 Research Article
 Open Access
Partial Interference and Its Performance Impact on Wireless Multiple Access Networks
 KaHung Hui^{1},
 Wing Cheong Lau^{2}Email author and
 Onching Yue^{2}
https://doi.org/10.1155/2010/735083
© KaHung Hui et al. 2010
 Received: 12 February 2010
 Accepted: 12 August 2010
 Published: 17 August 2010
Abstracts
To determine the capacity of wireless multiple access networks, the interference among the wireless links must be accurately modeled. In this paper, we formalize the notion of the partial interference phenomenon observed in many recent wireless measurement studies and establish analytical models with tractable solutions for various types of wireless multiple access networks. In particular, we characterize the stability region of IEEE 802.11 networks under partial interference with two potentially unsaturated links numerically. We also provide a closedform solution for the stability region of slotted ALOHA networks under partial interference with two potentially unsaturated links and obtain a partial characterization of the boundary of the stability region for the general Mlink case. Finally, we derive a closedform approximated solution for the stability region for general Mlink slotted ALOHA system under partial interference effects. Based on our results, we demonstrate that it is important to model the partial interference effects while analyzing wireless multiple access networks. This is because such considerations can result in not only significant quantitative differences in the predicted system capacity but also fundamental qualitative changes in the shape of the stability region of the systems.
Keywords
 Stability Region
 Slot Aloha
 Backoff Stage
 Admissible Region
 Successful Transmission Probability
1. Introduction
In a wireless network, all stations communicate with each other through wireless links. A fundamental difference between a wireless network and its wired counterpart is that wireless links may interfere with each other, resulting in performance degradation. Therefore in the study of wireless networks, one important performance measure is the capacity of the network when the effects of interlink interference are considered.
In establishing the capacity of a wireless network, we have to predict whether the wireless links interfere with each other. Two most common interference models in the wireless networking literature, namely, for example, protocol model and physical model [1], were proposed to predict whether transmissions in a wireless network are successful. In these interference models, one key assumption is that interference is a binary phenomenon, that is, either the links mutually interfere with each other to result in total loss of throughput of a target link, or there is no link throughput degradation at all. In other words, these models exclude the possibility that interfering links can be active simultaneously and still realize their capacity partially. However, recent empirical studies [2–6] have shown that these binary interference models are not valid in practice. Instead, measurement results have confirmed that there is a nonbinary transitional region [2, 4] (also known as the gray zone in some literature [3]) for the successful packet reception rate (PRR) of a wireless link which changes from zero, that is, 100% lossy, to almost 100%, that is, perfectly reliable, as its signaltointerferenceplusnoise ratio (SINR) increases. These studies have indicated that the range of the transitional regional (in SINR) can exceed 10dB for various types of practical networks including IEEE 802.11a wireless mesh [3, 7] and other lowpower multihop sensor networks [2, 4]. More importantly, measurement studies on largescale wireless mesh testbeds [8, 9] found that a significant number of links in those testbeds were indeed operating at the SINR transitional region, that is, with intermediate level of PRR between zero and 100%. In this paper, we call this phenomenon partial interference. From the physical layer implementation perspective, the partial interference phenomenon can be viewed as a consequence/manifestation of the probabilistic nature of signal decoding in the receiver, its interaction with the wellknown capture effect [10, 11], and the specific implementation of the frame reception and capture algorithms in individual chipsets [12].
While the phenomenon of partial interference in wireless networks has been widely observed as mentioned above, its incorporation in the performance modeling of such networks is still in its infancy. Most of the efforts in this direction so far ([2, 7, 12, 13]) have been limited to the characterization of the nonbinary transitional region in the PRRversusSINR curve based on measurement data [7, 12, 13] or some analytical means [2, 14]. However, once the PRRversusSINR curve is obtained, they only resort to simulations to evaluate the effects of partial interference on the system performance.
While the phenomenon of partial interference in wireless networks has been widely observed as mentioned above, its incorporation in the performance modeling of such networks is still in its infancy. Most of the efforts in this direction so far ([2, 7, 12, 13]) have been limited to the characterization of the nonbinary transitional region in the PRRversusSINR curve based on measurement data [7, 12, 13] or some analytical means [2, 14]. However, once the PRRversusSINR curve is obtained, they only resort to simulations to evaluate the effects of partial interference on the system performance.
In this paper, our focus is to develop analytical models with tractable solutions for various types of wireless multiple access networks which can accurately capture the performance impact of partial interference. Via analytical and numerical results throughout this paper, we demonstrate that it is important to model the partial interference effects while analyzing wireless multiple access networks. This is because such considerations can result in not only significant quantitative differences in the predicted system capacity but also fundamental qualitative changes in the shape of the stability region of the systems (e.g., from a concave to a convex region).
To quantify the impact of interference on multiple access networks, we propose an analytical framework to characterize partial interference for two representative types of multiple access wireless networks, namely, the IEEE 802.11 Wireless LANs and the classical slotted ALOHA networks. For IEEE 802.11 Wireless LANs, we extend the singlechannel Markov model in [15] to take into account the unsaturated traffic conditions, the SINR attained at the receivers, and the modulation scheme employed. These modifications result in a partial interference region, which cannot be captured by the binary interference models used in previous works. We also find out the stability (admissible) region of IEEE 802.11 networks with two interfering, potentially unsaturated links numerically. For slotted ALOHA networks, we extend the model in [16] to derive the exact stability region of slotted ALOHA with two links while considering partial interference. We show that as the link separation increases, the stability region obtained expands gradually under partial interference, as in the case of 802.11.
Despite the simplicity of slotted ALOHA, characterizing its exact stability region with unsaturated links is extremely difficult and has remained to be a key open problem for decades when there are more than two, potentially unsaturated links in the system [16–23]. However, by extending the FRASA (Feedback Retransmission Approximation for Slotted ALOHA) approach [24] to model the partial interference effects, we obtain a closedform approximation for the exact stability region for any number of links.
 (1)
After reviewing related work in Section 2, we formalize the notion of partial interference in Section 3 and then demonstrate its significant performance impact on different types of wireless networks via various examples and their analytical/numerical results throughout the rest of the paper. As an illustration, we show in Section 4 that, by considering partial interference effects while scheduling traffic in a wireless network of regular topology, the gain in network capacity across unit cut can be as high as 67%.
 (2)
In Section 5, we establish a model to analyze the effects of partial interference on the throughput of IEEE 802.11 networks with unsaturated links. Our approach enables one to compute numerically the stability region of any 2link 802.11 system under unsaturated traffic conditions.
 (3)
In Section 6, we investigate the effects of partial interference on the capacity of a slotted ALOHA system with unsaturated links by (i) establishing the exact stability region in closedform for the 2link case and (ii) providing a closedform, partial characterization of the stability region of the general Mlink case.
 (4)
In Section 7, we extend the FRASA approach in [24] to yield a closedform approximation for the stability region of the general Mlink slotted ALOHA system while considering partial interference effects. The capacity region derived by our approximation and the corresponding simulation results are provided for some sample cases. Again, this is to demonstrate the potential qualitative and quantitative differences in the system capacity region when the effect of partial interference is taken into account. We then conclude the paper in Section 8.
2. Related Work
In [1], two interference models, called the protocol model and the physical model, were introduced. The protocol model states that a transmission is successful if the corresponding receiver is located inside the transmission range of the transmitter, and all other active transmitters are located outside the interference range of the receiver. In the physical model, the transmissions from other transmitters are considered as noise, and a transmission is successful if the SINR attained at the receiver exceeds a certain threshold. Based on these models, the capacities of a multihop wireless network under random and optimal node placement were derived.
In [5], the authors measured the interference among links in a singlechannel, static 802.11 multihop wireless network. They measured the interference between pairs of links by the link interference ratio and observed that this ratio exhibited a continuum between 0 and 1. In [6], two interfering links were set up in a wireless network with multiple partially overlapped channels to measure TCP and UDP throughputs of an individual link. It was found that the throughputs increased smoothly when the separation between the links increased. The throughputs increased more rapidly as the channel separation between the links increased. Such nonbinary transitional region in the link throughput (or PRR equivalently) as the receiver SINR varies has also been observed by numerous measurement studies including [2–4]. These experimental results all confirmed that the binary assumption in the protocol or physical interference models are not valid in practice.
There has been some analytical work on finding the relationship between the SINR attained at a receiver and the throughput (or PRR equivalently) achieved by the corresponding wireless link. In [14], a methodology for estimating the packet error rate in the affected wireless network due to the interference from the interfering wireless network was presented. The throughput of the affected wireless network was found to increase continuously with the SINR attained at the corresponding receiver, which increased with the separation between the networks. Similarly, [2] derived expressions for the PRR as a function of distance, radio channel parameters, and the modulation/encoding scheme used by the radio. However, they did not provide analytical model on how the PRR function would impact the performance of the corresponding networks.
In [25], the throughput achieved by an Mlink IEEE 802.11 network under physical layer capture was derived. While their analysis can be viewed as another case study of the effects of the partial interference over 802.11 networks, their approach only works for the case where all of the links are always saturated, that is, with infinite backlog at the transmitter side. In contrast, the approach proposed in Section 5 of this paper can handle unsaturated links and has provided explicit numerical solutions for the stability region of the 2link case.
The study of the stability region of user infinitebuffer slotted ALOHA was initiated by the study in [17] decades before and is still an ongoing research. The authors in [17] obtained the exact stability region when under the collision channel (i.e., binary interference) model. References [18, 19] used stochastic dominance and derived the same result as in [17] for the case of .
For general , there were attempts to find the exact stability region, but there was only limited success. Reference [21] established the boundary of the stability region, but it involves stationary joint queue statistics, which still do not have closed form to date. Instead, many researchers focused on finding bounds on the stability region for general . Reference [17] obtained separate sufficient and necessary conditions for stability. References [18, 19] derived tighter bounds on the stability region by using stochastic dominance in different ways. Reference [22] introduced instability rank and used it to improve the bounds on the stability region. However, the bounds in [18, 22] are not always applicable. Also, the bounds obtained may not be piecewise linear.
With the advances in multiuser detection, researchers also studied this problem with the multipacket reception (MPR) model. Reference [23] studied this problem in the infiniteuser, singlebuffer, and symmetric MPR case. Reference [16] considered the problem with finite users and infinite buffer. They obtained the boundary for the asymmetric MPR case with two users, and also the inner bound on the stability region for general .
3. Partial Interference—Basic Idea
As an illustration to the methodology in [14], assume the underlying modulation scheme used is binary phase shift keying (BPSK). The distance between the transmitter and the receiver and that between the interferer and the receiver are and meters, respectively. The transmission power of the transmitter and the interferer are and watts, respectively.
In general, depends on the BER, which, in turn, is a function of the SINR at the receiver as well as the specific modulation scheme being used. While we use BPSK as an example here, the actual expression for under other modulation schemes can be readily derived as shown in [2, Table ].
We observe that if the value we assign to is too large (or the threshold distance is too large), we underestimate the throughput that the links can achieve. On the other hand, if is too small (or the threshold distance is too small), we introduce excessive interference into the network. In other words, it is difficult to use a single threshold to describe accurately the relationship between interference and throughput of each link in a network.
4. Capacity Gain When Partial Interference Is Considered
In this section, we demonstrate that there is a gain in system capacity when the effect of partial interference is considered. We consider one variation of the Manhattan network [27], that is, a network consisting of a rectangular grid extending to infinity in both dimensions. The horizontal and vertical separations between neighboring stations are denoted by and , respectively.
Under infinite transmitter backlog, the packetlevel capacity of each link, that is, the maximum packet reception rate without interference, is denoted by . We assume that differential binary phase shift keying (DBPSK) is employed and a packet consists of bits. We use the tworay ground reflection model (1) as in previous section to model the path loss. To apply the physical model, we let the SINR threshold be the case that the packet error rate is , that is, , where is the bit error rate of DBPSK [26]. We let and , therefore the SINR requirement is . Assuming that there is no interferer, this SINR requirement is met when the length of a link is smaller than 493 meters.
and is the Euclidean norm. Then the length of the cut occupied by an active transmitter is the length of , and the capacity across unit cut is therefore , where is the fraction of time that a link is active.
Capacity gain in the modified Manhattan network with different lengths of links.




 % increase 

350  3.02  0.2365  2.55  0.2671  12.93% 
400  3.48  0.1796  2.73  0.2163  20.45% 
450  5.58  0.0996  3.06  0.1661  66.82% 
If we allow partial interference, the active transmitters can be packed more closely. When decreases, more spatial reuse is allowed. The increase in the density of active transmitters outweighs the degradation in capacity, so there is an increase in the capacity across unit cut. However, if decreases further, interference will be the dominant factor in determining the capacity across unit cut. Therefore, the capacity across unit cut drops, and there exists for the optimal performance under partial interference. This behavior is depicted by the blue solid line in Figure 3. The optimal value of under partial interference is , and the capacity across unit cut is bits per second per kilometer. There is a percentage increase of 66.82% in the capacity across unit cut when the effect of partial interference is considered. Similar results are shown in Table 1 and Figure 3 for meters. The percentage increase is larger when the links are longer, but the capacity achieved by each link reduces. We can view as the carrier sensing range in the modified Manhattan network with the scheduling pattern in Figure 2, as it is the smallest horizontal separation allowed by the physical model. We observe that if the length of the links increases, the carrier sensing range needs to be increased in a larger proportion. Also, this carrier sensing range is much larger than double of the length of the links, which is the usual convention used in defining the relationship between carrier sensing range and transmission range.
5. Partial Interference in 802.11
In this section, we study partial interference in 802.11 networks, the prevalent wireless random access networks. We present an analytical framework to characterize partial interference in a singlechannel wireless network under unsaturated traffic conditions, which uses 802.11b with basic access scheme and DBPSK. We show that there is a partial interference region, in which the throughput of each link increases continuously with the separation between the links in the network. As a first attempt to relate the capacityfinding problem in wireless random access networks to the stability region of such networks, we derive the admissible (stability) region of an 802.11 network with two potentially unsaturated links numerically.
5.1. The 802.11 Model
We present our framework to characterize partial interference in a wireless network with random access protocols. In this framework, we derive the transmission probabilities and the packet corruption probabilities of the links in the network. is the probability that a station transmits in a randomly chosen slot, while is the probability that a packet is received with error.
 (i)
The network consists of two links and , where and denote the transmitter and the receiver of the links, respectively, .
 (ii)
There are a constant buffer nonempty probability that the transmission buffer of is nonempty and a constant channel idle probability that senses the channel to be idle, .
 (iii)
transmits with power , and the background noise power at is , .
 (iv)
Channel defects like shadowing and fading are neglected, and a generic path loss model is used to model the wireless channel, where is the propagation distance, is the path loss exponent, and is a constant.
 (v)
The interference from other transmitters plus the receiver background noise is assumed to be Gaussian distributed.
 (vi)
All bits in a packet must be received correctly for correct reception of the packet.
 (vii)
The size of an acknowledgement is much smaller than that of the payload, so the bit errors on acknowledgement are negligible.
We follow the approach as in [15], using a discretetime Markov chain to model the 802.11 Distributed Coordination Function (DCF) and obtain the transmission probability of a station. An ordered pair is used to denote the state of the Markov chain, where represents the backoff stage and is the current backoff counter value. In stage , is in the range , where is the contention window size in stage . is the maximum number of backoff stages. However, there are some discrepancies between the model in [15] and the actual behavior of 802.11 DCF. First, the model assumes that a station retransmits indefinitely until the packet is successfully transmitted. This assumption is inconsistent with 802.11 basic access scheme. Also, the model does not account for the unsaturated traffic conditions, which is the scenario appeared in practical situations.
The details of the Markov chain and the derivation of this equation can be found in [31].
The packet corruption probability is calculated according to the modulation scheme used in the PHY layer, the distance between the transmitter and the receiver, and the existence of nearby interferer(s). For a fixed carrier sensing threshold , we differentiate into two cases, whether both transmitters can sense the transmission of each other or not.
where , , and represent the number of bits in the PHY header, the MAC header, and the payload, respectively.
The channel idle probability is defined as follows. If can sense the transmission of , then will consider the channel to be idle whenever is inactive, that is, ; otherwise always senses the channel to be idle and .
Similarly, we can obtain three equations for link . With these six equations we can solve for the variables , , , , , by Newton's method [32] and obtain the loadings of these two links by (14).
5.2. Some Analytical Results
Parameters used for the analytical results.
 192 bits 
 272 bits 

 7 
 5 
 9020 s 
 9020 s 
 20 s 
 32 
,  24.5 dBm  ,  88 dBm 
,  1  ,  1.5 m 
In the following we attempt to find the maximum carried loads of each link in various scenarios. One observation from solving the system of equations in Section 5.1 is that the carried load will be smaller than the offered load when the offered load is too large. This corresponds to the instability of 802.11 observed in previous works (e.g., [15]). Therefore, we use binary search to find the maximum carried load under stable conditions. Initially, the search range for the offered load is between 0 and 1 Mbps. We choose the midpoint of the search range to be the offered load and solve the system of equations. If the resultant carried load is the same as the offered load, the offered load can be increased, and the next search range will be the upper half of the original one. Otherwise, the offered load results in instability, and the next search range will be the lower half of the original one. This procedure is repeated until the search range is sufficiently small.
Consider the curve corresponding to the carrier sensing threshold of −78 dBm in Figure 6(c), which is a common value used in NS2 simulation and the practical value used in Orinoco wireless LAN card. The corresponding carrier sensing range is 550 meters, which is in line with the carrier sensing range used in practice. In our model, we assume that carrier sensing works when the separation is within the carrier sensing range and fails otherwise and use two different sets of equations to model the system in these situations. Therefore there is an abrupt change in the aggregate throughput when the separation equals the carrier sensing range. If there is no carrier sensing in the system, the aggregate throughput will reduce to zero smoothly when the link separation reduces.
The curve in Figure 6(c) can be divided into three parts according to the link separation . When meters, both transmitters are in the carrier sensing range of each other. As a result, at most only one transmitter is active at a time. If meters, the transmitters are unaware of the existence of each other, and they contend for the wireless channel as if there were no interferers nearby. When meters, the separation is so large that there will not be any interference between the links. When lies between 550 and 800 meters, the aggregate throughput of the links increases smoothly as increases. We label this range of as the partial interference region. The existence of this partial interference region suggests that the interference models proposed by [1] that a single threshold can represent the interference relationship in wireless networks may be overly simplified.
The width of this partial interference region depends on the carrier sensing threshold used. Smaller , for example, −80 dBm, results in a narrower partial interference region as in Figure 6(d). Simultaneous transmissions are allowed only for the links separated far enough, and the throughput is suppressed significantly. For larger , for example, −75 and −70 dBm, more spatial reuse is allowed, and the width of the partial interference region is larger, as shown in Figures 6(a)6(b). However, excessive interference is introduced for larger , so there is a reduction in the aggregate throughput.
5.3. Admissible (Stability) Region
Although we are able to compute the admissible region for a twolink 802.11 network numerically, the closedform expression for the admissible region is unknown. Also, for an 802.11 network with two links, we have to solve a system of six nonlinear equations to compute the admissible region. When the number of links in the network grows, the number of equations involved will increase, and the system of equations will be more difficult to solve. Therefore the computation of the admissible region of general 802.11 networks seems to be forbiddingly intractable.
6. Partial Interference in Slotted ALOHA
In order to obtain insights in the stability region of general 802.11 networks, in this section, we study the stability of slotted ALOHA, which is a simpler random access protocol, under the assumptions of finite links and infinite buffer.
6.1. The FiniteLink Slotted ALOHA Model
Let be the set of links in the slotted ALOHA system. Time is slotted. The following assumptions apply to all links . Let and be the transmitter and the receiver of link , respectively. has an infinite buffer. The packet arrival process at is Bernoulli with mean and is independent of the arrivals at other transmitters. attempts a virtual transmission with probability , that is, if its buffer is nonempty, attempts an actual transmission with probability ; otherwise, always remains silent. Also define .
In the system, each time slot is just enough for transmission of one packet. Packets are assumed to have equal lengths. We assume that transmission results are independent in each slot. For , let be the probability that the transmission on link is successful when is the set of active transmitters. depends on the SINR at the receiver and the modulation scheme used. We also assume that the transmitters know immediately the transmission results, so that the transmitters remove successfully transmitted packets and retain only those unsuccessful ones.
We let be the queue length in at the beginning of slot and use an dimensional Markov chain to represent the queue lengths in all transmitters. We denote by the number of packets arrived at in slot and the number of packets successfully transmitted in slot by when . Then , where is used to account for the case that there is no packet transmitted when . We use the definition of stability in [16, 21, 22].
Definition 1.
then the process is substable. The process is unstable if it is neither stable nor substable.
The stability problem of slotted ALOHA we consider here is to determine whether the slotted ALOHA system with the set of links is stable given the parameters and . We use the result from [34]. On the assumption that the arrival and the service processes of a queue are stationary, the queue is stable if the average arrival rate is less than the average service rate, and the queue is unstable if the average arrival rate is larger than the average service rate. We also define the slotted ALOHA system to be stable when all queues in the system are stable.
6.2. Stability Region of 2Link Slotted ALOHA under Partial Interference
Similarly, we can derive expressions for and under binary and partial interference.
The union of these two regions constitutes the inner bound on the stability region of the original system .
The reason for the union of these two regions to be the outer bound on the stability region follows from the indistinguishability argument [16, 18]. Consider the dominant system . With a particular initial condition on the length of the queues, if the queue in is unstable, it is equivalent to the case that the queue in never empties with nonzero probability. Then and will be indistinguishable, in the sense that the packets transmitted from in are always real packets and is also unstable. Therefore, the union of the regions defined by (29) and (30) is the exact stability region for .
6.3. Some Illustrations
Parameters used for the analytical results.
,  24.5 dBm  ,  88 dBm 

,  1  ,  1.5 m 
 0.001  ,  450 m 
Figure 9(b) shows the corresponding results under partial interference. When the link separation is small, the amount of interference is so large that partial interference degenerates to the collision channel. As the link separation increases, the stability region expands gradually and changes from nonconvex to convex. At another extreme, when the links are sufficiently far apart, partial interference is identical to the orthogonal channel. Therefore, partial interference can be viewed as a generalization of binary interference that it interpolates the transition from the collision channel to the orthogonal channel. Notice that the results here are similar to the case in 802.11, therefore our results should be applicable to networks with practical random access protocols like 802.11.
6.4. Partial Characterization of the Stability Region for the MLink Case
to be a corner point corresponding to the case that is the set of persistent links. Notice that RHS of (35) is zero when . Then we obtain the following theorem.
Theorem 2.
All corner points lie on the boundary of the stability region.
Proof.
Refer to Appendix A.
By using stochastic dominance and the indistinguishability argument, we obtain the following theorem.
Theorem 3.
Let , be two corner points such that . Then the line segment joining these two points lies on the boundary of the stability region. This line segment represents the case that is the set of persistent links while is the only nonempty nonpersistent link in the system.
Proof.
Refer to Appendix B.
7. Stability Region of the General MLink Slotted ALOHA System under Partial Interference
Theorems 2 and 3 cover all cases with zero or one nonempty nonpersistent link in an Mlink system, respectively. However, if there are at least two nonempty unsaturated links, the stationary joint queue statistics must be involved in calculating the boundary. Unless we are able to compute the stationary joint queue statistics in closedform, we are unable to solve the capacityfinding problem, even assuming one of the simplest random access protocol, that is, slotted ALOHA. In fact, the characterization of the exact stability region of a general Mlink slotted ALOHA system with nonpersistent links has remained to be a key open problem for decades when [16–23] even under the simplified binary interference model.
To overcome the problem caused by the stationary joint queue statistics, we have introduced the FRASA (Feedback Retransmission Approximation for Slotted Aloha) model in [24] to obtain a closedform approximation for the stability region of a general Mlink slotted ALOHA system under binary interference (i.e., simple collision channel) assumptions. We refer the readers to [24, 31] for a detailed description of the FRASA approach. In the following subsection, we extend the model and analysis in [24] to cover the partial interference case. We remark that our results here automatically apply to the binary interference case if we allow to be either zero or one only by comparing the corresponding SINR, that is, , against a predefined threshold , as illustrated in (32) and (33).
7.1. FRASA under Partial Interference
with and is between zero and one for all . is the successful transmission probability vector under partial interference.
With this parametric form, we obtain the stability region of FRASA under partial interference as in the following theorem.
Theorem 4.
with and is between zero and one for all . Then , the stability region of FRASA under partial interference, is enclosed by , in the positive orthant.
Proof.
Refer to Appendix C.
7.2. Simulation Results
8. Conclusion
In this paper, we have introduced the notion of partial interference in wireless multiple access systems and illustrated the potential gain in system capacity when the effect of partial interference is taken into account. In particular, we characterize the stability region of IEEE 802.11 networks under partial interference with two potentially unsaturated links numerically. We also derive the stability region of slotted ALOHA networks under partial interference with two links analytically and obtain a partial characterization of the boundary of the stability region for the case of more than two, potentially unsaturated links in a slotted ALOHA system. By extending the FRASA model, we provide a closedform approximation for the stability region for general Mlink slotted ALOHA system under partial interference effects. Our analyses demonstrate that partial interference considerations can result in not only significant quantitative differences in the predicted system capacity but also fundamental qualitative changes in the shape of the stability region of the systems. This highlights the need of capturing the partial interference effects instead of adopting the conventional, overly simplified binary interference models while analyzing wireless MAC protocols.
Appendices
A. Proof of Theorem 2
Therefore lies on the boundary.
B. Proof of Theorem 3
The necessity follows directly from the indistinguishability argument. We observe that varies linearly with on the boundary, . It is trivial that and correspond to and , respectively.
C. Proof of Theorem 4
where we assume that , which is in general true because the probability of successful transmission is larger when there are less interferers. This implies that the stability region of FRASA obtained by assuming all links in in persistent conditions is contained inside the stability region of FRASA obtained by assuming all links in in persistent conditions. Hence, to obtain the boundary of stability region of FRASA under partial interference, we only have to consider the case that only one link is persistent. Then we can use the parametric form (40) to obtain the boundary when . By repeating over all possible values of , we get the desired result.
Declarations
Acknowledgments
The material in this paper was presented in part at the IEEE International Conference on Communications 2007, Glasgow, Scotland, June, 2007 and the 18th Annual IEEE International Symposium on Personal, Indoor and Mobile Radio Communications, Athens, Greece, September, 2007.
Authors’ Affiliations
References
 Gupta P, Kumar PR: The capacity of wireless networks. IEEE Transactions on Information Theory 2000, 46(2):388404. 10.1109/18.825799MathSciNetView ArticleMATHGoogle Scholar
 Zuniga M, Krishnamachari B: Analyzing the transitional region in low power wireless links. Proceedings of the 1st Annual IEEE Communications Society Conference on Sensor and Ad Hoc Communications and Networks (SECON '04), June 2004, Santa Clara, Calif, USA 517526.Google Scholar
 Kim W, Lee J, Kwon T, Lee SJ, Choi Y: Quantifying the interference gray zone in wireless networks: A Measurement Study. Proceedings of the IEEE International Conference on Communications (ICC '07), June 2007, Glasgow, UK 37583763.Google Scholar
 Maheshwari R, Jain S, Das SR: A measurement study of interference modeling and scheduling in lowpower wireless networks. Proceedings of the 6th ACM Conference on Embedded Networked Sensor Systems (SenSys '08), November 2008, Raleigh, NC, USAGoogle Scholar
 Padhye J, Agarwal S, Padmanabhan VN, Qiu L, Rao A, Zill B: Estimation of link interference in static multihop wireless networks. Proceedings of the Internet Measurement Conference, October 2005, Berkeley, Calif, USAGoogle Scholar
 Mishra A, Rozner E, Banerjee S, Arbaugh W: Exploiting partially overlapping channels in wireless networks: turning a peril into an advantage. Proceedings of the Internet Measurement Conference, October 2005, Berkeley, Calif, USAGoogle Scholar
 Maheshwari R, Cao J, Das SR: Physical interference modeling for transmission scheduling on commodity WiFi hardware. Proceedings of the 24th Annual Joint Conference of the IEEE Computer and Communications Societies (INFOCOM '09), April 2009, Rio de Janeiro, Brazil 26612665.Google Scholar
 Aguayo D, Bicket J, Biswas S, Judd G, Morris R: Linklevel measurements from an 802.11b mesh network. Proceedings of the Conference on Computer Communications (SIGCOMM '04), September 2004, Portland, Ore, USA 121131.Google Scholar
 Camp J, Robinson J, Steger C, Knightly E: Measurement driven deployment of a twotier urban mesh access network. Proceedings of the 4th International Conference on Mobile Systems, Applications and Services (MobiSys '06), June 2006, Uppsala, Sweden 96109.Google Scholar
 Lee J, Kim W, Lee SJ, Jo D, Ryu J, Kwon T, Choi Y: An experimental study on the capture effect in 802.11a networks. Proceedings of the 2nd ACM International Workshop on Wireless Network Testbeds, Experimental Evaluation and Characterization (WiNTECH '07), July 2007, Montreal, Canada 1926.View ArticleGoogle Scholar
 Kochut A, Vasan A, Shankar AU, Agrawala A: Sniffing out the correct physical layer capture model in 802.11b. Proceedings of the 12th IEEE International Conference on Network Protocols (ICNP '04), October 2004, Berlin, Germany 252261.Google Scholar
 Lee J, Ryu J, Lee SJ, Kwon T: Improved modeling of IEEE 802.11a PHY through finegrained measurements. Computer Networks 2010, 54(4):641657. 10.1016/j.comnet.2009.08.003View ArticleMATHGoogle Scholar
 Santi P, Maheshwari R, Resta G, Das S, Blough DM: Wireless link scheduling under a graded SINR interference model. Proceedings of the 2nd ACM international Workshop on Foundations of Wireless Ad hoc and Sensor Networking and Computing (FOWANC '09), 2009, New Orleans, La, USA 312.View ArticleGoogle Scholar
 Shellhammer SJ: Estimation of packet error rate caused by interference using analytic techniques—a coexistence assurance methodology. In IEEE 802.19 Technical Advisory Group Document Archive. Qualcomm, San Diego, Calif, USA; 2005.Google Scholar
 Bianchi G: Performance analysis of the IEEE 802.11 distributed coordination function. IEEE Journal on Selected Areas in Communications 2000, 18(3):535547. 10.1109/49.840210View ArticleGoogle Scholar
 Naware V, Mergen G, Tong L: Stability and delay of finiteuser slotted ALOHA with multipacket reception. IEEE Transactions on Information Theory 2005, 51(7):26362656. 10.1109/TIT.2005.850060MathSciNetView ArticleMATHGoogle Scholar
 Tsybakov BS, Mikhailov VA: Ergodicity of a slotted ALOHA system. Problems of Information Transmission 1979, 15(4):7387.MathSciNetGoogle Scholar
 Rao RR, Ephremides A: On the stability of interacting queues in a multipleaccess system. IEEE Transactions on Information Theory 1988, 34(5):918930. 10.1109/18.21216MathSciNetView ArticleMATHGoogle Scholar
 Szpankowski W: Stability conditions for multidimensional queueing systems with computer applications. Operations Research 1988, 36(6):944957. 10.1287/opre.36.6.944MathSciNetView ArticleMATHGoogle Scholar
 Anantharam V: The stability region of the finiteuser slotted ALOHA protocol. IEEE Transactions on Information Theory 1991, 37(3):535540.View ArticleGoogle Scholar
 Szpankowski W: Stability conditions for some multiqueue distributed systems: buffered random access systems. Advances in Applied Probability 1994, 26: 498515. 10.2307/1427448MathSciNetView ArticleMATHGoogle Scholar
 Luo W, Ephremides A: Stability of N interacting queues in randomaccess systems. IEEE Transactions on Information Theory 1999, 45(5):15791587. 10.1109/18.771161MathSciNetView ArticleMATHGoogle Scholar
 Ghez S, Verdú S, Schwartz SC: Stability properties of slotted aloha with multipacket reception capability. IEEE Transactions on Automatic Control 1988, 33(7):640649. 10.1109/9.1272View ArticleMathSciNetMATHGoogle Scholar
 Hui KH, Yue OC, Lau WC: FRASA: feedback retransmission approximation for the stability region of finiteuser slotted ALOHA. Proceedings of the International Conference on Network Protocols (ICNP '07), October 2007 330331.Google Scholar
 Chang H, Misra V, Rubenstein D: A general model and analysis of physical layer capture in 802.11 networks. Proceedings of the 25th IEEE International Conference on Computer Communications (INFOCOM '06), April 2006, Barcelona, BrazilGoogle Scholar
 Rappaport TS: Wireless Communications: Principles and Practice. 2nd edition. Prentice Hall, Upper Saddle River, NJ, USA; 2002.MATHGoogle Scholar
 Mergen G, Tong L: Stability and capacity of regular wireless networks. IEEE Transactions on Information Theory 2005, 51(6):19381953. 10.1109/TIT.2005.847728MathSciNetView ArticleMATHGoogle Scholar
 Wu H, Peng Y, Long K, Cheng S, Ma J: Performance of reliable transport protocol over IEEE 802.11 wireless LAN: analysis and enhancement. Proceedings of the 21st Annual Joint Conference of the IEEE Computer and Communications (INFOCOM '02), June 2002 599607.Google Scholar
 Wireless LAN Medium Access Control (MAC) and Physical Layer (PHY) Specifications IEEE Std. 802.11, 1999Google Scholar
 Malone D, Duffy K, Leith D: Modeling the 802.11 distributed coordination function in nonsaturated heterogeneous conditions. IEEE/ACM Transactions on Networking 2007, 15(1):159172.View ArticleGoogle Scholar
 Hui KH: Characterizing Interference in Wireless Mesh Networks, M.S. thesis. The Chinese University of Hong Kong, Shatin, Hong Kong; 2007.Google Scholar
 Kincaid D, Cheney W: Numerical Analysis: Mathematics of Scientific Computing. 3rd edition. Brooks/Cole, Pacific Grove, Calif, USA; 2002.MATHGoogle Scholar
 The Network Simulator—ns2 http://www.isi.edu/nsnam/ns/
 Loynes RM: The stability of a queue with nonindependent interarrival and service times. Proceedings of the Cambridge Philosophical Society 1962, 58: 494520.MathSciNetView ArticleGoogle Scholar
Copyright
This article is published under license to BioMed Central Ltd. This is an open access article distributed under the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.