A fluid reconfigurable intelligent surface (fRIS)-aided integrated sensing and communication (ISAC) system is proposed to enhance multi-target sensing and multi-user communication. Unlike the conventional RIS, the fRIS employs movable elements with adjustable positions, offering additional spatial degrees of freedom. In this system, a joint optimization problem is formulated to minimize sensing beampattern mismatch and symbol estimation error. An algorithm based on alternating minimization is devised to handle the resultant non-convex problem, where the subproblems are solved via augmented Lagrangian method, quadratic programming, semidefinite relaxation, and majorization-minimization. A key challenge is that the element positions affect both incident and reflective channels, leading to the high-order composite objective functions. As a remedy, the high-order terms are transformed into linear and linear-difference forms by exploiting the structural characteristics of fRIS and the channels. Numerical results demonstrate the superiority of the proposed scheme over conventional RIS-aided ISAC and other benchmarks.

Wireless sensing is recognized as a promising technology for next-generation wireless networks, utilizing signals from devices such as Wi-Fi to detect and interpret human information, including movement status and sleep quality. However, the broadcast nature of wireless signals poses significant privacy risks, as unauthorized users may intercept these signals, leading to potential privacy breaches. Given the sensitive personal information embedded in CSI data and the limitations of encryption at the transmission end, conventional privacy protection measures are inadequate. Thus, developing physical layer-based privacy protection technologies for wireless sensing is urgently needed. In this paper, we consider a wireless sensing system model addressing privacy leakage issues and characterize wireless sensing performance as a classification problem. We propose a novel multi-antenna signal processing-based privacy protection strategy. To illustrate the fundamental tradeoff between sensing and privacy protection, we model the wireless sensing process as a communication task based on non-cooperative joint source-channel coding and introduce the concept of a sensing rate region. Our main contribution is the characterization of sensing and privacy protection performance at two key points within the sensing rate region:POS, indicating the minimum achievable sensing rate of an illegitimate receiver constrained by the maximum sensing rate of a legitimate receiver, andPOPP, indicating the maximum sensing rate of a legitimate receiver constrained by the minimum sensing rate of an illegitimate receiver. Based on our analysis, we provide strategies to establish achievable boundaries betweenPOSandPOPP. Moreover, we define the secure sensing rateRPP, indicating the privacy protection performance of the system. Within this framework, we examine several illustrative examples, validated through numerical simulations.

Unmanned aerial vehicles (UAVs) and reconfigurable intelligent surfaces (RISs) have emerged as promising technologies for enhancing wireless coverage and connectivity due to their mobility and reconfigurability. In this paper, we investigate a multi-user system assisted by multiple UAV-mounted RISs, where each UAV carries a RIS to enhance the communication link between the base station (BS) and users. We aim to maximize the system sum-rate by jointly optimizing the BS beamforming, the phase shifts of RISs, and the location of UAVs. To address this problem, we develop an alternating optimization algorithm based on the fractional programming framework. The original problem is decomposed into three subproblems to obtain the optimal BS beamforming, RIS phase shift, and the UAV location. Numerical results demonstrate that the proposed algorithm effectively achieves the sum-rate performance.

Low-cost and low-power consumptions have become the trend for the future communication systems, where one-bit quantization is a promising candidate. However, due to the low resolutions, the performance loss of the one-bit system is serious. To alleviate this issue, we propose leveraging the fluid reconfigurable intelligent surface (fRIS) to compensate for the loss in downlink communication systems with one-bit digital-to-analog converters (DACs). Specifically, we formulate a symbol estimation error minimization problem by jointly optimizing the symbol estimator, the one-bit transmit signal, the fRIS phase shifts, and the element positions. To handle the resultant problem, an alternating algorithm is developed, where four subproblems are solved iteratively by using semidefinite-relaxation, discrete optimization, and quadratic programming. In optimizing fRIS element positions, the positions affect both the incident and reflective channels, leading to the high-order complex objective functions. We show that these terms can be simplified by using the characteristics of channels. Numerical results validate that the introduction of fRIS can alleviate the performance loss caused by one-bit quantization.

In this paper, we propose a PE-based parallelism configurable polar encoder hardware architecture for emerging high-speed 5G communication system . This encoder architecture is applied to the 5G-NR PBCH channel polar encoder, implemented with functional modules such as CRC generation and interleaving, channel encoding, and rate matching as specified in the 3GPP protocol. Next, based on the united power format (UPF) low-power management technology, the PBCH polar encoder architecture is divided into different power domains based on their operating characteristics to reduce the power consumption . Experimental results show that the proposed polar encoder achieves throughput up to 30 Gbps. Based on TSMC 40 nm CMOS technology, by applying the proposed low-power methodology, the power consumption of the PBCH polar encoder at parallelisms of 8, 16, and 32 are 1.236 mW , 1.170 mW , and 1.084 mW , achieving power reductions of 24%, 29%, and 35% when comparing to non-low-power design, respectively.

Unmanned aerial vehicle (UAV) data collection problems in wireless sensor networks (WSNs) under random and uncertain environments are critical challenging, due to massive burden of real-time communication among UAVs for aligning with observation and state information, ect. For this sake, this work investigates the UAV cooperation problem of WSN data collection by jointly maximizing collected data amount while minimizing cooperation cost. We formulate the problem as a constrained partially observable Markov decision process (CPO-MDP), which stimulates the design of a novel safe reinforcement learning aided lightweight cooperation (SRL-LC) framework for multi-UAV data collection. Speficially, the self-conscious cooperative communication scheme is developed to assist the optimization of the UAV trajectory decision making. Additionally, a safety module embedded in the decision network integrates a relaxed artificial potential field (APF) algorithm, enabling UAVs to maintain safety distance constraints during training. Simulation results demonstrate that the proposed SRL-LC framework achieves data collection performance comparable to the full-cooperation scheme for various settings of prior information, while reducing communication cost by approximately 85%. Moreover, the SRL-LC framework ensures zero violations of safety constraints throughout the training process.

Active reconfigurable intelligent surface (A-RIS) aided integrated sensing and communications (ISAC) system has been considered as a promising paradigm to improve spectrum efficiency. However, massive energy-hungry radio frequency (RF) chains hinder its large-scale deployment. To address this issue, an A-RIS-aided ISAC system with antenna selection (AS) is proposed in this work, where a target is sensed while multiple communication users are served with specifically selected antennas. Specifically, a cuckoo search-based scheme is first utilized to select the antennas associated with high-gain channels. Subsequently, with the properly selected antennas, the weighted sum-rate (WSR) of the system is optimized under the condition of radar probing power level, power budget for the A-RIS and transmitter. To solve the highly non-convex optimization problem, we develop an efficient algorithm based on weighted minimum mean square error (WMMSE) and fractional programming (FP). Simulation results show that the proposed AS scheme and the algorithm are effective, which reduces the number of RF chains without significant performance degradation.

In this paper, we analyse the reliability performance for the orthogonal time frequency space (OTFS) based low earth orbit (LEO)-satellite-air-terrestrial (LSAT) communication system. To facilitate the downlink transmission from the LEO satellite to the terrestrial node, a group of randomly distributed mobile unmanned aerial vehicles (UAVs) are employed to serve as the relays with decode-and-forward (DF) scheme. With the aid of stochastic geometry approach, the distribution of mobile UAVs is modeled by Poisson point processes (PPP) process with two motion modes: user dependent model (UDM) and user independent model (UIM). We derive the approximate closed-form expressions for the outage probabilities of the LSAT system under two UAV motion modes. Finally, the simulation results demonstrate that the reliability of the LSAT system can be significantly enhanced by using OTFS scheme, and by properly adjusting the UAV deployment parameters, corroborating the theoretical derivation.

The class imbalance problem significantly hinders the ability of the software defect prediction (SDP) models to distinguish between defective (minority class) and non-defective (majority class) software instances. Recent studies on the data resampling technique have shown that Random UnderSampling (RUS) is more effective than several complex oversampling techniques at alleviating this problem. However, RUS blindly removes majority class instances, leading to significant information loss. These studies have also pointed out that the conventional termination condition (i.e., terminating the data resampling technique when the number of instances for both the minority and majority classes are the same) of the data resampling technique can result in suboptimal performance. In fact, the undersampling technique can be likened to a recommender system or a web search engine that recommends majority class instances to SDP models. Therefore, we propose the Learning-To-Rank Undersampling technique (LTRUS). Our work is novel in two aspects: (1) We consider the undersampling process as a learning-to-rank task, optimizing a linear model to rank majority class instances and remove them from the bottom of the rank to alleviate the class imbalance problem . (2) We propose two termination conditions for the undersampling technique, which differ from the conventional termination condition. LTRUS significantly outperforms RUS, the clustering-based undersampling technique, the complexity-based oversampling technique, SMOTUNED, and Borderline-SMOTE in terms of F-measure, AUC, and MCC by 8.9%, 7.6%, and 18.0% on average under the conventional termination condition. Furthermore, LTRUS under the two termination conditions we propose yield similar performance, and both outperform LTRUS and all the other baselines under the conventional termination condition. The experimental results demonstrate the effectiveness of LTRUS and indicate that the conventional termination condition for the data resampling technique is improper.

In comparison with upper-layer authentication mechanisms, the tag-based Physical-Layer Authentication (PLA) attracts many research interests because of high security and low complexity. This paper mainly concerns two problems in prior tag-based PLA schemes, where the first one is extra overhead and vulnerability due to the reason that the parameter is broadcasted and the other one is the problem of setting the parameter empirically. Therefore, two new tag-based PLA schemes are proposed to address the above limitations. Specifically, a blind tag-based PLA scheme (BTP) is presented to achieve accurate authentication without knowing the tag parameter of the legitimate transmitter, which not only saves the communication overhead but also improves security. Then, an adaptive blind tag-based PLA scheme (ABTP) is further proposed, which adaptively sets the tag parameter according to the wireless channel state to achieve a better balance among robustness, security, and compatibility. Rigorous theoretical analyses are provided for the two proposed schemes and the prior schemes’ performance comparisons are given. The accuracy of the theoretical analyses is verified through simulation results. At last, the advantages and disadvantages of the two proposed schemes are discussed, and suggestions are given according to different scenarios.

This paper addresses the critical challenge of securing two-way Time-of-Arrival (ToA) localization in Wireless Sensor Networks (WSNs) against cooperative distance-enlargement attacks. Existing schemes often rely on heuristic test statistics, are limited to specific attack strategies e.g., Amplify and Forward (AF), or require collaboration among multiple anchor nodes, which hinders their effectiveness against sophisticated adversaries and often incurs high communication overhead. We propose two novel detection schemes based on signal characteristics that overcome these limitations, offering both high accuracy and low complexity. The first scheme, termed the Efficient Detection Scheme of Distance-Enlargement Attacks using Log-Likelihood Ratio (EDDEA-LLR), leverages the Neyman-Pearson (NP) lemma to derive an optimal test statistic for scenarios involving AF attacks. Recognizing the limitations of existing schemes and the EDDEA-LLR scheme in countering Decode and Forward (DF) attacks, we introduce the Efficient Detection Scheme of Distance-Enlargement Attacks by Embedding a Secret Tag (EDDEA-EST). This scheme exploits embedded secret tags within the challenge signal to effectively detect DF attacks. Importantly, the proposed schemes do not require collaboration among multiple anchor nodes, eliminating the need for additional communication and featuring low computational complexity. We provide a rigorous theoretical analysis, deriving closed-form expressions for the detection performance of both schemes. Through extensive simulations, we demonstrate the superiority of the proposed schemes over existing methods in terms of detection accuracy, robustness against both AF and DF attacks, and comprehensive performance considering communication overhead.

Spatial Modulation (SM) is a promising low-complexity modulation scheme for Multiple-Input Multiple-Output (MIMO) systems. In this paper, we address the problem of authenticating the transmitter device in the SM. We propose an authentication approach for an SM system by using Physical-Layer Authentication (PLA) mechanisms because the PLA has the following advantages: high security and low complexity. Based on the features of an SM system, we propose two PLA schemes:PLA with Superimposed Authentication Tag(PLA-SAT) andPLA with Superimposed Imaginary authentication Tag(PLA-SIT). We provide performance analyses of our schemes over fading channels in terms of robustness, compatibility, and security. Moreover, we derive their closed-form expressions under both perfect and imperfect channel estimates, including the Probability of Detection (PD), Probability of False Alarm (PFA), and Average Error Probability (AEP). Although the two proposed schemes have the same robustness and security, the PLA-SIT scheme has better compatibility than the PLA-SAT scheme. Our schemes were implemented and extensive performance comparisons through simulations were conducted. We observe that the simulation results of the two proposed schemes perfectly match their corresponding theoretical analyses. The authentication accuracy of the two proposed schemes is close to one when the received SNR is greater than 20 dB and the security performances of the two proposed schemes improve as the variance of estimation errors increases.

This paper addresses two issues of a contact-tracing system with edge-based wireless communication techniques: to locate close contacts and to provide confidentiality-preserving communications. In this paper, we propose the Confidentiality-Preserving Contact-Tracing (CPCT) system with multiple wireless edge-based nodes. The CPCT system consists of three stages: registration, authentication, and confidentiality-preserving communication stages. We propose two Physical-Layer Authentication (PLA) schemes for the authentication stage of the CPCT system: the Coordinate-based Location PLA (CLP) scheme using the estimated coordinates and the Time-of-Arrival (ToA) based Location PLA (TLP) scheme using the estimated ToAs. We propose a distributed encryption scheme for the confidentiality-preserving communication stage of the CPCT system named the Distributed Channel Impulse Response (CIR)-based Encryption (DCE) scheme. We provide the theoretical analysis of the proposed schemes and derive their closed-form expressions. We implement the proposed schemes and conduct extensive performance comparisons through simulations. We observe that the theoretical results perfectly match the corresponding simulation results. Moreover, the proposed PLA schemes provide higher authentication performance than the prior PLA scheme, while the proposed encryption scheme provides higher confidentiality performance than the prior encryption scheme.

This paper concerns the problem of defending against spoofing attacks without a secret key. We address the problem using the Physical-Layer-Authentication (PLA) because of its high security, low overhead, and high compatibility. However, many PLA schemes have the following limitations: quantization errors, local optimums, and performance loss due to the change in the communication environment. In this paper, two phase-noise-based PLA schemes are proposed to address the limitations of the prior schemes. We denote the first scheme as the Multiple Phase Noises PLA (MPP) scheme, which realizes the PLA by using multiple phase noise innovations. Note that since the MPP scheme avoids using any quantization algorithm, it outperforms the prior schemes on the authentication performance. We denote the second scheme as the Enhanced Multiple Phase Noises PLA (EMPP) scheme, which introduces an artificial random phase to the transmitted symbols at the transmitter to further improve the authentication performance. The theoretical analyses of the proposed schemes over fading channels are provided, where the closed-form expressions are derived. Theoretical comparisons between the proposed schemes and prior schemes are provided. The theoretical analyses and simulation results demonstrated the superiority of the proposed schemes. In comparison with the prior schemes, the MPP scheme achieves 13% authentication-performance gain without demodulation-performance loss, while the EMPP scheme achieves 42% authentication-performance gain with merely 16% demodulation-performance loss.

This paper concerns the problem of improving compatibility in the tag-based Physical-Layer Authentication (PLA) without sacrificing robustness of the PLA schemes. The prior PLA schemes for improving compatibility often sacrifice robustness or introduce extra communication overhead and security vulnerabilities. The main objective of our approach is to reduce the modification ratio of the source message given the tag length via an encoding approach. Based on a tag-encoding function, we propose two encoded tag-based schemes for the scenario of a single block and the scenario of multiple blocks, respectively, which are named as the Encoded Tag-based PLA scheme for Single Block (ET-SB) and the Encoded Tag-based PLA scheme for Multiple Blocks (ET-MB), respectively. We theoretically analyze the performance of the proposed schemes over fading channels and derive the closed-form expressions of the performance analyses. We implement the proposed schemes and conduct extensive performance comparisons through simulations. Our simulation results show that the closed-form expressions of the theoretical results of the proposed schemes perfectly match the corresponding simulation results. When the SNR is 10 dB, the compatibility of the ET-SB and ET-MB schemes improves to 15.63% and 23.45%, respectively, compared to the prior scheme.

In this study, the authors address antenna selection (AS)‐aided massive multi‐user multiple‐input‐multiple‐output (MU‐MIMO) system based on maximum signal‐to‐noise ratio, where imperfect channel state information (CSI), time‐varying channel and antenna spatial correlation are considered. More explicitly, a computationally simple training‐based channel estimator is firstly employed for obtaining the imperfect down‐link CSI. Channel quantisation (CQ) is subsequently introduced by the feedback link which is used for feeding back so‐obtained CSI to base station. Time varying coefficient is utilised to characterise channel time variety and antenna spatial correlation is modelled by Kronecker model. Furthermore, the authors derive closed‐form ergodic channel capacity of the AS‐aided MU‐MIMO system with channel estimation error, CQ error and delay feedback, which has not yet been addressed in the state‐of‐the‐art approaches. Simulation results are provided to demonstrate the tightness of their theoretical computations of the ergodic channel capacity.

Two decades of full-diversity high-rate MIMO research has created perfect Space-Time Block Codes (STBCs), including the Golden code. However, the major stumbling block of their wide-spread employment is their limited energy-efficiency. On one hand, the superposition of their signals results in a high Peak-to-Average Power Ratio (PAPR). On the other hand, the total number of equivalent Inter-Antenna Interference (IAI) contributions that the receiver has to deal with is increased to IAI = M2upon using M Transmit Antennas (TAs), which is a substantial extra price compared to the IAI = M of V-BLAST. Against this background, we propose a new family of Finite-Cardinality Generalized Space-Time Shift Keying (FC-GSTSK). More explicitly, the proposed FC-GSTSK is capable of outperforming both V-BLAST and STBC, which is the ultimate objective of full-diversity high-rate MIMO design. Furthermore, following the index modulation philosophy, the proposed FC-GSTSK replaces the signal-additions by the data-carrying signal-selection process. As a benefit, the FC-GSTSK substantially reduces the PAPR of signal transmission. As a further advantage, the equivalent IAI imposed on signal detection is reduced back to the same level as that of the V-BLAST. Moreover, the proposed FC-GSTSK is even capable of consistently outperforming the perfect STBCs in terms of its Peak Signal to Noise-power Ratio (PSNR) that takes into account the power consumption at the transmitter. As a further advance, the reduced-RF-chain based version of FC-GSTSK is also capable of outperforming both Generalized Spatial Modulation (GSM) and Space-Time Block Coded Spatial Modulation (STBC-SM) without increasing the PAPR and the equivalent IAI.

The matrix-based differential encoding invoked by Differential Space-Time Modulation (DSTM) typically results in an infinite-cardinality of arbitrary signals, despite the fact that the transmit antennas (TAs) can only radiate a limited number of patterns. As a remedy, the recently developed differential spatial modulation (DSM) is capable of avoiding this problem by conceiving a beneficial sparse signal matrix design, which also facilitates low-complexity single-RF signal transmission. Inspired by this development, the differential space-time block code using index shift keying (DSTBC-ISK) further introduces a beneficial diversity gain without compromising the DSM's appealingly low transceiver complexity. However, the DSTBC-ISK's performance advantage tends to diminish as the throughput increases, especially when an increased number of Receive Antennas (RAs) is used. By contrast, the classic Differential Group Code (DGC) that actively maximizes its diversity gain for different multiple-input-multiple-output (MIMO) system setups is capable of achieving a superior performance, but its detection complexity grows exponentially with the throughput. Against this background, we propose the differential space-time shift keying using Diagonal Algebraic Space-Time scheme, which is the first DSTM that is capable of achieving the DGC's superior diversity gain at high throughputs without compromising the DSM's low transceiver complexity. As a further advance, we also conceive a new differential space-time shift keying using Threaded Algebraic Space-Time arrangement, which is capable of achieving an even further improved diversity gain at a substantially reduced signal detection complexity compared to the best DGCs. Furthermore, in order to strike a practical tradeoff, we develop a generic multi-element and multi-level-ring Amplitude Phase Shift Keying design, and we also arrange for multiple reduced-size DSTM sub-blocks to be transmitted in a permuted manner, which exhibits an improved diversity-throughput tradeoff.

This paper considers the problem of reconstructing true targets in a single-channel synthetic aperture radar (SAR) imaging system, which has been disturbed by deceptive jammings. Since the deceptive jammings are usually confined to the main lobe of an SAR antenna, their time-frequency distributions are different from those of the true echoes. This enables us to utilize a dynamic synthetic aperture (DSA) scheme to extract the characteristics of the true and false targets. Dictionaries about the true and false targets are constructed by taking interactions between scatterers into account. Then a sparsity-driven optimization problem is solved to reconstruct the true and false targets separately with super-resolution. Moreover, the deceptively jammed SAR data are divided into different areas to handle various scenarios efficiently, and strategies for DSA selection are addressed as well. Simulations are provided to verify the effectiveness of the proposed algorithm.

We propose a norm-based joint transmit and receive antenna selection (NBJTRAS) aided near-capacity multiple-input-multiple-output (MIMO) system relying on the assistance of a novel two-tier channel estimation scheme. Specifically, a rough estimate of the full MIMO channel is first generated using a low-complexity, low-training-overhead minimum mean square error based channel estimator, which relies on reusing a modest number of radio frequency (RF) chains. NBJTRAS is then carried out based on this initial full MIMO channel estimate. The NBJTRAS aided MIMO system is capable of significantly outperforming conventional MIMO systems equipped with the same modest number of RF chains while dispensing with the idealized simplifying assumption of having perfectly known channel state information (CSI). Moreover, the initial subset channel estimate associated with the selected subset MIMO channel matrix is then used for activating a powerful semi-blind joint channel estimation and turbo detector-decoder, in which the channel estimate is refined by a novel block-of-bits selection based soft-decision aided channel estimator (BBSB-SDACE) embedded in the iterative detection and decoding process. The joint channel estimation and turbo detection-decoding scheme operating with the aid of the proposed BBSB-SDACE channel estimator is capable of approaching the performance of the near-capacity maximum-likelihood (ML) turbo transceiver associated with perfect CSI. This is achieved without increasing the complexity of the ML turbo detection and decoding process.

We propose a simple yet effective norm-based joint transmit and receive antenna selection (NBJTRAS) assisted and two-tier channel estimation (TTCE) aided space-time shift keying (STSK) system, which is capable of significantly outperforming the conventional STSK system, while efficiently utilising available radio frequency (RF) chains. Specifically, the NBJTRAS carries out antenna selection based on the channel estimation (CE) generated using a low-complexity training based least square channel estimator by reusing RF chains. The selected sub-channel matrix is further refined by an efficient semi-blind CE and data detection scheme. Our simulation results show that only a few iterations are sufficient for the TTCE scheme to approach the optimal maximum-likelihood detection performance associated with perfectly channel state information.

We propose a novel joint channel estimation and three-stage iterative detection/decoding scheme for near-capacity MIMO systems. In our scheme, as usual, the detected soft information is first exchanged a number of times within the inner turbo loop between the unity-rate-code (URC) decoder and the MIMO soft-demapper, and the information gleaned from the inner URC decoder is then iteratively exchanged with the outer decoder in the outer turbo loop. Our channel estimator however exploits the a posteriori information produced by the MIMO soft-demapper to select a sufficient blocks of high-quality detected soft bits, and it is naturally embedded into the original iterative three-stage detection/decoding process, without introducing the costly iterative loop between the decision-directed channel estimator and the three-stage turbo detector/decoder. Hence, the computational complexity of our joint channel estimation and three-stage turbo detection is similar to that of the three-stage turbo detection/decoding scheme associated with the perfect CSI. Moreover, our reduced-complexity semi-blind scheme is capable of achieving the optimal maximum-likelihood turbo detection performance attained under the perfect CSI, with the same number of turbo iterations.

We propose a low-complexity joint channel estimation (CE) and three-stage iterative demapping-decoding scheme for near-capacity coherent space-time shift keying (CSTSK) based multiple-input multiple-output (MIMO) systems. In the proposed scheme, only a minimum number of space-time shift keying training blocks are employed for generating an initial least square channel estimate, which is then used for initial data detection. As usual, the detected soft information is first exchanged a number of times within the inner turbo loop between the unity-rate-code (URC) decoder and the CSTSK soft-demapper, and the information gleaned from the inner URC decoder is then iteratively exchanged with the outer decoder in the outer turbo loop. Our CE scheme is embedded into the outer turbo loop, which exploits the a posteriori information produced by the CSTSK soft-demapper to select a sufficient number of high-quality decisions only for CE. Since the CE is embedded into the iterative three-stage demapping-decoding process, no additional iterative loop is required for exchanging information between the decision-directed channel estimator and the three-stage turbo detector. Hence, the computational complexity of the proposed joint CE and three-stage turbo detection remains similar to that of the three-stage turbo detection-decoding scheme with the given channel estimate. Moreover, our proposed low-complexity semi-blind scheme is capable of approaching the optimal maximum likelihood turbo detection performance attained with the aid of perfect channel state information, with the same low number of turbo iterations as the latter, as confirmed by our extensive simulation results.

We develop a semi-blind adaptive space-time shift keying (STSK) based multiple-input multiple-output system using a low-complexity iterative channel estimation and data detection scheme. We first employ the minimum number of STSK training blocks, which is related to the number of transmitter antennas, to obtain a rough least square channel estimate (LSCE). Low-complexity single-stream maximum likelihood (ML) data detection is then carried out based on the initial LSCE and the detected data are utilised to refine the decision-directed LSCE. We show that a few iterations are sufficient to approach the optimal ML detection performance obtained with the aid of perfect channel state information.