Spectrum Sharing Techniques in Dynamic Frequency Hopping Systems

DynFH=sys where fc hops based on real-time spec env via ctrl alg. Enables high resiliency, anti-jam, LPI/LPD comms. Spec sharing in DynFH req concurrent multi-user access w/o co-channel int. Key tech: CGSS (Cogn Spectrum Sensing), GDMA (Game-Dynamic Multichannel Access), DBS (Database-Driven Band Selection), ML-PA (ML-Powered Adaptation). CGSS uses cyclo-stat det, energy det, or coop sensing to id unused subbands. SNR wall limits det perf in low-SNR regimes. Sensing overhead (SO) ≈ T_sense / T_hop; must minimize via intelli-scheduling. GDMA applies non-coop games (e.g., Cournot/Bertrand models) where users bid for subcarriers; NE (Nash Eq) ensures fair share. Regret-matching alg allow convergence to CCE (Correlated CE). DBS integrates with GDB (Geo-DB) or SAS (Spectrum Access System) to obtain incumbent-free bands; 3.5 GHz CBRS model used in US. Latency in DB query req local caching & pred fetch. ML-PA employs DRL (Deep RL) agents (e.g., DQN, PPO) trained on env states (int lvl, ch occupancy, BER). State space: S = {γ_k(t), O_k(t), P_tx(t)}, Act space: A = {Δf_hop, T_hop, P_tx}. Reward: R = α·SE + β·LPI - δ·P_tx - ε·int. SE=Spectral Eff (bps/Hz). LPI metric: P_detect = Q(√(2·SNR + 1) · T_obs·B). DRL trains via exp replay & target net stab. CNN used for spatial-freq topo mapping from rasterized heatmap inputs. Transfer learning allows cross-scene adaptation. Int mitigation: null-space proj (NSP) w/ MIMO, power capping, orthog hop pat (OHP) via Welch-Costas arrays. OHP ensures min Δf & no hit consec twice. Code-aided FH: per-user FH seq gen via LDPC-coded index maps; enables ID-recognition at RX. Sync chall: time-drift in dist nodes; solved via UWB ref pulses or NTP+PLL hybrid. Cross-layer opt: TCP throughput degrades under freq switch; sol: MAC-layer buffering + TCP spoofing. 5G/6G integration: DynFH in mmWave bands (24–71 GHz); beam-hopping combined w/ freq-hop for 3D spatio-freq dith. NTN (Non-Terrestrial Net) use case: LEO sats employ DynFH to avoid ground int & comply with ITU-R RA.769. Reg constraints: FCC Part 15, ETSI EN 300 328 mandate dwell time ≤ 0.4s & freq agility. Violation risks null cert. Perf metrics: ACPR (Adj Ch Power Ratio), MER (Mod Error Ratio), SCG (Spec Conc Gain). ACPR > 50 dBc req for coexistence. SCG = ∫|S(f)|² df / max(|S(f)|²) → higher=better spreading. Pitfalls: (1) sensing-blind spots due to shadowing; mit: UAV-mounted sensing relays, (2) DRL overfitting to train env; mit: domain randomization, (3) sync loss in high-Doppler; mit: Doppler comp via CFO est, (4) game-theory impract assump (full info); mit: Bayesian games w/ uncertain types, (5) spec fragmentation in dense nets; mit: cluster-based CH (Cluster Head) negotiation. Current SOTA: Federated DynFH (Fed-DynFH) allows dist ML train w/o data centralization; uses local model updt + param server agg. Achieves 92% ch utl vs 76% in static FH. DARPA SC2 Phase 3 demonstrated AI-driven DynFH achieving 3x SE gain over LTE in contested env. Open chall: quantum-inspired hop seq (QFH) using superposition states for exponential seq space; proto shows 10^6 uncorr seqs in 64-dim Hilbert space. Std: IEEE 802.11af, 802.22 leverage DynFH for TVWS; 802.11be explores for RU hopping in Wi-Fi 7. Future dir: ISAC (Int Sensing & Comms) w/ DynFH: dual-use chirp-FH waveforms for radar-like sensing + data tx. Key trade: agility vs coherence time T_coh; rule: T_hop < 0.1·T_coh to track fading. Multi-objective opt sol: Pareto-front between SE, LPI, latency via NSGA-III. Tools: MATLAB Comm Toolbox, GNU Radio + UHD for prototyping. Eval: use fading ch models (TU, RA, HT) w/ int models (Poisson, alpha-stable).