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David Ripplinger, Aradhana Narula-Tam, Katherine Szeto AIAA 2013 August 21, 2013 Scheduling vs Random Access in Frequency Hopped Airborne Networks This work is sponsored by the Assistant Secretary of Defense (ASD R&E) under Air Force Contract #FA C Opinions, interpretations, conclusions and recommendations are those of the author and are not necessarily endorsed by the United States Government.

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Scheduling vs Random Access - 2 DCR 08/21/2013 Background Information Frequencies Frequency Hopping: Break up a packet into small pulses or hops Pseudo-randomly choose a new frequency for each hop Frequency Hopping spreads the packet transmission over multiple frequencies

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Scheduling vs Random Access - 3 DCR 08/21/ Frequencies Frequency Hopping Enables Jam Resistance If you stay on one frequency: A jammer can concentrate his energy on a single frequency An entire user’s packet can be lost

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Scheduling vs Random Access - 4 DCR 08/21/ Frequencies encode Frequency Hopping Enables Jam Resistance With Frequency Hopping, a jammer targeting a single frequency only impacts part of a user’s packet With Forward Error Correction, the loss of some hops can be tolerated k info symbols transmit decode w coded symbols (code rate = k/w) i received symbols (doesn’t matter which ones) i ≥ k, success i < k, failure

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Scheduling vs Random Access - 5 DCR 08/21/ Frequencies Synchronous Frequency Hopping Each user transmits on a single frequency for each hop User hops are synchronous in time –Users move to a new frequency simultaneously User hopping patterns are orthogonal Requires user receptions to be synchronized at the hop level –Many relevant systems have hop durations in the microseconds With synchronous hopping, there is no multi-user interference

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Scheduling vs Random Access - 6 DCR 08/21/ Frequencies Asynchronous Frequency Hopping Airborne networks can have up to 2-ms propagation delays –Hop receptions are no longer time aligned –A hop is only a few microseconds, so 2-ms guard times are impractical Large numbers of users result in many hop collisions, even if transmitted patterns are orthogonal We have asynchronous hopping, which has multi-user hop collisions

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Scheduling vs Random Access - 7 DCR 08/21/2013 MAC Comparison Problem Formulation This simple model is used to determine the throughput and delay of random access and scheduled MACs All users within transmission range It takes one slot to transmit a user’s packet –Packet is transmitted over many hops –Each slot consists of many mini-slots or hops Multiple users transmit simultaneously Collisions due to asynchronous frequency hopping are modeled using synchronous frequency hopping with random transmission patterns Full erasure model: If two users hop to same frequency in the same mini-slot, those hops are erased A node can send on one frequency and receive on another at the same time All users within transmission range It takes one slot to transmit a user’s packet –Packet is transmitted over many hops –Each slot consists of many mini-slots or hops Multiple users transmit simultaneously Collisions due to asynchronous frequency hopping are modeled using synchronous frequency hopping with random transmission patterns Full erasure model: If two users hop to same frequency in the same mini-slot, those hops are erased A node can send on one frequency and receive on another at the same time System Model

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Scheduling vs Random Access - 8 DCR 08/21/2013 Scheduled System with FH (Illustrative Example) Time Slots User 1: REDUser 2: GREEN User 3: BLUE User 4: ORANGE # Contending users Observations: Scheduling controls exactly how many users in a slot Requires coordination – increased complexity Total Successful Hops: 42 # Successful hops Frequencies

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Scheduling vs Random Access - 9 DCR 08/21/2013 Random Access System with FH (Illustrative Example) Time Slots User 1: REDUser 2: GREEN User 3: BLUE User 4: ORANGE # Contending users Observations: Random access controls the average number of users in a slot But sometimes too many or too few users contend Total Successful Hops: 36 # Successful hops p = 1/2 p = probability of transmission Frequencies

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Scheduling vs Random Access - 10 DCR 08/21/2013 System throughput is maximized by choosing optimal n, θ – n: optimal number of users transmitting in a slot, and – θ = k/w: forward error correction (FEC) code rate With scheduling, the number of users, n, can be controlled exactly With random access (RA), the transmission probability, p, in a slot determines average of n, –However, n varies from slot to slot –Inability to control n exactly, results in more collisions Hence, compared to scheduling, RA needs either smaller average n or a smaller code rate θ to ensure packets can be decoded –This implies lower throughput for Random Access Systems General Observations Conclusion: Random Access systems need to be more robust to collisions thereby resulting in lower throughput

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Scheduling vs Random Access - 11 DCR 08/21/ Parameter optimization for scheduling –Determine n (# users) and θ (code rate) to maximize throughput 2.Parameter optimization for random access –Determine p (transmission probability) and θ (code rate) to maximize throughput 3.Throughput comparison for scheduling vs random access 4.Delay comparison for scheduling vs random access Analysis Objectives

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Scheduling vs Random Access - 12 DCR 08/21/2013 Hop success probability with n active users: Probability of i out of w hop successes: Probability packet is successfully decoded: Throughput Analysis: Packet Success Probability

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Scheduling vs Random Access - 13 DCR 08/21/2013 Normalized throughput for scheduling system: Under RA, n is a random variable With transmit probability p, RA normalized throughput: Throughput Analysis

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Scheduling vs Random Access - 14 DCR 08/21/2013 Parameter Sweep Results SchedulingRandom Access For scheduling, the optimal operating point in all cases was near n ≈ q and θ ≈ 1/e = For random access, optimal θ was slightly smaller and optimal p ensured average n ≈ q Note: Can get close to optimal throughput with n or θ “in the neighborhood” of the optimal solution q = number of frequencies; here, q = 50 Throughput w = n Throughput w = n

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Scheduling vs Random Access - 15 DCR 08/21/2013 Assume code rate, θ = For each q, find optimal n Packet length w = 1000 In most cases: –Scheduling: choose n = 0.9q –RA: want n = 0.8q N is number of backlogged users Choose p = 0.8q/N Alternatively, could have fixed n and optimized θ Optimizing n Given Fixed Code Rate 0 Frequencies Average Number of Active Users Scheduling Random Access

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Scheduling vs Random Access - 16 DCR 08/21/2013 Throughput Comparison for θ = 1/e RA: throughput increases with increasing q, getting closer to scheduling throughput –n has lower variance At q = 50, scheduling is 16% better in this example N = 100, w = 1000, θ = 1/e For large w, as the number of channels q becomes large, the throughput difference between RA and scheduling decreases Scheduling Random Access Frequencies Agg Throughput (per frequency) 100% 80% 60% 40% 20% Scheduling Throughput Gain Frequencies

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Scheduling vs Random Access - 17 DCR 08/21/2013 Each node has i.i.d. Poisson packet arrivals Deterministic departures –Assume all packets are received 500 users Static scheduling (TDMA) –Schedule n users in each time slot RA knows how many backlogged users each slot –Back-off strategy: p = q/N, where N = # of backlogged users Delay Analysis and Simulation: Assumptions

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Scheduling vs Random Access - 18 DCR 08/21/2013 Delay Performance: Analysis and Simulation with Poisson Arrivals TDMA Simulation Random Access Simulation TDMA Analysis Random Access Analysis Random Access Scheduling Arrival Rate (packets per slot per frequency) Delay (slots) Poisson Arrivals Static time slot allocation results in unused time slots Result is extra delay Static time slot allocation results in unused time slots Result is extra delay SchedulingScheduling Very low delay, even for moderate loads Slightly less maximum throughput Very low delay, even for moderate loads Slightly less maximum throughput Random Access

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Scheduling vs Random Access - 19 DCR 08/21/2013 Delay Performance: Simulation Results for Bursty Arrivals Scheduling (Bursty Arrivals) Random Access (Bursty Arrivals) Delay Performance: Bursty vs Poisson Arrivals Scheduling (Poisson Arrivals) Random Access (Poisson Arrivals) Bursty Arrival Model: Geometrically distributed bursts of average length 5 Static scheduling handles bursty traffic poorly, but RA measures the traffic and adapts TDMA Bursty Random Access Bursty TDMA (Poisson Model) Random Access (Poisson) Arrival Rate (packets per slot per frequency) Delay (slots)

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Scheduling vs Random Access - 20 DCR 08/21/2013 Optimal users in a slot is n ≈ q (num frequencies) The optimal code rate is θ ≈ 1/e = –Assumes no jamming or noise Random access can’t control n exactly, just average n –Needs to be more robust than scheduling to packet loss RA needs smaller n or θ Scheduling achieves higher throughput –RA throughput improves with more hopping frequencies q –At q = 50, scheduling gets 10% to 20% more throughput, depending on codeword length w –As the number of frequencies gets large, scheduling and random access achieve similar throughputs RA gets lower delay especially with bursty traffic Conclusions

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Scheduling vs Random Access - 21 DCR 08/21/2013 Dynamic scheduling –Significant reduction in delay possible –Delay may be comparable to RA for both Poisson and Bursty traffic –Potentially higher throughput than RA Requires significant overhead for coordination thereby lowering effective throughput Incorporate transmit while receive constraints –Many systems do not enable receiving while transmitting –This will result in more collisions for random access Possible solution is time hopping –Scheduling can reduce transmit while receive issues Future Model Improvements and Research Time (and Frequency) Hopping

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