---
title: Transmit Antenna Selection (TAS)
url: https://www.emergentmind.com/topics/transmit-antenna-selection-tas
type: topic
---

# Transmit Antenna Selection (TAS)

Transmit antenna selection (TAS) is a fundamental spatial processing technique for multi-antenna wireless systems, whereby a subset of transmit antennas is selected for communication based on certain channel metrics or system constraints. TAS enables spatial diversity, reliability, power efficiency, and security, while reducing hardware complexity by minimizing the number of active RF chains required at the transmitter. The methodology, theoretical analyses, and practical implications of TAS depend strongly on system architecture, channel conditions, available channel state information (CSI), optimization metrics, and additional functionalities such as space-time coding, multiple-access, or physical-layer security enhancements.

## 1. Core Algorithms and Selection Criteria

The canonical TAS procedure involves measuring the instantaneous or statistical channel quality associated with each transmit antenna, and selecting one or more antennas to optimize a specific metric; typically, instantaneous SNR at the receiver, secrecy capacity, or outage probability. The selection may be based on:

- **Main channel CSI-driven selection**: The transmitter selects the antenna(s) yielding the maximum main-channel SNR; e.g., for MIMO wiretap channels, the two antennas maximizing $\|\mathbf{f}_\alpha\|$ are chosen when applying an Alamouti code [1303.5157].
- **Shadowing side information (SSI)-based selection**: Selection is performed based solely on slowly-varying shadowing coefficients $g_\ell$, significantly reducing feedback rate and channel estimation overhead; the antenna with the highest $g_\ell$ is chosen [1209.4065].
- **Majority-based voting**: In multi-user contexts (e.g., downlink NOMA), each user votes for the transmit antenna maximizing its estimated channel, and the antenna with the highest vote count is selected [1904.01885].
- **Optimization-driven selection**: Advanced frameworks employ sequential quadratic programming or combinatorial search to select antennas that, in conjunction with optimal power allocation, minimize total transmit/reception power while meeting rate constraints [2204.03356].
- **Security-oriented selection**: To maximize secrecy rates, selection may be performed based on the minimization of eavesdropper SNR or by leakage-based criteria, especially when the eavesdropper's channel is strong [2105.14950, 1801.04775].

## 2. TAS in Coded and Diversity-Enhancing Structures

TAS is often integrated with space-time coding (STBC), MIMO diversity schemes, and physical-layer security coding for simultaneous diversity and reliability:

- **TAS + Alamouti/OSTBC**: Selecting two strongest antennas, followed by Alamouti (2×2 OSTBC) transmission, achieves order-2 spatial diversity and improves secrecy for wiretap channels; a closed-form secrecy outage probability for arbitrary $N_A$, $N_B$, $N_E$ is available [1303.5157]. Similar constructs apply to IRS-assisted massive MIMO links with hybrid beamforming [2212.05854].
- **TAS/STBC under Feedback Errors**: Cross-analysis of joint transmit-receive antenna selection (TRAS/STBC) and TAS/STBC reveals that diversity is lower-bounded by STBC order even with feedback link errors. Analytical outage and BER/SER performance is quantified using Lauricella and hypergeometric functions for Nakagami-m fading [1208.2719].
- **Physical-Layer Network Coding**: For two-way relay channels, antenna selection based on maximizing Euclidean inter-cluster distance yields diversity order $\min\{N_A, N_B\}$, outperforming strongest-SNR selection which can suffer diversity degradation in higher-order modulations [1709.04450].

## 3. TAS for Secure and NOMA Communications

Security, spectral efficiency, and multi-user fairness are advanced with TAS in several ways:

- **PLS-Enhanced Wiretap/TAS Schemes**: In MIMO wiretap channels, TAS-Alamouti performs selection using only main-channel CSI, boosting Bob's SNR more than Eve's when $\overline\gamma_B/\overline\gamma_E$ exceeds a threshold; secrecy outage probabilities and $\epsilon$-outage capacities are derived [1303.5157].
- **Strong Eavesdropper Regime**: When eavesdropper SNR is comparable/superior, eavesdropper-based TAS (minimizing Eve's channel gain) substantially outperforms conventional Bob-centric selection; closed-form average secrecy capacities for both criteria are provided [2105.14950].
- **Secure MIMO-NOMA Networks**: For near/far users in NOMA, selection rules maximizing received signal power for either user deliver closed-form and asymptotic secrecy outage probabilities. Near-user diversity scales with transmit antennas only if selection is near-user-centric, while far-user diversity order saturates [1809.01306, 2512.00609]. Majority-based TAS further optimizes fairness for most users under imperfect CSI and feedback delay [1904.01885].

## 4. Impact of TAS in System-Level and Hardware Design

TAS enables marked improvements in several hardware and system aspects:

- **Reduced RF-chain and ADC Complexity**: TAS allows system operation with only one (or a few) RF chains even in large antenna arrays, considerably reducing cost and power [1805.10386]. In low-resolution ADC systems, TAS increases ergodic rate substantially, saturating at a closed-form limit determined by quantization parameter $\alpha$.
- **Cognitive Radio and Interference Control**: Underlay cognitive radio systems deploy TAS to strictly bound instantaneous interference to primary receivers; closed-form outage and ergodic capacity calculations incorporate the selection and guarantee primary protection [1404.5083].
- **Intelligent Reflecting Surface (IRS) Integration**: In mmWave IoT designs, combining TAS, OSTBC, hybrid analog/digital beamforming, and IRS yields full diversity and high SNR, with error-rate performance scaling strongly with the number of reflecting elements and subarray/antenna sizes [2212.05854].
- **Channel Estimation and Feedback**: Shadowing-based TAS radically decreases feedback channel usage and channel estimation overhead, especially beneficial in slow-fading or massive antenna settings [1209.4065].

## 5. Performance Analysis, Diversity, and Asymptotics

Theoretical analyses provide closed-form and asymptotic expressions for most relevant performance metrics:

- **Diversity Orders**: Variable across protocols: TAS-Alamouti provides linear scaling with $N_A$ for secrecy capacity at mid-high SNR [1303.5157], while some schemes for finite-alphabet modulations yield zero secrecy diversity order due to MI saturation [1903.00626]. TAS/STBC achieves full diversity under ideal feedback, falling to the STBC order under feedback error [1208.2719].
- **Outage/Capacity Expressions**: Closed-form formulas for outage probability, moment generating functions, ergodic rates, and symbol error probabilities under various fading models (Rayleigh, Nakagami-m, Generalized-K, $\alpha$–$\mu$) are derived for standard and security-enhanced TAS protocols [1303.5157, 1809.01306, 1209.4065, 2512.00609].
- **Zero Diversity Floor and Practical Saturation**: In NOMA and finite-alphabet wiretap channels, overall system SOP or ergodic secrecy rates saturate at high SNR; the diversity order can collapse to zero if AN-induced noise or imperfect SIC dominates, or due to MI bounds in BPSK/QPSK [1903.00626, 2512.00609].

## 6. Advanced Optimization and Machine Learning for TAS

Modern research applies advanced optimization and learning techniques:

- **Sequential Quadratic Programming (SQP) and Boolean Programming**: Power-minimization with discrete antenna switching is solved via AD-based SQP with Boolean QP substeps; near-exact Boolean solutions are obtained in a handful of iterations [2204.03356].
- **Subarray Selection in Hybrid Precoding Architectures**: Secure spatial modulation systems employ three main TASS methods—Max-ASR (approximate secrecy rate maximization via exhaustive search), Max-EV (largest singular value eigen-selection), and Max-P-SINR-ANSNR (product SINR/AN-SNR)—to balance complexity with secrecy performance [2001.02047].
- **Deep Learning-Based Selection**: In secure relay networks, deep neural networks can learn highly nonlinear mappings from channel features to optimal antenna subsets, achieving near-exhaustive performance at negligible online complexity [1901.02005].

## 7. Comparative Summary of TAS Schemes

| TAS Scheme                   | Selection Metric            | Complexity      | Diversity Scaling / Practical Impact     |
|------------------------------|----------------------------|-----------------|------------------------------------------|
| Max-SNR (main channel)       | $\max_\alpha \|\mathbf{f}_\alpha\|$         | O(N) – O(N^2)   | Linear at mid-high SNR; limited for BPSK/QPSK [1303.5157, 1903.00626] |
| Shadowing-based              | $\max_\ell g_\ell$         | O(L), infrequent| High feedback/estimation savings; SSI gain [1209.4065]         |
| Majority-voting (multi-user) | Per-user vote tally        | O(N^2)          | Optimizes for majority fairness, full diversity under ideal CSI [1904.01885]      |
| Sequential QP / AD-SBQP      | Power + rate constraints   | O(N^3) per pass | Near-exact Boolean selection, fast convergence [2204.03356]    |
| Leakage/Min-Eve based        | Minimize Eve SNR/SLNR      | O(N log N)      | Superior secrecy in strong-eavesdropper regimes [2105.14950, 1801.04775]       |
| Secure-NOMA, TASS-II         | Max-Far-user SNR           | O(N)            | Far-user outage minimized, overall SOP improved [1809.01306, 2512.00609]        |
| Deep learning (DNN)          | Learned mapping            | O(1) online     | Near-exhaustive optimal subset, nonlinear mapping [1901.02005]                  |

## References

- "Transmit Antenna Selection with Alamouti Scheme in MIMO Wiretap Channels" [1303.5157]
- "Secure Transmit Antenna Selection Protocol for MIMO NOMA Networks over Nakagami-m Channels" [1809.01306]
- "On the Performance of Transmit Antenna Selection Based on Shadowing Side Information" [1209.4065]
- "Alternating Direction Based Sequential Boolean Quadratic Programming Method for Transmit Antenna Selection" [2204.03356]
- "Transmit Antenna Selection in Underlay Cognitive Radio Environment" [1404.5083]
- "Analysis of Ergodic Rate for Transmit Antenna Selection in Low-Resolution ADC Systems" [1805.10386]
- "Majority Based TAS/MRC Scheme in Downlink NOMA Network with Channel Estimation Errors and Feedback Delay" [1904.01885]
- "Two High-performance Schemes of Transmit Antenna Selection for Secure Spatial Modulation" [1801.04775]
- "Transmit Antenna Selection for Physical-Layer Network Coding Based on Euclidean Distance" [1709.04450]
- "Outage Analysis of TAS-NOMA Systems With Multi-Antenna Users Over α-μ Fading" [2512.00609]
- "Deep Learning Assisted Antenna Selection in Untrusted Relay Networks" [1901.02005]
- "A New Transmit Antenna Selection Technique for Physical Layer Security with Strong Eavesdropping" [2105.14950]
- "Characterization of Effective Capacity in Antenna Selection MIMO Systems" [1608.00098]
- "Error Performance of Various QAM Schemes for Nonrenegerative Cooperative MIMO Network with Transmit Antenna Selection" [1809.00890]
- "Secrecy Performance of Antenna-Selection-Aided MIMOME Channels with BPSK/QPSK Modulations" [1903.00626]
- "IRS-Assisted Millimeter-wave Massive MIMO with Transmit Antenna Selection for IoT Networks" [2212.05854]
- "Precoding and Transmit Antenna Subarray Selection for Secure Hybrid Spatial Modulation" [2001.02047]
- "Unified Analysis of Transmit Antenna Selection/Space-Time Block Coding with Receive Selection and Combining over Nakagami-m Fading Channels in the Presence of Feedback Errors" [1208.2719]

Source: https://www.emergentmind.com/topics/transmit-antenna-selection-tas