The advancements in diffusion model enables the creation of highly detailed images. However, concurrently fusing texts and images poses significant challenges, often struggling with the maintenance of text accuracy across languages, optimal placement, and appropriate typography. To address these challenges, we introduce the Multilingual Accurate Textual Image Customization (MATIC) framework. MATIC employs the Chain-of-Thought (CoT) concept to decompose the textual image generation process into multiple steps, leveraging diverse generative artificial intelligence, including Multimodal Large Language Model (MMLLM) and diffusion model. The framework first generates the desired text and a corresponding prompt for the diffusion model based on user input. The diffusiongenerated image is then examined to remove any undesired text. Meanwhile, the typographic elements are designed to align with the visual content. Finally, the textual image is fused with the aid of a grid coordinate system, evaluated by MMLLM, and further customized by the user through natural language. Experimental results demonstrate that MATIC can produce accurate, high-quality, multilingual textual images that meet user requirements across various domains, including digital marketing, graphic design, and educational content creation.

Spatial permutation modulation (SPM) is a new multiple-input-multiple-output (MIMO) technology for next-generation communication systems, which is an extension of spatial modulation (SM) that conveys data information at multiple time instants. By encoding the permutation of activated antennas at several time instants, the SPM system gains benefits of transmit and time diversities enabling reliable mobile communications. This study proposed a low-complexity SPM detector, called multiple-candidate-selection matching maximal ratio combining detector (MCSMMRC), for the SPM system. The MCSMMRC detector has very low complexity, fixed throughput, and scalable computing structure, which makes MCSMMD suitable for hardware implementation. This study designed and implemented the proposed MCSMMD detector by using a Xilinx Virtex-7 FPGA chip. The FPGA implementation results showed that it achieved a maximum throughput of 538 Mbps and exhibited better normalized throughput than those of other SM-based detector chips in the literature.

Opportunistic links are emerging as a novel technology for state-of-the-art wireless networks, such as cognitive radio networks, energy-harvesting networks, and low-latency vehicular networks. To increase the reliability of these networks, path-permutation codes (PPCs) were advocated in [1]. These codes are based on a virtual multiple-input multiple-output (MIMO) model on the session/network layer, and have been tailored for increased reliability of opportunistic links. By exploiting multipath-multihop routes, PPCs access one relay path at a time while repeatedly transmitting the same QAM symbol. In this paper, we introduce a generalized PPC scheme which simultaneously accesses multiple relay paths and comprises multiple QAM symbols in the packet. Phase rotations are also introduced in the PPC codewords for additional performance improvement. Numerical simulation results validate our analyses and demonstrate the superior performance of this novel coding scheme, i.e., better error rate performance, higher throughput, and stronger robustness to transmission outages.

The trellis-coded generalized spatial modulation (TCGSM) system with multiple code rates is investigated so as to provide configurability and to enhance efficiency in different fading channels. Various high-radix stages are then adopted to support the optimal sequence detection for different configurations. A double K-best technique is then proposed to strike a good balance between performance and complexity of Viterbi-sphere decoding. From the analysis and simulation results, 29%-69% complexity can be saved with about 1dB performance degradation for the code rate of 4/5 in the spatial domain with QPSK and 16-QAM constellations. The first K-best selection for generating branch metrics from sphere decoding can reduce the detection complexity of the constellation while the second K- best selection can be considered if a high-radix trellis is used when the number of transmit antennas is large.

Based on spatial modulation (SM), spatial permu- tation modulation (SPM) has been recently proposed to enhance the performance of the multiple-input multiple-output (MIMO) system. SPM maps data bits to both the QAM symbol and permutation array. At successive time instants, different transmit antennas are activated according to the mapped permutation array to transmit the QAM symbol. In this work, the error rate of SPM in slow-fading channels is analyzed. The performance is first analyzed with the closed-form expression for the special case, and then is generalized to arbitrary cases by using the approximation of Gamma random variables. The machine learning algorithm is adopted to simplify the generalization and estimate the diversity. Through the analyses, we discover that by simply adding transmit antennas, the performance of SPM in slow-fading channels can be greatly enhanced due to the reduction of the time dependency. Numerical simulations demonstrate the accuracy of our analyses and show that by adding one transmit antenna, the time dependency can almost be removed, leading to around 3 dB SNR gain for the BER performance.

Cognitive radio ad hoc networks (CRAHNs) emerge as a spectrum efficient networking technology to enable autonomous machine-to-machine communication among massive number of IoT devices. However, spectrum sharing results in opportunistic links and CRAHN becomes a kind of opportunistic networks. To reduce latency in CRAHN and to achieve overall spectrum efficiency by avoiding tremendous feedback signaling, CRAHNs of open-loop physical layer transmission open a new avenue under massive operations. The new technology challenge associated with such new CRAHNs lies in error control with only local networking information without relying on feedback control over each opportunistic link. Path-time codes virtually realizing multi-input-multi-output over network layer have been innovated to resolve such a dilemma. However, effective multipath routing considering interference remains unclear. In this paper, be taking network topological factors and interference into account, we analytically derive SINR approximations to design power control and multi-path greedy routing. By stochastic geometry analysis, we also show that the resilient operation for large-scale CRAHNs can be facilitated with the aid of path-time codes.

Multiple-input multiple-output (MIMO) is an essential technology for modern wireless communication systems. Spatial modulation (SM) is an evolving MIMO transmission scheme for energy-efficient massive MIMO systems. SM conveys the information of transmit antenna indices and modulated symbols in MIMO systems. A variant spatial permutation modulation (SPM) was further proposed to include transmit and time diversities by transmitting a permutation array of antenna indices during several time instants. The SPM achieves better error rate performance than the SM especially in fast fading channel. This paper investigates sphere decoding algorithms for the SPM receiver. An ordering scheme was proposed to reduce the number of visited nodes in the spherical tree search. The improved ordered sphere decoding saves about 95.6% computational complexity corresponding to 22.7 times throughput in the software implementation.

We consider some problems arising when a wireless communication system performance must be assessed in the presence of model uncertainties due to poorly known or unknown dependences of the random variables used to model the channel. We argue that performance bounds based on probability boxes can measure the extent of uncertainty caused by inaccurate models.

Networks formed by energy-harvesting devices impose new technological challenges on data transmission due to uncertainty of the amount of energy that can be harvested. This is even more challenging with multihop networks, in which transmission outage occurs when one single device along the relay path cannot harvest enough energy. A virtual multiple-input multiple-output (MIMO) model for multihop, multipath networks has recently been developed to facilitate reliable open-loop end-to-end transmissions, making networks robust to random transmission outages without the necessity of bandwidth-consuming and complicated end-to-end feedback and control. In this paper, a framework based on a virtual MIMO model of multihop, multipath opportunistic networks formed by energy-harvesting devices is developed. We propose rotated-algebraic path-time codes (RA-PTC), by which data are encoded using Givens rotation and cyclic division algebras. Without rate loss, a form of time diversity is exploited by repeatedly transmitting the RA-PTC-coded data. Extensive theoretical analyses are carried out using amount of fading and diversity as metrics. Both the performance enhancement due to the proposed RA-PTC and the performance loss due to energy shortage are quantified. Furthermore, a simple yet effective cyclic power control is proposed to improve transmission reliability. Numerical results demonstrate that RA-PTC and cyclic power control enable efficient and reliable end-to-end transmission in multipath, multihop opportunistic networks formed by energy-harvesting devices.

We propose an efficient iterative multiple-input multiple-output (MIMO) detection algorithm based on the local search. Specifically, since the MIMO channel matrix twists the lattice structure of the received symbols, the proposed channel-aware local search (CA-LS) defines its search neighborhood according to the instantaneous channel realization. Such channel-dependent neighborhood can be efficiently identified by using the sphere decoder in a set that comprises the differences between pairs of QAM vectors, which is termed as delta vectors. The delta vectors with small quadratic norms with respect to the channel matrix are identified and then used as the search directions of the CA-LS. Features like sparsity and non-uniformity of delta vectors are exploited to reduce the SD complexity. Furthermore, by reformulating the detection criterion, the log-likelihood ratio (LLR) computations and searches of the CA-LS are greatly simplified. Numerical simulations demonstrate that compared with other practical iterative MIMO detectors, e.g., the list sphere decoder, the CA-LS achieves superior performance in both error rate and complexity aspects.

Wireless communication with nodes capable of harvesting energy emerges as a new technology challenge. In this paper, we investigate the problem of utilizing energy cooperation among energy-harvesting transmitters to maximize the data rate performance. We consider a general framework which can be applied to either cellular networks with base station energy cooperation through wired power grid or sensor networks with transmitting node energy cooperation through wireless power transfer. We model this energy cooperation problem as an infinite horizon Markov decision process (MDP), which can be optimally solved by the value iteration algorithm. Since the optimal value iteration algorithm has high complexity and requires non-causal information, we propose a distributed algorithm by using reinforcement learning and splitting the MDP into several small MDPs, each associated with a transmitter. Simulation results demonstrate the effectiveness of the proposed distributed energy cooperation algorithm.

In this paper, we investigate the joint design of transmit beamforming and power control to maximize the weighted sum rate in the multiple-input single-output (MISO) cognitive radio network constrained by arbitrary power budgets and interference temperatures. The nonnegativity of the physical quantities, e.g., channel parameters, powers, and rates, is exploited to enable key tools in nonnegative matrix theory, such as the (linear and nonlinear) Perron-Frobenius theory, quasi-invertibility, and Friedland-Karlin inequalities, to tackle this nonconvex problem. Under certain (quasi-invertibility) sufficient conditions, we propose a tight convex relaxation technique that relaxes multiple constraints to bound the global optimal value in a systematic way. Then, a single-input multiple-output (SIMO)-MISO duality is established through a virtual dual SIMO network and Lagrange duality. This SIMO-MISO duality proved to have the zero duality gap that connects the optimality conditions of the primal MISO network and the virtual dual SIMO network. Moreover, by exploiting the SIMO-MISO duality, an algorithm is developed to optimally solve the sum rate maximization problem. Numerical examples demonstrate the computational efficiency of our algorithm, when the number of transmit antennas is large.

Cognitive radios (CRs) improve the spectrum efficiency in wireless communications. Nonetheless, owing to the intrinsic randomness of ad hoc cognitive radio networks (CRNs), e.g., the opportunistic links, the traditional realization of ad hoc networking that demands the end-to-end control information, is unscalable and impractical. A virtual multiple-input multiple-output (MIMO) framework has been recently developed for the realization of error-resilient end-to-end transmission without the necessity of feedback information. In this paper, we propose an end-to-end path-permutation coded (PPC) transmission in which one relay path is accessed at a time and the transmission is hopped among multiple relay paths. The hopping order is specified by a permutation array that encodes the data, meaning that the data is conveyed by the order of indices of the accessed paths. With the PPC scheme, the control overhead and data processing complexity of the end-to-end transmission become relatively low. The PPC technique can also be utilized as a multiuser technique. At the destination node, a joint sphere decoder efficiently implements the maximum a posteriori (MAP) probability decoding that simultaneously identifies the order of accessed paths and erasures. Comprehensive theoretical analyses and simulations are conducted to demonstrate the superior performance of the PPC technique in ad hoc CRNs.

The inter-cell interference (ICI) may considerably degrade the system performance of heterogeneous networks (HetNets) and is generally tackled by the transmitters, which often leads to lower spectrum efficiency. To avoid such disadvantage, this work presents a new receiver function that identifies the precoders assigned to active users in interfering cells, and thus enables more effective ICI mitigation. We theoretically derive the design criterion and analyze the performance of the receiver equipped with the proposed active precoder identification (API) scheme. We also propose two low-complexity techniques: the recursive structure based on the Sherman-Morrison formula/matrix determinant lemma, and the mode-switching technique adapting the search space on-the-fly. Numerical simulations demonstrate that such receiver can achieve error rate performance close to a receiver with perfect knowledge of the active precoders. The recursive structure achieves 68% complexity saving, while the mode-switching technique further increases the saving to 83%.

Cognitive radio ad hoc network (CRAHN) is considered as a key technology to enhance the spectrum efficiency for diverse applications. However, due to the opportunistic links, the intrinsic randomness of the CRAHN makes the traditional precise control of the end-to-end transmission unscalable and generally infeasible. The recently-proposed virtual multiple-input multiple-output (MIMO) framework exploits the multipath routing to create the diversity at the network layer. With only local information and no feedback control channel, the path-time code (PTC) of the virtual MIMO system is able to efficiently provide the error resilient end-to-end transmission. In particular, while the transmission latency should be minimized, more attempts for accessing the opportunistic links can be made if a larger latency is allowed, which improves the error rate performance of the end-to-end transmission. By theoretically analyzing the error rate performance and erasure statistics, we propose a design guideline to determine the waiting period as the limit of the transmission latency. This designed waiting period not only preserves the diversity gained by the PTC but also provides a low transmission latency, resulting in a good balance of the reliability-latency trade-off of the end-to-end PTC transmission in CRAHNs.

In this paper, we study the performance of the recently proposed class of path-time codes (PTCs), which exploit time and path diversity to increase the reliability of end-to-end transmission in ad hoc multihop networks. Using the central limit theorem, we prove that PTCs reshape the statistics of a relay path including multiple cascaded links. In particular, as the number K of relay paths becomes large, the cascaded channel statistic is transformed from K-product Rayleigh to Rayleigh. This is numerically demonstrated and analytically verified by using the concept of “amount of fading” (AF). Our results show that, by using PTCs, the end-to-end error-rate performance can be boosted in addition to the diversity and coding gains.

Ad hoc cognitive radio networks (CRNs) have the potential for meeting the challenge of increasing radio spectrum efficiency. However, traditional control of ad hoc networking demands end-to-end information channel feedback, whose feasibility is hard in ad hoc CRNs due to the opportunistic nature of spectrum access. In this paper, we propose a virtual multiple-input multiple-output (MIMO) approach to facilitate error-resilient end-to-end transmission with no need for feedback information. An erasure-channel model is used to describe the randomness of outage caused by opportunistic links. At source node, a discrete Fourier transform (DFT)-based path-time code (PTC) is used to exploit path diversity. The a priori erasure probability is analyzed, and a pipeline scheduling scheme with unequal waiting periods is designed to reduce such probability. At destination node, knowledge of the a priori erasure probability is exploited to overcome the decoding challenge raised by the presence of random erasures. A joint sphere decoder (JSD) with a minimum mean-squared error sorted QR decomposition (MMSE-SQRD) effectively implements maximum a posteriori (MAP) probability decoding. This decoder simultaneously performs erasure identification and data decoding. Numerical results show that the proposed virtual MIMO framework can pave the way to efficient and reliable end-to-end transmission in ad hoc CRNs.

The performance of heterogeneous networks (Het-Nets) is strongly affected by inter-cell interference. Instead of relying on coordination among base stations, this study presents a new receiver block that identifies the precoders assigned to active users in an adjacent cell. Equipped with this proposed active precoder identification (API), the inter-cell interference at the receiver becomes feasible. The low complexity API is based on the maximum likelihood (ML) criterion and Sherman-Morrison formula. Moreover, by leveraging the fact that the list of active precoders often changes one precoder at a time, an efficient mode-switching API algorithm is proposed for further complexity reduction. Demonstrated by numerical results, the proposed API enables the receiver to achieve error rate performance close to that of the receiver with perfect knowledge of the active precoders used for transmission in the adjacent cell.

In this paper, we derive a closed-form expression for the probability density/mass function (PDF/PMF) and the moment generating function (MGF) of the quantized detector soft output, i.e., extrinsic log-likelihood ratio (LLR), for multiple-input multiple-output (MIMO) bit-interleaved coded modulation with iterative decoding (BICM-ID) systems. The effect of either LLR clipping or LLR clipping with rounding, often applied in practical implementations, are considered. Using the derived expression, we analyze the asymptotic coded bit error rate (BER) for MIMO BICM-ID systems with the two quantization operations under a flat Rayleigh fading channel. The error rate degradation caused by quantizing the extrinsic LLR is then interpreted as an additional signal-to-noise ratio (SNR) loss. Rather than Monte Carlo simulations, this theoretical treatment provides a more convenient alternative to determining the clipping level and optimal word-length (the number of bits needed to represent signals) for LLR in a BICM-ID implementation. Finally, several other applications of this proposed theoretical analysis are also demonstrated.

In this paper, we theoretically analyze the asymptotic coded bit error rate (BER) for multiple-input multiple-output bit-interleaved coded modulation (MIMO-BICM) with linear minimum mean-squared error (MMSE) detection and estimation for a flat Rayleigh fading channel. The numerical simulations validate the accuracy of our theoretical analysis. With such study, we can model the BER improvement of MMSE detection compared with zero-forcing detection as a signal-to-noise (SNR) gain.

For soft-input soft-output sphere decoding (SD), the combination of the Schnorr-Euchner (SE) enumeration and the radius reduction shrink the search space quickly but with considerable computational complexity. In this paper, a low complexity approach is proposed to reduce the tree search space before SD starts. Particularly, such reduction is achieved by 1) tightening the initial radius; 2) restricting the search space to a subset of the symbol lattice. With further enhancement on the tree pruning constraint, simulation results demonstrate significant complexity savings with negligible error rate performance loss.

This paper proposes a new concept of multiple-input multiple-output (MIMO) detection, aiming at minimizing the average computational cost. The detection methods are adapted according to the estimated channel state information to deliver just-acceptable error rate (JAER) performance. Error rate models for two popular MIMO detection algorithms are derived given the channel matrix. From these models, a channel-adaptive-MIMO (CA-MIMO) receiver with detector-switching strategies is proposed. Simulation results demonstrate that the proposed CA-MIMO detector meets the JAER criterion efficiently. Compared with the sophisticated Sphere Search (SS) MIMO detector, the average saving at moderate signal-to-noise ratio (SNR) is around 58% to 72%, depending on different modulation alphabets. At high SNR, the CA-MIMO detector almost always switch to Zero-Forcing (ZF) detection, where the complexity is several orders lower than the SS detector.

In this paper, we analyze the error rate degradation caused by the finite-precision extrinsic log-likelihood ratio (LLR), i.e., demapper output, for multiple-input multiple-output (MIMO) bit-interleaved coded modulation with iterative decoding (BICM-ID). A closed-form expression for the probability density function (pdf) of the metric difference with finite precision is derived. This pdf is well-approximated by two approaches, namely the pseudo quantization noise (PQN) model and the pdf tail truncation. Moreover, we interpret such performance degradation as an additional signal-to-noise ratio (SNR) loss. As validated by Monte Carlo simulations, this SNR loss can be directly applied at the first iteration, extending our analysis to non-iterative scenario. This work provides a convenient indicator for deciding the LLR precision in real implementations.

In this paper, the iterative scheduling issue for multiple-input multiple-output (MIMO) systems with turbo codes is addressed based on the informed asynchronous scheduling, initially used for scheduling low-density parity-check (LDPC) decoding. For the iterative MIMO demapping and turbo decoding, the convergence behavior by using the informed asynchronous scheduling is first demonstrated to be similar to the standard sequential scheduling. As the extrinsic log-likelihood ratio (LLR) calculation at both the MIMO demapper and turbo decoder costs high complexity, an informed message update (IMU) rule is further proposed to select only a subset of extrinsic LLRs for updates within each iteration. By avoiding the re-calculations for the extrinsic LLRs with small changes between successive iterations, significant complexity savings are achieved with negligible error rate performance loss.

This paper introduces a new framework of the multiple-input multiple-output (MIMO) detection in iterative receivers. Unlike the conventional methods processing with symbol lattice, we consider the delta symbol lattice, i.e., the difference between two arbitrary points in the symbol lattice. The inherent flexible, symmetric, and sparse properties of the delta lattice enhance the detection in both complexity and performance aspects. Consequently, we propose a delta-list MIMO (DL-MIMO) detection which separately exploits the channel information and the a priori information so that a soft-input soft-output sphere decoder is dispensable. Simulation results demonstrate this hardware-friendly DL-MIMO detection delivers nearly-optimal performance at affordable cost in a practical scenario.

This paper presents a low-complexity interpolation-based channel estimator suited for scattered-pilot OFDM systems in fast-fading channel. The proposed estimator consists of two interpolators: one RLS interpolator for estimating scattered subcarriers along the time direction and another raised-cosine (RC) interpolator for estimating data subcarriers along the frequency direction. A low-complexity architecture tailored for DVB-T/H is demonstrated as an exemplar. Simulation results show that the proposed channel estimator has comparable accuracy with almost order-of-magnitude lower complexity than conventional adaptive channel estimators.

Using the Schnorr-Euchner (SE) order for soft-input sphere decoders is inefficient for implementation, because it requires exhaustive calculation and sorting of partial metrics of all constellation points. Instead, low-complexity methods can be applied by separating the partial metric into channel information and a priori information and solely enumerating based on one of them. With such an orthogonalization, this paper presents an algorithm that effectively combines these two enumerations to deliver an order close to the SE one. Mathematical analyses and simulation results demonstrate that this is the first algorithm allowing for a low-complexity implementation with optimal error rate performance for any number of iterations.

Library based waveform (WF) development approaches have the potential to address one of the key challenges in software defined radio technology, developing portable and at the same time implementation-efficient WFs. The term WF, in this paper, represents a complete wireless standard with several modes. Upon standardizing the library and the interfaces of its components, different vendors can provide Flavors, which are efficient implementations, for some/all components of a WF as a board-support-package. Flavors might provide different trade-offs with respect to key performance related properties like bit error rate, throughput, latency, area, etc. This paper analyzes such a scenario by using FFT as an example. We use 10 Flavors of FFT from 2 vendors for 3 processing elements to analyze the influence of configuration parameters like input data-width, scaling, etc. on the performance properties. Analysis has been performed by doing two tests: using a simple OFDM transceiver system, calculating root mean square error. Based on our analysis, we have identified the main parameters that influence the properties. Our investigations stress the need for modeling the differences in performance on key properties in terms of the influencing parameters. Such a model can assist in selecting a set of appropriate Flavors for implementing a WF and enable tool assisted WF-development. Furthermore, it is evident from our investigations that Flavors exhibit trade-offs in properties and pose tough constraints in several aspects.

This paper proposes a new concept of multiple-input multiple-output (MIMO) detection, aiming at minimizing the average computational cost. The detection methods, according to the estimated channel state information, are adapted to deliver just acceptable error rate. Performance of two popular MIMO detectors is analyzed. Based on these analyses, a channel adaptive MIMO (CA-MIMO) receiver with a novel method- selection strategy is investigated. Simulation results demonstrate that the proposed CA-MIMO detector, operating at as low SNR as the sphere search does, achieves huge average complexity saving.

In this paper, we derive the asymptotic bit error rate (BER) for multiple-input multiple-output bit-interleaved coded modulation (MIMO-BICM) with linear zero-forcing (ZF) receivers for a temporally correlated flat Rayleigh fading channel. Pilot symbol assisted modulation (PSAM) in combination with linear minimum mean-squared error (LMMSE) channel estimation is considered. We also demonstrate that the deterioration due to imperfect channel state information (CSI) can be represented by a signal-to-noise ratio (SNR) degradation.

In this correspondence, we analytically derive the bit error rate (BER) for bit-interleaved coded modulation (BICM) under the assumption of a temporally correlated flat Rayleigh fading channel. We assume the channel state information (CSI) to be unknown at receiver side and consider a linear minimum mean-squared error (LMMSE) channel estimator that relies on periodically inserted pilot symbols. We give asymptotic results for both non-iterative BICM and BICM with iterative decoding (BICM-ID). Furthermore, we show that the interpretation of the channel estimation error as an SNR degradation holds true in these cases.

Orthogonal frequency-division multiplexing (OFDM) modulation provides a high data rate communication service. However, in mobile reception, a loss of sub-carrier orthogonality due to Doppler-spread leads to inter-carrier interference (ICI). This paper proposes a channel estimation scheme based on one-dimensional raised cosine interpolation. A time-domain raised cosine interpolation plus a frequency-domain raise cosine interpolator with adaptive roll-off factor can estimate the channel response very well in high-mobility environments.