Constellation design plays a critical role in achieving a balance between communication and sensing performance in integrated sensing and communication (ISAC). Traditional constellations often exhibit strong performance in either communication or sensing, but not both. While probabilistic constellation shaping and neural network (NN)-based constellations offer flexible alternatives, they are complex to implement or require extensive training data. To address these challenges, we propose a novel constellation design framework for ISAC, in which the constellation points are modeled as samples drawn from a parameterized two-dimensional probability density function—with a Gamma distribution for the amplitude and a uniform distribution for the phase. The optimization of the Gamma distribution parameters is performed using particle swarm optimization where the objective function is defined by end-task performance metrics. We compare our method against constellations designed by an end-to-end NN and demonstrate that the proposed framework achieves competitive performance across various operating conditions favoring sensing-centric, communication-centric, and tradeoff scenarios, while requiring significantly fewer parameters and no training data.

Non-orthogonal multiple access (NOMA) has gained significant attention as a potential next-generation multiple access technique. However, its implementation with finite-alphabet inputs faces challenges. Particularly, due to inter-user interference, superimposed constellations may have overlapping symbols leading to high bit error rates when successive interference cancellation (SIC) is applied. To tackle the issue, this paper employs autoencoders to design interference-aware super-constellations. Unlike conventional methods where superimposed constellation may have overlapping symbols, the proposed autoencoder-based NOMA (AE-NOMA) is trained to design super-constellations with distinguishable symbols at receivers, regardless of channel gains. The proposed architecture removes the need for SIC, allowing maximum likelihood-based approaches to be used instead. The paper presents the conceptual architecture, loss functions, and training strategies for AE-NOMA. Various test results are provided to demonstrate the effectiveness of interference-aware constellations in improving the bit error rate, indicating the adaptability of AE-NOMA to different channel scenarios and its promising potential for implementing NOMA systems.

In multiple access channels (MAC), multiple users share a transmission medium to communicate with a common receiver. Traditional constellations like quadrature amplitude modulation are optimized for point-to-point systems and lack mechanisms to mitigate inter-user interference, leading to sub-optimal performance in MAC environments. To address this, we propose a novel framework for constellation design in MAC that employs deep autoencoder (DAE)-based communication systems. This approach intelligently creates flexible constellations aware of inter-user interference, reducing symbol error rate and enhancing the constellation-constrained sum capacity of the channel. Comparisons against analytically derived constellations demonstrate that DAE-designed constellations consistently perform best or equal to the best across various system parameters. Furthermore, we apply the DAE to scenarios where no analytical solutions have been developed, such as with more than two users, demonstrating the adaptability of the model.

The next-generation multiple access (NGMA) serves as an umbrella term encompassing transmission schemes distinct from conventional orthogonal methods. As a prominent candidate of NGMA, nonorthogonal multiple access (NOMA) emerges as a promising solution, enhancing connectivity by allowing multiple users to concurrently share time, frequency, and space. However, NOMA faces challenges in practical implementation, particularly in canceling interuser interference (IUI). In this article, first, we discuss the principles behind NOMA and review the conventional NOMA methods and results. Then, to address the above challenges, we present asynchronous transmission and interference-aware modulation techniques, leading to decoding free from successive interference cancellation (SIC). The goal is to design constellations that dynamically adapt to interference, minimizing bit error rates (BERs) and enhancing user throughput in the presence of IUI, intercarrier interference, and intercell interference (ICI). The traditional linkage between minimizing BER and increasing spectral efficiency is addressed, with the exploration of deep autoencoders (AEs) for end-to-end (E2E) communication as a new concept with significant potential for improving BERs. Interference-aware modulation techniques can revolutionize constellation design and communication over nonorthogonal channels. rate-splitting multiple access (RSMA) is another promising interference management technique in multiuser systems. Beyond addressing existing challenges and misconceptions in finite-alphabet NOMA, this article offers fresh insights into the field and provides an overview of code-domain NOMA (C-NOMA) schemes, trellis-coded NOMA (TC-NOMA), and RSMA as other potential candidates for NGMA. Additionally, we discuss the evolution of channel coding toward low-latency communication and examine the modulation and coding schemes (MCSs) in fifth-generation (5G) cellular networks. Finally, we examine future research avenues and challenges, highlighting the importance of addressing them for the practical realization of NOMA from a theoretical concept to a functional technology.

A deep autoencoder (DAE)-based structure for end-to-end communication over the two-user Z-interference channel (ZIC) with finite-alphabet inputs is designed in this paper. The proposed structure jointly optimizes the two encoder/decoder pairs and generates interference-aware constellations that dynamically adapt their shape based on interference intensity to minimize the bit error rate (BER). An in-phase/quadrature-phase (I/Q) power allocation layer is introduced in the DAE to guarantee an average power constraint and enable the architecture to generate constellations with nonuniform shapes. This brings further gain compared to standard uniform constellations such as quadrature amplitude modulation. The proposed structure is then extended to work with imperfect channel state information (CSI). The CSI imperfection due to both the estimation and quantization errors are examined. The performance of the DAE-ZIC is compared with two baseline methods, i.e., standard and rotated constellations. The proposed structure significantly enhances the performance of the ZIC both for the perfect and imperfect CSI. Simulation results show that the improvement is achieved in all interference regimes (weak, moderate, and strong) and consistently increases with the signal-to-noise ratio (SNR). For instance, more than an order of magnitude BER reduction is obtained with respect to the most competitive conventional method at weak interference when$\rm SNR > 15 dB$and two bits per symbol are transmitted. The improvements reach about two orders of magnitude when quantization error exists, indicating that the DAE-ZIC is more robust to the interference compared to the conventional methods.

Radio frequency signal modulation classifications find broad applications in cognitive sensing and RF spectrum coexistence. Recently, deep neural networks have been shown to be a powerful tool for automatic modulation classification (AMC). Accounting for different signal variations is paramount towards reliable classifications. In this paper, we examine the performance of AMC under varying sampling rates and signal-to-noise ratio (SNR). We consider a dynamic environment where the signal modulation and channel conditions can be assumed constant over a number of consecutive observations. We also show that a single ResNet can be used for both modulation classification and estimating SNR which allows network training and testing at the same noise levels. It is shown that significant signal modulation classification accuracy improvement can be achieved using multiple observations and known SNR.

A deep autoencoder (DAE)-based end-to-end communication over the two-user Z-interference channel (ZIC) with finite-alphabet inputs is designed in this paper. The design is for imperfect channel state information (CSI) where both estimation and quantization errors exist. The proposed structure jointly optimizes the encoders and decoders to generate interference-aware constellations that adapt their shape to the interference intensity in order to minimize the bit error rate. A normalization layer is designed to guarantee an average power constraint in the DAE while allowing the architecture to generate constellations with nonuniform shapes. This brings further shaping gain compared to standard uniform constellations such as quadrature amplitude modulation. The performance of the DAE-ZIC is compared with two conventional methods, i.e., standard and rotated constellations. The proposed structure significantly enhances the performance of the ZIC. Simulation results confirm bit error rate reduction in all interference regimes (weak, moderate, and strong). At a signal-to-noise ratio of 20dB, the improvements reach about two orders of magnitude when only quantization error exists, indicating that the DAE-ZIC is highly robust to the interference compared to the conventional methods.

A deep autoencoder (DAE)-based communication over the two-user Z-interference channel (ZIC) is introduced in this paper. The proposed DAE-ZIC is designed to minimize the bit error rate (BER) in the presence of interference by jointly optimizing the encoders and decoders. Effectively, this is an end-to-end communication that designs new constellations for the ZIC. Normalization layers are embedded in the proposed DAE design to realize an average power constraint so that there are no regular shape restrictions on the constellation symbols. We compare the performance of the DAE-ZIC with two baseline methods, which are ZIC with regular and rotated constellations. Simulation results show a significant gain in BER reduction. On average, in weak, moderate, and strong regimes, 31%–75% BER improvement is achieved compared to the best existing methods.

In this article, we investigate the performance of hybrid satellite–terrestrial relay networks with multiuser downlink nonorthogonal multiple access. The satellite applies Alamouti space–time block coding, and users exploit a receive antenna selection technique. Communication between the satellite and users is assumed to be established with the aid of a half-duplex terrestrial relay equipped with multiple receive antennas and operating in amplify-and-forward mode, because of the heavy masking effects attributed to environmental obstacles. Subsequently, the satellite–relay link is exposed to shadowed-Rician fading, whereas the relay–users links undergo Nakagami-$m$fading, which is a generic statistical channel model for nonterrestrial networks. To be more practical, we consider imperfect successive interference cancellation. The exact outage probability of each user and the corresponding asymptotic expression at the high signal-to-noise ratio are derived to demonstrate the system performance. The analysis indicates that the shadowing conditions do not affect the array gain when the diversity order is dominated by the multiantenna configuration of the second hop. The theoretical derivations are validated by using Monte Carlo simulations. The numerical results indicate that the proposed scheme significantly improves the performance of each user under various shadowing conditions. It also outperforms orthogonal multiple access when proper power levels are allocated to users.

The$K$-receiver wiretap channel is a channel model where a transmitter broadcasts$K$independent messages to$K$intended receivers while keeping them secret from an eavesdropper. The capacity region of the$K$-receiver multiple-input multiple-output (MIMO) wiretap channel has been characterized using dirty-paper coding and stochastic encoding. However,$K$factorial encoding orders may need to be enumerated to evaluate the capacity region, which makes the problem intractable. In addition, even though the capacity region is known, optimal signaling to achieve the capacity region is unknown. In this paper, we determine one optimal encoding order to achieve every point on the capacity region, and thus reduce the encoding complexity$K$factorial times. We prove that the optimal decoding order for the$K$-receiver MIMO wiretap channel is the same as that for the MIMO broadcast channel without secrecy. To be specific, the descending weight ordering in the weighted sum-rate (WSR) maximization problem determines the optimal encoding order. Next, to achieve the secrecy capacity region boundary, we form a WSR maximization problem and apply the block successive maximization method to solve this nonconvex problem and find the input covariance matrices corresponding to each message. Numerical results are used to verify the optimality of the encoding order and to demonstrate the efficacy of the proposed signaling design.

Using a deep autoencoder (DAE) for end-to-end communication in multiple-input multiple-output (MIMO) systems is a novel concept with significant potential. DAE-aided MIMO has been shown to outperform singular-value decomposition (SVD)-based precoded MIMO in terms of bit error rate (BER). This paper proposes embedding left- and right-singular vectors of the channel matrix into DAE encoder and decoder to further improve the performance of the MIMO DAE. SVD-embedded DAE largely outperforms theoretic linear precoding in terms of BER. This is remarkable since it demonstrates that DAEs have significant potential to exceed the limits of current system design by treating the communication system as a single, end-to-end optimization block. Based on the simulation results, at SNR=10dB, the proposed SVD-embedded design can achieve a BER of about 10−5and reduce the BER at least 10 times compared with existing DAE without SVD, and up to 18 times compared with theoretical linear precoding. We attribute this to the fact that the proposed DAE can match the input and output as an adaptive modulation structure with finite alphabet input. We also observe that adding residual connections to the DAE further improves the performance.

The Gaussian multiple-input multiple-output (MIMO) multi-receiver wiretap channel is studied in this paper. The base station broadcasts confidential messages to K intended users while keeping the messages secret from an eavesdropper. The capacity of this channel has already been characterized by applying dirty-paper coding and stochastic encoding. However, K factorial encoding orders may need to be enumerated for that, which makes the problem intractable. We prove that there exists one optimal encoding order and reduced the K factorial times to a one-time encoding. The optimal encoding order is proved by forming a secrecy weighted sum rate (WSR) maximization problem. The optimal order is the same as that for the MIMO broadcast channel without secrecy constraint, that is, the weight of users’ rate in the WSR maximization problem determines the optimal encoding order. Numerical results verify the optimal encoding order.

The problem of maximizing secrecy rate of multiple-input multiple-output multiple-eavesdropper (MIMOME) channels with arbitrary numbers of antennas at each node is studied in this paper. First, the optimization problem corresponding to the secrecy capacity of the MIMOME channel is converted to an equivalent optimization based on Givens rotations and eigenvalue decomposition of the covariance matrix. In this new formulation, precoder is a rotation matrix which results in a positive semi-definite (PSD) covariance matrix by construction. This removes the PSD matrix constraint and makes the problem easier to tackle. Next, a Broyden-Fletcher-Goldfarb-Shanno (BFGS)-based algorithm is developed to find the rotation and power allocation parameters. Further, the generalized singular value decomposition (GSVD)-based precoding is used to initialize this algorithm. The proposed rotation-BFGS method provides an efficient approach to find a near-optimal transmit strategy for the MIMOME channel and outperforms various state-of-the-art analytical and numerical methods. In particular, the rotation-BFGS precoding achieves higher secrecy rates than the celebrated GSVD precoding, with a reasonably higher computational complexity. Extensive numerical results elaborate on the effectiveness of the rotation-BFGS precoding. The new framework developed in this paper can be applied to a variety of similar problems in the context of multi-antenna channels with and without secrecy.

This paper introduces a unified deep neural network (DNN)-based precoder for two-user multiple-input multiple-output (MIMO) networks with five objectives: data transmission, energy harvesting, simultaneous wireless information and power transfer, physical layer (PHY) security, and multicasting. First, a rotation-based precoder is developed to solve the above problems independently. Rotation-based precoding is a new precoding and power allocation scheme that beats existing solutions for PHY security and multicasting and is reliable in different antenna settings. Next, a DNN-based precoder is designed to unify the solution for all objectives. The proposed DNN concurrently learns the solutions given by conventional methods, i.e., analytical or rotation-based solutions. A binary vector is designed as an input feature to distinguish the objectives. Numerical results demonstrate that, compared to the conventional solutions, the proposed DNN-based precoder reduces on-the-fly computational complexity more than an order of magnitude while reaching near-optimal performance (99.45% of the averaged optimal solutions). The new precoder is also more robust to the variations of the numbers of antennas at the receivers.

This paper studies secure precoding and power allocation for the two-user multiple-input multiple-output broadcast channel (MIMO-BC) with common and confidential messages. In this setting, the base station provides three services: A public service (a multicast or common message) to serve both users and two confidential messages to serve each of the two users secretly. The capacity region of this channel is characterized using secret dirty-paper coding. This scheme is, however, prohibitively complex for practical implementations. Under this scenario, a linear precoding method based on a power splitting scheme among the three services is proposed to optimize the corresponding rate equations in a separated manner by decomposing the MIMO-BC into two wiretap channels and a multicast channel. This approach leverages the wiretap channel solutions to design a precoder for the above channel. Particularly, alternative optimization is applied to the modified wiretap channels. For the multicast channel, analytical solutions together with a nonlinear random search are developed in different cases. Numerical results illustrate the efficacy of the proposed linear precoding and power allocation method. The proposed method also outperforms existing solutions over related problems such as the MIMO-BC with one confidential broadcasting and multicasting, etc.

This paper investigates a precoding design for a two-user multiple-input multiple-output (MIMO) network with various objectives, including simultaneous wireless information and power transfer, energy harvesting, and security. Conventionally, precoding and power allocation matrices for these objectives are obtained via different solutions. While in some cases analytic solutions are known, in other cases only time-consuming iterative methods are available. To overcome this issue and unify the solutions for multi-objective networks, a deep learning-enabled framework is proposed in this paper. The proposed deep neural network (DNN)-based precoding learns how to optimize multiple objective functions and find their corresponding input covariance matrices concurrently, efficiently, and reliably. Compared to conventional iterative precoding methods, the proposed approach reduces on-the-fly computational complexity 91.19% while reaching near-optimal performance (99.64% of the optimal solution). The proposed DNN-based precoding can flexibly adapt itself to the different needs of the network and is faster and more robust than transitional approaches, making it an attractive solution for current and future communication networks.

The exponential growth of the Internet of things (IoT) necessitates long-lasting, low-cost, and sustainable green IoT network designs. In this paper, relay deployment is proposed to increase the battery lifetime of IoT devices in smart homes. Then, the effect of this deployment on the battery lifetime of IoT sensors is analyzed and evaluated in large scales using tools from stochastic geometry. Such analysis is more realistic as it captures real-world spatial correlation of IoT devices and reveals better insights to network design. Further, a closed-form expression of the battery consumption model is presented, and its cumulative density function is derived analytically. Numerical and analytic results match and confirm that relay deployment is a promising opportunity that can extend smart homes' IoT battery lifetime from about one year to several years.

In this paper, we consider the problem of energy harvesting maximization in a wiretap channel model while keeping the secrecy rate higher than a given threshold and the transmit power lower than a given constant. In this model, the transmitted radio signal that conveys information is also considered as an energy carrier. The energy receiver (Eve) is a legitimate user who may benefit from the energy of the received signal that is sent to the information receiver (Bob) but she should not be able to decode the message itself. Energy harvesting maximization at Eve's side is a non-convex optimization problem and therefore intractable. To tackle this problem, we use rotation matrices to represent the covariance matrix of the transmitted signal and then apply the Karush-Kuhn-Tucker conditions. We derive an analytical solution for the case in which the number of transmit antennas is two whereas the numbers of receive antennas at Bob's and Eve's sides are arbitrary and finite. Finally, we verify the results by simulations.

The problem of maximizing the secrecy rate of multiple-input multiple-output multiple-eavesdropper (MI-MOME) channels with arbitrary numbers of antennas at each node is studied in this paper. It is shown that optimal precoding can be formed using a rotation matrix in an nt-dimensional space where ntis the number of antennas at the transmitter. Next, a gradient-descent based method is developed to find the rotation and power allocation parameters. The proposed rotation-based method can be applied to the MIMOME channel with arbitrary numbers of antennas at each node and outperforms state-of-the-art solutions. In particular, it achieves higher secrecy rates than generalized singular value decomposition (GSVD)-based precoding. Further, GSVD-based precoding is used to initialize the rotation-based precoding and this combination provides an efficient approach to find a near-optimal transmit strategy for the MIMOME channel. The rotation-based precoding can be applied to related problems in multi-antenna channels. Numerous simulation results show that the rotation-based method outperforms existing methods in terms of performance and computational complexity.

This work exploits the advantages of two prominent techniques in future communication networks, namely caching and non-orthogonal multiple access (NOMA). Particularly, a system with Rayleigh fading channels and cache-enabled users is analyzed. It is shown that the caching-NOMA combination provides a new opportunity of cache hit which enhances the cache utility as well as the effectiveness of NOMA. Importantly, this comes without requiring users' collaboration, and thus, avoids many complicated issues such as users' privacy and security, selfishness, etc. In order to optimize users' quality of service and, concurrently, ensure the fairness among users, the probability that all users can decode the desired signals is maximized. In NOMA, a combination of multiple messages are sent to users, and the defined objective is approached by finding an appropriate power allocation for message signals. To address the power allocation problem, two novel methods are proposed. The first one is a divide-and-conquer-based method for which closed-form expressions for the optimal resource allocation policy are derived, making this method simple and flexible to the system context. The second one is based on the deep reinforcement learning method that allows all users to share the full bandwidth. Finally, simulation results are provided to demonstrate the effectiveness of the proposed methods and to compare their performance.

A downlink single-input single-output non-orthogonal multiple access setting is considered, in which a base station (BS) is communicating with two legitimate users in two possible scenarios of unsecure environments: existence of an external eavesdropper and communicating through an untrusted relay. For the first scenario, a number of trusted cooperative half-duplex relays is employed to assist with the BS's transmission and secure its signals from the external eavesdropper. Various relaying schemes are proposed and analyzed for that matter: cooperative jamming, decode-and-forward, and amplify-and-forward. For each scheme, secure beamforming signals are devised at the relays to maximize the achievable secrecy rate regions. For the second scenario, with the untrusted relay, achievable secrecy rate regions are derived for two different relaying schemes, compress-and-forward and amplify-and-forward, under two different modes of operation. In the first mode, coined passive user mode, the users receive signals from both the BS and the untrusted relay and combine them to decode their messages. In the second mode, termed the active user mode, the users transmit a cooperative jamming signal simultaneously with the BS's transmission to further confuse the relay. Focusing on half-duplex nodes, the users cannot receive the BS's signal while jamming the relay, i.e., while being active, and rely only on the signals forwarded to them by the relay. It is shown that the best relaying scheme highly depends on the system parameters, in particular the distances between the nodes, and also on the part of the secrecy rate region at which the system is to operate.

The problem of merging multiple services over multiple-input multiple-output Gaussian broadcast channels with two receivers is studied. The transmitter provides two independent services: a public (multicast) service with a common message for both users, and a private service with a confidential message intended only for one of the users which is to be kept secret from the other. Under this scenario, a new linear precoding scheme is developed to maximize secrecy-multicast rate region. This scheme decomposes input covariance matrix using rotation matrices and results in a reformulation of the secrecy-multicast capacity region which is easier to be optimized. Next, by finding the distributions to which eigenvalues and rotation angles fit in, a search algorithm is proposed to solve the new capacity expression to find precoding and power allocation matrices. Numerical results illustrate that the proposed precoding method significantly increases the rate region compared to the existing methods.

The effect of beam misalignment on rate performance of the downlink hybrid beamforming-based non-orthogonal multiple access (HB-NOMA) systems is analyzed in this paper. First, an HB-NOMA framework is introduced in the context of multiuser millimeter wave (mmWave) communications, a sum-rate maximization problem is formulated for that, and an algorithm is introduced to design digital and analog precoders and power allocation. Then, a lower bound for the achievable rate of the perfectly aligned line-of-sight (LoS) channels is derived. Subsequently, the impact of beam misalignment is evaluated when the users experience misaligned LoS or non-LoS channels. To this end, a misalignment factor is modeled and each misaligned effective channel is described in terms of the perfectly aligned effective channel parameters and the misalignment factor. Furthermore, a lower bound for the achievable rate, and an upper bound for the rate gap expression between the aligned and misaligned HB-NOMA systems are established. The analyses reveal that a large misalignment can remarkably degrade the rate. Extensive numerical simulations are conducted to verify the findings.

A downlink single-input single-output nonorthogonal multiple access system is considered in which a base station (BS) is communicating with two legitimate users in the presence of an external eavesdropper. A group of trusted cooperative half-duplex relay nodes, powered by the BS, is employed to assist the BS's transmission. The goal is to design relaying schemes such that the legitimate users' secrecy rate region is maximized subject to a total power constraint on the BS and the relays' transmissions. Three relaying schemes are investigated: cooperative jamming, decode-and-forward, and amplify-and-forward. Depending on the scheme, secure beamforming signals are carefully designed for the relay nodes that either diminish the eavesdropper's rate without affecting that of the legitimate users, or increase the legitimate users' rates without increasing that of the eavesdropper. The results show that there is no relaying scheme that fits all conditions; the best relaying scheme depends on the system parameters, namely, the relays' and eavesdropper's distances from the BS, and the number of relays. They also show that the relatively simple cooperative jamming scheme outperforms other schemes when the relays are far from the BS and/or close to the eavesdropper.

Social and communication networks interact with each other in multifaceted ways, yet these interactions are often considered to be secondary in throughput, privacy and security analysis for communication networks. In this paper, full-duplex (FD) and half-duplex (HD) multi-antenna cooperative communication systems are studied by taking both physical links and social connections into account. An optimal beamformer for maximizing communication rate in the proposed socio-technological setting aims to balance between the direct link and the cooperating link as well as respecting the trust degree between the users. The resulting optimization problems are nontrivial to solve, even numerically, as they are not convex. The complexity of the problems is significantly reduced by showing that a linear combination of the direct and cooperating links' channel vectors maximizes the achievable rate. Then, a computationally efficient numerical solution is used to maximize the rates both in the FD and HD modes. Numerical results demonstrate that significant gains in communication rates can be obtained with the proposed optimal beamforming design.

In this paper, beamforming design is investigated for a multi-antenna cooperative communication system in which both physical links and social connections (trust degrees) between nodes are taken into account. An optimal beamformer aims to balance between the direct link and the cooperating link as well as respecting the trust degree. The resulting optimization problem is nontrivial to solve, even numerically, as it is not convex. The complexity of the problem is largely reduced by showing that a linear combination of the direct and cooperating links' channel vectors maximizes the achievable rate. Then, a computationally efficient numerical solution is used to maximize the rate. Numerical results demonstrate that significant gains in communication rates can be obtained with the proposed optimal beamforming design.

A Gaussian multiple-input multiple-output wiretap channel in which the eavesdropper and legitimate receiver are equipped with arbitrary numbers of antennas and the transmitter has two antennas is studied in this paper. It is shown that the secrecy capacity of this channel can be achieved by linear precoding. The optimal precoding and power allocation schemes achieving the secrecy capacity are developed subsequently, and the secrecy capacity is compared with the generalized singular value decomposition (GSVD)-based precoding, which is the best previously proposed precoding for this problem. Numerical results show that substantial gain can be obtained in secrecy rate between the proposed and GSVD-based precodings.

A new relay-aided non-orthogonal multiple access (NOMA) technique is proposed for multi-cell uplink cellular networks in which each cell supports K single-antenna users by its respective base station (BS) equipped with N(4Z ≪K) antennas. Cooperative relaying transmission is used to accommodate more than one user per orthogonal resource block in the context of interference-limited cellular networks. With the proposed relayaided NOMA, an Alamouti structure of the desired symbol can further be generated at each BS free of interference, which gives rise to diversity gain of two. The proposed scheme does not require any channel state information (CSI) at users. Further, only limited CS! is required at relays and BSs, which can greatly reduce the control overhead.

A Gaussian multiple-input multiple-output wiretap channel in which the eavesdropper and legitimate receiver are equipped with arbitrary numbers of antennas and the transmitter has two antennas is studied in this paper. The input covariance matrix that achieves the secrecy capacity is determined. In particular, it is shown that the secrecy capacity of this channel can be achieved by linear precoding. Precoding and power allocation schemes that maximize the achievable secrecy rate, and thus achieve the secrecy capacity, are developed. The secrecy capacity is then compared with the achievable secrecy rate of generalized singular value decomposition (GSVD)-based precoding, which is the best previously proposed technique for this problem. Numerical results demonstrate that substantial gain can be obtained in secrecy rate between the proposed and GSVD-based precodings.

The Han-Kobayashi (HK) encoding scheme is simplified for the one-sided Gaussian interference channel and a class of mixed interference channels, with Gaussian codebooks. The simplified region significantly decreases the computational complexity of the HK region. It also provides better insight into how to use the HK scheme for these channels. It shows that time-sharing with power allocation over two dimensions is enough to achieve the border of the HK inner bound, for these channels. Moreover, a new representation of the HK region for these channels is introduced.

We investigate mobile sensor deployment algorithms when the sensors have a limited communication range and they estimate other sensors location from the messages they receive from them. These pragmatic constraints bring two implications: the former implies that each sensor will only be aware of the presence of those sensors whose communication ranges include that sensor, and the latter indicates that location information can be inaccurate, due to estimations error. Consequently, the conventional Voronoi-based mobile sensor deployment algorithms fail to guarantee a simple, reliable coverage detection; additionally, the sensors are prone to collision. We introduce a new set of Voronoi-based diagrams, named guaranteed Voronoi diagrams with limited communication, to tackle the above problems.

Single-cell-based coverage hole detection algorithms are developed for mobile sensors deployment under inaccurate location information and non-identical sensing ranges. Existing Voronoi-based diagrams require the exact location of sensors to guarantee a simple, single-cell-based coverage detection, and they miss the mark if the location information is inaccurate. The guaranteed Voronoi-based diagrams, proposed in this paper, extend the existing diagrams in a way to guarantee the single-cell based coverage hole detection when upper bounds on location errors are provided. Simulation results show that with inaccurate location information the proposed algorithms can largely increase the network coverage. Even if the location information is exactly known, we suggest assuming some error margins to improve the network coverage based on the proposed algorithms.

Discrete Fourier transform (DFT) codes have been used to provide robustness against errors and erasures in various applications. This paper focuses on improving error localization of the Bose-Chaudhuri-Hocquenghem (BCH) DFT codes. First, we analyze how the subspace-based error localization outperforms the coding-theoretic one. Then, we propose an extension of the subspace-based error localization, based on additional syndrome, that improves the existing one and is naturally suitable for rate-adaptive distributed source coding (DSC). Further, we propose a new generic subspace-based algorithm to decode BCH-DFT codes. The proposed approach generalizes the encoding and decoding of this important class of DFT codes. It introduces many different decoding matrices for a DFT code; this diversity is then used to diminish the effect of the quantization noise and thus to improve the decoding. Finally, the extended and generalized approaches are combined to maximize the decoding gain. Simulation results demonstrate the capability of the proposed algorithms to perform significantly better than the existing subspace-based error localization, in the presence of quantization noise.

A subspace-based approach for rate-adaptive distributed source coding (DSC) based on discrete Fourier transform (DFT) codes is developed. Punctured DFT codes can be used to implement rate-adaptive source coding, however they perform poorly after even moderate puncturing since the performance of the subspace error localization degrades severely. The proposed subspace-based error localization extends and improves the existing one, based on additional syndrome, and is naturally suitable for rate-adaptive distributed source coding architecture.

In a recent paper [1], Rini et al. proved a capacity result for the discrete memoryless cognitive interference channel, under the condition named “better cognitive decoding.” We show that this capacity region is the same as the capacity region characterized by Wu et al. in [2].

Motivated by a host of recent applications requiring some amount of redundancy, frames are becoming a standard tool in the signal processing toolbox. In this paper, we study a specific class of frames, known as discrete Fourier transform (DFT) codes, and introduce the notion of systematic frames for this class. This is encouraged by application of systematic DFT codes in distributed source coding using DFT codes, a new application for frames. Studying their extreme eigenvalues, we show that, unlike DFT frames, systematic DFT frames are not necessarily tight. Then, we come up with conditions for which these frames can be tight. In either case, the best and worst systematic frames are established from reconstruction error point of view. Eigenvalues of DFT frames, and their subframes, play a pivotal role in this work.

We show how real-number codes can be used to compress correlated sources, and establish a new framework for distributed lossy source coding, in which we quantize compressed sources instead of compressing quantized sources. This change in the order of binning and quantization blocks makes it possible to model correlation between continuous-valued sources more realistically and correct quantization error when the sources are completely correlated. The encoding and decoding procedures are described in detail, for discrete Fourier transform (DFT) codes. Reconstructed signal, in the mean-squared error sense, is seen to be better than or close to quantization error level in the conventional approach.

In this paper, we derive the exact closed-form expression for the symbol-error rate (SER) of carrierless amplitude and phase/quadrature amplitude modulation (CAP/QAM) where the number of symbols is an odd power of two. The presented formula is simply in the form of the well known Gaussian Q-function and its second power. In the low signal-to-noise ratio range where the bounding techniques fail, our expression accurately predicts the SER. Furthermore, based on this expression, we introduce a simple upper bound for the SER which is tighter and more accurate than the existing bounds.

In this paper, we consider the problem of peak to mean envelope power ratio (PMEPR) in orthogonal frequency division multiplexing (OFDM) systems. First, we propose a power-efficient 8-QAM signal constellation and extend it to higher order QAM signal constellations as well; and then we deploy these constellations to reduce the PMEPR in OFDM systems using the constellation expanding idea. All these constellations have less average power compared to the existing constellations like quadrature constellation and therefore are appropriate for PMEPR reduction in OFDM and other discrete multi-tone (DMT) techniques