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Nicolò Michelusi ([email protected]), Muddassar Hussain, David Love (Purdue) Anoosheh Heidarzadeh, Alex Sprintson (TAMU) 1 2 3 4 5 6 7 8 9 10 11 12 NSF funded project: NSF EARS CNS-1642982: "Real-time Control of Dense, Mobile, Millimeter Wave Networks Using a Programmable Architecture" (2016-2019), Nicolò Michelusi (PI), David Love (co-PI),James Krogmeier (co-PI), Purdue University; Alex Sprintson (co-PI), TAMU; Chris Anderson (co-PI), USNA CNS-1642982 0 0.2 0.4 0.6 0.8 1 False alarm probability, 0 0.1 0.2 0.3 0.4 0.5 0.6 0.7 0.8 0.9 1 1-P MD worst ( , ) = 0.02 ( , ) = 0.08 ( , ) = 0.14 [1] Mm-wave in mobile, dense environments Architecture Energy efficient beam alignment (BA) v Problem: align tx/rx beams with mobile users; QoS constraints; min power Adaptive BA protocol Fractional search Performance Summary v Mobility may disrupt gains of mm-wave Ø Beam-alignment (BA) bottleneck 1. SDN architecture Ø Real-time control in fast-varying networks Ø X-layer to reduce comm. overhead 2. Adaptive energy efficient BA protocol 3. Neyman-Pearson beam design v Future mobile & dense networks demand a . . High flexibility to address overwhelming ... ..communication overhead 1) We propose a flexible architecture for dynamic network control via SDN 2) We design flexible adaptive beam-alignment protocol to minimize energy cost & support QoS 3) We address beam-design to directly incorporate detection performance v “Fractional search” optimal: v iteratively, in closed form Rate constraint Detectability constraint Coverage constraint 2) Data Communication 1) Beam alignment ITA’17, Asilomar’17 ! φ s μ ⇤ = arg min μ E " L-1 X k=0 E k +(N - L)P L T slot S 0 # s.t. N - L N W tot log 2 ✓ 1+ γ P L ! L ◆ ≥ R, E k = φ s ! k , 8k < L, Z ↵ L +! L /2 ↵ L -! L /2 S L (✓ )d✓ =1, ⇢ k ⇢ k = ( 4⇢ 2 k+1 +4⇢ k+1 -1 8⇢ k+1 , ⇢ k+1 > 1 2 , ⇢ k+1 , 0 < ⇢ k+1 1 2 ⇢ L = φ d φ s Energy/radiant to attain rate constraint Energy/radiant for detectability 0 5 10 15 20 25 30 35 40 45 0 0.5 1 R = 1 Mbps R = 10 Mbps R = 100 Mbps R = 1000 Mbps Slot index, k 10 −1 10 0 10 1 10 2 10 3 10 4 34 36 38 40 42 Bisection search Fractional search exact Rate (Mbps) Power (dBm) MDP formulation v Adaptive beam alignment protocol as finite . horizon Markov decision process Ø Time interval k=0,…,L-1 Ø State U k : width of uncertainty area Ø Action : beam width Ø Cost : sensing energy Ø Final cost : data comm. energy Ø Transitions: depend on ACK/NACK v Analysis of cost-to-go function & structural . properties φ s ! k φ d U L ! k ! ⇤ k (U k )= 1 2 f k U k ,f k = ✓ 1 - 1 2⇢ k+1 ◆ + f k Fractional factor NP Beam Design v Hypothesis test in each sensing slot: v Neyman-Pearson detector: v Beam design problem: |s H y| 2 ksk 2 2 H 0 7 H 1 γ min c2C M t P MD (c) s.t. P FA (c) δ , kck 2 2 =1 H 1 : y = ↵ p P d H (✓ )cs + w, ✓ ⇠ U (R main c ) H 0 : y = ↵ p P d H (✓ )cs + w, ✓ ⇠ U (R sec c ) NP Beam Design Problem v Modified problem via relaxation: v Optimal beam: D λ,⌫ , D 1 diag(λ)D H 1 - D 0 diag(⌫ )D H 0 max z 2R,kck 2 2 =1 z s.t. z Gain(c, ✓ ), 8✓ 2 D main c Gain(c, ✓ ) φ(γ , δ ), 8✓ 2 D sec c Performance [1]. J. Song, J. Choi and D. J. Love, "Codebook design for hybrid beamforming in millimeter wave systems," 2015 IEEE International Conference on Communications (ICC), London, 2015, pp. 1298-1303. 33% v Main-love gain improves . . by decreasing v Less energy dispersion ... . outside main-lobe v Better detection performance . by NP-based design v Up to 30% improvement over . state-of-art [1] φ(γ , δ ) -1.5 -1 -0.5 0 0.5 1 1.5 AoD, 0 0.1 0.2 0.3 0.4 0.5 0.6 0.7 0.8 0.9 1 Gain(c, ) ( , ) = 0.02 ( , ) = 0.08 ( , ) = 0.14 [1] R guard R guard c ? (λ, ⌫ )= v max (D λ,⌫ ) kv max (D λ,⌫ )k 2 Weighted array response matrix main side dual vars optimized via SDP - : beam-width -U: interval of uncertainty - : energy/radiant ACM mmNets’17 SDN framework goals v Enable ......... . programmability . . at MAC & PHY ... . layers v Enable different .. . per-packet .. ... . behaviors v Define and ..... .. incorporate ..... . wireless primitives Apply Clear Write Metadata Meter Instruction Set Goto Output(1) Group(2) eth(src=) icmpv6(type=) Action List Action Set table: 0 buffer: 0 meter: 0 queue: 0 Action Set eth(src=) eth(dst=) queue(5) output(2) Ethernet VLAN VLAN IPv4 TCP Payload 14B 4B 4B 20B 20B 1000B 1063B src: 00:02:03:04:05:06 dst: 00:20:30:40:50:60 type: VLAN (0x8100) pcp: 0 vid: 1000 type: VLAN (0x8100) pcp: 0 vid: 100 type: IPv4 (0x0800) dscp: 0 ecn: 0 protocol: TCP (6) src: 10.0.0.1 dst: 11.1.1.1 src: 10000 dst: 5060 m / a / c / w / md / g Instructions Choice Arrival Extraction Selection Execution Egress Group
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