Performance Impact on Mobile Broadband Data in a Mixed Voice Over LTE Environment
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In this paper, we consider the operational impact that VoLTE will have on the performance of mobile broadband data in the real world from a throughput perspective when the two service types are mixed on the same channel. We begin by proposing an M/G/1 priority queueing model with non-preemptive scheduling to analyze scheduler latencies that can be expected when there are services with different levels of priority in service on the same channel. We then discuss the direct impact that latency has on packet data convergence protocol layer throughputs, and simulate scheduler scenarios in a long-term evolution environment. Scheduler algorithms are highly proprietary and vary greatly from one vendor to another; however, there are some laws and constraints of both space and time that cannot be avoided. In this paper, we quantify these limitations using modeling and simulations. Finally, we provide solutions from a hardware configuration and capacity planning perspective to help to overcome these hidden limitations that are typically difficult to measure and quantify, and complex to understand.
KeywordsVoLTE capacity LTE scheduler Simulations Latency MBB throughput
To meet exponential increases in demand for quality and capacity from wireless networks, technology is being evolved at an ever-increasing speed. In the early days of wireless, call models for voice-only networks were simple: user traffic patterns were predictable and capacity limits were well defined. At the present time, the wireless industry is experiencing radio access network physical channel architectures that create many ill-defined scenarios in terms of capacity because of channel resources that serve both voice and mobile broadband (MBB). Capacity depends on many factors, such as signal quality, equipment capabilities, and scheduler implementations. Additionally, the data world has introduced users who request resources, make a transaction, and could leave within a few milliseconds, as opposed to voice callers who may arrive and hold resources for up to a few minutes, on average. Both voice and data are becoming complicated in their own right with regard to modeling because of the aforementioned issues with capacity. As the wireless industry adopts voice over long-term evolution (VoLTE) as the primary voice network of the future, these issues will further increase because of the additional complexity of mixing voice and data traffic on the same channel. In this paper, we attempt to quantify the impact of higher priority traffic, such as a VoLTE user, on the throughput of an MBB data user. This is a particularly difficult aspect to quantify numerically. This paper equips the reader with two quantification methods: queueing theory and simulation.
2 Queueing Theory Basics
Queueing theory dates back to 1909, when it was introduced by Erlang . It is currently used in many fields, such as computer design, server architecture design, manufacturing, and the evaluation of scheduler efficiencies in computer science . Schedulers are not new to wireless. As wireless becomes more data-centric and depends on schedulers that use the same resources for voice and data with varying priority levels, queueing theory models can be adopted to quantify the impact of latency on wireless networks.
As shown by the two examples, it is possible to reduce the wait time for users in a priority queue system for higher priority users; however, this is at the expense of making lower priority users wait longer, on average. In the next section, we further discuss how these concepts apply to LTE in terms of VoLTE users (k = 1) and MBB users (k = 2). For simplicity, we only discuss two levels of priority; LTE offers nine levels of priority. However, the form of Eq. (2) allows it to be expanded to an infinite number of priority levels as needed for additional types of services.
3 Volte and MBB Assumptions
Before applying queueing models to LTE using the equations presented in the previous section, assumptions must be made about VoLTE and MBB users that allow them to be defined in terms of resource utilization and service rates (i.e., ρ and µ, respectively).
Queueing models often implement schedulers, thus, it is convenient to consider scheduling capabilities as a resource. Each vendor varies by equipment type; however, all vendors have a limited number of users that can be scheduled per TTI. This TTI is always 1 ms in accordance with 3GPP standards . This number of users per TTI is referred to as the user equipment per TTI (UE/TTI) limit. For example, a vendor may only be able to schedule a maximum of six downlink users during each TTI. Hardware upgrades may allow it to schedule more. As stated previously, this varies by vendor and hardware type.
This provides ρ for the first priority class (VoLTE) for the queueing model calculations, which will be discussed later.
This provides ρ for the second priority class (MBB) when applying queueing theory models to LTE.
4 Queueing Theory Applied to Volte and MBB
In this example, once a user makes a request for service from the system, the average scheduler delay for any packet, regardless of class or type, is expected to be 4.083 ms.
Expected delays for priority k = 1
Expected delays for priority k = 2
5 Latency Impact on PDCP Layer Throughputs
Although it is helpful to know how latency is impacted by scheduling for users of different classes, the goal of this paper is to discuss specifically the impact that VoLTE users have on MBB users. To more clearly quantify this, in the next section, we will discuss the impact that scheduler delays have directly on the downlink throughputs of MBB users that receive data through the hybrid automatic repeat request (HARQ) process.
Bits per re by CQI
Maximum theoretical throughputs
However, maximum throughputs in the PDCP layer are directly limited by scheduler latency because of the delay between the time that a scheduling request is made by the active HARQ process and the time that it is actually sent over the air link. Maximum throughputs are only achievable if every TTI can be scheduled with zero latency. We consider two cases to illustrate this concept.
Maximum throughputs given latency (5 MHz only)
Because the HARQ process is asynchronous, with parallel processes, the previous method can only be used as a guide. It remains more valid for large file sizes than small, bursty transmissions. If the file size is large and the above HARQ transmissions need to be repeated many times, then using an average scheduler delay is valid for calculating the average throughput over the duration of that file transfer. However, for a small transmission, suppose that it is sufficiently small that it only needs to use the three HARQ processes from the example once to complete its download (i.e., total volume is very small). The first two processes could be delayed by the scheduler, but the third process could be scheduled immediately. In this case, the download would appear to be complete sooner, from start to finish, and the actual throughput for the duration of the very short transfer would appear faster than that given by Eq. (5). If the HARQ process was in fact synchronous, the equation would be valid for all cases. Because it is asynchronous, it is very difficult, perhaps even impossible, to model every scenario with a single mathematical equation. Fortunately, for such complex systems, the industry can use simulations to model most scenarios with greater accuracy and efficiency.
Creating simulations that behave like a pseudo scheduler can further our understanding of this topic. Each vendor has its own implementations of schedulers. In this section, we present the results of a simple scheduling algorithm described as a first in, first out (FIFO) algorithm, with priority always given to VoLTE users, if present. In the following are some of the rules and operations of the simulation, assumptions, limitations, and results.
Bandwidth (MHz) is used to determine the number of PRBs that are available to be scheduled during each TTI. The results are for a 5 MHz bandwidth.
Hourly volume (MB) defines the expected arrival rate and traffic intensity for MBB users.
Hourly MOUs define the expected arrival rate and traffic intensity for VoLTE users.
UE/TTI limits define the hardware limitation for the number of UEs that can be scheduled during each TTI in the downlink. The results are for hardware that supports 12 UE/TTI in the downlink.
MIMO/SISO distributions are sampled from live networks and used to represent the mix of MIMO versus SISO usage probabilities for all users by CQI.
CQI distributions are sampled from real networks to provide the probability of CQI for each user given a mean sector CQI value. The simulations were run for a sector with a mean CQI = 9.
VAF is assumed to be 50% for all simulations.
PRBs and UE/TTI limits are the limiting factors.
PDCCH is quite challenging in itself and therefore is not simulated here; it is certainly a limiting factor in real-world applications.
Downlink-only is simulated in this paper; other uplink limitations that are not discussed here may apply.
TTI bundling is not considered because this is a downlink-only simulation.
Semi-persistent scheduling (SPS) is not considered in this simulation; however, we consider the need to implement SPS because it provides a benefit to PDCCH and scheduler loading .
Retransmission rates for the simulated system are considered to be zero. Many normally operating systems in reality have between 0 and 10% retransmission rates. The benefit of adding this to simulations does not outweigh the complexity it adds to the simulation process.
During each simulation cycle (i.e., 1 ms TTI), some basic functions were performed, which belong to two categories: traffic simulation and scheduling. Traffic simulation randomizes the arrival rates and intensity of newly arriving traffic based on samples from distributions. Scheduling manages what resources are used and by whom to send data during each TTI.
Checked the users in the queue and ranked them by service type first (VoLTE or MBB), and then by length of time in the queue. Those who had been in the queue longer were ranked higher, within their service type.
PDSCH resources were assigned by rank until either TTI limits were exhausted or PRBs were exhausted, whichever occurred first. Resources were allocated per CQI requirement, as defined in 3GPP 36.213 .
The status of each user in the queue was updated. For example, time in the queue (to maintain a record of scheduler delay), remaining data volume still to be served for each user, remaining MOU to be served for each user, whether the next transmission should be VAF silent, whether the next transmission should be in MIMO or SISO, and what the CQI should be for the next transmission. These factors were randomly sampled from distributions provided as inputs to the simulation.
New traffic timers were checked to determine whether it was time for new traffic to arrive. If so, a new user was added to the queue. A random sample was then taken from the arrival rate distribution to determine when the next user should arrive.
This simple FIFO approach is appropriate for simulations in this study for manageability. However, vendor implementations may include other factors in their scheduling decisions, and may even have multiple options for schedulers that could be used for different types of desired scheduler behaviors.
The resulting output of these simulations shows the expected values for the average scheduling request latency per MBB user, in addition to the average throughput per MBB user at varying combinations of voice and data volume loading. Additionally, provided for reference are PRB utilization and TTI utilization.
These simulations show that increasing levels of VoLTE traffic had a direct impact on MBB users in the form of increased latency, which caused reduced throughput per user. It is also interesting to note that MBB traffic had a stronger impact on PRB utilization, whereas VoLTE had a stronger impact on TTI usage. Figure 8 shows that MBB traffic could drive PRB utilization to 100%. Figure 8 also shows that, under heavy VoLTE loading, it was nearly impossible to reach 100% PRB utilization. Figure 9 shows that, under heavy MBB loading, it was nearly impossible to use all the TTIs available. However, under heavy VoLTE loading, it was easy to exhaust or use all the TTIs available. These results are all based on a 5 MHz channel with an average CQI = 9. Other channel bandwidths with different CQI distributions may provide different results.
7 Final Thoughts and Recommendations
In this paper, we provided two methods for studying the impact that VoLTE can have on MBB user throughputs. The queueing theory method was convenient because it could be performed on paper and quickly scaled for different inputs, such as bandwidth, RF quality, or CODEC. However, it could only provide expectations for a single CQI. In reality, the users on a site have a distribution of CQIs. Simulations are complex and take time; however, they are powerful where queueing theory alone is inadequate. Simulations can utilize many forms of distributions to better simulate the varying RF conditions within a cell. Because of these differences, it is not accurate to directly compare the results of the simulations with those of the queueing theory method. Both methods showed that, as VoLTE traffic increased, the MBB throughput per user decreased. At lower levels of loading, the decrease could be negligible or acceptable. Users may be able to apply this method to begin setting initial expectations for new VoLTE networks before network data is available to target where issues may exist.
Depending on a carrier’s perspective and approach to setting capacity thresholds, it may need to evolve or expand how it views the design requirements and thresholds for adding capacity. In an MBB user-only environment, an approach may be to consider the number of connected users or PRB utilization as indicators of congestion or capacity exhaustion. However, in a mixed user environment, these KPIs no longer provide sufficient information. For example, it is entirely possible to exhaust the UE/TTI limits while only utilizing 50% of the PRBs. The remaining PRBs will be wasted because the scheduling resources are not available to make use of them unless the UE/TTI capability of the hardware is somehow increased through configuration or hardware upgrades. This scenario is demonstrated in Fig. 8, in which the VoLTE MOUs begin to exceed 20,000 MOUs. The traffic intensity is high; however, PRB utilization is well below 100% because there are not sufficient UE/TTI slots remaining to schedule data users who can utilize the remaining PRBs after VoLTE traffic has been assigned.
The user could set thresholds based on the simulation results; however, this would create a complex set of matrices that would need to be managed and articulated to engineers to set thresholds for many combinations of data volumes and MOUs at varying bandwidths and mean sector CQIs.
8 Future Uses
In addition to building concepts and developing guideline thresholds as a starting point, the methods in this paper can also be used to predict network loading and congestion points as carriers begin migrating VoLTE traffic onto the same network as their MBB users. Forecasting could be performed per cell, by which data volumes are forecasted from historical data with traditional ARIMA models. The impact of VoLTE on MBB users could then be modeled over time by moving voice traffic onto an MBB network. This is the most likely scenario because it will take time to place VoLTE capable handsets into the market, and adaptation rates may vary based on many factors, such as geography, device availability, and demand.
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