuration. Figure 9 illustrates how a configuration could look like in frequency domain. Here, as an example, half of the total system bandwidth is used with the derived optimized power pattern, while the other half is transmitted with the default power settings of each serving node, to schedule UEs which have decreased rates through the applied power pattern.

### 2.2 Decentralized Synchronous Adapted Scheduling

*P*for each one hop UE

*k*on every PRB

*n*in TTI

*i*are calculated.

*R*is defined as the instantaneous supportable rate, depending on the latest received UE CQI reports on an \(m_{\text{th}}\) subband. The parameter \(\alpha\) is an exponential scaling factor which is set to 1. The average throughput

*T*is recursively updated, as defined in Eq. 18.

*T*of the unscheduled UEs/RNs is multiplied with a forgetting factor \(\beta\). Scheduled UEs’

*T*is updated by multiplying with \(\beta\) and adding the instantaneous data rate of the current TTI with a weight of \(1-\beta\). Based on the calculated priority matrix the PRB allocation is done by taking the UE

*U*with the maximum priority

*P*for each individual PRB, defined in Eq. 19.

*R*of Eq. 17 for all two hop UE

*j*, served by the considered RN, based on [22]. The specific two hop connection takes into account both, the instantaneous rates

*R*of the BHSF and the ALSF. The harmonic mean of both hops (

*L*) considers the loss in time of the transmission, as well. Finally, two priorities are calculated with the throughput

*T*of the AL, \(T_{AL}\) and the BH link \(T_{BH}\). The maximum is used as a priority of the two hop UEs. In most of the cases the priority of the AL might be used, because the UE individual past throughput might be equal or less then the total past throughput of the BH link.

#### 2.2.1 Overhead Consideration and Resource Allocation for Different Types of Channel Quality Feedback

When frequency selective scheduling is applied, typically subband CQI reports are used to gain knowledge of the channel states and derive a resource allocation decision with better resource utilization. Typically, in conventional networks the UE takes measurements and send back the report to the MBS where the MAC scheduler uses the reports to take a decision. In RN extended networks, it is additionally necessary to feedback the reports from RN to MBS for all UEs attached to the RN. This can heavily increase the additional signalling overhead in the uplink (UL) and influence the performance. Besides that, the control signalling overhead is already increased, as different subframe types occur and the MBS needs accurate information of both shadow fading (SF) types. For instance, if periodic CQI reporting is assumed, the UE needs to generate at least two CQI reports per frame, one for each occurring SF type, where different interference situations appear and additionally different optimized power patterns are applied. The reports consist of a defined number of subband reports including MCS recommendation among other information. The reports of all two-hop UEs need to be collected and sent back to the MBS. To reduce this potential large amount of overhead, different types of reports with reduced amount of overhead are compared in this study. The reference case assumes a full subband report for frequency selective decisions as input for Eq. 20. Alternative RN to MBS feedbacks with a potential overhead reduction of are used and compared in terms of throughput, SINR and fairness. Instead of frequency selective subband CQI reports either the recommended maximum, average supportable rate derived out of the CQI reports for the RN access link is fed back to the MBS. This reduces the amount of signalling by \(\frac{{1}}{N}\), where N is the defined number of subband reports per UE. For further clarification it should be kept in mind, that the actual scheduling decision at the RN is still done in a frequency selective manner based on RN UEs’ CQI feedback to the RNs.

#### 2.2.2 Scheduling Strategies for Optimized Subbands

As already mentioned, different scheduling strategies can be applied, how the optimized subbands can be allocated to UE. Advantages and drawbacks of different strategies are discussed in the following and compared by means of SLS in Sect. 3.

Scheduling strategy 1:

Only UE with previously calculated chance to gain from optimized subband are allowed to use such resources, based on UE specific individual positive delta of the rates. If no UE with potential benefit is attached to MBS or RNs the resources will be left unallocated. This results in a better SINR in neighbouring cells on the one hand, but higher loss in terms of unallocated radio resources on the other.

Scheduling strategy 2:

UE with negative \(\varDelta\) rates can potentially also make use of the optimized subband. Dependent on the adapted two hop proportional fair, those UE will be less prioritized on those PRBs. For a single UE it is possible to allocate the total amount of PRBs independent if transmitted with optimized or default power. This results in a higher variation of SINR values for a single UE transmission and thus in potentially less efficient link adaptation.

Scheduling strategy 3:

UE with less rate can also make use of the optimized subband. Dependent on the adapted two hop proportional fair, those UE will be less prioritized. For a single UE it is only possible to allocate the optimized or the default subband.

## 3 System Level Simulations

In this section, the performance analysis of the hybrid RRM scheme is presented. First, the most important simulation assumptions are summarized, followed by the results.

### 3.1 Assumptions

Comparable results as a reference can be found in [26] based on the agreed assumptions in the 3GPP [27]. Typical performance gains by introducing relays with decentralized scheduling schemes are approximately an average user throughput of arround 20% when 4 RNs per sector are deployed in an urban or sub-urban scenario [28]. However, one of the major contributions of this work is to improve the backhaul link quality of the deployed RNs without the deployment of an additional receiving antenna to save additonal deployment costs and planning effort. This assumption makes the scenario more challenging and also difficult to compare with other existing results in the literature. In addition, the simulation results are quite sensitive and dependent on their underlying assumptions. In a realistic urban scenario an additional directed receiving antenna requires LoS connection to the donor MBS. Otherwise, the SINR might be decreased in an unwanted manner and possible theoretical gains are not possible due to a received signal with a high delay spread [29, 30]. This might be difficult to achieve in a realistic scenario due to limited possible RN site locations in the vicinity of a user hotspot, when RNs are deployed to increase the capacity of the network. Furthermore, possible deployment heights are limited as well in realistic scenarios. This typically decreases the LoS probability as well in the applied PL models and is often overseen in existing literature. For instance in [31] an additional RN antenna gain of 13 dBi and an RN antenna height of 15 m is assumed. In [13] an additional directional receive antenna with 7 dBi antenna gain is assumed. Nearly comparable results can be found in [15, 17, 32], with slightly deviating assumptions in terms of the deployed scenario and the considered number of users. In principle the centralized part of the proposed hybrid scheme could be applied in the considered scenarios and an additional gain could be achieved when using the solutions in a combined manner. In Fig. 15 the final result is shown of the proposed solution. In the first step a macro cell network is compared with a RN extended system. As stated before an additional gain of 18% could be reached in the average overall user throughput under fairness constraints comparable with the achieved gains in [26, 28]. When the centralized part of the hybrid RRM scheme is enabled an additional average gain of 10% could be observed. This makes it an attractive solution as no directed antennas have been applied in receiving or sending direction at the RNs. In a nutshell the chosen scenario in this work is one of the most realistic but also one of the most challenging ones to outperform the conventional MBS network and increase the performance by the hybrid RRM approach in RN extended networks. Furthermore, realistic non prohibitive low amount of feedback overhead is assumed, which limits the possible gains of the approach but shows trustworthy results. This is also often assumed way too optimistic in the existing literature, as summarized in Sect. 1.

Summary GA parameters

Parameter | Value |
---|---|

Fitness function type | Equation 12 |

Number of generations | 400 |

Number of individuals | 160 |

Parental selection | Fitness proportionate |

Crossover inheritance type | Random one point |

Mutation rate | 0.05 |

TX power adaptation values (PAV) | [− 46, − 40, − 30, − 20, − 10, − 6, − 5, − 4, − 3, − 2, − 1, 0] dB |

A comparison of the resulting UE throughput, SINR, fairness and potential energy savings on the DL physical shared channel is described in the following.

### 3.2 Results

In the reference simulation a conventional MBS network is considered. In the next step, the scheduling strategy is based on the decentralized adapted two hop proportional fair metric, defined in Sect. 2.2, while the centralized heuristic algorithm is disabled. To reduce RN signalling to the MBS a comparison is carried out for the different aforementioned signalling information. Finally the centralized algorithm is additionally enabled based on the defined fitness functions based on Eqs. 11 and 12, described in detail in Sect. 2.1 and further defined as o1 and o2.

#### 3.2.1 Comparison with Different Two Hop UE Feedbacks Forwarded from RN to MBS

#### 3.2.2 Throughput Comparison with Different Fitness Functions and Scheduling Policies

#### 3.2.3 Final Comparison for Best Feedback, Power Reduction Pattern and Scheduling Settings

#### 3.2.4 SINR Comparison for Best Settings

#### 3.2.5 Fairness Comparison and Energy Savings for Best Settings

The fairness index according to the 3GPP criteria is defined in [27]. It is defined as the normalized UE throughput CDF with respect to the average. If it proceeds on the right side of the identity function the system performance fulfils a fair throughput distribution among UE. The limit defines a linear relation between the UE throughput and the probability to experience a certain throughput. For instance 50% of the time the UE should perceive 50% or less of the average UE throughput. In Fig 18a the fairness evaluation of the investigated system is presented. Compared to the reference network (light grey) the RN extended network has a slightly higher variance of throughput values, since it is a bit flatter. All example RN network settings shown here fulfil the fairness requirement of 3GPP.

## 4 Conclusions

Existing resource allocation schemes for RN extended networks fail to combine multiple targets. The hybrid scheme is separated in the decentralized part to allocate the resources in a two hop proportional frequency selective manner with co-scheduling of UEs and RNs. In the centralized part adapted subband power allocation is improved by reducing the transmission power. The scheme unites multiple objectives under detailed modelling of the wireless BH link. While improving the spectral efficiency of the BH link under fairness constraints and the limitation of possible co-scheduled single hop transmissions, it reduces the interference in the system by only reducing the transmission power on the shared channel. For the centralized part only very limited feedback is required, while for the decentralized part the necessary feedback on two hop access link is minimized. Simulation results show performance gains with stable fairness and savings in energy consumption.

## Notes

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