RDcomm: Multifaceted Communication Paradigms

• RDcomm is a research shorthand encompassing diverse models such as rate‐decomposition in C-RAN, remote coordination, semantic communication, and integrated radar–communication.
• It underpins advanced methodologies that optimize interference management, beamforming, and task-oriented rate–distortion in applications like collaborative perception and function computation.
• The term also denotes robust reliability solutions and quantum secure protocols, balancing performance metrics like weighted sum-rate, secrecy capacity, and end-to-end delivery.

Searching arXiv for recent and relevant papers on “RDcomm” and its domain-specific usages. RDcomm is a context-dependent research shorthand rather than a single standardized term. In the literature, it denotes at least six distinct but technically related constructs: rate-decomposition communications in cloud radio access networks, remote strong coordination and reliable communication, randomized distributed function computation and semantic communication, pragmatic rate–distortion communication for collaborative perception, dual-functional radar–communication integration, and several reliability- or security-oriented communication architectures at the protocol and physical layers (Ahmad et al., 2019, Cervia et al., 2020, Liu et al., 26 Sep 2025, Xu et al., 2021, Liu et al., 2024, Elangovan et al., 4 Mar 2025, Khalilov et al., 8 May 2025). This multiplicity is not merely terminological. Each usage fixes a different optimization variable, operational objective, and performance criterion: weighted sum-rate in C-RAN, total variation coordination regions, Bayes-risk-constrained message design, radar–communication trade-offs, secrecy capacity, or end-to-end reliability.

1. Terminological scope and major usages

The term appears across information theory, wireless systems, radar–communication integration, quantum secure communication, and large-scale networking. This suggests that RDcomm functions as a local shorthand inside individual subfields rather than as a universally accepted acronym.

Usage of RDcomm	Core meaning	Representative papers
Rate-decomposition communications	RS-CMD with private/common splitting, SIC, clustering, and coordinated beamforming in C-RAN	(Ahmad et al., 2019)
Remote coordination and reliable communication	Strong coordination of an external observer’s distribution while communicating reliably over a DMC	(Cervia et al., 2020)
Randomized distributed semantic communication	RDFC and DeepRDFC for channel simulation, function computation, and privacy	(Bergström et al., 11 Mar 2026, Günlü, 10 Mar 2026)
Pragmatic rate–distortion communication	Task-relevant and redundancy-less message design for collaborative perception	(Liu et al., 26 Sep 2025)
Radar–communication integration	DFRC/RadCom waveform, beamforming, RSMA, IRS/RIS, and secure ISAC designs	(Xu et al., 2021, Yin et al., 2022, Li et al., 2022, Zheng et al., 2024, Liu et al., 2021, Cao et al., 2021, Oliveira et al., 2023)
Secure/reliable communication substrates	Receiver-device-independent QSDC, reliable UDP command transport, and software-defined RDMA reliability	(Liu et al., 2024, Elangovan et al., 4 Mar 2025, Khalilov et al., 8 May 2025)

A related but separately named rate–distortion formulation appears in communication-efficient federated learning, where gradient quantizers are optimized under an explicit encoded-rate budget rather than under an RDcomm label (Hamidi et al., 2024). This suggests a broader conceptual neighborhood in which “RD” can denote rate–distortion, reliability design, or rate decomposition depending on disciplinary context.

2. RDcomm as rate-decomposition communications in C-RAN

In the downlink C-RAN formulation, RDcomm is explicitly identified with RS-CMD, where each user message is split into a private part and a common part. A central processor connects to $N$ multi-antenna BSs over finite-capacity backhaul links $C_b$, serves $K$ single-antenna users, and jointly designs beamformers, serving clusters, and decoding sets under per-BS backhaul and transmit-power constraints (Ahmad et al., 2019). The BS-$b$ transmit signal is

$$x_b = \sum_{k=1}^{K}\Big(\mathbf{w}^{p}_{b,k}\,s^{p}_{k} + \mathbf{w}^{c}_{b,k}\,s^{c}_{k}\Big),$$

with private and common serving clusters

$$\mathcal{B}^{p}_{k} \triangleq \big\{b : \mathbf{w}^{p}_{b,k}\neq \mathbf{0}\big\},\quad \mathcal{B}^{c}_{k} \triangleq \big\{b : \mathbf{w}^{c}_{b,k}\neq \mathbf{0}\big\}.$$

The essential RDcomm mechanism is rate decomposition together with common message decoding. For user $u$,

$$R_{u} = R^{p}_{u} + R^{c}_{u},\quad R^{c}_{u} \le \min_{k\in\mathcal{D}_{u}} \log_{2}\big(1+\gamma^{c}_{u\to k}\big),$$

where $\mathcal{D}_u$ is the decoding set for the common message. At each user, SIC is performed on designated common streams in a prescribed order, followed by decoding of the private stream. The private-stream SINR and common-stream SINR are explicitly coupled through undecoded private streams, non-decoded common streams, and SIC ordering. The weighted sum-rate objective is

$$\max_{\{\mathbf{w}\},\{\mathcal{B}^{p}_{k},\mathcal{B}^{c}_{k}\},\{\mathcal{D}_{u}\}} \sum_{k=1}^{K} w_{k}\Big(R^{p}_{k}+R^{c}_{k}\Big).$$

Backhaul enters through data-sharing constraints of the form

$C_b$0

where the binary indicators induce group sparsity in the beamformers. The resulting problem is mixed discrete–continuous and nonconvex. The proposed solution relaxes the induced $C_b$1-structure by the concave surrogate

$C_b$2

and then applies inner-convex approximation, slack-variable decoupling of bilinear backhaul terms, first-order linearizations of $C_b$3, and proximal regularization. Each convexified subproblem can be cast as SOCP and solved in $C_b$4 per iteration, with convergence to a stationary point of the relaxed problem under standard ICA conditions (Ahmad et al., 2019).

Operationally, the design is most favorable in interference-limited and backhaul-limited regimes. The paper states that RDcomm yields substantial weighted-sum-rate gains over treating interference as noise, especially under moderate-to-strong inter-user interference, limited backhaul, and dense BS deployments. It also identifies the relevant limiting cases: setting all common beamformers to zero reduces RDcomm to TIN; enlarging $C_b$5 and $C_b$6 moves the design toward richer cooperation and BC-like coordinated multipoint; and very weak interference or extremely tight backhaul drives the solution toward purely private transmission (Ahmad et al., 2019).

3. RDcomm as coordination-theoretic and semantic communication

In information theory, RDcomm denotes “Remote Joint Strong Coordination and Reliable Communication.” The canonical model is a three-node DMC in which an encoder communicates reliably with a legitimate decoder while constraining the distribution observed by an external agent. Messages $C_b$7 are i.i.d. uniform, the encoder and decoder share common randomness $C_b$8 at rate $C_b$9, and the induced $K$0 must approach $K$1 in total variation while the block error probability vanishes (Cervia et al., 2020). The single-letter region is characterized by an auxiliary $K$2 satisfying $K$3, with

$K$4

and $K$5 (Cervia et al., 2020). Under strong secrecy, the message-rate bound sharpens to

$K$6

This coordination-theoretic reading of RDcomm is extended by randomized distributed function computation. In RDFC, the sender communicates only the information needed for the receiver to generate a randomized function output $K$7 such that the blockwise synthesized law approximates $K$8 in total variation. With common randomness $K$9 and local randomness $b$0, the strong-coordination rate region is

$b$1

for $b$2 and $b$3 (Bergström et al., 11 Mar 2026). The two corner points are operationally important: without common randomness the minimum communication rate is Wyner’s common information $b$4, whereas with sufficient common randomness the rate can approach $b$5 (Bergström et al., 11 Mar 2026, Günlü, 10 Mar 2026).

DeepRDFC provides a neural instantiation of this framework by learning distributed channel simulation from samples only. An encoder $b$6 maps $b$7 to a latent vector $b$8, a vector quantizer imposes a codebook of size $b$9, and a decoder $$x_b = \sum_{k=1}^{K}\Big(\mathbf{w}^{p}_{b,k}\,s^{p}_{k} + \mathbf{w}^{c}_{b,k}\,s^{c}_{k}\Big),$$0 produces a distribution over $$x_b = \sum_{k=1}^{K}\Big(\mathbf{w}^{p}_{b,k}\,s^{p}_{k} + \mathbf{w}^{c}_{b,k}\,s^{c}_{k}\Big),$$1. Training minimizes a categorical cross-entropy surrogate for TVD through the induced distribution

$$x_b = \sum_{k=1}^{K}\Big(\mathbf{w}^{p}_{b,k}\,s^{p}_{k} + \mathbf{w}^{c}_{b,k}\,s^{c}_{k}\Big),$$2

using Pinsker’s inequality to relate KL and total variation (Bergström et al., 11 Mar 2026). The reported experiments on a BSC target law show large TVD improvements from common randomness: for $$x_b = \sum_{k=1}^{K}\Big(\mathbf{w}^{p}_{b,k}\,s^{p}_{k} + \mathbf{w}^{c}_{b,k}\,s^{c}_{k}\Big),$$3 and $$x_b = \sum_{k=1}^{K}\Big(\mathbf{w}^{p}_{b,k}\,s^{p}_{k} + \mathbf{w}^{c}_{b,k}\,s^{c}_{k}\Big),$$4, test TVD drops from $$x_b = \sum_{k=1}^{K}\Big(\mathbf{w}^{p}_{b,k}\,s^{p}_{k} + \mathbf{w}^{c}_{b,k}\,s^{c}_{k}\Big),$$5 without common randomness to $$x_b = \sum_{k=1}^{K}\Big(\mathbf{w}^{p}_{b,k}\,s^{p}_{k} + \mathbf{w}^{c}_{b,k}\,s^{c}_{k}\Big),$$6 with common randomness at the same rate (Bergström et al., 11 Mar 2026).

The privacy-oriented RDFC formulation interprets RDcomm as semantic communication for randomized outputs under strong coordination and local differential privacy. It uses the standard coordination region

$$x_b = \sum_{k=1}^{K}\Big(\mathbf{w}^{p}_{b,k}\,s^{p}_{k} + \mathbf{w}^{c}_{b,k}\,s^{c}_{k}\Big),$$7

and emphasizes the gap between the no-common-randomness Wyner common information point and the unlimited-common-randomness mutual-information point (Günlü, 10 Mar 2026). In the reported continuous Gaussian-LDP case, sufficient common randomness can reduce the semantic communication rate by up to a factor of $$x_b = \sum_{k=1}^{K}\Big(\mathbf{w}^{p}_{b,k}\,s^{p}_{k} + \mathbf{w}^{c}_{b,k}\,s^{c}_{k}\Big),$$8 compared to the WCI point, while finite-blocklength analysis shows that the privacy-parameter gap closes exponentially fast with input length (Günlü, 10 Mar 2026).

4. RDcomm as pragmatic rate–distortion communication for multi-agent perception

In collaborative perception, RDcomm denotes a concrete communication framework rather than a coordination region. The setting involves multiple agents that exchange intermediate BEV features under a bandwidth constraint, with the receiver already holding local features $$x_b = \sum_{k=1}^{K}\Big(\mathbf{w}^{p}_{b,k}\,s^{p}_{k} + \mathbf{w}^{c}_{b,k}\,s^{c}_{k}\Big),$$9 that partially inform the downstream task $$\mathcal{B}^{p}_{k} \triangleq \big\{b : \mathbf{w}^{p}_{b,k}\neq \mathbf{0}\big\},\quad \mathcal{B}^{c}_{k} \triangleq \big\{b : \mathbf{w}^{c}_{b,k}\neq \mathbf{0}\big\}.$$0. The central quantity is pragmatic distortion, defined as the increase in Bayes risk caused by replacing the sender’s raw observation $$\mathcal{B}^{p}_{k} \triangleq \big\{b : \mathbf{w}^{p}_{b,k}\neq \mathbf{0}\big\},\quad \mathcal{B}^{c}_{k} \triangleq \big\{b : \mathbf{w}^{c}_{b,k}\neq \mathbf{0}\big\}.$$1 with a compressed message $$\mathcal{B}^{p}_{k} \triangleq \big\{b : \mathbf{w}^{p}_{b,k}\neq \mathbf{0}\big\},\quad \mathcal{B}^{c}_{k} \triangleq \big\{b : \mathbf{w}^{c}_{b,k}\neq \mathbf{0}\big\}.$$2 conditioned on the receiver’s observation $$\mathcal{B}^{p}_{k} \triangleq \big\{b : \mathbf{w}^{p}_{b,k}\neq \mathbf{0}\big\},\quad \mathcal{B}^{c}_{k} \triangleq \big\{b : \mathbf{w}^{c}_{b,k}\neq \mathbf{0}\big\}.$$3 (Liu et al., 26 Sep 2025): $$\mathcal{B}^{p}_{k} \triangleq \big\{b : \mathbf{w}^{p}_{b,k}\neq \mathbf{0}\big\},\quad \mathcal{B}^{c}_{k} \triangleq \big\{b : \mathbf{w}^{c}_{b,k}\neq \mathbf{0}\big\}.$$4 The associated rate–distortion problem is

$$\mathcal{B}^{p}_{k} \triangleq \big\{b : \mathbf{w}^{p}_{b,k}\neq \mathbf{0}\big\},\quad \mathcal{B}^{c}_{k} \triangleq \big\{b : \mathbf{w}^{c}_{b,k}\neq \mathbf{0}\big\}.$$5

and the paper states the minimal achievable rate as

$$\mathcal{B}^{p}_{k} \triangleq \big\{b : \mathbf{w}^{p}_{b,k}\neq \mathbf{0}\big\},\quad \mathcal{B}^{c}_{k} \triangleq \big\{b : \mathbf{w}^{c}_{b,k}\neq \mathbf{0}\big\}.$$6

Two necessary optimality conditions are then identified. Pragmatic relevance requires

$$\mathcal{B}^{p}_{k} \triangleq \big\{b : \mathbf{w}^{p}_{b,k}\neq \mathbf{0}\big\},\quad \mathcal{B}^{c}_{k} \triangleq \big\{b : \mathbf{w}^{c}_{b,k}\neq \mathbf{0}\big\}.$$7

and redundancy-less communication requires

$$\mathcal{B}^{p}_{k} \triangleq \big\{b : \mathbf{w}^{p}_{b,k}\neq \mathbf{0}\big\},\quad \mathcal{B}^{c}_{k} \triangleq \big\{b : \mathbf{w}^{c}_{b,k}\neq \mathbf{0}\big\}.$$8

These conditions are operationalized by two modules. First, task entropy discrete coding uses layered vector quantization with base and residual codebooks, confidence-based spatial selection, and Huffman coding weighted by confidence frequency

$$\mathcal{B}^{p}_{k} \triangleq \big\{b : \mathbf{w}^{p}_{b,k}\neq \mathbf{0}\big\},\quad \mathcal{B}^{c}_{k} \triangleq \big\{b : \mathbf{w}^{c}_{b,k}\neq \mathbf{0}\big\}.$$9

Second, mutual-information-driven message selection estimates local complementarity between the sender’s coarse feature and the receiver’s feature using a neural MI estimator trained with the logistic f-GAN lower bound

$u$0

A coarse “handshake” sends the base indices first; the receiver then constructs a redundancy map and requests only low-MI regions (Liu et al., 26 Sep 2025).

The empirical results tie the theoretical construction to practical bandwidth savings. On DAIR-V2X and OPV2V, the framework is reported to achieve state-of-the-art accuracy while reducing communication volume by up to $u$1 times relative to CodeFilling on OPV2V detection, and by $u$2 times on segmentation relative to the same baseline (Liu et al., 26 Sep 2025). Additional ablations attribute large savings to both coding and selection: confidence-weighted Huffman coding saves $u$3 and $u$4 bits over fixed-length coding for detection and segmentation, while MI-driven selection reduces bits by $u$5 and $u$6 versus confidence-based or LiDAR-coverage baselines (Liu et al., 26 Sep 2025).

A related but separately named rate–distortion formulation appears in federated learning. RC-FED minimizes $u$7 subject to $u$8, where the rate is the expected code length after entropy coding, and its convergence theorem exposes the quantization term $u$9 explicitly in the optimization error bound (Hamidi et al., 2024). This is not called RDcomm in that paper, but it occupies the same rate-aware, task-oriented design space.

5. RDcomm as dual-functional radar–communication integration

In radar and ISAC research, RDcomm commonly denotes DFRC or RadCom: the joint use of shared spectrum, hardware, and signal processing for sensing and communication. One line of work emphasizes RSMA. In a multi-antenna DFRC transmitter, the transmit signal

$$R_{u} = R^{p}_{u} + R^{c}_{u},\quad R^{c}_{u} \le \min_{k\in\mathcal{D}_{u}} \log_{2}\big(1+\gamma^{c}_{u\to k}\big),$$0

combines a common stream, private communication streams, and optionally a radar sequence. The joint design maximizes weighted sum-rate while minimizing beampattern MSE under per-antenna power constraints. A central conclusion is that the RSMA common stream can simultaneously manage inter-user interference, manage radar–communication interference, and support beampattern approximation, so the framework with and without an additional radar sequence achieves the same tradeoff performance (Xu et al., 2021).

This RSMA perspective is extended to multibeam satellites. There, the DFRC beamforming problem minimizes the trace of the CRB for target-angle estimation subject to per-user QoS constraints and the equal per-feed power constraint $$R_{u} = R^{p}_{u} + R^{c}_{u},\quad R^{c}_{u} \le \min_{k\in\mathcal{D}_{u}} \log_{2}\big(1+\gamma^{c}_{u\to k}\big),$$1. The paper formulates the problem with covariance matrices $$R_{u} = R^{p}_{u} + R^{c}_{u},\quad R^{c}_{u} \le \min_{k\in\mathcal{D}_{u}} \log_{2}\big(1+\gamma^{c}_{u\to k}\big),$$2, common-rate allocations $$R_{u} = R^{p}_{u} + R^{c}_{u},\quad R^{c}_{u} \le \min_{k\in\mathcal{D}_{u}} \log_{2}\big(1+\gamma^{c}_{u\to k}\big),$$3, and Schur-complement LMIs, then solves it by SCA together with an iterative penalty enforcing rank-one beamforming matrices (Yin et al., 2022). The reported result is that RSMA-assisted DFRC yields lower Root-CRB than SDMA for all tested QoS thresholds and nearly matches a radar-only benchmark at high radar SNR (Yin et al., 2022).

Another major thread is intelligent-surface-aided RDcomm. In an IRS-aided DFRC system with one target and multiple communication receivers, the radar-side round-trip channel is

$$R_{u} = R^{p}_{u} + R^{c}_{u},\quad R^{c}_{u} \le \min_{k\in\mathcal{D}_{u}} \log_{2}\big(1+\gamma^{c}_{u\to k}\big),$$4

the communication channel is $$R_{u} = R^{p}_{u} + R^{c}_{u},\quad R^{c}_{u} \le \min_{k\in\mathcal{D}_{u}} \log_{2}\big(1+\gamma^{c}_{u\to k}\big),$$5, and the objective maximizes

$$R_{u} = R^{p}_{u} + R^{c}_{u},\quad R^{c}_{u} \le \min_{k\in\mathcal{D}_{u}} \log_{2}\big(1+\gamma^{c}_{u\to k}\big),$$6

subject to unit-modulus IRS coefficients and a waveform-covariance constraint (Li et al., 2022). The radar-IRS phase subproblem is quartic in the IRS phases and is handled on the complex circle manifold by Riemannian gradient descent with elementwise retraction. The secure-RIS extension formulates secrecy-rate maximization under radar-detection constraints for both RCCE and DFRC, with robust variants under bounded CSI uncertainty. The reported comparison states that the RCCE system can provide a higher secrecy rate than the DFRC system, even when Eve’s CSI is imperfect (Zheng et al., 2024).

Waveform-centric RDcomm is equally diverse. Symbol-level precoding for MIMO DFRC optimizes the instantaneous transmit vector per symbol under constructive-interference QoS inequalities and strict constant-modulus constraints. Two algorithms are proposed: a Euclidean PDD–MM–BCD method and a Riemannian ALM–RBFGS method on the complex-circle manifold. The second is reported to be about $$R_{u} = R^{p}_{u} + R^{c}_{u},\quad R^{c}_{u} \le \min_{k\in\mathcal{D}_{u}} \log_{2}\big(1+\gamma^{c}_{u\to k}\big),$$7 faster, at the price of a slight performance loss (Liu et al., 2021). Integrated CPM–LFM RDcomm defines a radar rate

$$R_{u} = R^{p}_{u} + R^{c}_{u},\quad R^{c}_{u} \le \min_{k\in\mathcal{D}_{u}} \log_{2}\big(1+\gamma^{c}_{u\to k}\big),$$8

and maximizes $$R_{u} = R^{p}_{u} + R^{c}_{u},\quad R^{c}_{u} \le \min_{k\in\mathcal{D}_{u}} \log_{2}\big(1+\gamma^{c}_{u\to k}\big),$$9 via greedy user selection and an MMLM beamforming/power-allocation algorithm, while also deriving PSD, BER, and ambiguity-function characteristics for the integrated waveform (Cao et al., 2021). Shift-register-based PMCW RadCom instead modulates entire PRBS blocks with BPSK symbols, uses Schmidl–Cox-compatible binary preambles and pilot blocks for synchronization and residual SFO correction, and demonstrates zero BER for three of the four measured parameter sets under the reported proof-of-concept setup (Oliveira et al., 2023).

Taken together, these works show that “RDcomm” in the radar literature is not a single waveform family. It spans RSMA-driven interference management, symbol-level precoding, integrated chirp or PMCW signaling, IRS/RIS-aided propagation control, and secrecy-constrained ISAC. The common structural theme is a coupled optimization over sensing utility and communication utility, but the metrics vary widely: weighted SNR, WSR, CRB, beampattern MSE, secrecy rate, BER, ambiguity function, or pilot-aided synchronization error.

6. RDcomm as receiver-device-independent quantum secure direct communication

In quantum communication, RDcomm denotes receiver-device-independent quantum secure direct communication. The protocol is prepare-and-measure, uses a trusted single-photon source, and treats all receiving devices in both laboratories as black boxes (Liu et al., 2024). Security is certified solely from observed statistics. Alice prepares equatorial single-photon states

$\mathcal{D}_u$0

with $\mathcal{D}_u$1, partitions them into three sequences, and uses two security-check rounds plus a masked message round. Bob encodes message bits through $\mathcal{D}_u$2 and $\mathcal{D}_u$3, and the parties compare empirical versus theoretical projection statistics

$\mathcal{D}_u$4

$\mathcal{D}_u$5

If the deviations exceed the tolerated threshold, the protocol aborts (Liu et al., 2024).

The secrecy analysis is given in wiretap form. With total gains $\mathcal{D}_u$6 and $\mathcal{D}_u$7, and total error rates $\mathcal{D}_u$8 and $\mathcal{D}_u$9, the secrecy message capacity is

$$\max_{\{\mathbf{w}\},\{\mathcal{B}^{p}_{k},\mathcal{B}^{c}_{k}\},\{\mathcal{D}_{u}\}} \sum_{k=1}^{K} w_{k}\Big(R^{p}_{k}+R^{c}_{k}\Big).$$0

The protocol is stated to provide the same security level as MDI QSDC, while avoiding entanglement and Bell-state measurements. Under the simulated practical parameters, it achieves practical communication efficiency about $$\max_{\{\mathbf{w}\},\{\mathcal{B}^{p}_{k},\mathcal{B}^{c}_{k}\},\{\mathcal{D}_{u}\}} \sum_{k=1}^{K} w_{k}\Big(R^{p}_{k}+R^{c}_{k}\Big).$$1 times that of DI QSDC and a secure communication distance about $$\max_{\{\mathbf{w}\},\{\mathcal{B}^{p}_{k},\mathcal{B}^{c}_{k}\},\{\mathcal{D}_{u}\}} \sum_{k=1}^{K} w_{k}\Big(R^{p}_{k}+R^{c}_{k}\Big).$$2 times that of DI QSDC (Liu et al., 2024). The secure-distance examples include $$\max_{\{\mathbf{w}\},\{\mathcal{B}^{p}_{k},\mathcal{B}^{c}_{k}\},\{\mathcal{D}_{u}\}} \sum_{k=1}^{K} w_{k}\Big(R^{p}_{k}+R^{c}_{k}\Big).$$3 km for $$\max_{\{\mathbf{w}\},\{\mathcal{B}^{p}_{k},\mathcal{B}^{c}_{k}\},\{\mathcal{D}_{u}\}} \sum_{k=1}^{K} w_{k}\Big(R^{p}_{k}+R^{c}_{k}\Big).$$4 and $$\max_{\{\mathbf{w}\},\{\mathcal{B}^{p}_{k},\mathcal{B}^{c}_{k}\},\{\mathcal{D}_{u}\}} \sum_{k=1}^{K} w_{k}\Big(R^{p}_{k}+R^{c}_{k}\Big).$$5 km for $$\max_{\{\mathbf{w}\},\{\mathcal{B}^{p}_{k},\mathcal{B}^{c}_{k}\},\{\mathcal{D}_{u}\}} \sum_{k=1}^{K} w_{k}\Big(R^{p}_{k}+R^{c}_{k}\Big).$$6 under the stated efficiency assumptions (Liu et al., 2024).

This usage is conceptually distinct from the rate–distortion or coordination-theoretic meanings of RDcomm. Its defining idea is not semantic compression or interference management, but one-sided device-independent certification of direct communication.

7. RDcomm as reliable control and transport architecture

At the networking and systems level, RDcomm appears as a reliability-oriented communication substrate. In large detector control, it denotes a reliable datagram-based command interface over UDP, derived from the Handshaking Protocol based Command Interface for the INO ICAL experiment (Elangovan et al., 4 Mar 2025). The setting involves 28,800 RPCs with RPC-DAQ front ends, each a distinct Ethernet node, controlled over a LAN. Each DAQ uses two UDP sockets, one for multicast and one for unicast. Reliability is added through a handshake scheme, CRC-16 with polynomial $$\max_{\{\mathbf{w}\},\{\mathcal{B}^{p}_{k},\mathcal{B}^{c}_{k}\},\{\mathcal{D}_{u}\}} \sum_{k=1}^{K} w_{k}\Big(R^{p}_{k}+R^{c}_{k}\Big).$$7, sequence numbers, duplicate suppression, and selective unicast retries to the non-responsive set $$\max_{\{\mathbf{w}\},\{\mathcal{B}^{p}_{k},\mathcal{B}^{c}_{k}\},\{\mathcal{D}_{u}\}} \sum_{k=1}^{K} w_{k}\Big(R^{p}_{k}+R^{c}_{k}\Big).$$8. Command packets carry start markers $$\max_{\{\mathbf{w}\},\{\mathcal{B}^{p}_{k},\mathcal{B}^{c}_{k}\},\{\mathcal{D}_{u}\}} \sum_{k=1}^{K} w_{k}\Big(R^{p}_{k}+R^{c}_{k}\Big).$$9 and acknowledgments $C_b$00, along with DAQ ID, data type, command word, sequence number, payload length, payload, and checksum (Elangovan et al., 4 Mar 2025).

The protocol is explicitly tuned by measured latency. In the Mini-ICAL proof-of-concept with five DAQs, average cycle times were $C_b$01 for 18-byte packets, $C_b$02 for 26-byte packets, and $C_b$03 for 100-byte packets in the non-busy model; under concurrent TCP event traffic at $C_b$04–$C_b$05 Mbps, the 100-byte command latency rose only to $C_b$06–$C_b$07 (Elangovan et al., 4 Mar 2025). The design therefore preserves multicast-friendly semantics while adding deterministic delivery and integrity checks.

A different systems-level meaning appears in planetary-scale RDMA reliability. SDR-RDMA introduces a software-defined reliability stack for long-haul RDMA, motivated by the observation that at $C_b$08 Gbit/s and $C_b$09 ms RTT the BDP is about $C_b$10 GiB, which makes selective-repeat recovery fundamentally RTT-limited for many message sizes (Khalilov et al., 8 May 2025). The architecture exposes unreliable multi-packet RDMA writes together with a receive buffer bitmap, so applications can exploit partial completion to implement SR, erasure coding, or hybrids while retaining zero-copy semantics. The analysis models SR completion time by

$C_b$11

and EC fallback by

$C_b$12

The reported performance gains reach up to $C_b$13 on average and $C_b$14 at the $C_b$15th percentile for RDMA write completion in lossy, high-RTT regimes, while DPA offload saturates $C_b$16 Gbit/s using as few as $C_b$17 of $C_b$18 hardware threads at message sizes of at least $C_b$19 KiB (Khalilov et al., 8 May 2025).

These protocol and transport uses show that RDcomm can also denote reliability engineering rather than physical-layer waveform design or information-theoretic coordination. The shared thread is again contextual: the term names the communication architecture that is central to the problem, whether that architecture is a multicast command plane, a bitmap-aware RDMA stack, or a single-photon secure direct channel.