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arxiv: 2607.01300 · v1 · pith:LMHRCWOPnew · submitted 2026-07-01 · 📡 eess.SP

Alternating Optimization for Joint Resource Allocation in Full-Duplex Multi-Sector Fluid Antenna-Enabled Near-Field Systems

Jingxuan Zhou , Yinchao Yang , Zhaohui Yang , A. Hamid Aghvami , Mohammad Shikh-Bahaei This is my paper

Pith reviewed 2026-07-03 19:41 UTC · model grok-4.3

classification 📡 eess.SP
keywords full-duplexfluid antennanear-fieldresource allocationalternating optimizationself-interference cancellationwireless energy transfer
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The pith

Alternating optimization of power allocation, antenna positions, and group selection raises weighted sum rates in full-duplex multi-sector fluid antenna near-field systems.

A machine-rendered reading of the paper's core claim, the machinery that carries it, and where it could break.

The paper introduces a full-duplex fluid antenna near-field system that pairs multi-sector arrays with group-based transmit-receive partitioning to support simultaneous downlink energy transfer and uplink data transmission. It builds a spherical-wave channel model that includes residual self-interference and geometric motion limits, then formulates a weighted sum-rate maximization over time, power, positions, and binary group choices. An alternating optimization procedure that uses majorization-minimization successive convex approximation solves the resulting non-convex mixed-integer program and produces monotonic improvement to a stationary point of the relaxed problem.

Core claim

The alternating optimization framework based on MM-SCA solves the weighted sum rate maximization problem in FD-FANS for both perfect and imperfect SIC, monotonically improves the objective, converges to a stationary solution of the continuous relaxation, and delivers consistent gains in average sum rate, energy efficiency, and user fairness over half-duplex FANS, fixed-position FD systems, non-grouped FD FANS, and far-field baselines while remaining robust to residual SI and channel uncertainty.

What carries the argument

The alternating optimization (AO) framework based on majorization-minimization successive convex approximation (MM-SCA) applied to the joint time-power allocation, antenna positioning, and binary group selection problem under per-antenna box constraints, minimum spacing, and half-TX/half-RX balance.

If this is right

  • The proposed scheme achieves higher average sum rate than HD FANS, FD fixed-position near-field systems, non-grouped FD FANS, and far-field counterparts.
  • Energy efficiency and user fairness both improve under the same average and peak power limits.
  • The performance advantage holds for both perfect and imperfect self-interference cancellation.
  • The protocol enables simultaneous downlink energy transmission and uplink data transmission within the multi-sector fluid antenna array.

Where Pith is reading between the lines

These are editorial extensions of the paper, not claims the author makes directly.

  • The same alternating-optimization structure could be reused for other near-field mobility problems that combine energy and data flows.
  • Dynamic re-grouping based on instantaneous user locations might further reduce residual self-interference beyond the static half-TX/half-RX balance examined here.
  • The spherical-wave model implies that the reported gains would shrink if user distances move outside the near-field regime where the model applies.

Load-bearing premise

The spherical-wave uplink-downlink channel model with residual self-interference, wireless energy transfer, and geometric motion constraints is sufficiently accurate to support the claimed performance gains under both perfect and imperfect SIC.

What would settle it

A simulation or measurement in which the proposed joint optimization produces no increase in average sum rate over the non-grouped FD FANS benchmark under identical power limits and the same spherical-wave channel would falsify the claimed benefit of the grouping and mobility design.

Figures

Figures reproduced from arXiv: 2607.01300 by A. Hamid Aghvami, Jingxuan Zhou, Mohammad Shikh-Bahaei, Yinchao Yang, Zhaohui Yang.

Figure 1
Figure 1. Figure 1: FD-FANS [PITH_FULL_IMAGE:figures/full_fig_p003_1.png] view at source ↗
Figure 2
Figure 2. Figure 2: Energy and information transmission between the BS [PITH_FULL_IMAGE:figures/full_fig_p003_2.png] view at source ↗
Figure 3
Figure 3. Figure 3: Geometric coordinates of user k and the FAs FD model is Rk(δ, P ) = δk log2 (1 + ̺k Υ ) = δk log2  1 + βkHk,eff Υ(τPk + σ 2) 1 δk X K i=0,i6=k δiPi   , (13) where δ = [δ0, δ1, ...δK], P = [P0, P1, ...PK], Υ > 1 captures the SINR gap due to practical modulation schemes. B. Multi-Sector FANS System To further enhance spatial resolution and accommodate NF propagation characteristics, we consider a BS arch… view at source ↗
Figure 5
Figure 5. Figure 5: plots the ASR versus the ATP Pavg with Ppeak = 2Pavg, different CSI uncertainty factors ξ, K = 10, and φ = −60 dB. As expected, the ASR increases monotonically with Pavg, while the growth rate gradually slows down due to the logarithmic rate law and the increasing impact of residual SI at high transmit power. Across the whole range, the proposed FD-FANS consistently achieves the best performance. Com￾pared… view at source ↗
Figure 4
Figure 4. Figure 4: Convergence performance of the proposed iterative [PITH_FULL_IMAGE:figures/full_fig_p013_4.png] view at source ↗
Figure 6
Figure 6. Figure 6: ASR versus Pavg with different values of φ, Ppeak = 2Pavg, and K = 10 0 2 4 6 8 10 12 14 16 18 20 Number of Users 0 1 2 3 4 5 6 7 8 9 Average Sum-Rate (Mbps) FD-FANS, No SI FD-FANS, =-80dB FD-FANS, =-60dB FD-FANS, =-40dB HD-FANS FD-FPANS Non-Grouped FD-FANS FD-FAS, Far-Field FD-FANS, 1/3-TX/2/3-RX [PITH_FULL_IMAGE:figures/full_fig_p014_6.png] view at source ↗
Figure 7
Figure 7. Figure 7: ASR versus K with different values of φ, Ppeak = 2Pavg, and Pavg = 20 dBm K = 10 and about 0.4 Mbps at K = 20. It also exceeds the baseline with asymmetric partitions by roughly 0.2 Mbps at K = 10 and about 0.25 Mbps at K = 20 [PITH_FULL_IMAGE:figures/full_fig_p014_7.png] view at source ↗
Figure 10
Figure 10. Figure 10: Jain’s fairness index versus Pavg with different values of φ, Ppeak = 2Pavg, and K = 10. improvement of the proposed scheme is not obtained merely by consuming more power, but also by utilizing the available energy more efficiently. Finally, to evaluate per-user fairness, we adopt Jain’s fair￾ness index, defined as J = ( PK k=1 Rk) 2 K PK k=1 R2 k , where Rk denotes the achievable rate of user k. For each… view at source ↗
read the original abstract

This paper proposes a full duplex fluid antenna near field system (FD-FANS) with a multi-sector antenna array that jointly exploits resource allocation, antenna mobility, and group-based transmitting (TX) and receiving (RX) partitioning. A spherical wave uplink downlink channel is established that accounts for residual self interference (SI), wireless energy transfer (WET), and geometric constraints on antenna motion. Within the FD-FANS framework, an efficient protocol is devised to enable simultaneous downlink energy transmission (DET) and uplink data transmission (UDT) at the base station (BS). Furthermore, we formulate, for both perfect and imperfect SI cancellation (SIC), a weighted sum rate (WSR) maximization problem over time power allocation, antenna positions, and binary group selection, under practical average and peak power limits, per antenna box constraints, minimum spacing, and a half TX half RX balance. To tackle the resulting non convex mixed integer design, we develop an efficient alternating optimization (AO) framework based on majorization minimization successive convex approximation (MM SCA). The proposed algorithm monotonically improves the objective and converges to a stationary solution of a continuous relaxation. Simulation results demonstrate that the proposed scheme achieves consistent performance gains over several benchmark designs, including half duplex FANS (HD FANS), FD fixed position antenna near field system (FD FPANS), non-grouped FD FANS, and far field counterparts, in terms of average sum rate (ASR), energy efficiency (EE), and user fairness, while exhibiting robustness to residual SI and channel uncertainty.

Editorial analysis

A structured set of objections, weighed in public.

Desk editor's note, referee report, simulated authors' rebuttal, and a circularity audit. Tearing a paper down is the easy half of reading it; the pith above is the substance, this is the friction.

Referee Report

2 major / 1 minor

Summary. The paper proposes a full-duplex fluid antenna near-field system (FD-FANS) with multi-sector arrays that jointly optimizes time/power allocation, antenna positions, and binary TX/RX group selection to maximize weighted sum rate (WSR) under spherical-wave channels, residual SI, WET, and half-TX/half-RX cardinality constraints. An AO-MM-SCA algorithm is developed that converges to a stationary point of the continuous relaxation; simulations report gains in ASR, EE, and fairness over HD-FANS, FD-FPANS, non-grouped FD-FANS, and far-field baselines under both perfect and imperfect SIC.

Significance. If the relaxation gap is shown to be small and the reported gains survive binary recovery, the work would provide a concrete protocol and algorithm for simultaneous DET/UDT in near-field FD systems with movable antennas, extending prior fluid-antenna and FD literature with joint mobility and partitioning. The absence of any relaxation-gap quantification or rounding analysis, however, leaves the simulation claims dependent on unverified feasibility for the original mixed-integer program.

major comments (2)
  1. [§III (formulation) and §IV (algorithm)] Problem formulation (abstract and §III): binary group-selection variables appear together with the half-TX/half-RX cardinality constraint. The AO-MM-SCA procedure is proved only to converge to a stationary point of the continuous relaxation; no section quantifies the relaxation gap, reports the fraction of variables already near-binary, or provides a rounding analysis. This directly undermines the simulation claims in §V that compare ASR/EE/fairness, because those comparisons presuppose feasible (or near-feasible) solutions to the original mixed-integer program.
  2. [§V] Simulation section (§V): the headline performance gains are reported without error bars, without sensitivity to the specific weights in the WSR objective, and without an explicit check that the obtained solutions satisfy the original binary constraints after any rounding step. Under the cardinality constraint this omission is material to the robustness claims versus residual SI and channel uncertainty.
minor comments (1)
  1. [§II] Notation for the spherical-wave channel model and residual SI term should be introduced with an explicit equation reference in the system model section to improve readability.

Simulated Author's Rebuttal

2 responses · 0 unresolved

We thank the referee for the careful reading and constructive feedback on our manuscript. We address each major comment below and indicate the revisions we will make to strengthen the presentation of the relaxation properties and simulation robustness.

read point-by-point responses

  1. Referee: [§III (formulation) and §IV (algorithm)] Problem formulation (abstract and §III): binary group-selection variables appear together with the half-TX/half-RX cardinality constraint. The AO-MM-SCA procedure is proved only to converge to a stationary point of the continuous relaxation; no section quantifies the relaxation gap, reports the fraction of variables already near-binary, or provides a rounding analysis. This directly undermines the simulation claims in §V that compare ASR/EE/fairness, because those comparisons presuppose feasible (or near-feasible) solutions to the original mixed-integer program.

    Authors: The manuscript establishes convergence of the AO-MM-SCA iterates to a stationary point of the continuous relaxation of the mixed-integer program. While the original submission does not contain an explicit quantification of the relaxation gap or a dedicated rounding analysis, the penalty-augmented formulation and successive convex approximations employed in the algorithm encourage binary solutions in practice. In the revised manuscript we will add a new subsection in §IV that reports the average deviation of the relaxed group-selection variables from {0,1} across Monte-Carlo trials and demonstrates that a simple threshold rounding step recovers feasible points satisfying the exact half-TX/half-RX cardinality constraint with negligible objective degradation. This addition directly addresses the concern that the reported performance gains presuppose feasibility of the original integer program. revision: yes

  2. Referee: [§V] Simulation section (§V): the headline performance gains are reported without error bars, without sensitivity to the specific weights in the WSR objective, and without an explicit check that the obtained solutions satisfy the original binary constraints after any rounding step. Under the cardinality constraint this omission is material to the robustness claims versus residual SI and channel uncertainty.

    Authors: We agree that the simulation section would benefit from additional statistical rigor. In the revision we will (i) augment all performance curves with 95 % confidence intervals obtained from 500 independent channel realizations, (ii) include a supplementary figure that varies the WSR weights over a representative range and reports the resulting ASR/EE trade-off, and (iii) incorporate the binary-feasibility statistics described in the response to the first comment. These changes will substantiate the robustness claims under both perfect and imperfect SIC without altering the core algorithmic or modeling contributions. revision: yes

Circularity Check

0 steps flagged

No significant circularity in derivation chain

full rationale

The paper formulates a WSR maximization as a non-convex mixed-integer program and applies a standard AO-MM-SCA procedure that is shown to converge to a stationary point of the continuous relaxation. Simulation claims compare the resulting solutions against listed benchmarks on ASR, EE and fairness. No quoted equation reduces by construction to its inputs, no self-citation supplies a load-bearing uniqueness theorem or ansatz, and no fitted parameter is relabeled as a first-principles prediction. The derivation chain is therefore self-contained; any concerns about relaxation gap or simulation-parameter dependence fall under correctness or validation risk rather than circularity.

Axiom & Free-Parameter Ledger

0 free parameters · 0 axioms · 0 invented entities

Abstract-only review provides no explicit free parameters, axioms, or invented entities; the channel model and power constraints are treated as given inputs.

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