- Open Access
Avoiding collisions between IEEE 802.11 and IEEE 802.15.4 through coexistence aware clear channel assessment
© Tytgat et al; licensee Springer. 2012
Received: 12 September 2011
Accepted: 10 April 2012
Published: 10 April 2012
More and more devices are becoming wirelessly connected. Many of these devices are operating in crowded unlicensed bands, where different wireless technologies compete for the same spectrum. A typical example is the unlicensed ISM band at 2.4 GHz, which is used by IEEE 802.11bgn, IEEE 802.15.4, and IEEE 802.15.1, among others. Each of these technologies implements appropriate Media Access Control (MAC) mechanisms to avoid packet collisions and optimize Quality of Service. Although different technologies use similar MAC mechanisms, they are not always compatible. For example, all CSMA/CA-based technologies use Clear Channel Assessment (CCA) to detect when the channel is free; however, in each case it is specifically designed to improve detection reliability of the specific technology. Unfortunately, this approach decreases the detection probability of other technologies, increasing the amount of cross-technology collisions. In this article, we introduce the concept of coexistence aware CCA (CACCA), which enables a node operating in one technology to backoff for other coexisting technologies as well. As a proof of concept, we analyze the Packet Error Rate(PER) incurred by an IEEE 802.15.4 network in the presence of IEEE 802.11bg interference, and assess the PER reduction that is achieved by using CACCA.
The problem when using different wireless technologies in the same frequency band is that most of them are not designed to be compatible with each other. Even if different technologies use a similar Medium Access Control (MAC) protocol, they might still impede each other.
Within this article we study the collisions between two heterogeneous CSMA/CA-based MAC technologies. As a proof of concept, we analyze the collisions between IEEE 802.11bg and IEEE 802.15.4. Throughout the article, we refer to IEEE 802.11bg with the term Wi-Fi, and to IEEE 802.15.4 with the term Zigbee. Note that IEEE 802.15.4 only defines the physical (PHY) layer and MAC layer, in contrast to Zigbee that also specifies higher layers of communication above IEEE 802.15.4. However, for the sake of simplicity we use the terms IEEE 802.15.4 and Zigbee to denote the same thing.
The co-existence behavior of Wi-Fi and Zigbee has been studied extensively. The physical layer effects of Wi-Fi and Zigbee coexistence are already considered in the IEEE 802.15.4 standard . Zhen et al.  study the cross-technology detection probability of Clear Channel Assessment (CCA) between Zigbee and Wi-Fi. They conclude that Zigbee is oversensitive to Wi-Fi, while Wi-Fi is insensitive to Zigbee beyond a Heterogeneous Exclusive CCA Range, which they calculate to be 25m with the free space pathloss model. Yuan et al.  study the co-existence behavior of Zigbee and saturated Wi-Fi. They conclude through a model and simulation that 5.75% of the Zigbee throughput remains under the assumption that Wi-Fi and Zigbee CCA can avoid all cross-technology collisions. They also conclude through simulation that no throughput remains in case Wi-Fi does not detect Zigbee. Pollin et al.  measure the coexistence impact of Zigbee and Wi-Fi. They conclude that standard Wi-Fi devices do not backoff for Zigbee traffic, even in very close proximity. They also show that the CCA mechanism of Zigbee can reduce collisions with Wi-Fi, but it is too slow to avoid all Wi-Fi traffic. Thonet et al.  measure up to 85% Zigbee packet loss due to 802.11b traffic. Consequently, we conclude that Zigbee might incur severe packet loss when it coexists with Wi-Fi. However, no model predicting the performance degradation has been proposed. Out of , it is possible to determine the Packet Error Rate (PER) depending on the signal-to-interference ratio (SIR) and the size of the collision window, given there is a collision. However, the amount of collisions is dependant on the channel access mechanism of both Wi-Fi and Zigbee. Hence, a detailed model for cross-technology collisions that considers realistic Wi-Fi and Zigbee channel access mechanisms is a key open issue. In [6, 7], we propose such a model and focus on exploring the economic value of introducing sensing engines in one specific business scenario. In this article, we focus on a thorough theoretical study of this model, and verify it against real-life measurements in a testbed environment.
The remainder of the article is organized as follows. In Section 2, we analyze the CCA-based medium access in Wi-Fi and Zigbee. In Section 3, we derive the Zigbee PER model under Wi-Fi interference, look at the sensitivities it has and verify it through measurements. Out of this model, the major mechanism leading to the high Zigbee PER is identified. In Section 4, we analyze the different Coexistence Aware CCA (CACCA) implementation alternatives, and the implications of using a spectrum sensing engine as a CACCA agent. Section 5 gives an overview of potential topics for further research, while Section 6 concludes this article.
2. CCA operating principle
Both Wi-Fi and Zigbee allow preamble detection instead of energy detection as CCA. Preamble detection can improve sensitivity, but prevents cross-technology detection due to the differences in preambles between technologies. In Zigbee, this is usually disabled as the sensing time defined by the standard is sufficiently long to allow adequate sensing sensitivity. However, Wi-Fi enables this by default in order to reach the maximum sensing sensitivity within the short Wi-Fi CCA timeframe. We can therefore assume that standard Wi-Fi does not backoff at all for Zigbee traffic.
3. Zigbee PER under Wi-Fi interference
3.1. Analytical PER model
In the following, we assume that every collision between a WiFi packet and a Zigbee packet results in the Zigbee packet being lost. Although this is undoubtedly an oversimplification, it allows us to clearly show the plausible PER reduction through the usage of CACCA.
is a random variable that represents the time until the current Inter Packet Delay (IPD) of Wi-Fi terminates and a new Wi-Fi packet starts, T Z is the average Zigbee packet length, TZ,CCA is the Zigbee CCA time, TZ,Rx2Tx, is the Zigbee Rx to Tx turnaround time.
Since Wi-Fi CCA does not detect Zigbee transmissions, the instants of time at which Wi-Fi transmissions start are independent of the Zigbee transmissions. We assume that the distribution of Wi-Fi IPD can be approximated by the exponential distribution, with average . Note that it is typically assumed that the Wi-Fi IPD has a self-similar distribution (i.e., traffic bursts). However, traffic bursts can be divided into periods of intense traffic, and periods of less intense traffic. Within each period, we assume the distribution of IPD can reasonably be approximated by the exponential distribution, respectively, with a high and a low rate. This assumption allows to determine the PERZ,W during intense traffic as well as during low traffic periods, which is the major intent of this study.
MAC frame size (bytes)
127, 100, 50, 5
1 Mbps, 11 Mbps, 54 Mbps
MAC load (Kbps)
Packet duration (μs)
127 b: 4256
1 Mbps: 10416
100 b: 3392
11 Mbps: 1121
50 b: 1792
54 Mbps: 212
5 b: 352
where R is the average Wi-Fi packet rate (packets/s), TW is the average Wi-Fi packet duration (s).
3.2. Sensitivity analysis
The largest deviation to the base model is caused with the smallest Wi-Fi packets possible (28 μs). This possibility is also visualized in Figure 7. There is a factor 1.8 difference for 100 kbps Wi-Fi, and the 10% PERZ,W point shifts from 279 to 486 kbps.
3.3. Experimental model verification
All experiments are run with 100 bytes Zigbee packets and 1278 bytes Wi-Fi packets sent at bitrates of 1, 11, and 54 Mbps. All packets are transmitted with a fixed IPD.
As mentioned earlier, these tests are conducted with constant IPD for both Wi-Fi and Zigbee, and still the PER measurements are rather close to our calculations. The error remains below 25% in the region where the Zigbee network stays operational (PERZ,W < 10%). This indicates that the sensitivity of our model to the probability distribution of and is rather low.
4. Deployment of sensing engine-based CACCA
4.1. Sensing engine characteristics
Regular CCA versus sensing engine based CACCA timings
T S ,CCA (μs)
T S ,Rx2Tx (μs)
The power consumption of a sensing engine detecting Wi-Fi is presented in , and equals 110 mW for the analog part, and 4 mW for the digital part to detect Wi-Fi, totaling to 114 mW. The sensing engine needs to be switched on during the 9 μs long CCA + Rx2Tx window, resulting in a total energy consumption of 9 μs * 114 mW = 1.03 μJ. The minimal power consumption of a current CC2520 Zigbee Radio in transmit mode equals 45 mW, and the smallest Zigbee packet lasts 320 μs, resulting in a total minimal transmit energy of 12.8 μJ. Hence, the total impact on the power consumption of the sensing engine equals at most 8% per transmitted packet.
When deployed in a Wi-Fi device there is no need for a separate receive chain, as common Wi-Fi devices have the necessary bandwidth and sensitivity. A Zigbee packet is detected by the sensing engine within a timeframe of 4 μs. The standard Wi-Fi CCA time is 4 μs, hence we assume that the implementation of a sensing engine in Wi-Fi devices will not change TW,CCA and TW,Rx2Tx.
Only the digital part of a sensing engine will contribute to the energy consumption in a WiFi device. This 4 mW is only consumed during an 8-μs long timeframe, totaling to 32 nJ per transmission. An 18-dBm Wi-Fi transmission consumes at least 63 mW, using a 100% efficient radio. The shortest packet lasts 24.5 μs , resulting in an energy consumption of 1.5 μJ. The sensing engine energy consumption will thus contribute to at most 2% of the energy consumption per packet transmitted at 18 dBm.
4.2. Case 1: Zigbee side CACCA
PERZS,W calculated at the 100 kbps point equals 1.05%. In other words, the inclusion of the sensing engine results in a PERZ,W drop of 24%. The 10% PERZ,W point shifts from 279 to 324 kbps.
4.3. Case 2: Wi-Fi side CACCA
4.4. Case 3: Wi-Fi and Zigbee CACCA
In typical operating conditions, the Zigbee load is low, thus most of the contribution to PERZ,W comes from the first part of (12). This part highly depends on the Zigbee CCA + Rx2Tx time; therefore, it makes sense to also examine the effect of implementing the sensing engine on both Zigbee and Wi-Fi.
Again, we look at the two parts of the formula separately. The probability of Wi-Fi starting its transmission during the TZS,CCA + TZS,Rx2Tx window is significantly lower compared to case 2, as this window now only lasts for 9 μs instead of 320 μs. The 100 kbps point has a calculated PERZS,WS of 0.01%. In comparison with COTS hardware, this creates a drop in PERZ,W of 99.6%. The 10% PERZ,W point shifts from 279 kbps to 37 Mbps.
The dependence of PERZS,WS on (second part of the formula) is identical to case 2.
4.5. Case comparisons
Case 1 handles the usage of the sensing engine on the Zigbee nodes. We conclude that PERZ,W is highly dependent on TZ and . The analysis shows reduction of 8-48% in PERZ,W (at 100 kbps Wi-Fi load), depending on the size of the Zigbee packets. The Wi-Fi load which leads to 10% Zigbee packet loss equals 324 kbps (for default size Zigbee packets of 100 bytes).
Case 2 handles the inclusion of the sensing engine in the Wi-Fi devices. The model shows that the dependence on is reduced, while the dependence on the Zigbee packet size is almost completely removed. This case reduces PERZ,W at 100 kbps Wi-Fi load by 75% while the Wi-Fi load which leads to 10% Zigbee packet loss becomes 3130 kbps.
Case 3 considers the implementation of the sensing engine on both Zigbee and Wi-Fi nodes. This case has the lowest dependence on . It reduces PERZ,W at 100 kbps Wi-Fi load by 99.6%, and achieves a Wi-Fi load resulting in a 10% Zigbee packet loss of 37 Mbps.
Comparison of regular CCA with the three CACCA deployment alternatives
Zigbee + Wi-Fi CACCA
PERZ,W @ 100 kbps (%)
Wi-Fi load @ 10% PERZ,W (Kbps)
PERZ,W dependence on
5. Future study
We instantiated the CACCA analysis within a Zigbee ⇔ Wi-Fi context. However, similar analysis can be done in other combinations of technologies, as well as identical technologies that operate in partially overlapping bands (e.g., IEEE 802.11bgn @ 2.4 GHz).
Another aspect we did not consider is the impact the sensing engine has on the Wi-Fi side. It does not only reduce PERW,Z-which is a positive effect-but it also reduces the throughput of Wi-Fi-which is a negative effect. As such this remains an open issue.
This article only considers Wi-Fi broadcast traffic, without acknowledges or request to send/clear to send. An elaboration on their impact remains for future study.
A final direction for future study is to study the combination of the time domain collision avoidance, together with frequency and/or space domain collision avoidance. This will exploit the possible benefits of a spectrum sensing engine to its fullest.
As more and more wireless technologies emerge, more of these technologies have to coexist with one another. One of the major open Wi-Fi ⇔ Zigbee coexistence issues is a model for cross-technology packet collisions. We propose a new analytical model for Zigbee packet loss due to collisions with Wi-Fi packets, analyze it theoretically and validate it experimentally. Out of this model, we conclude that the major cause of Zigbee packet loss is the inability of Wi-Fi to detect Zigbee transmissions.
In order to solve this problem, we propose the CACCA concept. CACCA enables Wi-Fi to detect Zigbee, and can be implemented through a sensing engine. There are three different deployment alternatives, namely, only Zigbee side deployment, only Wi-Fi side deployment, and Zigbee as well as Wi-Fi deployment. Deploying CACCA only on Zigbee results in 24% packet loss reduction, deploying it on Wi-Fi results in 75% packet loss reduction while deploying it on both sides reduces Zigbee packet loss by 99.6%. The maximum allowable Wi-Fi load in order to have less than 10% Zigbee packet loss rises from 279 kbps in the regular CCA case to 324 kbps in the Zigbee only deployment alternative, 3.1 Mbps in the Wi-Fi only deployment alternative and 37 Mbps when deploying it on both sides. The added energy consumption of a sensing engine-based CACCA deployment equals to less than 8% per packet transmitted on the Zigbee side, and less than 2% on the Wi-Fi side.
We can conclude that the deployment of CACCA achieves substantial reduction of the Zigbee incurred packet loss, without needing any additional information exchange (and the incurred overhead), nor having a severe impact on the energy consumption. It can inherently cope with dynamic environments, and is backwards compatible with the IEEE 802.15.4 and IEEE 802.11 standards. Consequently, implementing CACCA increases the reliability of Zigbee while coexisting with Wi-Fi to an unprecedented level, without losing backwards compatibility with existing technologies.
As a final remark, we believe that while in the short-term CACCAmight be seen as a quick-fix for IEEE 802.11bgn ⇔ IEEE 802.15.4 coexistence, it can easily be extended to allow coexistence beyond current state-of-the-art technologies.
The research leading to these results has received funding from the European Union's Seventh Framework Programme FP7/2007-2013 under grant agreements no. 257542 (CONSERN project) and no. 258301(CREW project). It has also received funding from IWT under projects ESSENCES and SYMBIONETS and from IBBT under the project NGWINETS.
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