4.1 Downlink Interference Control of LAA-LTE for Coexistence with Asym-
4.1.7 Discussion
4.1.7.1 Convergence and Computational Complexity
and ii) the power allocation module in (65). Since the final converted problems of (64) and (65) for given AHAP suppression set, i.e.,Bi in (64) andBˆin (65), are convex problems, converged solutions can be easily obtained by well-studied convex programmings such as the SDP. If there exist no feasible sets of Bi and B, the proposed scheme simply allocates equal power for eachˆ subcarrier. Therefore, the proposed scheme always finds a solution.
Computational complexity in flops: the problem of each module is solved via SDP, which is known to be a powerful mathematical method for large-dimensional problems due to its
(a) 3 APs
(b) 5 APs
(c) 10 APs
Figure 42: Percentage of the number of searches performed in the proposed AHAP selection algorithm for various scenarios.
relatively low computational complexity and fast convergence. In general, the SDP has polyno- mial worst-case complexity [70] in flops with respect to the variable dimension. For instance, the interior-point SDP with rank-1 relaxation has the average complexity of less thanO(t2), wheret is the variable size [71]. Thus, the main factor determining the overall computational complexity is the number of brute-force searches in the AHAP selection module, i.e., the number ofBicases considered to find a feasible one. Fig. 42 shows the percentage of the number of searches on the AHAP combination list under 3 APs, 5 APs, and 10 APs deployment setups. The results present that the proposed AHAP selection algorithm ends in a single search for the 3 APs and 5 APs deployment, i.e., all the AHAPs can be suppressed. On the other hand, it ends in five searches for the 10 APs deployment. In practice, fewer than 10 APs would share a single channel (not the whole bandwidth), since there are abundant channels in the 5 GHz unlicensed spectrum [72].
Therefore, it is confirmed that the computational complexity of the proposed algorithm stays at polynomial-time complexity in worst-case and much lower complexity on average, which is affordable in practical systems with less than 10 APs sharing the same shared channel.
0 0.5 1 1.5 2 2.5 3 ED margin [dB]
1.5 2 2.5
Throughput [Mbps]
AP1 AP2 AP3
(a)
0 0.5 1 1.5 2 2.5 3
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10 20 30 40
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WLAN's throughput eNB's throughput WLAN + eNB
(b)
Figure 43: Throughput for each ED margin in Location 1 of 3 APs deployment 4.1.7.2 Trade off between the throughput of EAPs and AHAPs A trade-off between the throughput of EAPs and AHAPs can be obtained by controlling ΓED in (15). Specifically, we consider the following modified inequalities which can replace (62c) and (62d):
Jα ≥ΓED+4ED, ∀α∈ AE, (66)
(1−cα)·Jα ≥(1−cα)·(ΓED+4ED), ∀α∈ BAH, (67) where4ED ≥0is a non-negative ED margin.
Three APs Deployment: Fig. 43(a) shows the throughput of each AP with respect to 4ED. Fig. 43(b) presents the throughput of an eNB and WLAN and the sum-throughput of the eNB and WLAN. Note that the result for4ED= 0 is obtained by the original proposed scheme.
The deployment ‘Location 1’ of the three APs deployment in Section 4.2.5 was used for the simulations. In the simulations over time, it was observed that AP1 was an EAP at all time while the other APs turned into EAPs or AHAPs over time depending on channel condition. As4ED increases, the throughput of EAPs is increased owing to more certain collision avoidance from (66). In Fig. 43(a), for increasing 4ED, the throughput of AP1 tends to increase, as expected.
On the other hand, the constraint (67) becomes more stringent to satisfy as4ED increases, and hence we may end up with a feasible AHAP suppression set, i.e., Bi (17) or Bˆ(18), with very small cardinality; that is, only a few AHAPs can be suppressed. Due to this weak collision avoidance for AHAPs, the throughput of AHAPs is expected to decrease for increasing 4ED. Fig. 43(a) shows that the throughput of other AHAPs tends to decrease, since they were in the status of an AHAP in many time slots.
Interestingly, the WLAN’s total throughput remains almost the same as seen in Fig. 43(b).
On the other hand, the eNB’s throughput decreases gradually for increasing 4ED. Therefore, the sum-throughput of the eNB and WLAN is rather decreased by introducing positive ED margin.
Fig. 44 is obtained under ‘Location 4’ of the 3 APs deployment in Section 4.2.5. In this deployment, AP1 and AP2 were observed to be EAPs, and AP3 turned into an EAP or AHAP
0 0.5 1 1.5 2 2.5 3 ED margin [dB]
2 3 4
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(a)
0 0.5 1 1.5 2 2.5 3
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10 20 30 40 50
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WLAN's throughput eNB's throughput WLAN + eNB
(b)
Figure 44: Throughput for each ED margin in Location 4 of 3 APs deployment
0 0.5 1 1.5 2 2.5 3
ED margin [dB]
0 2 4 6 8
Throughput [Mbps]
AP1 AP2
AP3 AP4
AP5
(a)
1 2 3 4 5 6 7
ED margin 20
40 60 80
Throughput [Mbps]
WLAN's throughput eNB's throughput WLAN + eNB
(b)
Figure 45: Throughput for each ED margin in 5 APs deployment
depending on channel condition. Fig. 44 shows a similar tendency compared to Fig. 43, where the throughput of EAPs increases with respect to 4ED, whereas the throughput of the AHAP decreases. The throughput of the eNB also decreases, leading to reduced sum-throughput of the eNB and WLAN.
Many APs Deployment: Fig. 45 and Fig. 46 are obtained under the 5 APs deployment and 10 AP deployment setups in Section 4.2.5, respectively. All the APs can become AHAPs depending on channel condition in these deployment setups. In the 5 APs deployment, AP5 has the lowestηAH. In the 10 APs deployment, AP2, 4, 8, and 10 have relatively lowηAH.
As in Fig. 43 and Fig. 44, the throughput of APs with lowηAH, i.e., the APs which are EAPs with high probability, tends to increase. However, in general, an increased 4ED increases the throughput of some APs while decreasing the throughput of the other APs. Consequently, the total throughput of WLAN remains almost the same.
As the number of APs increases, the eNB’s throughput is significantly decreased due to the positive ED margin, since the eNB’s transmission suffers from collisions with more AHAPs which are not suppressed. Therefore, the sum-throughput of the eNB and WLAN also severely decreases while the throughput of WLAN almost remains the same, compared to that in the proposed scheme without the positive ED margin.
In summary, the introduction of the positive ED margin 4ED does not improve the total
0 0.5 1 1.5 2 2.5 3 ED margin [dB]
0 1 2 3 4
Throughput [Mbps]
AP1 AP2
AP3 AP4
AP5 AP6
AP7 AP8
AP9 AP10
(a)
0 0.5 1 1.5 2 2.5 3
ED margin [dB]
10 20 30 40
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WLAN's throughput eNB's throughput WLAN + eNB
(b)
Figure 46: Throughput for each ED margin in 10 APs deployment
throughput of WLAN but significantly lowers the throughput of the eNB. Therefore, the choice of 4ED = 0 is the best for improving the total sum-throughput of the eNB and WLAN, as in the original proposed formulation.
4.1.7.3 Explicit consideration of Wi-Fi throughput To justify the benefit of the pro- posed scheme, we consider a new algorithm in which an explicit consideration on Wi-Fi’s through- put is included. A reasonable and tractable method for protecting Wi-Fi’s throughput to some extent would be additionally introducing the following convex inequality:
JSTA,α≤ΓInterf,∀α∈ AAH\B,ˆ (68) whereJSTA,αis interference from an LAA-LTE eNB to STA associated with APα, andΓInterfis the maximum-allowed interference from the eNB to each STA. Note thatBˆis the feasible set of AHAPs to be suppressed. Hence,AAH\Bˆdenotes the set of AHAPs that will not be suppressed.
By introducing the inequality (68), the eNB reduces the generating interference to the STAs of the AHAPs inAAH\B. As a result, the eNB tries to avoid collision by suppressing some AHAPsˆ and protect the throughput of the rest of the AHAPs at the same time. By lowering the value of ΓInterf, a better protection on the Wi-Fi’s throughput can be achieved.
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0 2 4
Throughput [Mbps]
AP1 AP2 AP3
(a)
-101 -92 -82 -77 -70 -65 with IC Maximum-allowed interference [dBm]
0 20 40
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WLAN's throughput eNB's throughput WLAN + eNB
(b)
Figure 47: Throughput for each maximum-allowed interference in three AP deploy- ment
-101 -92 -82 -77 -70 -65 with IC Maximum-allowed interference [dBm]
0 2 4 6
Throughput [Mbps]
AP1 AP2
AP3 AP4
AP5
(a)
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WLAN's throughput eNB's throughput WLAN + eNB
(b)
Figure 48: Throughput for each maximum-allowed interference in five AP deployment Since the new constraint is also convex, the proposed algorithm still works with trivial modifications for finding solutions. Note that as ΓInterf → ∞, the new inequality becomes inactive.
Based on simulations under the deployment of Section 4.2.5, we evaluate the performance of LAA-LTE and Wi-Fi with respect to ΓInterf. In the figures, the throughput of ‘with IC’ is obtained from the original proposed scheme, where ΓInterf→ ∞.
Three APs deployment: Fig. 47(a) presents that although the new constraint improves the throughput of some APs according to the choice ofΓInterf, it rather reduces the total through- put of APs and the throughput of the eNB at the same time. To satisfy the new constraint, the eNB would reduce its overall transmission power, resulting in degradation of the eNB’s through- put. The protected AHAPs would then recklessly transmit even during the eNB’s ongoing transmission, leading to more frequent collision and eventually degradation on the throughput and delay performance of the collided APs.
Many APs deployment: As the number of APs becomes five or more, the change on the total WLAN throughput due to the additional new constraint becomes insignificant as in Figs.
48 and 49. On the other hand, the eNB’s throughput is significantly decreased by introducing the new constraint, resulting in decreased sum-throughput of WLAN and LAA-LTE.
-101 -92 -82 -77 -70 -65 with IC Maximum-allowed interference [dBm]
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AP3 AP4
AP5 AP6
AP7 AP8
AP9 AP10
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0 20 40
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WLAN's throughput eNB's throughput WLAN + eNB
(b)
Figure 49: Throughput for each maximum-allowed interference in ten AP deployment In summary, through the extensive numerical simulations, we have confirmed that the throughput of WLAN is rather decreased with the new inequality. The lesson from the simulation results is that if the eNB must transmit in the presence of Wi-Fi, it should do its best to successfully transmit as quickly as possible without collision; that is, the eNB should suppress as many AHAPs as possible, as in the proposed formulation. This seemingly selfish strategy will eventually be the best also for Wi-Fi because of minimized collisions.
4.1.8 Conclusion
In this paper, we have proposed the downlink interference control scheme based on power allo- cation for the eNB, which protects its transmission from the interference of asymmetric hidden terminals. Through extensive simulation results, we conclude that our proposal allows the eNB to effectively prevent the exposure boundary APs from becoming asymmetric hidden terminals.
As a result, the proposed scheme increases throughput and reduces long delay of the eNB while preserving or even improving the performance of WLAN in various deployment scenarios.
4.2 Downlink MU-MIMO LTE-LAA for Coexistence with Asymmetric Hid- den Wi-Fi APs
4.2.1 Introduction
As the mobile traffic demand is expected to become 8.2 times larger in 2020 than in 2015 [9], mobile communications operators should secure more licensed bands to meet the needs of their customers. However, the spectrum below 10GHz, which is suitable for wide-range wireless communications, is being exhausted as other wireless applications such as radar and broadcast are requiring more bandwidth to expand their capabilities. In addition, since the lease and usage of licensed bands cost astronomical expenses, using licensed bands even for the communications in relatively isolated environment such as indoors or for the communications of less QoS-sensitive traffics is not effective in terms of cost and spectral efficiency.
To cope with the scarcity of licensed bands while maximizing the spectral efficiency, 3GPP has standardized a new type of LTE, termed LTE License Assisted Access (LAA) (LTE-LAA) in its recent releases [46]. The core idea of LTE-LAA is to offload downlink data traffics to unli- censed bands by combining the UNII (Unlicensed National Information Infrastructure) bands at 5 GHz with LTE’s licensed bands via the carrier aggregation functionality. Since the unlicensed spectrum can be utilized without charge and becomes extremely useful in relatively isolated en- vironment, the new type of LTE is expected to increase the spectral efficiency without significant financial burden.
To coexist with already pervasive Wi-Fi in the unlicensed bands, LTE-LAA adopts the Listen Before Talk (LBT) policy with Clear Channel Assessment (CCA) and designs its MAC mecha- nism similarly to Wi-Fi’s Distributed Coordination Function (DCF) employing the binary expo- nential backoff [46]. LTE-LAA should also limit its transmission duration by Maximum Channel Occupancy Time (MCOT) for better Wi-Fi protection. These new features have motivated sev- eral researches on the contention window (CW) size of LTE-LAA’s exponential backoff [47] and MCOT [5]. The authors of [47] optimize the CW size of the LTE-LAA’s exponential backoff for fair channel access. In [5], it is proposed to adapt the length of MCOT to allow LTE-LAA to use a shared channel as much as possible not affecting Wi-Fi.
Extensive researches have been conducted to enable a peaceful coexistence of LTE-LAA and Wi-Fi via resource allocation [48–54]. The majority of the literature on resource allocation [48–53] mainly focus on how to allocate the channel usage time to LTE-LAA and Wi-Fi. On the other hand, the work [54] investigates a user’s preference towards LTE-LAA and Wi-Fi depending on the user’s satisfaction and affordability when the user has to choose between LTE-LAA and Wi-Fi.
Several researchers have studied LTE-LAA with multiple antennas [73–77]. In [73], the authors evaluate the performance of single-user MIMO LTE-LAA in the scenario where two different operators coexist without Wi-Fi devices. The work [74] combines the multiple signal classification technique for the direction of arrival estimation with null steering techniques to
allow LTE-LAA and Wi-Fi to transmit concurrently, yet without any evaluation on the through- put of LTE-LAA. The authors of [75] considers the use of massive MIMO antenna arrays for LTE-LAA mobile handsets without the consideration of WLAN. The works [76, 77] propose multi-antenna transmission techniques to enhance spatial reuse efficiency for LTE-LAA and Wi-Fi coexistence. Those works, however, do not take into account the distortion in Wi-Fi’s signals received at LTE-LAA and LTE-LAA’s signals received at Wi-Fi due to the difference of physical-layer parameters between LTE-LAA and Wi-Fi.
Recently, several researchers have addressed issues due to the difference of the CCA thresh- olds in LTE-LAA and Wi-Fi [55–57]. By extensive simulations, the authors of [55] evaluate the throughput and delay performance of LTE-LAA and Wi-Fi with varying energy detection thresholds of LTE-LAA and Wi-Fi. The same analysis is conducted mathematically in [56].
Our recent work [57] first identifies a new phenomenon in the scenario of the LTE-LAA and Wi-Fi coexistence, and defines the asymmetric hidden terminal problem. We also verify that the asymmetric hidden terminal problem is indeed inevitable and induced by the difference in the CCA thresholds of LTE-LAA and Wi-Fi. We confirm that asymmetric hidden terminals severely degrade the throughput and delay performance of LTE-LAA.
This paper proposes a Multi-User MIMO (MU-MIMO) transmit precoding and power control technique for an LTE-LAA eNodeB (eNB) to mitigate the performance degradation due to the CCA threshold difference between LTE-LAA and Wi-Fi. The proposed scheme is motivated by understanding the transmission behaviors of neighboring Wi-Fi access points (APs) taking into account the difference in the CCA thresholds and physical-layer parameters between LTE-LAA and Wi-Fi. We propose a method for an eNB to control the start of APs’ transmission during the transmission of LTE-LAA via the design of precoding vectors and transmission power on the subcarriers of LTE-LAA, which does not require any modification to the Wi-Fi’s standard.
Our contribution is summarized as follows:
• Based on the stochastic geometry framework [78], we derive the medium access probabili- ties of APs, which can be calculated by an eNB when the eNB has the channel information from APs to itself by listening to APs’ beacon signals.
• With the derived medium access probability, we formulate a problem for optimizing the multi-user precoding vectors and transmission power on each LTE-LAA’s subcarrier, in pursuit of mitigating the performance degradation due to the CCA threshold difference between LTE-LAA and Wi-Fi. The goal is to prevent the asymmetric hidden APs from recklessly transmitting, which would co-destruct its own and LTE-LAA’s signals due to severe mutual interference.
• For evaluation of the proposed scheme, we implement a realistic event-driven simulator with important MAC and PHY analytics including the energy detection mechanism, binary exponential backoff, collisions, signal interference, and distortion due to the difference of physical-layer parameters between LTE-LAA and Wi-Fi. Through extensive simulations
Figure 50: LAA-WLAN coexistence scenario with the proposed MU-MIMO LTE-LAA system.
in various scenarios, we show that the proposed scheme improves the throughput and delay performance of LTE-LAA as well as that of Wi-Fi in most of the scenarios by circumventing the co-destruction of concurrent Wi-Fi’s and LTE-LAA’s transmission. In addition, we show that the proposed scheme enhances the throughput and delay fairness between Wi-Fi APs.
To the best of the authors’ knowledge, for the LAA-WLAN coexistence, this is the first attempt to utilize multi-antenna precoding and power control as a remedy to the asymmetric hidden terminal problem with the consideration of the impact of the difference in physical-layer parameters between LTE-LAA and Wi-Fi.
4.2.2 System Model
4.2.2.1 LAA-WLAN Coexistence Scenario The LAA-WLAN coexistence scenario is considered, where an LTE-LAA eNB serves NUE user equipments (UEs) paired with the eNB and where there are NAP Wi-Fi APs around the eNB. Each AP serves a paired Wi-Fi station (henceforth referred to as ‘STA’) as in Fig. 50.23 To focus on the LAA-WLAN coexistence
23In this work, although we assume that an AP is associated with a single STA, it can be readily extended to the case of multiple STAs based on a similar approach considered in this paper.
problem, we assume that there are no other interfering eNBs. This assumption is reasonable if mobile network operators deploy eNBs at strategically chosen locations for interference manage- ment and capacity enhancement. Moreover, intercell interference can be mitigated sufficiently via intercell interference management techniques such as Inter-Cell Interference Coordination to only focus on the interference between LTE-LAA and Wi-Fi. An eNB is equipped with NT antennas while the other nodes are equipped with a single antenna, operating in a shared chan- nel in the 5 GHz ISM band. It is reasonable to assume that both an eNB and UE have Wi-Fi modules [61].24 Finally, this work sets the transmit power of an eNB and an AP to the same value, 23 dBm complying with the regulation [34].25
The eNB and APs conduct LBT based on CCA and employ the binary exponential backoff for collision avoidance. The parameters for the exponential backoff of LTE-LAA and Wi-Fi are differently defined in their respective standards. Once the eNB obtains a channel access opportunity, it will continue to transmit during an MCOT period, usually 8 msec whereas the transmission duration of APs can be varied.
The procedure of the binary exponential backoff is as follows. A transmitter sets its con- tention window (CW) as an initial CW and doubles it at every collision up to the maximum CW.
In addition, the CW is reset to the initial CW at successful transmission or after the maximum CW is reached. In the case of Wi-Fi, if a packet is not successfully transmitted to a receiver, the transmitter determines that a collision has occurred in the packet. However, in the case of LTE-LAA, an eNB transmits 8 subframes, and even a single subframe can have several packets to multiple UEs. According to [46], an eNB decides whether to double its CW based on the ratio of the number of NACKs to the number of all ACKs and NACKs in the first subframe out of the 8 subframes. If the ratio is larger than a threshold, the eNB doubles its CW. Otherwise, the eNB’s transmission is regarded successful.
As assumed in [57], this work focuses only on saturated downlink traffic in unlicensed band, which can effectively reflect the reality since the downlink traffic is expected to become 8 times heavier in 2020 than the uplink traffic [30]. In the LTE-LAA network, the licensed band is used for control channels, through which a UE informs control messages to the eNB such as channel state information [62].
If clear line-of-sight (LOS) between the transmitter and receiver is secured for each commu- nication channel, such as in wireless backhaul channels, transmit beamforming becomes pinpoint directional beamforming towards serving UEs or STA as in radar systems. However, we consider non-line-of-sight (NLOS) channels with many unpredictable indirect paths due to the presence of many scatterers between a transmitter and receiver, such as buildings, walls, etc. In fact, the NLOS environment is much more frequently encountered both in indoor and outdoor chan-
24The Wi-Fi modules at eNBs and UEs are simply utilized to collect information from Wi-Fi APs such as channel information and SSID by listening to APs’ beacon signals.
25In practice, each AP can only perform egocentric optimization by transmitting at its maximum power since there is no interference coordination method across APs. Therefore, it is reasonable to set AP’s transmission power at its maximum, 23 dBm.