Ultra-wideband (UWB) radio has license-free access to a substantial bandwidth of over 500 MHz, which makes it a promising candidate for integrated sensing and communication (ISAC), because the performance of both functionalities relies on the availability of sufficient bandwidth. However, due to its regulated power constraint and spectral mask, UWB sensing performance is susceptible to blockage, while the UWB data rate remains low. To enhance both functionalities, we propose a novel two-tier frequency-domain equalization (FDE) scheme for UWB ISAC, in which the first FDE removes the user-specific sequence from the received UWB header in support of sensing, while the second FDE equalizes the effect of channel impulse responses (CIRs) for data detection. The data-carrying amplitude/phase of UWB pulses, which are inherently repeated in the UWB header, naturally follow the circular convolution model without requiring a cyclic prefix (CP) for the proposed sensing operations. Furthermore, the CIRs estimated by the first FDE stage of bistatic sensing can be directly utilized by the second FDE stage of data detection. Compared to the conventional matched filtering (MF) aided UWB sensing, the proposed FDE approach improves the peak-to-sidelobe power ratio and facilitates multi-target sensing. Compared to conventional UWB MF template-based data detection, the proposed approach is capable of improving synchronization, channel estimation and equalization. Furthermore, the proposed two-tier FDE approach inspires a new pulse repetition tradeoff (PR-T) scheme, which reduces pulse repetition to achieve an improved data rate. Our simulation results demonstrate that the proposed UWB FDE sensing achieves centimeter-level accuracy even at a low Ricean K-factor of -4 dB. Additionally, the proposed FDE data detection designed for PR-T is capable of improving the UWB data rate from the single Megabits-per-second (Mbps) range to over 100 Mbps, at the cost of reduced pulse repetition gain.

Visible Light Positioning and Communication (VLPC) is a promising candidate for implementing Integrated Sensing And Communication (ISAC) in the unlicensed 400 THz to 800 THz band. The current Visible Light Positioning (VLP) systems mainly operate based on the Received Signal Strength (RSS) of the Line-of-Sight (LoS) path. However, its accuracy is degraded by interferences from Non-LoS (NLoS) paths. Furthermore, in Visible Light Communication (VLC) systems, the estimation of Channel State Information (CSI) also becomes challenging, when the optical channel becomes dispersive. Against this background, we propose a new VLPC scheme using Direct Current (DC) biased Optical Orthogonal Frequency-Division Multiplexing (VLPC-DCO-OFDM), where OFDM-based sensing is applied for the sake of improving the resolution of the estimated Channel Impulse Response (CIRs) exploited for positioning functionality. The CIRs estimated by sensing are further exploited to provide enhanced CSI for communication data detection. Moreover, we propose a hybrid Radar-RSS based solution, where the conventional RSS-aided VLP method is invoked for the sake of refining OFDM radar. Our simulation results demonstrate that the proposed VLPC-DCO-OFDM scheme - which simultaneously supports the triple functionalities of illumination, bi-static sensing and communication - is capable of achieving centimeter-level positioning accuracy and Giga-bits-per-second data rate.

Cell-free (CF) MIMO has emerged as a promising next-generation technology, primarily due to its ability to provide uniformly high-quality service to all user equipment (UEs), regardless of their location. While existing research has extensively explored various aspects of CF systems—including scalability, clustering strategies, power control, and precoding designs—there remains a notable gap in the literature concerning the physical-layer performance of 5G New Radio (NR) within CF architectures. This paper addresses this gap by focusing on the Physical Uplink Shared Channel (PUSCH) transmission over frequency-selective channels. We develop a comprehensive, 3GPP-compliant link-level simulator to evaluate the performance of CF MIMO under realistic propagation conditions. First, we generate results for selected modulation and coding schemes (MCSs) to confirm the simulator’s alignment with expected performance. Then, the effects of key physical-layer parameters—such as subcarrier spacing (SCS), the number of distributed radio units (RUs), and the number of RU antennas—are evaluated using Block Error Rate (BLER) as the primary performance metric. We also compare the results of the CF-MIMO system with a co-located antenna scenario, serving as the baseline for a traditional MIMO system, and confirm that the CF-MIMO system achieves superior performance due to its spatial diversity advantages. The results also show that employing higher SCS values effectively exploits frequency diversity, particularly when the signal bandwidth exceeds the channel’s coherence bandwidth. As expected, increasing the number of RUs significantly improves BLER due to enhanced spatial diversity and reduced UE-RU path loss. We further examine the impact of practical channel estimation by evaluating four different DMRS configurations, confirming that Type 1 with length 2 provides superior performance under the tested conditions. Finally, we investigate the effect of carrier frequency, showing that higher frequencies lead to increased path loss and degraded performance. The findings offer valuable insights into spatial, frequency, and estimation-related interactions in CF 5G NR, while guiding MCS selection for target BLER-SNR levels and enabling PHY abstraction for higher-layer simulations.

We demonstrate that the proposed optical orthogonal time frequency space (O-OTFS) is capable of improving the bandwidth-/power-/energy-efficiencies of optical orthogonal frequency-division multiplexing (O-OFDM). The bandwidth-efficiency is improved because only a single cyclic prefix (CP) is needed for an entire O-OTFS frame. The power-efficiency is enhanced thanks to the diversity gain achieved by its symplectic finite Fourier transform (SFFT), which also leads to a reduced peak-to-average power ratio (PAPR), hence improving its energy-efficiency. These features are facilitated by the proposed layered asymmetrically clipped O-OTFS (LACO-OTFS), which is capable of removing the direct current (DC) bias while retaining the full optical throughput. Nonetheless, there exists an inherent trade-off, where increasing the O-OTFS frame size leads to a commensurately reduced CP percentage at the cost of an increased PAPR. In order to mitigate this, we propose to perform discrete Fourier transform based spreading (DFT-S) in the delay-Doppler (DD)-domain. Furthermore, we demonstrate that regardless of the choice of domain in which the information is modulated (i.e. O-OFDM/O-OTFS with/without DFT-S), the frequency-selectivity of the quasi-static but dispersive optical channel can always be equalized by single-tap frequency-domain equalization (FDE). Moreover, the channel estimation techniques are conceived to operate in the time-/frequency-/DD-domains for both O-OFDM and O-OTFS. Our simulation results demonstrate that for a multi-user optical wireless system associated with M = 64 subcarriers and the OTFS frame length of N = 64, LACO-OTFS is capable of achieving a 7 dB power-efficiency gain over LACO-OFDM, where the CP overhead is reduced by a factor of N = 64. DFT-S-LACO-OTFS is also capable of providing a 7 dB power-efficiency gain over DFT-S-LACO-OFDM, where the low PAPR of single-carrier transmission is retained.

In order to maximize the spectral efficiency (SE) in multicarrier-division duplex (MDD) enabled cell-free massive MIMO (CF-mMIMO), a heterogeneous graph neural network (HGNN), referred to as CF-HGNN, is specifically introduced to optimize the power allocation (PA). To efficiently manage the interference invoked, a meta-path based mechanism is applied in CF-HGNN to enable individual access point (AP) and mobile station (MS) nodes to aggregate information from the interfering and communication paths with different priorities during message passing. Moreover, the proposed CF-HGNN employs the adaptive node embedding layer and adaptive output layer to make it scalable to the various numbers of APs, MSs and subcarriers. For comparison, a quadratic transform and successive convex approximation (QT-SCA) algorithm is proposed to solve the PA problem in classic way. Numerical results show that CF-HGNN is capable of achieving 99% of the SE achievable by QT-SCA but using only 10(-4 )times of its operation time, and it can outperform the conventional learning-based and greedy unfair methods in terms of SE performance. Furthermore, CF-HGNN exhibits good scalability to the CF networks with various numbers of nodes and subcarriers, and also to the large-scale CF networks when assisted by user-centric clustering.

A Deep Learning (DL) aided Logarithmic Likelihood Ratio (LLR) correction method is proposed for improving the performance of Multiple-Input Multiple-Output (MIMO) receivers, where it is typical to adopt reduced-complexity algorithms for avoiding the excessive complexity of optimal full-search algorithms. These sub-optimal techniques typically express the probabilities of the detected bits using LLRs that often have values that are not consistent with their true reliability, either expressing too much confidence or not enough confidence in the value of the corresponding bits, leading to performance degradation. To circumvent this problem, a Deep Neural Network (DNN) is trained for detecting and correcting both over-confident and under-confident LLRs. We demonstrate that the complexity of employing the DL-aided technique is relatively low compared to the popular reduced-complexity receiver detector techniques since it only depends on a small number of real-valued inputs. Furthermore, the proposed approach is applicable to a wild variety of iterative receivers as demonstrated in the context of an iterative detection and decoding aided MIMO system, which uses a low-complexity Smart Ordering and Candidate Adding (SOCA) scheme for MIMO detection and Low-Density Parity Check (LDPC) codes for channel coding. We adopt Extrinsic Information Transfer (EXIT) charts for quantifying the Mutual Information (MI) and show that our DL method significantly improves the BLock Error Rate (BLER). Explicitly, we demonstrate that about 0.9 dB gain can be achieved at a BLER of $10^{-3}$ by employing the proposed DL-aided LLR correction method, at the modest cost of increasing the complexity by 16% compared to a benchmarker dispensing with LLR correction.

The recently-developed orthogonal time frequency space (OTFS) modulation is capable of transforming the time-varying fading of the time-frequency (TF) domain into the time-invariant fading representations of the delay-Doppler (DD) domain. The OTFS system using orthogonal frequency-division multiplexing (OFDM) as inner core naturally requires the subcarrier spacing (SCS) Delta f$ to be larger than the maximum Doppler frequency theta(max), i.e. Delta f > theta(max), when perfect channel state information (CSI) knowledge is assumed. However, for the first time in literature, we explicitly demonstrate that the practical OFDM-based OTFS systems have to double their SCS in order to facilitate CSI estimation, requiring Delta f' = 2 Delta f > 2 theta(max). In order to mitigate this loss, we propose a novel noncoherent OTFS system, which is capable of operating at Delta f > theta(max). The major challenge in this context is the mitigation of the DD-domain interference without CSI. Against this background, we draw an analogy between the input-output model of OTFS and that of V-BLAST, where V-BLAST's blind inter-antenna interference mitigation technique is invoked. Moreover, we propose to partition the DD-domain modulated symbols into groups, where space-time block coding is invoked in order to eliminate the DD-domain interference within each group. Our simulation results demonstrate that the proposed noncoherent OTFS is capable of substantially outperforming its coherent counterparts relying on CSI estimation.

The demodulation reference signal of the 5G Multiple-Input Multiple-Output Orthogonal Frequency-Division Multiplexing (MIMO-OFDM) waveform has been designed for supporting Minimum Mean-Square Error-Interference Rejection Combining (MMSE-IRC) equalization, which has become the state-of-the-art, owing to its enhanced performance in the case of dense frequency reuse, which is typical in 5G. By contrast, in the 4G LTE system, typically turbo equalization techniques were used. The family of Non-Linear receiver techniques tend to be eminently suitable for tough rank-deficient scenarios, when the received signal constellation becomes linearly non-separable. Hence, we propose a novel receiver for interference-constrained MIMO-OFDM systems, relying on a linear MMSE-IRC detector intrinsically amalgamated with an additional NL equalizer. In this way, we may achieve the best of both worlds, retaining the interference rejection capability of the MMSE-IRC detector and the superior performance of the NL equalizer. Our solution circumvents the potential failure of the MMSE-IRC, when the MIMO channels' degree freedom is completely exhausted by the desired users in case the transmitter has a high number of transmission layers for example. Based on this concept, we then design a novel NL equalizer relying on the Smart Ordering and Candidate Adding (SOCA) algorithm. This reduced complexity NL detection algorithm is particularly well suited for practical hardware implementation using parallel processing at a low latency. Briefly, the proposed scheme employs the MMSE-IRC detector for mitigating the interference. It makes the first estimate of the desired user signals and then uses the SOCA detector for further decontaminating the received signals. It also generates the soft information, enabling turbo equalization, wherein iterative detector and decoder iteratively exchange their soft information. We present BLock Error Rate (BLER) results, which show that the proposed scheme can always achieve superior performance to the conventional MMSE-IRC detector at the cost of increasing the complexity. In some cases, our proposed scheme can obtain about 1.5 dB gain, at the cost of 4 times higher complexity. We demonstrate that the complexity of the SOCA detector can be reduced by adjusting its parameterization or at the cost of reducing the self-consistency of the soft information produced by the SOCA detector, which slightly erodes the BLER performance. In order to mitigate this, we propose to use Deep Learning (DL) for enhancing the accuracy of the soft information. Using this technique, we show that the MMSE-IRC-NL-SOCA detector relying on DL attains about 3 dB gain at the cost of only marginally increasing the complexity, compared to the proposed MMSE-IRC-NL-SOCA scheme.

Novel techniques are conceived for joint compressive sensing (CS) and low-density parity check (LDPC) coding in wireless sensor networks (WSNs), namely, a soft-input soft-output (SISO) tree search sphere decoding (SD) technique and an SISO Hamming distance (HD)-based solution. Factor graphs are utilized to describe the connectivity between the signals and sensors, as well as with the LDPC codes. In the fusion center (FC), the factor graphs may be used for iterative joint LDPC-CS decoding in order to recover the signals observed. However, the CS decoder of the FC suffers from high complexity if the exhaustive Maximum A Posteriori (e-MAP) technique is employed, which considers all possible combinations of source signals detected by each of the associated sensors. Hence, in the proposed SD and HD schemes, only the more likely combinations of source signals are tested for reducing the CS decoding complexity. More specifically, a tree search technique is used in the first step to find the most likely combination of source signal values. Then, in the second step, the proposed SD continues the tree search to find a set of alternative hypotheses. This facilitates the generation of high-quality extrinsic information, which may be iteratively exchanged with the LDPC decoder. By contrast, in the HD approach, the second step obtains the alternative hypotheses within a certain HD of the most likely source signal combination. Both our BLock Error Rate (BLER) results and EXtrinsic Information Transfer (EXIT) charts show that the proposed SD and HD techniques approach the performance of the full-search e-MAP approach at a significantly reduced complexity. In particular, we show that the e-MAP solution is about 56 times more complex than the SD approach and around 210 times more complex than the HD approach. Compared to a separate source-channel coding (SSCC) hard information benchmarker, the proposed SISO schemes improve the decoding performance by about ${1}.{7} \text {dB}$ . Furthermore, the SISO schemes allow the iterations inside the CS decoding to eliminate the error floors and obtain a further ${2}.{45}$ -dB gain.

A novel Soft-Input Soft-Output (SISO) polar decoding algorithm is proposed, which is capable of iterating between an inner and outer decoder in a three-stage serial concatenated iterative receiver. The proposed polar decoding algorithm leverages a hybrid of Soft Cancellation (SCAN) and g -function aided-SCAN (G-SCAN) decoding. The SCAN decoder enables iterative soft-information exchange with the outer decoders and the G-SCAN decoder facilitates iterative soft-information exchange with the inner decoders while exploiting the error correction capability of the classic Successive Cancellation List (SCL) decoder. Furthermore, we present the Three-Dimensional (3D) Extrinsic Information Transfer (EXIT) chart analysis of polar codes for the first time, in order to characterise the iterative exchange of extrinsic information between these three concatenated stages. This offers an insight into the interactions of these three decoders and characterises their iterative convergence. In this three-stage serial concatenated scheme the first stage is a Joint Source Channel Coding (JSCC) decoder, the second stage is a 5th Generation (5G) 3rd Generation Partnership Project (3GPP) New Radio (NR) polar decoder based on our novel hybrid SISO polar algorithm, and the third stage is a 2 × 2 Multiple Input Multiple Output (MIMO) detector. We characterized the Symbol Error Rate (SER) vs. complexity of the proposed scheme, and compare it to various soft- and hard-decision benchmarkers, as well as to the relevant JSCC and Separate Source Channel Coding (SSCC) schemes. In comparison to a three-stage serial concatenated JSCC benchmarker, the proposed SISO scheme offers 11 ${\%}$ complexity reduction over to the state-of-the-art SISO SCAN polar decoder at a similar SER performance. Additionally, the proposed SISO scheme achieves a 0.75 dB SNR gain over the SCAN polar decoder of a two-stage serial concatenated SSCC benchmarker.

The novel concept of joint Compressive Sensing (CS) and Low Density Parity Check (LDPC) coding is conceived for Joint Source-Channel Coding (JSCC) in Wireless Sensor Networks (WSNs) supporting a massive number of signals.More explicitly, we demonstrate this concept for a specific scheme, which supports a massive number of signals simultaneously, using a small number of Internet of Things Nodes (IoTNs) based on the concept of CS.The compressed signals are LDPC coded in order to protect them from poor transmission channels.We also propose the new iterative joint source-channel decoding philosophy for exchanging soft extrinsic information, which combines CS decoding and LDPC decoding by merging their respective factor graphs.We then characterize this scheme using Extrinsic Information Transfer (EXIT) chart analysis.Our BLock Error Rate (BLER) results show that the proposed iterative joint LDPC-CS decoding scheme attains about 1.5 dB gain at a BLER of 10 -3 compared to a benchmarker, which employs separate CS and LDPC decoding.Naturally, this gain is achieved at the cost of approximately doubling the complexity of the proposed iterative joint LDPC-CS decoding scheme.

A universal decoding scheme is conceived for quantum stabilizer codes (QSCs) by appropriately adapting the ‘guessing random additive noise decoding’ (GRAND) philosophy of classical domain codes. We demonstrate that the generalized quantum decoder conceived is eminently suitable for different QSC decoding paradigms, namely for both stabilizer-measurement-based as well as the inverse-encoder-based decoding. We then harness the resultant decoder for both quantum Bose-Chaudhuri-Hocquenghem (BCH) codes and quantum polar codes and quantify both their quantum block error rate (QBLER), and QBLER per logical qubits as well as their decoding complexity. Furthermore, we provide a parametric study of the associated design trade-offs and offer design guideline for the implementation of GRAND-based QSC decoders.

Providing Internet service above the clouds is of ever-increasing interest and in this context aeronautical ad-hoc networking (AANET) constitutes a promising solution. However, the optimization of packet routing in large ad hoc networks is quite challenging. In this article, we develop a discrete $\epsilon$ multi-objective genetic algorithm ( $\epsilon$ -DMOGA) for jointly optimizing the end-to-end latency, the end-to-end spectral efficiency (SE), and the path expiration time (PET) that specifies how long the routing path can be relied on without re-optimizing the path. More specifically, a distance-based adaptive coding and modulation (ACM) scheme specifically designed for aeronautical communications is exploited for quantifying each link's achievable SE. Furthermore, the queueing delay at each node is also incorporated into the multiple-objective optimization metric. Our $\epsilon$ -DMOGA assisted multiple-objective routing optimization is validated by real historical flight data collected over the Australian airspace on two selected representative dates.

Joint Source-Channel Coding (JSCC) is a powerful technique that allows for the efficient transmission of information by simultaneously considering the characteristics of both the source and the channel. The recently proposed Exponential Golomb Error Correction (ExpGEC) and Rice Error Correction (REC) codes provide generalized JSCC schemes for the near capacity coding of symbols drawn from large or infinite alphabets. Yet these require impractical decoding structures, with large buffers and inflexible system design, this was mitigated by the introduction of the Reordered Elias Gamma Error Correction (REGEC) which itself had limited flexibility with regards to source distribution. In this paper, we propose a novel Reordered Exponential Golomb Error Correction (RExpGEC) coding scheme, which is a JSCC technique designed for flexible and practical near-capacity performance. The proposed RExpGEC encoder and decoder are presented and its performance is analysed using Extrinsic Information Transfer (EXIT) charts. The flexibility of the RExpGEC is shown via the novel trellis encoder and decoder design. Finally, the Symbol Error Rate (SER) performance of RExpGEC code is compared when integrated into the novel RExpGEC-URC-QPSK scheme against other comparable JSCC and Separate Source Channel Coding (SSCC) benchmarkers. Specifically the RExpGEC-URC-QPSK scheme is compared against the REGEC-URC-QPSK scheme, and a serial concatenation of the Exponential Golomb and Convolution Code, which becomes the novel Exp-CC-URC-QPSK scheme. Our simulation results demonstrate the performance gains and flexibility of the proposed RExpGEC-URC-QPSK scheme against the benchmarkers in providing reliable and efficient communications. Specifically, the RExpGEC-URC-QPSK scheme outperforms the SSCC in a uncorrelated Rayleigh fading channel by 2 to 3.6 dB (dependent on source distribution). Furthermore, the RExpGEC-URC-QPSK scheme consistently operates within 2.5 dB of channel capacity when measuring $E_{b}/N_{0}$ , whilst providing flexibility in SNR performance when compared to the REGEC-URC-QPSK scheme. These performance gains come at the cost of complexity, whereby the RExpGEC-URC-QPSK scheme is 3.6 times more complex than Exp-CC-URC-QPSK scheme under certain conditions. This paper highlights the unique capabilities of RExpGEC as a high performance, practical and flexible JSCC technique.

The recently developed reconfigurable intelligent surfaces (RISs) are capable of improving the coverage of space–air–ground integrated networks (SAGINs), where the signals can be reflected in the desired direction without relying on power-thirsty radio-frequency (RF) chains. However, in the face of the substantially increased Doppler frequency, the classic orthogonal frequency-division multiplexing (OFDM) becomes inadequate in supporting RIS for the following reasons. First, the detrimental doubly selective fading leads to intersymbol interference (ISI) and intercarrier interference (ICI), which result in error floors for OFDM operating in the time–frequency (TF) domain. Second, it is far from trivial to configure RIS based on the time-varying fading channels. Third, the interpolation-based TF-domain channel estimation methods become impractical for the high-Doppler and high-dimensional RIS systems. Against this background, in this article, we propose the powerful 2-D orthogonal time–frequency space (OTFS) modulation for RIS-aided SAGINs, which transforms the time-varying fading encountered in the TF-domain to the time-invariant fading in the delay-Doppler (DD) domain. More explicitly, first, for the first time in the literature, we devise the DD-domain channel model of RIS-assisted SAGINs in the face of doubly selective fading. Second, in order to facilitate the RIS configuration in the DD-domain, we propose to create “virtual” Doppler frequencies that guide the phase changes at the RIS, even though the RIS phase rotations do not suffer from Doppler effects. Third, we conceive an attractive DD-domain RIS channel estimation method that can support both OFDM and OTFS, where the TF-domain interpolation is eliminated. Our simulation results demonstrate that the proposed DD-domain RIS configuration and channel estimation methods for both OFDM and OTFS are capable of mitigating the error floors encountered in the TF-domain. Furthermore, our simulation results confirm that OTFS-based RIS-assisted SAGIN systems are capable of outperforming their OFDM counterparts and exhibit excellent performance across a wide range of SAGIN channel parameters including the Ricean K factor, Doppler frequency, delay spread, coverage distance, and carrier frequency.

Reconfigurable intelligent surface (RIS) is capable of improving the wireless system performance by steering the reflected signal in the desired direction.One of the major challenges is that both the transceiver and RIS have to be jointly optimized, where the optimization problems have to be reformulated for different system models and scenarios.To circumvent this challenge, new low-complexity antenna selection (AS) algorithms for transceiver-agnostic RIS configuration are proposed.Given a multiple-input multiple-output (MIMO) channel, the proposed RIS-AS opts for accurately aligning the RIS both with the transmit antenna (TA) and receive antenna (RA) for the sake of maximizing the MIMO channel's overall output power.The proposed RIS-AS only has to configure the RIS alone, i.e. without iterations with the transceiver optimization.As a result, the proposed RIS-AS has the compelling benefit that they are generically applicable, regardless of the specific transceiver architecture.Our simulation results confirm that the proposed RIS-AS is capable of supporting any MIMO configuration, regardless of their closed/open-loop, single-/full-RF and multiplexing-/diversity-oriented setups.

A multicarrier-division duplex (MDD)-based cell-free (CF) scheme, namely MDD-CF, is proposed, which enables downlink (DL) data and uplink (UL) data or pilots to be concurrently transmitted on mutually orthogonal subcarriers in distributed CF massive MIMO (mMIMO) systems. To demonstrate the advantages of MDD-CF, we firstly study the spectral-efficiency (SE) performance in terms of one coherence time (CT) interval associated with access point (AP)-selection, power- and subcarrier-allocation. Since the formulated SE optimization is a mixed-integer non-convex problem that is NP-hard to solve, we leverage the inherent association between involved variables to transform it into a continuous-integer convex-concave problem. Then, a quadratic transform (QT)-assisted iterative algorithm is proposed to achieve SE maximization. Next, we extend our study to the case of one radio frame consisting of several CT intervals. In this regard, a novel two-phase CT interval (TPCT) scheme is designed to not only improve the SE in radio frame but also provide consistent data transmissions over fast time-varying channels. Correspondingly, to facilitate the optimization, we propose a two-step iterative algorithm by building the connections between two phases in TPCT through an iteration factor. Simulation results show that, MDD-CF can significantly outperform in-band full duplex (IBFD)-CF due to the efficient interference management. Furthermore, compared with time-division duplex (TDD)-CF, MDD-CF is more robust to high-mobility scenarios and achieves better SE performance.

A variety of deep learning schemes have endeavoured to integrate deep neural networks (DNNs) into channel coded systems by jointly designing DNN and the channel coding scheme in specific channels. However, this leads to limitations concerning the choice of both the channel coding scheme and the channel paramters. We circumvent these impediments and conceive a turbo-style multi-carrier auto-encoder (MC-AE) for orthogonal frequency-division multiplexing (OFDM) systems, which is the first one that achieves the flexible integration of DNN into any given channel coded systems while achieving an iteration gain. More explicitly, first of all, we design the MC-AE independently of both the channel coding arrangement and of the channel model, where the output layer of the MC-AE decoder is designed for both accepting and producing reliable soft-bit decisions. Owing to the fact that bit-dependency is imposed by the MC-AE mapping, our bespoke MC-AE decoder becomes capable of achieving a beneficial iteration gain, when the extrinsic information is exchanged between the soft-decision MC-AE decoder and the soft-decision channel decoder. Secondly, in order to be able to interpret the performance advantages of our MC-AE over the conventional OFDM, we map the MC-AE’s input-output relationship to an equivalent model-based representation. The associated theoretical analysis verifies the fact that during the process of data-driven signal reconstruction across OFDM’s subcarriers, a beneficial frequency diversity gain is achieved by the proposed MC-AE design. Finally, our simulation results demonstrate that the MC-AE is capable of achieving substantial performance advantages over both conventional OFDM and OFDM based index modulation (OFDM-IM) in channel coded systems.

Soft-Input Soft-Output (SISO) polar decoding algorithms, such as Belief Propagation (BP) and Soft Cancellation (SCAN) polar decoding, offer iteration capability for facilitating turbo-style detection. However, at lower Signal-to-Noise Ratios (SNRs), the performance of the BP and SCAN decoders is about 1.5 dB and 0.5 dB worse than that of the state-of-the-art hard-decision Successive Cancellation List (SCL) decoding algorithm, respectively, despite iteratively exchanging information with a Multiple Input Multiple Output (MIMO) detector. Motivated by this gap, we conceive a novel G-SCAN polar decoder, which generates both soft-decision and hard-decision outputs. This is achieved by intrinsically amalgamating a list decoder with a novel SISO decoder. These soft-decision outputs may be used for turbo-detection, but they also support the hard-decision outputs of the SCL algorithm for achieving superior block error rate (BLER) performance. As a result of these benefits, the proposed G-SCAN algorithm using a list size of $L = 2$ offers around 1 dB BLER gain compared to the conventional hard-decision SCL decoder relying on $L = 32$. Furthermore, we have carried out its Extrinsic Information Transfer (EXIT) chart analysis, and characterized the performance vs. the complexity of the proposed G-SCAN algorithm, and compared it to various soft- and hard-decision output benchmarks for a wide variety of different rate-matching modes and block lengths. Furthermore, in order to reduce the complexity of the proposed algorithm, a novel Cyclic Redundancy Check (CRC)-aided G-SCAN algorithm is also proposed, which facilitates early termination and improves the BLER performance.