BeamSeek: Structured Beam Management

• BeamSeek is an umbrella term for algorithmic strategies that reduce search overhead in beam management by prioritizing high-value beam candidates.
• It exploits structural information such as beam statistics, geometry, and mobility to optimize beam alignment, training, and tracking in mmWave and massive-MIMO systems.
• It balances performance metrics like signal strength and misalignment error with overhead costs by integrating methods from statistical ranking, two-stage elimination, bandit approaches, and Bayesian optimization.

BeamSeek is an overloaded research term applied to several algorithmic strategies for searching over structured candidate spaces, most prominently in millimeter-wave and massive-MIMO beam management. In that dominant usage, it denotes methods for beam training, beam alignment, beam prediction, beam tracking, or beam acquisition under severe search-space and latency constraints, using side information such as beam statistics, user position, mobility, environmental sensing, or online posterior uncertainty. The same label also appears in adjacent optimization settings, including feature selection, visual tracking, and candidate extraction in gravitational-wave radiometry, but these uses share only the abstract idea of prioritizing a small, high-value subset of candidates rather than a single canonical algorithm (Tiwari et al., 2019, Hong et al., 2020, Chege et al., 7 Apr 2025, Maggi et al., 2023, Sharma et al., 18 Aug 2025, Fraiman et al., 2022, Sharma et al., 21 Nov 2025).

1. BeamSeek as a beam-management problem

In wireless communications, BeamSeek addresses the core problem that highly directional mmWave and THz links require transmit and receive beams to be aligned before reliable data transmission can occur. With analog or hybrid beamforming and finite codebooks, initial access and beam refinement are fundamentally discrete search problems over beam indices. Exhaustive search tests all candidate beam pairs and is accurate but incurs overhead proportional to codebook size, while hierarchical search reduces measurements but may sacrifice gain at early stages because broad beams use fewer active elements or coarser sectors (Shokri-Ghadikolaei et al., 2015, Li et al., 2018).

The underlying objective is usually to maximize an RSS, RSRP, RSSI, SNR, or rate surrogate. Representative formulations include maximizing $|\mathbf{w}^{H}\mathbf{H}\mathbf{f}|$, maximizing subcarrier-averaged received power over a DFT codebook, or minimizing beam selection error under a fixed training budget (Hong et al., 2020, Chege et al., 7 Apr 2025, Ghatak, 2023). Across these formulations, the same structural constraints recur: sparse multipath, narrow beams, limited RF chains, block or quasi-static fading during training, and strict control-plane budgets for pilots, measurements, and feedback (Tiwari et al., 2019, Wei et al., 2022, Maggi et al., 2023).

A central conceptual point is that BeamSeek methods are not merely search heuristics. Many explicitly optimize a performance–overhead trade-off. In short-range mmWave scheduling, beamwidth and alignment time enter the effective throughput

$$T_{\mathrm{eff},i} = \Big(1-\frac{\tau_{\mathrm{align},i}}{T}\Big)\,R_i,$$

so narrower beams can increase directivity while simultaneously lengthening alignment time; the result is that extremely narrow beams are in general not optimal (Shokri-Ghadikolaei et al., 2015). This same trade-off reappears in beamset design, probabilistic candidate pruning, and online tracking.

2. Search mechanisms and algorithmic families

Within beam management, BeamSeek spans several distinct algorithmic families.

Family	Core mechanism	Representative papers
Statistical ranking	Order beam tests by learned beam probabilities or entropy structure	(Tiwari et al., 2019)
Two-stage elimination	Explore all beams coarsely, eliminate weak candidates, refine survivors	(Li et al., 2018, Liu et al., 2020)
Bandit-based acquisition	Treat beams as arms and minimize identification error or stopping time	(Wei et al., 2022, Ghatak, 2023)
Side-information guidance	Use position, trajectory, rotation, or path skeletons to pre-prune beams	(Hong et al., 2020, Rea et al., 2018, Khosravi et al., 2019)
Bayesian or learned prediction	Infer a small candidate set from posterior models or learned mappings	(Chege et al., 7 Apr 2025, Maggi et al., 2023, Zhao et al., 6 Jun 2025)

The memory-assisted statistically-ranked method for sparse MIMO formalizes BeamSeek as ordering beam pairs by their success probabilities. Under the independence assumption between transmit and receive beam statistics, the joint success probability factorizes as $p_{i,j}=p_i^{(\mathrm{Tx})}p_j^{(\mathrm{Rx})}$, and the expected number of tests is the expected rank under the sorted Kronecker-product PMF. Beam entropy,

$E=-\sum_{i=1}^{N} p_i\log p_i,$

governs savings: lower entropy implies a more concentrated PMF and earlier success. In the paper’s low-entropy example, MarS yields $m=4.7$ average tests versus $27$ for exhaustive and $12$ for multi-level search, with Monte Carlo validation over $10^7$ realizations (Tiwari et al., 2019).

Two-stage elimination variants implement a different logic. OTSS allocates a fraction $\alpha$ of the training energy budget to a first pass over all beam pairs, eliminates $K$ weak pairs, then coherently combines first- and second-stage measurements over the survivors. Under a single-path ideal-beam model, OTSS is shown to asymptotically outperform exhaustive and hierarchical search, and in a practical-beam LOS example it reaches a $$T_{\mathrm{eff},i} = \Big(1-\frac{\tau_{\mathrm{align},i}}{T}\Big)\,R_i,$$0 misalignment target using about $$T_{\mathrm{eff},i} = \Big(1-\frac{\tau_{\mathrm{align},i}}{T}\Big)\,R_i,$$1 of exhaustive search training time and $$T_{\mathrm{eff},i} = \Big(1-\frac{\tau_{\mathrm{align},i}}{T}\Big)\,R_i,$$2 of hierarchical search time (Li et al., 2018). IDBS removes the need for prior SNR knowledge by iteratively deactivating beams through a Bayesian comparison under a uniform improper prior and refining boundary cases through beam shifting, so that training effort adapts to the realized SNR and path geometry (Liu et al., 2020).

Bandit-based BeamSeek formulations reinterpret beam acquisition as best-arm identification. In 2PHTS, beams are grouped into super-arms, the reward model is heteroscedastic Gaussian with $$T_{\mathrm{eff},i} = \Big(1-\frac{\tau_{\mathrm{align},i}}{T}\Big)\,R_i,$$3, and the algorithm combines grouping, a heteroscedastic GLR stopping rule, and tracking allocations to reduce the number of samples needed to identify the best beam with confidence $$T_{\mathrm{eff},i} = \Big(1-\frac{\tau_{\mathrm{align},i}}{T}\Big)\,R_i,$$4 (Wei et al., 2022). A different bandit line uses concurrent beam exploration, in which beam groups are activated simultaneously and decoded through a Hamming-style procedure; for abruptly changing environments, sequential halving and K-SHES provide fixed-budget guarantees when a near-optimal beam becomes optimal during the acquisition process (Ghatak, 2023).

3. Geometry, mobility, and environmental side information

A large portion of BeamSeek research exploits the fact that beam directions are strongly constrained by geometry. In the simplest location-aware form, indoor positions are converted to angular predictions through $$T_{\mathrm{eff},i} = \Big(1-\frac{\tau_{\mathrm{align},i}}{T}\Big)\,R_i,$$5, quantized to the nearest codebook beam, and expanded to a small neighborhood to account for location error and RF distortion. On a 13.8 GHz testbed with 8-element ULAs, this reduces the beam search from $$T_{\mathrm{eff},i} = \Big(1-\frac{\tau_{\mathrm{align},i}}{T}\Big)\,R_i,$$6 probes to $$T_{\mathrm{eff},i} = \Big(1-\frac{\tau_{\mathrm{align},i}}{T}\Big)\,R_i,$$7, with measured alignment time decreasing from approximately $$T_{\mathrm{eff},i} = \Big(1-\frac{\tau_{\mathrm{align},i}}{T}\Big)\,R_i,$$8 s to approximately $$T_{\mathrm{eff},i} = \Big(1-\frac{\tau_{\mathrm{align},i}}{T}\Big)\,R_i,$$9 s; the reported speed-up is about $p_{i,j}=p_i^{(\mathrm{Tx})}p_j^{(\mathrm{Rx})}$0, and the best beam found matches the exhaustive optimum under LoS in the reported tests (Hong et al., 2020).

Location-aware beam alignment can also be coordinated across BS and UE. With bounded location uncertainty, the predicted AoD and AoA are converted into angular windows and then into reduced beam subsets. Search alternates between BS and UE slots, uses local optimal beams to recenter the next search window, and stops when a target rate threshold is achieved. A CRB-based treatment in this framework explicitly links constrained angular probing to channel-parameter estimation error and effective rate (Igbafe et al., 2019).

Mobility introduces an additional layer. SLASH uses sub-6 GHz WiFi Time-of-Flight rather than inertial sensors to infer both UE position and handheld rotation. Position uncertainty is mapped to an angular half-width

$p_{i,j}=p_i^{(\mathrm{Tx})}p_j^{(\mathrm{Rx})}$1

which narrows the sector search space during link establishment, while a PSD-based estimator of rotation speed drives two-sector maintenance probes during beam tracking. The reported evaluation shows more than $p_{i,j}=p_i^{(\mathrm{Tx})}p_j^{(\mathrm{Rx})}$2 data-rate gain for link establishment and $p_{i,j}=p_i^{(\mathrm{Tx})}p_j^{(\mathrm{Rx})}$3 for link maintenance relative to prior work (Rea et al., 2018).

A related but distinct mobility-aware construction is the path-skeleton framework. Here a path skeleton stores dominant AoA/AoD/gain tuples for a grid location, and a similarity metric such as $p_{i,j}=p_i^{(\mathrm{Tx})}p_j^{(\mathrm{Rx})}$4 triggers beam-search re-execution only when the old skeleton becomes obsolete. In outdoor simulations with real building map data, this substantially reduces skeleton queries while preserving near-optimal rate (Khosravi et al., 2019). Trajectory-informed regioning pushes this further by partitioning a user path into regions that share similar path skeletons; one reference point per region supplies a candidate-beam database for the whole region, and a dynamic program minimizes the number of regions subject to a similarity constraint (Khosravi et al., 2022).

4. Probabilistic prediction, Bayesian optimization, and learned inference

Another major BeamSeek line replaces explicit sweeping with posterior inference from data. In probabilistic position-aided beam selection, the joint PMF of position and optimal beam indices is modeled as a low-rank tensor and estimated by variational Bayes using a CP decomposition. For an $p_{i,j}=p_i^{(\mathrm{Tx})}p_j^{(\mathrm{Rx})}$5 BS UPA and a $p_{i,j}=p_i^{(\mathrm{Tx})}p_j^{(\mathrm{Rx})}$6 UE UPA with DFT codebooks, the full search space contains $p_{i,j}=p_i^{(\mathrm{Tx})}p_j^{(\mathrm{Rx})}$7 beam pairs; the learned PMF model achieves approximately $p_{i,j}=p_i^{(\mathrm{Tx})}p_j^{(\mathrm{Rx})}$8 of the maximum achievable rate by sweeping only the top $p_{i,j}=p_i^{(\mathrm{Tx})}p_j^{(\mathrm{Rx})}$9 pairs, corresponding to about $E=-\sum_{i=1}^{N} p_i\log p_i,$0 of the codebook and about $E=-\sum_{i=1}^{N} p_i\log p_i,$1 search-overhead reduction (Chege et al., 7 Apr 2025).

Collaborative-filtering BeamSeek treats UEs as users, beams as items, and RSS values as ratings. A truncated SVD of the UE-beam matrix yields latent UE factors; a new UE is projected into that latent space from a small elicitation pattern, nearest neighbors are identified by cosine similarity, and beam scores are aggregated from their ratings. In multi-BS simulations with $E=-\sum_{i=1}^{N} p_i\log p_i,$2 beams, this approach nearly reaches oracle performance with many fewer probes than 3DPF-style baselines and automatically concentrates recommendations on the strongest BSs (Yammine et al., 2022).

Bayesian-optimization variants are posterior-search methods rather than offline predictors. For mobile 5G beam tracking, a GP prior over beam index and time, together with a greedy approximation to a submodular parallel expected-improvement objective penalized by beamset size, selects a beamset $E=-\sum_{i=1}^{N} p_i\log p_i,$3 that balances RSRP and reporting overhead. In 3GPP-compliant simulations with $E=-\sum_{i=1}^{N} p_i\log p_i,$4 beams, the reported high-accuracy configuration reaches accuracy at least $E=-\sum_{i=1}^{N} p_i\log p_i,$5 and RSRP error below $E=-\sum_{i=1}^{N} p_i\log p_i,$6 dB with overhead around $E=-\sum_{i=1}^{N} p_i\log p_i,$7, while a lower-overhead configuration maintains roughly $E=-\sum_{i=1}^{N} p_i\log p_i,$8 accuracy and about $E=-\sum_{i=1}^{N} p_i\log p_i,$9 dB error at speeds up to $m=4.7$0 km/h with overhead around $m=4.7$1 (Maggi et al., 2023). In RIS-assisted tracking, a discrete TPE-based BO over RIS codebooks achieves accuracy approximately $m=4.7$2 to $m=4.7$3 as the measured fraction $m=4.7$4 increases from $m=4.7$5 to $m=4.7$6, with execution times approximately $m=4.7$7 s to $m=4.7$8 s on a $m=4.7$9 RIS grid (Liu et al., 2023).

Large-model and deep-learning BeamSeek methods target predictive inference directly from sensor observations. MLM-BP combines position text and multi-view visual embeddings through DeepSeek Janus-Pro-1B, with LoRA on the image encoder and frozen LLM weights except RMSNorm. It reports $27$0 Top-1 accuracy on a simulated dataset and, on DeepSense 6G Scenario 41, $27$1 Top-1 and $27$2 Top-3 accuracy with only $27$3 of the labeled data, outperforming small-model baselines by more than $27$4 Top-1 (Zhao et al., 6 Jun 2025). A different deep BeamSeek, aimed at low-complexity phased arrays, uses agile beam switching to construct a power profile and feeds it to a SwiGLU MLP for pilot-free DOA regression; on the 60 GHz COSMOS testbed it achieves up to an $27$5 degree reduction in average estimation error relative to a correlation-based baseline, with especially large gains in low-SNR and small-$27$6 regimes (Sharma et al., 18 Aug 2025).

5. Performance criteria and recurring trade-offs

The unifying performance question in BeamSeek is not simply whether the best beam can be found, but how much cost is incurred in finding it. Overhead appears as pilot duration, number of probed beams, beamset size, feedback payload, database maintenance, or inference latency. Benefit appears as rate, RSRP, alignment accuracy, or probability of correct beam identification. Because these quantities are coupled, BeamSeek papers frequently optimize a penalized or constrained objective rather than a pure search success metric (Shokri-Ghadikolaei et al., 2015, Maggi et al., 2023).

Several recurring trade-offs are explicit. In statistical ranking, lower beam entropy improves ordering quality, so MarS can front-load the most probable pairs and obtain large savings, whereas high entropy motivates hybridization with multi-level search (Tiwari et al., 2019). In beamwidth optimization and transmission scheduling, narrower beams improve directivity and interference suppression but raise alignment time approximately inversely with the product of transmit and receive beamwidths; the result is a unique optimum for effective throughput rather than monotonic preference for the narrowest beam (Shokri-Ghadikolaei et al., 2015). In OTSS, training energy must be split between broad exploration and concentrated refinement; the optimal survivor count scales on the order of $27$7 rather than $27$8 or $27$9 (Li et al., 2018).

A common misconception is that all reduced-overhead methods are merely approximate replacements for exhaustive search. The literature is more specific. Some methods, such as the 13.8 GHz location-aware testbed, report no observable degradation relative to exhaustive search under the studied LoS geometry because the true beam lies within the reduced neighborhood with high probability (Hong et al., 2020). Others explicitly accept approximation in exchange for overhead reduction: the PMF tensor model scans only a top-$12$0 list, BO methods select a beamset rather than all beams, and collaborative filtering ranks beams from similar users rather than evaluating the entire codebook (Chege et al., 7 Apr 2025, Maggi et al., 2023, Yammine et al., 2022).

Another misconception is that hierarchical search is always the natural baseline for efficient beam alignment. The results repeatedly show context dependence. Hierarchical and multi-level procedures are attractive when the codebook is large and entropy is high, but they may be inferior at long range or low SNR because early broad beams have lower gain (Tiwari et al., 2019). Bayesian deactivation methods and best-arm identification schemes, by contrast, are designed to adapt training effort to uncertainty or to a fixed risk budget (Liu et al., 2020, Wei et al., 2022).

6. Assumptions, limitations, and the broader use of the term

BeamSeek methods are typically specialized to strong structural assumptions. Statistical-ranking methods assume beam PMFs are stable enough to be learned and, in one formulation, that Tx and Rx beam statistics are independent (Tiwari et al., 2019). Location-aware methods assume reliable position estimates, stable orientation conventions, and a geometry in which LoS or a small set of reflectors dominates (Hong et al., 2020, Igbafe et al., 2019). Rotation-aware methods rely on suitable AP geometry and ToF quality (Rea et al., 2018). Path-skeleton and trajectory databases presume that the environment changes slowly enough for stored path structures to remain useful (Khosravi et al., 2019, Khosravi et al., 2022). BO and learned predictors inherit model-specific limitations: GP kernels may underfit abrupt changes, large multimodal models may be too heavy for edge deployment, and offline-trained PMF or neural predictors require periodic refresh under environmental drift (Maggi et al., 2023, Zhao et al., 6 Jun 2025, Chege et al., 7 Apr 2025).

These limitations explain why BeamSeek is better understood as a design pattern than as a single algorithm. The pattern consists of exploiting structure—statistical, geometric, temporal, modal, or combinatorial—to shrink a large candidate set to a small one while preserving the probability that the optimal or near-optimal candidate remains inside it.

That abstraction also explains the term’s migration outside wireless communications. In feature selection, beam search generalizes forward selection by keeping the top-$12$1 subsets at each depth and can outperform greedy selection when predictive power arises from correlated features; the reported experiments include cases where beam-selected ten-feature subsets match or approach all-feature performance (Fraiman et al., 2022). In visual tracking, a multi-agent reinforcement-learning beam search maintains multiple trajectory hypotheses and selects the one with maximum accumulated score, improving robustness to occlusion and fast motion over greedy top-1 tracking (Wang et al., 2022). In all-sky gravitational-wave radiometry, a Peak Finder strategy selects local maxima of the SNR sky map to suppress correlated beam-smeared candidates; at $12$2 Hz, following up two Peak Finder candidates reduces false dismissal rate by a factor of $12$3 relative to the full-sky method (Sharma et al., 21 Nov 2025).

In this broader sense, BeamSeek denotes a family resemblance rather than a unified formalism: systematic reduction of search cost through structured candidate selection, with technical implementations varying from entropy-ranked codebook scans and Bayesian optimization to latent-factor recommendation, deep inference, or local-maximum suppression.