5G-EnhanceNewsletter

27th Apri l 2020
Issue 1

Proje ct we bsite : h ttp:/ /5 g -enh a nce .eu/
Pa rtn e rs:- EU re se arch te a m ( VTT, F HG, UO ULU an d
A cce lle ra n )- Ja pan re se arch tea m (T UA T, NI CT, UEC,CA TV , JCTA and RWJ )INSIDE THIS ISSUE

Introduction to 5G-Enhance

1. Introduction to 5GEnhance

2. Advanced radio accesstechnologies for eMBB

3. Conclusion

5G Enhanced Mobile Broadband Access Networks in Crowded
Environments (5G-Enhance) is a Horizon 2020 joint projectcomposed of EU research team (VTT, FHG, UOULU and
Accelleran) and Japan research team (TUAT, NICT, UEC,
CATV, JCTA and RWJ). 5G-Enhance intends to develop andexecute large scale trial activities on actual testbed in EU andJapan.
A key objective for 5G-Enhance is to define and evaluate
interoperable 5G eMBB and efficient network solutions indense area. In order to fulfil this the 5G-Enhance consortium
has identified a number of key project objectives; each ofwhich is associated with a distinct set of research andinnovations. These specific objectives are:

“5G-Enhance develop
and execute large scale
trial activities on actual
testbed in EU and Japan.”Objective 1: Develop, plan and execute large scale trial
activities on actual testbeds between EU and Japan.
Objective 2: Clarify the design and specification required forthe demonstration and trials based on two wirelessapplications with the 5G network.
Objective 3: Development of 5G enhanced mobile broadband
technologies in dense area for achieving the requirements (5G
KPI) of the two applications.
Objective 4: Establish long-term research collaboration
between leading industry players, top research institutes anduniversities in Europe and Japan

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2. Advanced radio accesstechnologies for eMBB
In order to provide enhanced mobile broadband (eMBB) in a highdemand area, the co-existence of macro operator and locally
deployed micro operators is becoming essential. Micro operators are
locally deployed small cell networks in specific areas and tailored forvertical specific service delivery. While traditional mobile network
operators' (MNO) continue to be the major stakeholders deploying 5G
networks similar to prior generations, local 5G micro operators aim atcomplementing the MNO offerings in specific high-demand locations.
The micro operator concept is built on top of the 5G technicalfeatures of dense indoor small cell networks, and the deployment of“We develop dynamic
TDD and multiconnectivity for local
small cell networks.”

such local small cell networks requires spectrum with quality guarantees, see Figure 1. This call for thehighly efficient and flexible radio resource allocation, and interference coordination techniques. 5GEnhance WP3 primarily focus is on developing interference mitigation techniques for dynamic time division
duplexing (TDD) system and multi-connectivity techniques, focusing on local small cell networks.

Figure 1: High-level approach for 5G-Enhance with local 5G micro operators.

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1. Dynamic time division duplexing (TDD)
Small cell systems and dynamic TDD have been gaining traction as possible solutions to the high data rate andlow latency demands of the 5G technologies. They have also been proposed as important research items inthe 3GPP New Radio standardization. As the instantaneous traffic demand between uplink (UL) and downlink(DL) can vary greatly among the cells in small cell systems, dynamic TDD with flexible frame structures is an
obvious solution to provide low latency services with high data rates. However, varying the cells transmit UL
and DL modes can induce difficult interference characteristics, for example UL-DL crosslink interference(CLI), where a user in an uplink cell is interfering with a downlink user in the adjacent cell, see Figure 2.

Figure 2: DL-UL and UL-DL interference scenarios in dynamic TDD system
A. Dynamic UL/DL Mode Selection and Resource Allocation in Multi-Cell MIMO TDD Systems
Previous research, for single antenna systems, in dynamic TDD CLI mitigation via mode selection has mostly
focused on various time-slot allocation algorithms, see [1]– [3] and the references therein. Cell mode selection
problems have also been a focus in device-to-device communications, for example [4], [5]. However, the singleantenna schemes lack the interference mitigation benefits of beamforming.
The literature in beamforming-based interference mitigation is plentiful. Considering the interference
characteristics of TDD systems with fixed UL and DL transmit modes of the cells, the cross-link power leakage
can be mitigated for example via beamformer training schemes, [6], where precoded pilots are applied toiteratively train optimal beamformers. This approach along with the UL/DL mode selection is considered in [7];however, the approach proposed in [7] is myopic and does not consider the long-term statistics of the dynamic
network.

Figure 3: Queue dynamic illustrations

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We model the joint UL/DL mode selection and resource allocation problem as a time-dynamic optimization
problem, see Figure 3. In Figure 3, exogenously arriving UL/DL data is first admitted to a transport layer
queues / , then admitted to network layer queue / according to flow control decisions/
made ineach time slot, where / are binary flow control variables and / denote fixed flow rates. The Lyapunov
drift-plus-penalty framework is used to derive a dynamic control algorithm, to provide a solution for a longterm time-dynamic UL/DL mode selection and resource allocation problem by solving a series of
instantaneous subproblems [13]. The proposed dynamic algorithm decouples the UL/DL mode selection and/ rate allocation (see Figure 3), guided by the accumulating network layer and virtual queues. The
decoupling makes the rate allocation problem a known weighted sum-rate maximization problem under afixed UL/DL mode, which can be solved separately at the BSs using, for example, over-the-air training [6].
Simulation results from a system with 7 BSs and 14 UEs (2 UEs per BS) operating under asymmetric arrival rate
are given below, see Figures 4 and 5. The arrival asymmetry is modeled by setting a subset of cells to haveprimarily downlink arrival, while the rest have primarily uplink arrival. Figure 4 plots the queue backlog withrespect to time for the proposed dynamic algorithm in guided (centralized) form and the decoupled form,
with TDMA as a comparison case. It is evident that TDMA is unable to handle the asymmetric traffic and isunstable, however, our proposed algorithm stabilizes after around 400 time instances.

Figure 4. Network layer queue evolution as a function of time.
Figure 5 shows the network utility and backlog as a function of the design parameter C [13], which can be set
to emphasize either maximizing the utility (high C) or minimizing the backlog (low C). The utility function we
use aims to maximize the flow rate between the transport and network layer queues, while simultaneously
minimizing interference. Setting C high enough, our proposed algorithm performs significantly better than
TDMA in asymmetric traffic scenarios. The TDMA backlog is not included as it is unstable.

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Page 5Figure 5. Top: Objective (or utility) value with respect to design parameter C.
Bottom: Network layer queue backlog with respect to C.
B. Direct Beamformer Estimation for Hybrid Architecture in mmWave Dynamic TDD System
There is growing interest in the exploration of underutilized millimeter wave (mm-wave) frequency spectrum
for the future-generation wireless networks [8], because the available multi-GHz bandwidth in the mm-wave
band is essential to meet very high peak data rate and low latency requirements of the next-generation
wireless networks. Moreover, the small wave-length of mm-wave band signal allows the use of very largeantenna arrays at both the transmitter and receiver, and it can provide high array gain, which is essential tocompensate for the large path loss in the radio channel, and to provide large numbers of spatial degrees of
freedom.

Figure 6: Hybrid architectures: a) fully-onnected, b) partially-connected

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Page 6The use of dynamic TDD in mmWave band can further increase the capacity of communication networks.
However, the mm-wave communication requires a large scale MIMO system to combat the propagation loss,
and the use of fully-digital precoding/combining techniques for mm-wave communication is not practicallyfeasible, because of its huge implementation cost and power consumption. Given such constraints, there is
growing interest in the use of hybrid (analog/digital beamforming) architecture. In fully connected hybridarchitecture, each RF chain is connected to every antenna and in partially connected each RF chain is connected
to set of antennas as shown in figure 6.

Figure 7: analog beamforming toward users; this also minimizes large part of interference due to the main
lobe
Proper selection of analog precoder/combiner for hybrid architecture can suppresses large part ofinterference, and it also ensures that less coordination is required for digital precoder/combiner design. In
designing the analog precoders, users’ position can be exploited, which is also optimal in case of LOS.
Another option is beam sweeping which is considered as an important feature of 5G-NR in mmWave
communications [9]. Aided with the analog beamformers, we design digital precoders/combiners by using
over-the-air (OTA) pilot-aided beamforming design technique [6] in order to maximize the weighted sum-rate(WSR).
Figure 8 shows the average WSR versus signal-to-noise ratio (SNR) for different values of RF chain in LOS
scenario. Fully-digital beamforming method is considered as a benchmark which has same number of RF chain
as compared to number of antennas, thus providing better performance. We can see that both position-based
and beam sweeping for analog precoder/combiner achieve similar performance for various values of RF chain.
This is because in LOS scenario, same set of analog beams are optimal for both position-based and beam
sweeping methods. Moreover, results show that proposed method outperform APA-OTA (APA[14] inconjunction with OTA method) which assumes known channel state information (CSI) to design analog
precoder/combiner. In case of non-LOS scenario, beam sweeping method in designing analog beams
performs better, see Figure 9.

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Figure 8: LOS scenario: average WSR versus SNR

Figure 9: Non-LOS scenario: average WSR versus SNR

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2. Multi-connectivity
Multi-connectivity is essential to fulfil 5G requirements. In a dense deployment, each user equipment (UE)can be connected to multiple cells to enhance its throughput and the reliability and robustness of its
transmission. The selection of the cells must take into account not only the signal-to-interference-plus-noise
ratio (SINR) or the quality of the link in each cell, but also many other parameters, both at cells level and at
network level in order to manage interference, quality of service (QoS) and energy efficiency, for example.
5G Enhance WP3 aims to study the cell selection process as well as traffic management to further enhance
the throughput and QoS for all users.
A. Rate and Reliability Trade-off for mmWaveCommunication via Multi-point Connectivity
Next-generation mm-wave cellular system will enable throughput-intensive and low-latency applications suchas augmented reality and autonomous driving. However, mmWave radio channel is inherently unreliable due
to its susceptibility to blockages, high path and penetration losses. Furthermore, when a finite-size buffer isoverloaded due to subsequent unsuccessful transmissions, new arrival leads to packet drops and results inunreliable communication. To overcome such challenges, use of Coordinated Multi-point (CoMP) schemes,
where each UE is concurrently connected to multiple spatially distributed BS (see Figure 10), are imperative
for more robust and resilient communication, [10],[11],[12].
·To reduce end-to-end latency, a large number of data packets should be transmitted in a given
time slot. To do that, the intuitive way is to send as many data packages as possible.

·

To increase the end-to-end reliability, the network should model SINR over all possible subset
combinations of CoMP set and choose the worst-case estimate to avoid any potential link
blockage during the transmission phase.

Thus, there is a trade-off between achievable latency, rate and reliable connectivity. Here we model thistrade-off by lower bounding the SINR estimate over subset combination of potentially blocked links (i.e., =|ℬ |, ℬ ⊆ ℬ ) [10],[11],[12]. For example, by using smaller subset size combinations of CoMP set, we can
improve the system reliability. However, it will provide lower SINR estimate and hence lower rate andincreased latency to each user. Conversely, larger subset size of CoMP combinations leads to higher SINR
estimate and reduced latency, but system is more susceptible to outage thus resulting less stableconnectivity.

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Page 9Figure 10. Downlink communication system model showing BSs and UEs in the presence of random blockers.
From Figure 11, it can be concluded that system outage performance improves by decreasing the subset sizefor SINR estimate. Clearly, lower provides more stable connectivity and highly reliable communication
with reduced latencies. For example, the specific rate allocation with = 1 can withstand blockage up to asingle active link.

Figure 11. Outage performance as function of increasing blockage probability

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It can be seen form Figure 12 that the system throughput decreases with reduced subset size , when the link
blockage probability , = 0, ∀( , ). This behavior is expected as with smaller , the aggregate SINR estimate
for each user will be relatively lower. On contrary, with increasing , the effective throughput with = 3
quickly approaches zero due to the impact of outage and link blockage during transmission. Clearly, there istrade-off between achievable throughput and stable connectivity. More specifically, for a given , and outage
threshold, we can guarantee minimum achievable system throughput and vice-versa

Figure 12. Effective throughput as a function increasing blockage probability.

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3. Conclusion
One research activity of 5G-Enhance is to develop advanced radio access technologies for the eMBB usage
scenario where multiple local 5G networks are deployed in the same geographic area by different micro
operators. The specific objectives are to define the architecture for 5G-Enhance, specify interference
mitigation techniques for sharing and coexistence between multiple local 5G network deployments, and develop
multi-connectivity techniques. In this newsletters, we introduce dynamic TDD and multi/connectivity research
activities.

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Page 12References
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