Performance of Massive MIMO Self-Backhauling for Ultra-Dense Small Cell Deployments
Summary (3 min read)
- The integration of radio access and backhauling – advocated in s-BH solutions – has been addressed by the Third Generation Partnership Project (3GPP) with a list of requirements detailed in .
- The UEs benefit from more radio resources allocated and from the SCs proximity, given by the higher probability of the UEs to be close to – and in LOS with – the serving SCs.
- Indeed, when compared to mMIMO DA solutions with pilot reuse 3 and reuse 1, ultra-dense SCs deployments supported by mMIMO s-BH provide rate improvements for cell-edge UEs that amount to 30% and a tenfold gain, respectively.
- Capital and lower-case bold letters denote matrices and vectors, respectively, while [·]∗, [·]T and [·], also known as Notation.
II. SYSTEM MODEL
- The mMIMO-BSs are connected to the core network through a high-capacity wired connection, while all SCs receive backhaul traffic through mMIMO-BSs and function as access points for UEs.
- The authors consider a self-backhaul configuration, where mMIMO-BSs are solely dedicated for the backhaul, while SCs are solely dedicated for the access.
- For comparison purposes, the authors also consider the conventional DA approach where each mMIMO-BS directly serves the UEs.
A. Macro cell and user topologies
- Furthermore, the authors denote by Ki the number of UEs randomly and uniformly distributed over the sector’s area, and let k denotes single-antenna UE.
- The authors assume that each UE is connected with the SC (in the s-BH approach) or with the mMIMO-BS (in the DA approach) that provides the largest reference signal received power (RSRP) .
B. Small cell topologies
- The authors denote by Li the set of SCs deployed per sector and connected to the i-th mMIMO-BS that provides the largest RSRP.
- This scenario is used as a baseline and follows the set of parameters specified by the 3GPP in  to evaluate the relay scenario.
- Self-backhauled SCs are positioned targeting nearby UE locations, also known as (b) Ad-hoc deployment.
- It is worth noting that even when the 2-D distance d = 0, UEs and SCs are still separated in space because the antennas are positioned at different heights.
C. Frame structure
- As shown in Fig. 2a, the authors consider the time-slot T as a single scheduling unit in the time domain, and they partition the access and backhauling resources through the parameter α ∈ [0, 1].
- Therefore, α time-slots are allocated to the backhaul links, while 1−α time-slots are allocated to the access links.
- This approach entails that each SC equally shares the system bandwidth B with its UEs.
- In each time slot, the mMIMO BSs precode the access signals, and the UEs are spatially multiplexed on the entire system bandwidth.
- Details about the channel training procedure will be discussed in Section III.
D. Channel model
- Since all the RBs are assigned to each SC, the authors removed the RB index q from the massive MIMO channel notation.
- C the single-input singleoutput (SISO) channel between the l-th SC and the k-th UE in the q-th RB.
- Because of its slow-varying characteristic, it does not change rapidly with time, and it can be assumed constant over the observation time-scale of the network.
- Throughout the paper, the authors assume a composite fading (i.e. large scale fading and small scale fading together) for the SC-UE and the mMIMO-BS-UE links (in the DA approach), which changes between successive time-slots and between different RBs.
- The authors describe the channel training procedure, the mMIMO DL backhaul transmission, and the DL access transmission, which is treated separately below for both the s-BH and the mMIMO DA setups.
A. Massive MIMO channel training
- To calculate the DL precoder, the authors consider that the channel is estimated through uplink (UL) pilots, assuming UL/DL channel reciprocity .
- Hi denote the channel between the i-th mMIMOBS and the UEs located in the same sector.
- Let us define P ⊆ I as the subset of sectors, whose UEs share identical pilot sequences with the UEs served by the i-th mMIMO-BS.
- The use of the same set of orthogonal pilot sequences among different sectors leads to the well-known pilot contamination problem, which can severely degrade the performance of mMIMO systems , .
- Two pilot allocation schemes are here compared: Pilot reuse 1 scheme (R1): All Ki UEs per sector are trained in τ = 1 OFDM symbol.
C. Small cell DL transmission
- The authors recall from the channel model that glkq denotes the SISO channel between the l-th SC and the k-th UE corresponding to the q-th RB.
- The authors assume that the backhaul capacity is equally divided between the Kl UEs served by the l-th SC.2.
D. Massive MIMO direct access transmission
- In contrast to s-BH setups, mMIMO systems providing DA dedicate all their time resources to DL data transmission.
- Ki between the i-th mMIMO-BS and its connected UEs is plugged into (3), to subsequently derive (4) and (9).
IV. NUMERICAL RESULTS
- To realistically evaluate the mMIMO s-BH network performance, in this paper, the authors adopt the methodology described by 3GPP in  for heterogeneous network.
- The authors perform system level simulations accounting for all signal and interfering radio links between each SC and the UEs, as well as between each mMIMO-BS and all SCs.
- Subsequently, the authors measure the performance in terms of cumulative distribution function (CDF) of the end-to-end UE rate (8).
- To compare s-BH against DA, the authors also simulate the links between mMIMO-BSs and UEs, and compute the resultant rates (9).
- Table I contains the relevant parameters used to conduct the simulation campaign.
A. Small cell random and ad-hoc deployments with mMIMO s-BH
- In Fig. 3, the authors assume α = 0.5, and analyze the results for the two SC topologies described in Sec. II-B, namely the ad-hoc and random SC deployments.
- The gains provided by more radio resources and proximity are outweighed by the detrimental impact of interference, and from the curves shown in Fig. 3, the authors can see that the end-toend UE rates increase marginally when doubling the number of SCs deployed.
- The performance enhancement is caused by two complementary effects: i) the signal improvements provided by the larger antenna gain of the directive Yagi, and ii) the reduced interference created towards neighboring UEs served by other SCs.
- In fact, assuming that the network uses α = 0.85, which is the optimal value for cell-edge UEs (5-th percentile of the CDF), the median UEs (50-th percentile of the CDF) can achieve an end-to-end rate of 19.5 Mbps, which represents a 16% reduction with respect to the maximum end-to-end rate achievable of 23.3 Mbps.
- A more detailed comparison is further developed in the next section.
C. Comparison between DA and s-BH systems
- From Fig. 5, the authors identify two different regions: .
- This is because pilot contamination severely degrades the rate of UEs at the cell edge in the mMIMO DA setup with pilot reuse 1.
- S-BH architecture works better because: 1) access links benefit from the UE-to-SC proximity, which reduces the path loss and improve the LOS propagation condition, and 2) backhaul links benefit from the absence of pilot contamination, and the higher height of the SC compared to the UE.
- The latter leads to an improved path-loss and LOS conditions with respect to those modelled for the macro to UE link .
- At the top of the CDF, i.e. over the 50-th percentile, the mMIMO DA architecture exceeds the performance of sBH mMIMO.
- The authors analyzed the performance of the mMIMO based s-BH architecture below 6 GHz frequencies.
- The authors adopted a system-level simulation approach to investigate the UE rate performance for different SC deployments, and to analyze the effect of the variation of the backhaul/access partition.
- H. H. Yang et al., “Energy-efficient design of MIMO heterogeneous networks with wireless backhaul,” IEEE Trans.
- Y. G. Lim et al., “Performance analysis of massive MIMO for cellboundary users,” IEEE Trans.
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Q1. What contributions have the authors mentioned in the paper "Performance of massive mimo self-backhauling for ultra-dense small cell deployments" ?
A key aspect of the fifth-generation wireless communication network will be the integration of different services and technologies to provide seamless connectivity. In this paper, the authors consider using massive multiple-input multiple-output ( mMIMO ) to provide backhaul links to a dense deployment of self-backhauling ( s-BH ) small cells ( SCs ) that provide cellular access within the same spectrum resources of the backhaul. Through a comprehensive system-level simulation study, the authors evaluate the interplay between access and backhaul and the resulting end-to-end user rates. Moreover, the authors analyze the impact of different SCs deployment strategies, while varying the time resource allocation between radio access and backhaul links. The results show that dense SCs deployments supported by mMIMO s-BH provide significant rate improvements for cell-edge users ( UEs ) in ultradense deployments with respect to mMIMO DA, while the latter outperforms mMIMO s-BH from the median UEs ’ standpoint.