With both small-cell LTE and Wi-Fi networks available as alternatives for deployment in unlicensed bands (notably 5 GHz), the investigation into their coexistence is a topic of active interest, primarily driven by industry groups. 3GPP has recently standardized LTE Licensed Assisted Access (LTE-LAA) that seeks to make LTE more co-existence friendly with Wi-Fi by incorporating similar sensing and back-off features. Nonetheless, the results presented by industry groups offer little consensus on important issues like respective network parameter settings that promote "fair access" as required by 3GPP. Answers to such key system deployment aspects, in turn, require credible analytical models, on which there has been little progress to date. Accordingly, in one of the first work of its kind, we develop a new framework for estimating the throughput of Wi-Fi and LTE-LAA in coexistence scenarios via suitable modifications to the celebrated Bianchi Bianchi model. The impact of various network parameters such as energy detection (ED) threshold on Wi-Fi and LTE-LAA coexistence is explored as a byproduct and corroborated via a National Instrument (NI) experimental testbed that validates the results for LTE-LAA access priority class 1 and 3.
Finally, while the imperative of much of the coexistence investigations are driven by progression to fair sharing as defined by 3GPP , we do not explore that issue in any depth in this work. This very important aspect deserves a much deeper investigation and is left for separate future work. Nonetheless, we quote a few relevant important prior art that has contributed to our understanding of the overall problem. In @cite_6 @cite_3 , the authors derive the proportional fair rate allocation Proportional fairness is well-known to achieve airtime fairness in rate-heterogeneous Wi-Fi networks. for Wi-Fi LTE-LAA (as well as Wi-Fi LTE-U) coexistence. Also in @cite_30 , the fairness in the coexistence of Wi-Fi LTE-LAA LBT based on the 3GPP criteria is investigated through a custom-built event-based system simulator. Their results suggest that LBT (and correct choice of LBT parameters) is essential to achieving proportional fairness.
@cite_30 @cite_3 @cite_6
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In this paper, we focus on the deployment of LTE technologies coexisting with Wi-Fi technologies in the unlicensed spectrum 5 GHz. Listen before talk (LBT) functionality is required in unlicensed band in order to ensure the fair coexistence among different operators. We propose a LBT enhancement algorithm with contention window size adaptation for LTE with Licensed-Assisted Access (LTE-LAA) in order to achieve not only channel access fairness but also the QoS fairness. Simulation results show that LTE-LAA with LBT mechanism does not impact Wi-Fi services more than an additional Wi-Fi network. Compared with the fixed LBT mechanism, our proposed LBT algorithm could achieve around 4 and 6 LTE-LAA performance gain in user perceived throughput (UPT) and transmission latency, respectively. And, more than 25 Wi-Fi transmission latency gain can be also achieved. Furthermore, the simulation result shows the advantage of proposed LBT algorithm over original LBT procedure in the case of two LTE operators coexistence. Continued growth in wireless data demands has driven the need for more spectrum. Both licensed and unlicensed spectrum solutions are being deployed and viewed as complementary in dealing with the wireless traffic explosion. For example, Wi-Fi is being used successfully to assist in offloading the more spectrally constrained licensed band systems. At the heart of Wi-Fi's success is the premise of fair co-existence so that multiple Wi-Fi system owners can deploy Wi-Fi access points in the same unlicensed spectrum in an unmanaged fashion. Given Wi-Fi's success, the idea of leveraging unlicensed spectrum with LTE is being proposed through a feature called Licensed Assisted Access (LAA-LTE). This paper reports our findings in designing LBT mechanisms for LAA-LTE to ensure that it operates at least as fairly as Wi-Fi in unlicensed spectrum. These findings are based on our simulation results of co-existence performance between LAA-LTE and Wi-Fi for different candidate LBT schemes as reported in this paper. It concludes that for fair co-existence LBT is essential for LAA-LTE, but also that the choice of LBT scheme and settings of that scheme matter, with those proposed in current European regulations likely to result in Wi-Fi being disadvantaged. The use of the unlicensed spectrum by LTE networks (LTE-U or LAA-LTE) is being considered by mobile operators in order to satisfy increasing traffic demands and to make better use of the licensed spectrum. However, coexistence issues arise when LTE-U coverage overlaps with other technologies currently operating in unlicensed bands, in particular WiFi. Since LTE uses a TDMA OFDMA scheduled approach, coexisting WiFi networks may face starvation if the channel is fully occupied by LTE-U transmissions. In this paper we derive a novel proportional fair allocation scheme that ensures fair coexistence between LTE-U and WiFi. Importantly, we find that the proportional fair allocation is qualitatively different from previously consideredWiFi-only settings and that since the resulting allocation requires only quite limited knowledge of network parameters it is potentially easy to implement in practice, without the need for message-passing between heterogeneous networks. Performing cellular long term evolution (LTE) communications in unlicensed spectrum using licensed assisted access LTE (LTE-LAA) is a promising approach to overcome wireless spectrum scarcity. However, to reap the benefits of LTE-LAA, a fair coexistence mechanism with other incumbent WiFi deployments is required. In this paper, a novel deep learning approach is proposed for modeling the resource allocation problem of LTE-LAA small base stations (SBSs). The proposed approach enables multiple SBSs to proactively perform dynamic channel selection, carrier aggregation, and f ractional spectrum access while guaranteeing fairness with existing WiFi networks and other LTE-LAA operators. Adopting a proactive coexistence mechanism enables future delay-tolerant LTE-LAA data demands to be served within a given prediction window ahead of their actual arrival time thus avoiding the underutilization of the unlicensed spectrum during off-peak hours while maximizing the total served LTE-LAA traffic load. To this end, a noncooperative game model is formulated in which SBSs are modeled as homo egualis agents that aim at predicting a sequence of future actions and thus achieving long-term equal weighted fairness with wireless local area network and other LTE-LAA operators over a given time horizon. The proposed deep learning algorithm is then shown to reach a mixed-strategy Nash equilibrium, when it converges. Simulation results using real data traces show that the proposed scheme can yield up to 28 and 11 gains over a conventional reactive approach and a proportional fair coexistence mechanism, respectively. The results also show that the proposed framework prevents WiFi performance degradation for a densely deployed LTE-LAA network.