Source-linked AI summary
Reconfigurable Intelligent Surface-Assisted Space Shift Keying
Ayse E. Canbilen, Ertugrul Basar, Salama S. Ikki
TL;DR
Future 6G systems face high-frequency engineering demands and the challenge of efficient, high-performance connectivity. This paper proposes RIS-assisted SSK with an ML-based ABER analysis. Simulations and analysis report low error rates and high energy efficiency, with results varying by RIS configuration and channel conditions.
Problem
Future 6G's higher-frequency bands require system re-engineering, while high-performance connectivity must be provided efficiently.
Source: §I · p. 1Method
The paper proposes RIS-assisted SSK and develops an ML-based analytical framework for ABER, supported by simulations and an asymptotic approximation.
Source: §I · p. 1Results
At ABER = 10^-3, RIS-SSK with N = 16 reflecting elements provides approximately 30 dB SNR gain over traditional SSK.
Source: §V · p. 4Takeaways & Limitations
The authors conclude that RIS-based SSK achieves very low error rates with unconventional energy efficiency in the studied system.
Source: §VI · p. 5
Abstract
from arXiv · showhide
The concept of reconfigurable intelligent surface (RIS)-assisted transmission, where phases of reflected signals are adjusted in an effective arrangement, has been recently put forward as a promising solution for 6G and beyond networks. Considering this and the undeniable potential of index modulation (IM) techniques, an RIS-based space shift keying (SSK) scheme is proposed in this letter to maintain all inherent advantages of both RISs and SSK. Specifically, a mathematical framework is presented by using a maximum likelihood (ML) detector for the calculation of the average bit error rate (ABER). Extensive computer simulation results are provided to assess the potential of the proposed RIS-based SSK system and to verify the theoretical derivations. The obtained results indicate that the proposed scheme enables highly reliable transmission with unconventionally high energy efficiency, however with the added cost of increased receiver complexity.
I. INTRODUCTION
Future 6G systems face high-frequency engineering demands while needing energy-, spectrum-, and cost-effective connectivity. The paper proposes RIS-assisted SSK and analyzes its error performance and complexity.
I. INTRODUCTION
- 6G's anticipated higher-frequency bands require system re-engineering, alongside the challenge of providing high-performance connectivity efficiently.The introduction also identifies new user requirements and applications as engineering challenges.Source: §I · p. 1
- Index modulation conveys additional bits through indices of available transmit entities; spatial modulation uses the activated antenna index and can trade spectral for energy efficiency.The introduction also attributes reduced inter-channel interference and synchronization problems to spatial modulation.Source: §I · p. 1
- RIS elements passively reflect incident signals with adjustable phases, without dedicated energy, decoding, encoding, or retransmission.The introduction notes that phase control can enhance useful signals, while passive elements avoid noise amplification and self-interference.Source: §I · p. 1
- The proposed RIS-SSK scheme uses transmit-antenna indices for SSK, aiming for reliable, energy-efficient transmission while avoiding synchronization and interference problems.Unlike [13], it realizes SSK at the transmitter by exploiting the transmit-antenna indices.Source: §I · p. 1
- The paper presents an ML-based analytical framework for ABER, verifies it with simulations, and derives an asymptotic approximation.It also investigates reflecting-element count, blind phases, channel phase-estimation error, and path loss across SNR regimes.Source: §I · pp. 1–2
II. SYSTEM MODEL
The system uses SSK to select one source antenna and reflect its unmodulated carrier through an RIS to a destination. The analysis considers both channel-phase-informed and blind RIS operation.
II. SYSTEM MODEL
- SSK maps incoming bits to one of Nt source antennas, which sends an unmodulated carrier through N RIS elements to a single destination antenna.The source-to-RIS and RIS-to-destination channel coefficients are modeled as zero-mean complex Gaussian with variance σ^2.Source: §II · p. 2
- The path-loss model assumes the direct path has little effect for large N and can be omitted; the assumed propagation scenario determines the distance dependence.The passage describes a near-field specular-reflection case and contrasts its path-loss dependence with the alternative case.Source: §II · p. 2
- The paper distinguishes intelligent RIS-SSK, which uses channel-phase knowledge, from blind RIS-SSK.The two implementation scenarios are defined by whether the RIS knows the channel phases.Source: §II · p. 2
A. Intelligent RIS-SSK
In the intelligent RIS-SSK scenario, software-controlled RIS phases maximize destination SNR by canceling channel phases. This is a best-case setup that assumes phase and activated-antenna information at the RIS.
A. Intelligent RIS-SSK
- The received baseband signal includes the RIS-reflected signal and noise; the passage defines the transmitted energy, noise variance, reflector phase, and selected antenna index.The RIS phase for the lth reflector is φ_l, and i denotes the selected transmit-antenna index.Source: A. Intelligent RIS-SSK · p. 2
- The RIS adjusts each reflector's phase to cancel channel phases and maximize destination SNR, assuming it knows the phases and activated transmit-antenna index.The paper describes this as a best-case scenario and assumes channel-state information is provided to the RIS via communication software.Source: A. Intelligent RIS-SSK · p. 2
- With multiple receive antennas, canceling channel phases would require more than one phase setting per reflector, which the paper says is impractical for this design.Each reflector can be set to just one specific phase for each reflection.Source: A. Intelligent RIS-SSK · p. 2
B. Blind RIS-SSK
Blind RIS-SSK operates without channel-phase or activated-antenna information at the RIS, so it cannot use the intelligent phase-maximization strategy. Its detector estimates the active antenna index.
B. Blind RIS-SSK
- Without channel or activated-antenna information, the RIS cannot maximize SNR through phase adjustment; the blind scheme reflects with φ_l = 0° and uses ML detection of the active antenna index.The paper characterizes this as the worst-case scenario in system performance relative to the intelligent case.Source: B. Blind RIS-SSK · p. 2
- The blind scheme can use multiple receive antennas because its reflector phase does not depend on l.Source: B. Blind RIS-SSK · p. 2
- The cited phase-sum identity reaches its maximum when the component phases are aligned.Source: B. Blind RIS-SSK · p. 2
III. PERFORMANCE ANALYSIS
The section presents analytical derivations for theoretical ABER in the proposed RIS-based SSK schemes over Rayleigh fading channels, covering intelligent and blind transmission.
III. PERFORMANCE ANALYSIS
- The analysis derives theoretical ABER for intelligent and blind RIS-based SSK transmission over Rayleigh fading channels.Source: §III · p. 3
A. Performance of the Intelligent RIS-SSK
For intelligent RIS-SSK, the analysis derives pairwise error probabilities and uses a large-reflector approximation to characterize average error behavior. The resulting expressions indicate that increasing the reflector count improves error probability at both low and high SNR, with different dependencies on SNR.
A. Performance of the Intelligent RIS-SSK
- The intelligent-scheme derivation begins with the conditional PEP for confusing activated antenna i with q and expands B as the channel difference Gi−Gq.Source: A. Performance of the Intelligent… · p. 3
- For sufficiently large N, the CLT models B as Gaussian using its mean and variance, enabling derivation of the conditional PEP and APEP.The APEP uses the MGF of λ = |B|^2, which follows a non-central chi-square distribution.Source: A. Performance of the Intelligent… · p. 3
- Increasing N can produce incredibly low APEP at relatively low SNR, owing to the N^2 term in the exponential form.Source: A. Performance of the Intelligent… · p. 3
- At high SNR, error probability decreases exponentially with N, while further SNR increases reduce APEP relatively little.Source: A. Performance of the Intelligent… · p. 3
B. Performance of the Blind RIS-SSK
For blind RIS-SSK, the ML detector selects the activated antenna, and the analysis derives its pairwise and average error probabilities using a CLT approximation.
B. Performance of the Blind RIS-SSK
- The blind RIS-SSK analysis uses the ML detector to assess the PEP when transmitted antenna i is detected as q.Source: B. Performance of the Blind RIS-SSK · p. 3
- With independent δ and Ψ, the CLT models C as zero-mean Gaussian with variance 2N; |C|^2 then follows a central chi-square distribution for the APEP derivation.Source: B. Performance of the Blind RIS-SSK · p. 3
C. Average Bit Error Rate (ABER)
The section relates ABER to APEP for binary signaling and gives a union-bound calculation for larger antenna sets.
C. Average Bit Error Rate (ABER)
- For Nt = 2, ABER equals APEP; for Nt > 2, a union bound calculates ABER using the identical APEP across antenna pairs.The bound uses the fact that APEP is independent of the activated antenna indices [13].Source: C. Average Bit Error Rate (ABER) · p. 3
IV. DETECTION COMPLEXITY ANALYSIS
The analysis compares detector arithmetic, finding conventional SSK simplest and blind RIS-SSK most complex, while RIS phase adjustment reduces complexity relative to the blind scheme.
IV. DETECTION COMPLEXITY ANALYSIS
- Intelligent RIS-SSK detection requires N + 3 real multiplications and N + 1 real summations, versus 4N + 4 and 3N + 2, respectively, for blind RIS-SSK.Source: §IV · p. 4
- For N ≫ 1, conventional SSK is simplest and blind RIS-SSK most complex; RIS phase adjustment significantly reduces complexity versus blind detection, especially with many reflectors.Table 1 reports complexity using real multiplications (M) and summations (S).Source: §IV · p. 4
- The complexity comparison counts real multiplications and summations, with conventional SSK included as a benchmark.Source: §IV · p. 4
V. SIMULATION RESULTS
Simulations test intelligent and blind RIS-SSK, verify analytical results, and compare the schemes with reference systems. The reported results show ABER gains from RIS assistance and increasing reflector counts, including under path loss.
V. SIMULATION RESULTS
- Simulations evaluate intelligent and blind transmission, verify analytical results, and neglect path loss unless otherwise stated, with σ^2=1.SNR is defined as E/N0.Source: §V · p. 4
- Doubling reflectors gives a 6 dB SNR gain at a target ABER in the waterfall region, and analytical and simulated results match for N ≥ 32.The intelligent scheme's performance enhancement with increasing N is reported to exceed that of AF-aided SSK.Source: §V · p. 4, Figure 2 · p. 4
- At ABER=10^-3, intelligent RIS-SSK with N=16 yields approximately 30 dB SNR gain over traditional SSK.Its ABER curves also have waterfall and saturation regions.Source: §V · p. 4, Figure 3 · p. 4
- For increasing spectral efficiency from 2 to 6 bps/Hz, RIS-SSK costs approximately 2 dB in SNR versus 12 dB for RIS-DH.The comparison indicates higher energy efficiency for RIS-SSK at high spectral efficiency, at the cost of more Tx antennas.Source: §V · p. 4, Figure 3 · p. 4
- Blind RIS-SSK gives 12 dB better results than traditional SSK at N=16; increasing Rx antennas yields a 15 dB SNR reduction.The performance enhancement with increasing Rx antennas is parallel to that of traditional SSK; intelligent phase adjustment enables a target ABER at lower SNR than blind operation.Source: §V · p. 4, Figure 4 · p. 5
- Under path loss, doubling N gives the proposed scheme 6 dB better results, while path loss has destructive effects on both systems.The comparison describes the proposed scheme as more robust to path loss than traditional SSK.Source: §V · p. 5, Figure 5 · p. 5
VI. CONCLUSION
The conclusion reports very low error rates and unconventional energy efficiency for RIS-based SSK, and identifies advanced SSK and partial-CSI RIS algorithms as future directions.
VI. CONCLUSION
- Simulations confirmed by analytical derivations indicate very low error rates and unconventional energy efficiency, making RIS-based SSK superior to the reference designs.The conclusion characterizes the scheme as a potential candidate for future wireless networks.Source: §VI · p. 5
- The scheme may avoid complex coding methods for reliable communication because it can operate effectively at extremely low SNR.Source: §VI · p. 5
- Advanced forms of SSK could yield much better results in the system design.Source: §VI · p. 5
- Developing algorithms that make the RIS intelligent under partial channel-state information remains future research.Source: §VI · p. 5