So near, and yet so far: Managing ‘far-away’ interferers in dense femto-cell networks

*+Huiguang Liang, *Hyong S. Kim, +Wai Leong Yeow, +Hwee-Pink Tan * Dept. of Electrical and Computer Engineering

Carnegie Mellon University Pittsburgh, PA, United States of America

+ Networking Protocols Department Institute for Infocomm Research 1 Fusionopolis Way, Singapore

{huiguang, kim}@ece.cmu.edu, {wlyeow, hptan}@i2r.a-star.edu.sg

Abstract—We expect femto-cells to be massively and densely deployed in the future. Numerous existing works on femto-cell interference management assume that the local topology of interfering femto-cells can be sufficiently approximated through sensing, if not already known in advance. We show that this assumption results in poor throughput performance in dense femto-cell networks. For some cell-edge users, using conventional sensing in dense deployments can result in almost 50 times less instantaneous throughput, as compared to having oracular knowledge of interference topology. This sub-optimality is caused by “far-away” interferers. These are femto-cells that are deployed just far enough such that their presence will not be detected by conventional sensing. We then introduce a mobile sensing scheme to detect these “far-away” interferers by exploiting the inherent mobility of femto-cell users. We show through packet-level simulation that this sensing scheme is able to better approximate the interference topology. This results in significantly improved performance over conventional sensing, in dense deployment scenarios.

I. INTRODUCTION In recent years, the concept of small and inexpensive end-

user deployed femto base-stations (fBSs) was conceived to augment wireless connectivity provided by traditional base-stations. These fBSs are typically designed to provide cellular coverage to a residential apartment or a small house, by forming femto-cells with coverage radii of around 10 meters. In LTE networks, they are also known as Home eNodeBs (HeNBs). They are envisioned to be deployed as customer-premises equipment, and exploit the customers' existing wired broadband connectivity to provide extended cellular coverage. Because of this ad-hoc deployment model, conventional planning, maintenance and management approaches such as RF drive-tests, frequency planning and assignment, manual installation, inspection and configuration, will likely be prohibitively expensive.

II. BACKGROUND REVIEW AND PROBLEM DESCRIPTION Prior work has considered the problem of allocating

spectrum resources to a neighborhood of arbitrarily-deployed

This work was supported in part by NSF grant 0756998, CyLab grants ARO DAAD19-02-1-0389 and W911NF-09-1-0273, and the Agency for Science, Technology and Research (A*STAR) Singapore.

femto-cells, in order to minimize mutual down-link interference [2][3][7]. In a conventional cellular network, the operator is able to manually assign spectrum resources based on intricate knowledge of base-station locations, as well as careful measurements at deployment sites to check for interference [1]. However, operators generally do not have physical access to a femto-cell’s deployment site. Femto-cells will potentially be deployed in large numbers; hence any approach that requires manual measurements or physical site access would be impractical. The operator also does not know where an fBS is exactly installed, and has no easy way to determine precisely the deployment location. Global Positioning Satellite (GPS) localization may provide position information with a small error, but they will not work indoors, where femto-cells are designed to be primarily deployed. Some proprietary indoor localization services exist, such as Skyhook. These however incur additional licensing costs, and their performances are typically dependent on the existence of known location reference points (e.g. a known WiFi AP) near the fBSs. IP localization is not useful, since it typically provides only the location of the DHCP server and not of the actual customer premise. The registered address of the customer is also not a dependable measure of physical femto-cell location, since it gives only the billing address. It is not guaranteed that the femto-cell will not be deployed elsewhere. It should also be noted that knowledge of an fBS’ location is inherently insufficient for frequency planning as its exact radio frequency (RF) operating environment is not known.

Hence, it is generally suggested that a conventional sensing approach will enable a femto-cell to be able to detect other nearby femto-cells [2]. This approach advocates that femto-cells periodically listen for the transmissions of other femto-cells, in order to learn of each other’s existence. Otherwise, femto-cells can direct their associated subscriber stations (SSs) to scan for transmissions of other femto-cells, and report their findings to their associated base-stations. In this way, it is often assumed that femto-cells can be locally divided into independent local neighborhoods, as illustrated in Figure 1. Interference avoidance and spectrum allocation techniques can then be independently applied to each neighborhood. Besides orthogonal frequency assignment techniques, power control is also another popular approach to reduce co-channel interference. However, emerging cellular standards such as 3GPP LTE [8] only specifies power control mechanisms for the uplink, while leaving other inter-cell interference coordination (ICIC) methods to deal with downlink interference [14].

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By segmenting femto-cells into independent neighborhoods, and applying interference avoidance techniques to each neighborhood separately, the implied assumption is that the transmissions of the femto-cells in one neighborhood do not significantly affect the transmissions of those in another neighborhood. However, when the density of femto-cells in a given locality (e.g. within an apartment building) increases, the distance between each neighborhood decreases. If the number of surrounding neighborhoods is sufficiently large and close together, they will inevitably result in interference with one another. This type of interference cannot be captured by conventional sensing alone, as the sensing range is inherently limited. A femto-cell or its associated SSs will only be able to sense a neighbor’s presence if it receives the neighbor’s transmission coherently (i.e. above the SINR required to decode the transmission). We refer to femto-cells that are just outside of sensing range as “far-away” interferers. These are illustrated in Figure 2. In [9], the problem of ignoring “far-away” interferers is described, but in the context of wireless multi-hop networks. The authors found that, by reducing the presence of interference to a binary decision using the protocol model instead of the physical transmission model [4], typical simulation results of throughputs can be over-estimated by 210%. Our work complements this by demonstrating that ICIC algorithm designers should consider local interference topology beyond using conventional sensing, since ignoring “far-away” interferers will evidently result in debilitating interference for dense fBS deployments. Hence, in this paper, we focus on how conventional sensing can be improved, so that downlink interference coordination and avoidance techniques that rely on interference topology knowledge can be enhanced.

III. PAPER CONTRIBUTIONS The contributions of this paper are as follows:

• We show through in-depth packet-level simulations that using conventional sensing to discover local interference topology may not result in satisfactory interference avoidance, due to undiscovered interferers. • We demonstrate on a per-user basis that users, by using conventional sensing, may suffer from poor performance. The onus is on the operator to resolve or avoid this situation. • We extend the conventional sensing range by implementing a “mobile sensing” scheme. This scheme is described in Section IV. • We show that our mobile sensing scheme reduces the effects of “far-away” interference and improves throughput in comparison with the conventional sensing scheme in

dense fBS deployment scenarios, yet remains simple with little overhead.

IV. MOBILE SENSING In this paper, we propose a simple, but powerful extension

to conventional sensing, in order to expand the potential range of fBS neighbor discovery. This therefore leads to better approximations of an fBS’s interference topology. This proposed technique exploits the inherent mobility of cellular users to discover nearby fBSs that are just out of conventional sensing range. We term this approach as mobile sensing. The key idea of mobile sensing is to make mobile SSs broadcast their last associated cell information periodically for some duration, after they disassociate from an fBS. This information contains the network connectivity information of last associated cell (e.g. fBS MAC address, IP address, any unique ID). This allows an overhearing fBS to identify the advertised fBS. It should also include the broadcasting device’s current time, and the time which it disassociated with its last-associated cell. Suppose fBS B overhears and decodes successfully an opportunistic broadcast from a mobile SS which was last associated with fBS A. fBS B then informs its management gateway of this received broadcast, together with the connectivity information of fBS A contained in the broadcast. The management gateway can then infer that fBS A and fBS B are within mobile sensing range of each other, and further infer that they should be connected with an edge in their interference topology graph. This example is illustrated in Figure 3.

A. Mobile sensing range Assuming typical pedestrian walking speeds, the parameter

that determines the potential mobile sensing range is the duration for which an SS broadcasts its last-associated-cell

Figure 1: An illustration of two neighborhoods derived from

conventional sensing. They are assumed to be independent (e.g. they can be colored independently). An edge connecting two fBSs indicates

that their transmissions are assumed to mutually interfere.

Figure 2: Frequency assignment in femto-cellular networks using conventional sensing. Co-channel femto-cells that are beyond sensing range can be numerous and their sum contribution to interference can

quickly become debilitating.

Figure 3: By using mobile sensing, neighbors that are typically outside

of conventional sensing range can be discovered, and the inferred interference topology can be colored such that conventional “far-

away” interference is better mitigated.

A

B

C

D

E

F

Neighborhood

Neighborhood

fBS A

fBS C

fBS B

fBS D

fBS E

fBS F

Physical topology Assumed interference topology

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information, after it disassociates from that cell. In [13], it is found that a pedestrian walking speed is approximately 1.22 m/s. Hence, depending on the desired sensing range and the assumed mobility model, the broadcast duration can be set accordingly. In Section V, we will explain that for our evaluation setup, where fBSs are densely deployed in a typical multi-level apartment building, a broadcast duration of about 6.5 s is sufficient to increase the median percentage of discovered neighbors (within a 30 m radius) from 12% to 33%. We will demonstrate appreciable throughput improvements due to better topology approximations in Section VI.

B. Channel access for SS broadcast One important feature of mobile sensing is that it does not

require additional infrastructure, special-purpose hardware or extensive modifications. However, spectral resources have to be made available for mobile SSs to asynchronously and opportunistically broadcast their last-association information. A medium access protocol must also regulate the way SS uses the channel. The SS should not have to perform a full association setup with every cell it comes into range with, since latency, bandwidth and battery life becomes significant considerations. Fortunately, cellular standards such as WiMAX [5] already define similar methods in which an SS can asynchronously signal and exchange information with a BS, for the purposes of ranging, network entry, and bandwidth requests, amongst others. This is done by allowing multiple-access to these signaling channels using predefined CDMA codes. In this paper, we assume an asynchronous broadcast channel exists for SSs to make opportunistic broadcasts. Since the broadcast packets are intended to be very small and limited (they only include timing and connectivity information), we assume that congestion, contention and broadcast storms will not typically pervade in a system using mobile sensing.

V. EXPERIMENTAL SETUP We use OPNET [11] to perform detailed femto-cell packet-

level simulations. The fBS, SS, and management server models are implemented on top of the OPNET WiMAX model, which uses OFDMA, the same downlink multiple access method as 4G LTE-Advanced. The OPNET built-in models are modified as needed to provide non-standard functionality, such as adding sensing support. Femto-cells are configured to transmit at a power such that a SS receives a downlink signal-to-noise ratio (SNR) of more than 9.4 dB within 10 meters from the fBS, in order to decode the minimum modulation and coding scheme, which is QPSK ½ [10]. Hence, the nominal cell coverage radius, or cell edge, is 10 m. The system has a total of 60 frequency sub-channels centered at 1.9 GHz with 20 MHz of overall bandwidth, following the WiMAX standard [5]. As the majority of femto-cells will be deployed indoors, we model indoor propagation effects such as path-loss and multi-path in accordance with ITU recommendations found in [6].

We simulate a 100 femto-cell dense deployment within an apartment-like scenario, as shown in Figure 4. Femto-cells are divided into 4 levels of deployment, with 25 fBSs per level. Femto-cells on each level are laid out in a 5 × 5 square grid with a predefined separation distance s, varied over 10.0 m, 20.0 m, 30.0 m, and 40.0 m horizontally and vertically from its adjacent neighbors, with a uniformly random jitter. This simulates dense deployment densities in apartment buildings of

different sizes. Each femto-cell has 1 subscriber-station associated with it. The SS is correspondingly placed at a radial distance d, which is varied from 5.0 m, 7.5 m to 10.0 m from the center of its corresponding femto-cell, but at random azimuth angles drawn uniformly from 0 to 2π radians. We then saturate the link layer with a full constant bit-rate layer-3 packet flow for each fBS-SS pair. For each of the 4 mean separation distances s, we generate 5 random topologies with the same parameters. We then simulate each topology over 5 different random seeds and aggregate their results whenever necessary.

We chose this deployment setting in order to demonstrate a realistic scenario in which “far-away” interference will likely occur. In other deployment scenarios, such as in a rural, or suburban setting, the likelihood of numerous “far-away” interferers being co-located together is low. Hence conventional sensing may suffice in such situations. Indeed, we show in Section VI that fBSs separated beyond 40 m contribute negligible “far-away” interference to one another, while those within 30 m can cause debilitating interference to each other’s downlink transmission.

Figure 4: Three-dimensional distribution of fBSs in a multi-floor,

apartment building-like scenario. 25 femto-cells are laid out on each floor, with a total of 4 floors simulated.

A. Conventional Sensing We implement the conventional sensing approach on all

femto-cells and SSs. Each element will use this approach to approximate its own local interference topology. A neighbor is considered to be found when its transmission can be picked up by a femto-cell or its associated SS with a SINR of more than 6.4 dB, which is required to decode the neighbor’s pilot tone coded with BPSK ½ [10]. Whenever a neighboring femto-cell is identified through sensing, the local management server is contacted and updated with this information. The server stores the topology in the form of a graph, with vertices representing fBSs. Each time a neighbor is discovered, the server inserts a bi-directional edge connecting the corresponding vertices of the neighboring pairs.

B. Mobile Sensing In the mobile sensing scheme, we first conduct

conventional sensing and build an initial topology as outlined in the prior section. We then simulate the mobility of each mobile SS using a two-phase mobility scheme, assuming a 1.22 m/s walking speed [13]. When a mobile SS is within the

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coverage area of its own cell, it follows a random walk model with no pause time. If an SS moves beyond the coverage area of its fBS, it is considered to have disassociated with its fBS, and will then follow a ‘corridor-mobility’. The mobile SS essentially seeks out a shortest path to the corridor, and then moves in either clockwise or anti-clockwise direction along the corridor with equal probability.

At the moment where each SS disassociates with its “home” fBS, it begins broadcasting its last known association every 100 ms, resulting in a broadcast of 10 beacons per second, which is a typical beaconing interval in wireless local area networks like IEEE 802.11. The SS continues to broadcast for 6.5 s (corresponding to moving approximately 8 m from the SS’s start point) after disassociation. Whenever an fBS “hears” the advertisement of a new neighbor from one of the mobile SSs, it contacts the local management server and instructs the server to update the topology with a new edge between the fBSs. Information contained in the advertisement, as described in Section IV, is also forwarded to the server. Empirically, if the SS walks in a straight line from the center of its cell towards the center of a neighboring cell, the resulting mobile sensing distance is about 33 m in our setup. However, we find that majority of the neighbors discovered lies within a 30 m radius, due to our two-phase random mobility model. We chose the broadcast period of 6.5 s to correspond to this 30 m sensing distance, because neighboring co-channel fBSs beyond that radial distance contributes negligible interference in our experiments, as explained in Section VI-B. If these fBSs were to be sensed, interference avoidance and spectrum allocation techniques can become over-conservative (e.g. avoid assigning the same channels to a pair of fBSs far away from each other, when their actual co-channel mutual interference is vanishingly small and channel reuse is actually beneficial rather than debilitating).

C. Orthogonal frequency assignment by management server In our setup, the management server uses a greedy

constrained central graph coloring approach to assign orthogonal frequencies to each fBS. In this approach, colors are sequentially assigned in greedy fashion to fBSs, such that no two fBSs connected by an edge in the interference topology are assigned the same color. We denote the number of colors used in this fashion as n. The 60 available sub-channels are then mapped to colors, such that each color has orthogonally 60 n sub-channels. We limit n to 10, so as to allow each fBS to have at least 6 sub-channels. When an fBS A cannot be assigned a color that is not already assigned to any of its neighbors (due to the constraint on n), it will be given a color that is used by another fBS B that is approximately furthest away from node A, in order to limit interference. The server infers this by looking at the elapsed time from which the UE disassociates from its fBS A, to the time where another fBS B picks up its opportunistic broadcast. The longer this elapsed time is, the longer the assumed distance between fBS A and fBS B will be. When all fBSs are eventually colored this way, the server then informs each fBS of its assigned spectrum and the fBSs will start using their assigned spectrum upon their next frame transmission. We used this approach because of its ease of implementation. However, we believe any ICIC approach that

relies on knowledge of interference topologies can benefit from mobile sensing.

D. Number of mobile users and system temporal evolution We evaluate the temporal evolution of a system employing

mobile sensing by evaluating different probabilities p of each fBS having an SS which is mobile, instead of being stationary. For example, if p = 0.6, this means that 60% of all the fBSs have mobile SSs performing mobile sensing, while the rest of the fBSs have a stationary SS associated with each fBS. We evaluate p from 0 to 1.0, in increments of 0.2. We expect that in a practical deployment, the system should evolve such that when it is first deployed, conventional sensing provides the only clue about the system’s interference topology. This corresponds to p = 0. As the system evolves over time, more and more fBSs experience some form of SS mobility (e.g. users follow a typical diurnal cycle by going to work, returning home), and thus benefits more and more from mobile sensing. We therefore emulate this temporal evolution by evaluating the performance of the system at different values of p.

VI. RESULTS AND DISCUSSION

A. Efficacy of neighbor discovery with mobile sensing In Figure 5, we first examine how effective mobile sensing

is, in terms of how many neighbors an fBS can potentially discover. We illustrate the result for s = 10 m, where fBSs are initially deployed in a square grid with an inter-fBS separation distance of 10 meters horizontally and vertically. We chose to illustrate this result because the problem of “far-away” interferers is expected to be most severe in the densest deployment. The cumulative distribution function of the percentage of neighbors, which we define as fBSs that are within a radial distance of 30 m, discovered by each fBS is illustrated in Figure 5.

With these evaluated parameters, conventional sensing results in just a median of 11.9% and mean of 12.2% of neighbors discovered, while the top-performing fBS finds only 32.4% of neighbors. This suggests that a significant number of “far-away” interferers exist, and remain undiscovered by conventional sensing alone. In order to improve the sensing capability and hence the discovery of interference topology, we

Figure 5: CDF of the percentage of neighbors (that are within 30 m

radial distance) discovered by each fBS, for s = 10.0 m.

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examine how mobile sensing improves on this result. We evaluated varying percentages of fBSs with mobile SSs, and explained in Section V-D that this in essence reflects how the system behaves with mobile sensing as time progresses.

At p = 0.2, mobile sensing results in a median of 16.3% and mean of 17.5% of discovered neighbors. The top-performing fBS finds 53.2% of neighbors. Evidently, we see clear and appreciable improvements with just 20% of fBSs using mobile sensing. As evinced in Figure 5, increasing values of p result in significant increases in the ratio of neighbor discovery across all percentiles.

One important result is when the system reaches steady-state at p = 1. Here, fBSs find a median of 32.1% and mean of 32.5% of neighbors, while the top-performing fBS finds 63.9% of neighbors. In comparison with just conventional sensing alone, the fBSs in a steady-state system with mobile sensing finds at least twice more neighbors within their 30 m radius, across all percentiles. Another important observation is that even when at steady-state, not all neighbors are discovered. This pertains to the apartment-like mobility model that we have evaluated, where beyond the coverage of their “home” cell, SSs move along fixed limited pathways as described in Section V-B. This is in accordance with a realistic and practical apartment-like scenario.

B. Effects of conventional sensing with varying fBS densities Next, we establish a base-line performance of conventional

sensing, with varying values of d, the distance of an SS to its associated fBS, and s, the inter-fBS separation distance. The value d is an important parameter, as SSs at different distances from their associated fBSs are affected by interference with varying severity. At the nominal cell-edge, an SS receives minimal received signal strength (RSS) and is likely to be nearer to “far-away” interferers in a dense deployment. Hence, it is likely to suffer from high co-channel interference. An SS very close to its associated fBS experiences the opposite: a high received signal strength, and likely less co-channel interference owing to undiscovered co-channel fBSs. The SINRs that SSs receive at different distances from their fBSs therefore behave differently. In the following discussions and figures, the reported instantaneous throughput is the instantaneous

downlink throughput achieved by the SS per sub-channel assigned to its associated fBS, normalized by the throughput per sub-channel achievable by that SS under an ideal, zero-interference deployment.

Figure 6 illustrates the case for d = 5.0 m. We begin to see that conventional sensing performs differently at various fBS densities. At s = 10.0 m, the first quartile of SSs see their instantaneous throughput dropping to 89.8% and below of the maximum, compared to 94.7%, 99.42% and 99.43% for s equaling to 20, 30 and 40 meters respectively. For d = 7.5 m, Figure 7 shows that the sub-optimality of conventional sensing is compounded further. The bottom 25% of SSs see their instantaneous throughput drop to 61.2% and below at s = 10.0 m. This contrasts with the 66.2%, 82.2% and 85.0% seen by SSs for s equaling to 20, 30 and 40 meters respectively. Finally, at the nominal cell-edge of d = 10.0 m, we see that “far-away” interferers affect conventional sensing severely. The first quartiles of SSs see 0.39%, 0.91%, 9.2% and 15.6% for s equaling 10, 20, 30 and 40 meters respectively. In this case, going from s = 10 m to 40 m results in 40 times the instantaneous throughput. These results suggest that potential improvements can be brought about by reducing “far-away” interference. From Figure 6 to Figure 8, we see that throughput is appreciably impaired for s < 30 m. This explains our choice of the 6.5 s broadcast period (described in Section V-B), which corresponds approximately to a mobile sensing range of 30 m.

C. Effects of mobile sensing Mobile sensing, as demonstrated in Section VI-A,

appreciably increases the number of neighbors discovered compared to conventional sensing. In Figure 9 to Figure 11, we demonstrate the impact of having extended topology knowledge, gleaned from mobile sensing in the densest simulated deployment, where s = 10 m. We also include, for comparison, the performance observed if an oracle provides complete knowledge of all neighbors within each fBS’s 30 m radial distance. At d = 5 m, Figure 9 shows that the first quartile of SSs for the system at steady-state (where p = 1), with mobile sensing, sees their instantaneous throughput going up to 94.8%. This compares favorably with the 89.8% seen by

Figure 6: Per-SS instantaneous throughput CDF observed at d = 5.0 m, p = 1.0, for various inter-

fBS separation distances s.

Figure 7: Per-SS instantaneous throughput CDF observed at d = 7.5 m, p = 1.0, for various inter-

fBS separation distances s.

Figure 8: Per-SS instantaneous throughput CDF observed at d = 10.0 m, p = 1.0, for various

inter-fBS separation distances s.

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SSs in the first quartile with conventional sensing. With oracular knowledge, SSs in the first quartile see up to 95% of maximum throughput. Figure 10 demonstrates further improvements owing to mobile sensing. At steady-state, first quartile of SSs at d = 7.5 m see up to 75.5% of maximum throughput, compared to that of only 61.2% from conventional sensing alone. Oracle knowledge provides up to 79.5% of the maximum, just 4 percentage points better than mobile sensing. The bottom 10th percentile sees even better improvements, going from 37.1%, to 59.8% then 67.3%, by using conventional to mobile sensing and oracle knowledge respectively. The gain of 22.7 percentage points in the bottom 10th percentile, going from conventional to mobile sensing is highly significant. Finally, at the cell-edge where d = 10.0 m, important improvements are further seen. Figure 11 shows that SSs at the first quartile see up to 11.6% of maximum throughput with mobile sensing, compared to just 0.39% using conventional sensing. This is an improvement of over 29 times. Having oracular knowledge in this case provides the first quartile of SSs with up to 19.4% of maximum throughput, over 50 times better when compared to using conventional sensing.

An important observation that can be made from Figure 9 to Figure 11 is the limiting effect of increasing p on the gain in interference reduction. In all 3 cases, we see that the throughput improvements from p = 0.6 to p = 1.0 is minimal. However, Figure 5 demonstrates that the percentage of neighbors discovered continue to appreciably increase from p = 0.6 to p = 1.0, with the same simulation setup as that of Figure 9 to Figure 11 (where s = 10 m). The likely explanation for this is that the neighbors detected from p = 0.6 onwards are likely to be far enough such that they cause little co-channel interference.

VII. CONCLUSION In this paper, we identified a common assumption that

numerous existing femto-cell interference avoidance algorithms make: that local interference topology can be sufficiently captured by conventional sensing. Although it is often implicitly assumed that conventional sensing works well across any fBS density, we showed that in a dense deployment, throughput of an SS can be 40 times less compared to that of a sparse deployment. We showed that the throughput performance of miniaturized cells can be improved by

mitigating the effects of "far-away" and undetected interferers using mobile sensing. Through detailed simulation, we observed that instantaneous throughput is much improved in cases where a mobile user is close to the cell edge, by as much as up to 29 times. Operators may therefore find this approach practical and interesting as it leads to improved service performance and customer satisfaction, while being simple and effective.

ACKNOWLEDGEMENT We will like to thank Joseph L. Heyman for his extensive

scripts which significantly aided data collection and processing.

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Figure 9: Per-SS instantaneous throughput CDF

observed at d = 5 m, s = 10 m. Figure 10: Per-SS instantaneous throughput

CDF observed at d = 7.5 m, s = 10 m. Figure 11: Per-SS instantaneous throughput

CDF observed at d = 10.0 m, s = 10 m.

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