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In this paper, we consider a new design problem of optimizing a linear transceiver for correlated multiple-input multiple-output (MIMO) interference channels in the presence of channel state information (CSI) errors, which is a more realistic and practical scenario than those considered in the previous studies on uncorrelated MIMO interference channels. By taking CSI errors into account, the optimization problem is initially formulated to minimize the average mean square error (MSE) under the general power constraints. Since the objective function is not jointly convex in precoders and receive filters, we split the original problem into two convex subproblems, and then linear precoders and receive filters are obtained by solving two subproblems iteratively. It is shown that the proposed algorithm is guaranteed to converge to a local minimum. The numerical results show that the proposed algorithm can significantly reduce the sensitivity to CSI errors compared with the existing robust schemes in the correlated MIMO interference channel. [ABSTRACT FROM AUTHOR]

Summary: Multiple‐input multiple‐outputs (MIMOs) have eminent quality in maximizing the throughput of wireless communication models. In MIMO, the antenna arrays can be utilized for fulfilling the needs of 5G by utilizing various spatial signatures of users. Even though 5G communication is imminent, there exist issues, like network interference that arise due to reused frequency spectrum resources. This delving presents an optimized deep model for suppressing interference occurring in the Rayleigh channel in the multiple‐user MIMO (MU‐MIMO) model. Here, an MU‐MIMO model is employed with correlated interference wherein there exist various users around the base station (BS) with several antennas at the transmitter and receiver. Here, a deep neuro‐fuzzy network (DNFN) is used to upgrade the performance of detectors underneath correlated interference. Here, the model comprises zero forcing‐maximum likelihood detection (ZF‐MLD) that assists to generate an initial estimate of broadcasted signals in a particular time slot. The DNFN is used to capture latent correlation among several symbols. Here, the DNFN training is performed using developed autoregressive Henry gas spider monkey optimization (RHGSMO), which is the combination of conditional autoregressive value at risk (CAViaR), Henry gas solubility optimization (HGSO), and spider monkey optimization (SMO). With the lowest symbol error rate (SER), bit error rate (BER), and signal to interference and noise ratio (SINR), the suggested RHGSMO‐based DNFN performed better than existing approaches. [ABSTRACT FROM AUTHOR]

Blind Interference Alignment (BIA) schemes create and exploit channel coherence patterns without the knowledge of channel realizations at transmitters, while beamforming schemes rely primarily on channel knowledge available to the transmit- ters without regard to channel coherence patterns. In order to explore the compatibility of these disparate ideas and the possibility of synergistic gains, this work studies the Degrees of Freedom (DoF) of the 2-user (?1 × ?1)(?2 × ?2) Multiple- Input Multiple-Output (MIMO) Interference Channel (IC) where Transmitter 1 is equipped with reconfigurable antennas and has no channel knowledge, while Transmitter 2 has partial channel knowledge but no reconfigurable antennas. Taking a fundamental dimensional analysis perspective, the main question is to identify which antenna configurations allow synergistic DoF gains. The main results of this work are two-fold. The first result identifies antenna configurations where both reconfigurable antennas and partial channel knowledge are individually beneficial, as those where ?1 < ?1 < min(?2,?2). The second result shows that synergistic gains exist in each of these settings, over the best known solutions that rely on either reconfigurable antennas or partial channel knowledge alone. Coding schemes that jointly exploit partial channel knowledge and reconfigurable antennas emerge as a byproduct of the analysis.

In this paper, we consider a new design problem of optimizing a linear transceiver for correlated multiple-input multiple-output (MIMO) interference channels in the presence of channel state information (CSI) errors, which is a more realistic and practical scenario than those considered in the previous studies on uncorrelated MIMO interference channels. By taking CSI errors into account, the optimization problem is initially formulated to minimize the average mean square error (MSE) under the general power constraints. Since the objective function is not jointly convex in precoders and receive filters, we split the original problem into two convex subproblems, and then linear precoders and receive filters are obtained by solving two subproblems iteratively. It is shown that the proposed algorithm is guaranteed to converge to a local minimum. The numerical results show that the proposed algorithm can significantly reduce the sensitivity to CSI errors compared with the existing robust schemes in the correlated MIMO interference channel.

This paper develops deep learning (DL) based beamforming approaches for multi-antenna interference channels where several base stations (BSs) individually optimize their own beamforming vectors in a decentralized manner. By exploiting the optimal beam structure, we propose an efficient method for beam decisions and coordination among BSs based solely on local information. Moreover, we show that the proposed approach allows a scalable design with respect to the number of users. We also present novel training strategies for the proposed deep neural networks, validating its potential as an innovative decentralized beamforming methodology. Consequently, the proposed DL based decentralized beamforming framework can achieve various optimal beamforming strategies. Numerical results demonstrate the advantages of the proposed framework over conventional methods.

A parameter regime is identified where the simple scheme of treating interference as Gaussian noise (TIN), with power control and jamming, is optimal for the secure generalized degrees of freedom (GDoF) region of Gaussian broadcast networks under the robust assumption of finite-precision channel state information at the transmitter (CSIT). The network consists of one transmitter equipped with K antennas, and K single-antenna receivers. The results are generalized to groupcast (equivalently, compound broadcast) settings where each message is desired by a disjoint group of receivers. Noting that messages are independently encoded in the GDoF-optimal scheme, the result for the broadcast channel is extended to its counterpart Gaussian interference channel under finite precision CSIT. Evidently, both secrecy constraints and finite precision CSIT limit the benefits of more sophisticated schemes, leading to optimality of simpler schemes for larger parameter regimes. Aligned Image bounds are the key to the proof of optimality for these larger parameter regimes under finite precision CSIT.

The Generalized Degrees of Freedom (GDoF) of the two user interference channel are characterized for all parameter regimes under the assumption of finite precision channel state information at the transmitters (CSIT), when a limited amount of cooperation is allowed between the transmitters in the form of π DoF of shared messages. In all cases, the number of over- the-air bits that each cooperation bit buys is shown to be equal to either 0, 1, 1/2 or 1/3.

Generalized degrees of freedom (GDoF) are characterized for the symmetric K-user multiple-input multiple-output interference channel under the assumption that the channel-state information at the transmitters (CSITs) is limited to finite precision. In this symmetric setting, each transmitter is equipped with M antennas, each receiver is equipped with N antennas, each desired channel (i.e., a channel between a transmit antenna and a receive antenna belonging to the same user) has strength ∼ P , while each undesired channel has strength ∼ Pα, where P is a nominal SNR parameter. The result generalizes a previous GDoF characterization for the SISO setting (M=N=1) and is enabled by a significant extension of the aligned image sets bound that is broadly useful. GDoF per user take the form of a W-curve with respect to α for fixed values of M and N. Under finite precision CSIT, in spite of the presence of multiple antennas, all the benefits of interference alignment are lost.

Generalized degrees of freedom (GDoF) are characterized for the symmetric K-user multiple-input multiple-output interference channel under the assumption that the channel-state information at the transmitters (CSITs) is limited to finite precision. In this symmetric setting, each transmitter is equipped with M antennas, each receiver is equipped with N antennas, each desired channel (i.e., a channel between a transmit antenna and a receive antenna belonging to the same user) has strength ∼ P , while each undesired channel has strength ∼ Pα, where P is a nominal SNR parameter. The result generalizes a previous GDoF characterization for the SISO setting (M=N=1) and is enabled by a significant extension of the aligned image sets bound that is broadly useful. GDoF per user take the form of a W-curve with respect to α for fixed values of M and N. Under finite precision CSIT, in spite of the presence of multiple antennas, all the benefits of interference alignment are lost.

The generalized degrees of freedom of the two-user symmetric multiple input multiple output interference channel are characterized as a function of the channel strength levels and the level of channel state information at the transmitters. In this symmetric setting, each transmitter is equipped with M antennas, each receiver is equipped with N antennas, and both cross links have the same strength parameter α and the same channel uncertainty parameter β. The main challenge resides in the proof of the outer bound which is accomplished by a generalization of the aligned image sets approach.

Optimal energy delay scheduling for capacity allocation problems in interference channel energy harvesting wireless sensor networks (EH-WSN) address of static data streams as well as power surveying. The goal is to diminish the general regression of network. However, the optimization problem is not curved, which makes it difficult to obtain optimal solution. The major goal of this document is to directly address that original non-convex formulation with enhanced Beetle Antennae Search (BAS) algorithm competently gets optimal solution. The simulations in delay, delivery ratio, drop, energy, network life, overload, and throughput are performed; the predictable result shows the improved BAS algorithm performs better convex approximation system. This plan is helpful for other complex optimization problems in WSN. [ABSTRACT FROM AUTHOR]

This paper studies the capacity of the two-user intensity-modulation/direct-detection (IM/DD) interference channel (IC), which is relevant in the context of multi-user optical wireless communications. Despite some known single-letter capacity characterizations for general discrete-memoryless ICs, a computable capacity expression for the IM/DD IC is missing. In this paper, we provide tight and easily computable inner and outer bounds for a general two-user IM/DD IC under peak and average optical intensity constraints. The bounds enable characterizing the asymptotic sum-rate capacity in the strong and weak interference regimes, as well as the generalized degrees of freedom (GDoF) in the symmetric case. Using the obtained bounds, the GDoF of the IM/DD IC is shown to have a ‘W’ shape similar to the Gaussian IC with power constraints. The obtained bounds are also evaluated numerically in different interference regimes to show their tightness, and used to study the performance of on-chip and indoor OWC systems. [ABSTRACT FROM AUTHOR]

End-to-end autoencoder (AE) learning has the potential of exceeding the performance of human-engineered transceivers and encoding schemes, without a priori knowledge of communication-theoretic principles. In this work, we aim to understand to what extent and for which scenarios this claim holds true when comparing with fair benchmarks. Our particular focus is on memoryless multiple-input multiple-output (MIMO) and multi-user (MU) systems. Four case studies are considered: two point-to-point (closed-loop and open-loop MIMO) and two MU scenarios (MIMO broadcast and interference channels). For the point-to-point scenarios, we explain some of the performance gains observed in prior work through the selection of improved baseline schemes that include geometric shaping as well as bit and power allocation. For the MIMO broadcast channel, we demonstrate the feasibility of a novel AE method with centralized learning and decentralized execution. Interestingly, the learned scheme performs close to nonlinear vector-perturbation precoding and significantly outperforms conventional zero-forcing. Lastly, we highlight potential pitfalls when interpreting learned communication schemes. In particular, we show that the AE for the considered interference channel learns to avoid interference, albeit in a rotated reference frame. After de-rotating the learned signal constellation of each user, the resulting scheme corresponds to conventional time sharing with geometric shaping. [ABSTRACT FROM AUTHOR]

Intelligent reflecting surface (IRS) is a promising 6G technology that can improve wireless communication capacity in a cost-effective and energy-efficient manner, by adjusting a large number of passive reflectors to appropriately change the signal propagation. In this study, we identified the achievable rate region of a two-hop interference channel with distributed multiple IRS relays. To do so, we formulated a non-convex problem that characterizes the rate-profile, and found its solution using successive convex approximation (SCA). We then proposed an alternating direction method of multipliers (ADMM) and alternating optimization (AO) based distributed and low-complex IRS control that maximizes the achievable sum-rate, and proved its convergence and optimality. We then compared the proposed IRS control with semi-definite relaxation (SDR)-, random phase-, deep reinforcement learning (DRL)- based IRS controls, and optimal amplify-and-forward (AF)-, interference neutralization (IN)-, and decode-and-forward (DF) based relaying schemes. We demonstrated that the proposed control with multiple IRS elements outperforms the benchmark controls in terms of the achievable rate region, achievable sum-rate, and energy efficiency under same power budget. We also confirmed that the discrete phase approximation of the proposed control provides near-optimal performance with fewer bits, and the proposed control is robust under imperfect CSI condition. The proposed controls can be efficiently applied to large-scale multi-pair multihop device-to-device and machine-type device communications in the interference-limited or low-powered dense networks of 5G and 6G environments. [ABSTRACT FROM AUTHOR]

Summary: Energy efficiency is one of the key challenges in fifth generation (5G) and beyond wireless communication systems. Recently, simultaneous wireless information and power transfer (SWIPT) systems are usually adopted to enhance energy efficiency. The harvested energy through power transfer is first rectified to generate the direct current (DC) voltage that is used to power the communication devices. This rectification reduces the energy efficiency due to losses in conversion to DC voltages. In this work, we consider integrated information decoding and energy harvesting receiver architecture through power splitting SWIPT network that enables alternating current (AC) computational logic. The AC computing method is used to supply the computing block of receivers to further enhance the energy efficiency. Multiuser multiple input single output (MISO) interference channel (IC) is considered, where receivers are required to compromise between quality of service (QoS) and energy consumption. The system sum rate is maximized under sum harvested energy and total transmit power constraints through joint beamforming and power splitting factors design. For the problem tractability in the proposed AC computing‐enabled receiver, standard beamforming schemes are deployed, namely, zero forcing (ZF), regularized zero forcing (RZF), maximum ratio transmission (MRT), and a hybrid scheme between MRT and ZF beamforming (MRT‐ZF). Moreover, the power splitting ratios design problem is transformed into a one dimension search problem. Simulation results show that different beamforming designs exhibit a great trade‐off between the achieved rate and the harvested energy. [ABSTRACT FROM AUTHOR]

In this paper, a deep learning (DL) framework for the optimization of the resource allocation in multi-channel cellular systems with device-to-device (D2D) communication is proposed. Thereby, the channel assignment and discrete transmit power levels of the D2D users, which are both integer variables, are optimized for maximization of the overall spectral efficiency whilst maintaining the quality-of-service (QoS) of the cellular users. Depending on the availability of channel state information (CSI), two different configurations are considered, namely 1) centralized operation with full CSI and 2) distributed operation with partial CSI, where in the latter case, the CSI is encoded according to the capacity of the feedback channel. Instead of solving the resulting resource allocation problem for each channel realization, a DL framework is proposed, where the optimal resource allocation strategy for arbitrary channel conditions is approximated by deep neural network (DNN) models. Furthermore, we propose a new training strategy that combines supervised and unsupervised learning methods and a local CSI sharing strategy to achieve near-optimal performance while enforcing the QoS constraints of the cellular users and efficiently handling the integer optimization variables based on a few ground-truth labels. Our simulation results confirm that near-optimal performance can be attained with low computation time, which underlines the real-time capability of the proposed scheme. Moreover, our results show that not only the resource allocation strategy but also the CSI encoding strategy can be efficiently determined using a DNN. Furthermore, we show that the proposed DL framework can be easily extended to communication systems with different design objectives. [ABSTRACT FROM AUTHOR]

Abstract Recent progress in the understanding of the behavior of the interference channel has led to valuable insights: first, discrete signaling has been discovered to have tangible benefits in the presence of interference, especially when one does not wish to decode the interfering signal, i.e., the interference is treated as noise, and second, the capacity of the interference channel as a function of the interference link gains is now understood to be highly irregular, i.e., non-monotonic and discontinuous. This work addresses these two issues in an integrated and interdisciplinary manner: it utilizes discrete signaling to approach the capacity of the interference channel by developing lower bounds on the mutual information under discrete modulation and treating interference as noise, subject to an outage set, and addresses the issue of sensitivity to link gains with a liquid metal reconfigurable antenna to avoid the aforementioned outage sets. Simulations illustrate the effectiveness of our approach.

In this paper, we consider the problem of joint beam selection and link activation across a set of communication pairs to effectively control the interference between communication pairs via inactivating part communication pairs in ultra-dense device-to-device (D2D) mmWave communication networks. The resulting optimization problem is formulated as an integer programming problem that is nonconvex and NP-hard. Consequently, the global optimal solution, even the local optimal solution, cannot be generally obtained. To overcome this challenge, this paper resorts to design a deep learning architecture based on graph neural network to finish the joint beam selection and link activation, with taking the network topology information into account. Meanwhile, we present an unsupervised Lagrangian dual learning framework to train the parameters of the GBLinks model. Numerical results show that the proposed GBLinks model can converge to a stable point with the number of iterations increases, in terms of the weighted sum rate. Furthermore, the GBLinks model can reach near-optimal solutions through comparing with the exhaustive scheme in small-scale ultra-dense D2D mmWave communication networks and outperforms GreedyNoSched and the SCA-based method. It also shows that the GBLinks model can generalize to varying network densities and network coverage regions of ultra-dense D2D mmWave communication networks. [ABSTRACT FROM AUTHOR]

This paper investigates the interference nulling capability of reconfigurable intelligent surface (RIS) in a multiuser environment where multiple single-antenna transceivers communicate simultaneously in a shared spectrum. From a theoretical perspective, we show that when the channels between the RIS and the transceivers have line-of-sight and the direct paths are blocked, it is possible to adjust the phases of the RIS elements to null out all the interference completely and to achieve the maximum $K$ degrees-of-freedom (DoF) in the overall $K$ -user interference channel, provided that the number of RIS elements exceeds some finite value that depends on $K$. Algorithmically, for any fixed channel realization we formulate the interference nulling problem as a feasibility problem, and propose an alternating projection algorithm to efficiently solve the resulting nonconvex problem with local convergence guarantee. Numerical results show that the proposed alternating projection algorithm can null all the interference if the number of RIS elements is only slightly larger than a threshold of $2K(K-1)$. For the practical sum-rate maximization objective, this paper proposes to use the zero-forcing solution obtained from alternating projection as an initial point for subsequent Riemannian conjugate gradient optimization and shows that it has a significant performance advantage over random initializations. For the objective of maximizing the minimum rate, this paper proposes a subgradient projection method which is capable of achieving excellent performance at low complexity. [ABSTRACT FROM AUTHOR]

In this paper, we propose one‐bit feedback‐based distributed beamforming (DBF) techniques for simultaneous wireless information and power transfer in interference channels where the information transfer and power transfer networks coexist in the same frequency spectrum band. In a power transfer network, multiple distributed energy transmission nodes transmit their energy signals to a single energy receiving node capable of harvesting wireless radio frequency energy. Here, by considering the Internet‐of‐Things sensor network, the energy harvesting/information decoding receivers (ERx/IRx) can report their status (which may include the received signal strength, interference, and channel state information) through one‐bit feedback channels. To maximize the amount of energy transferred to the ERx and simultaneously minimize the interference to the IRx, we developed a DBF technique based on one‐bit feedback from the ERx/IRx without sharing the information among distributed transmit nodes. Finally, the proposed DBF algorithm in the interference channel is verified through the simulations and also implemented in real time by using GNU radio and universal software radio peripheral.

This paper employs auxiliary receivers as a mathematical tool to identify Gallager-type auxiliary random variables and write outer bounds for some basic multiuser settings. This approach is then applied to the relay, interference, and broadcast channel settings, yielding new outer bounds that improve on existing outer bounds and strictly outperform classical outer bounds. For instance, we strictly improve on: the cutset outer bound for the scalar Gaussian relay channel, the outer bounds for the Gaussian Z-interference channel, and the outer bounds for the two receiver broadcast channel. [ABSTRACT FROM AUTHOR]

Blind Interference Alignment (BIA) schemes create and exploit channel coherence patterns without the knowledge of channel realizations at transmitters, while beamforming schemes rely primarily on channel knowledge available to the transmitters without regard to channel coherence patterns. In order to explore the compatibility of these disparate ideas and the possibility of synergistic gains, this work studies the Degrees of Freedom (DoF) of the 2-user ${(M_{1}\times N_{1})(M_{2}\times N_{2})}$ Multiple-Input Multiple-Output (MIMO) Interference Channel (IC) where Transmitter 1 is equipped with reconfigurable antennas and has no channel knowledge, while Transmitter 2 has partial channel knowledge but no reconfigurable antennas. Taking a fundamental dimensional analysis perspective, the main question is to identify which antenna configurations allow synergistic DoF gains. The main results of this work are two-fold. The first result identifies antenna configurations where both reconfigurable antennas and partial channel knowledge are individually beneficial, as those where $M_{1}< N_{1}< \min (M_{2},N_{2})$. The second result shows that synergistic gains exist in each of these settings, over the best known solutions that rely on either reconfigurable antennas or partial channel knowledge alone. Coding schemes that jointly exploit partial channel knowledge and reconfigurable antennas emerge as a byproduct of the analysis. [ABSTRACT FROM AUTHOR]

In the achievable rate region maximization for intelligent reflecting surfaces (IRSs)-assisted interference channel (IFC), where more than one pair of transceivers communicates and interferes with each other simultaneously. Specifically, we optimize the transmit power and passive beamforming vectors at all the IRSs to obtain the Pareto boundary of the achievable rate region in IFC, subject to the unit-modulus constraints for the passive beamforming vectors at the IRSs and transmit power budgets at the transmitters. To solve the formulated non-convex optimization problem efficiently, we resort to optimize each variable alternatively until convergence. The optimization of transmit power is based on a locally optimal fixed-point iteration method. Instead of using the conventional high-complexity and sub-optimal Gaussian randomization-aided semi-definite relaxation (SDR) technique for the passive beamforming design, we first propose a novel locally optimal homotopy optimization based majorization-minimization method. To further reduce the computational complexity, a locally optimal log-sum-exp approximation based manifold optimization method is also proposed. Simulation results validate that our proposed methods can significantly decrease the computational complexity and outperform the conventional SDR-based algorithm. [ABSTRACT FROM AUTHOR]

Topological interference management (TIM) can obtain degrees of freedom (DoF) gains with no channel state information at the transmitters (CSIT) except topological information of network in the interference channel. It was shown that TIM achieves symmetric DoF 1/2 when internal conflict does not exist among messages [7]. However, it is difficult to assure whether a specific topology can achieve symmetric DoF 1/2 without scrutinizing internal conflict. It is also hard to derive a specific topology directly from the conventional condition for symmetric DoF 1/2. Even except for the topology achieving symmetric DoF 1/2, topology achieving specific DoF less than 1/2 is not well known. With these problems in mind, we propose a method to derive all maximal topologies directly in TIM, named as alliance construction in K-user interference channel. That is, it is proved that a topology is maximal if and only if it is derived from alliance construction. Further we translate a topology design by alliance construction in the alignment-conflict graph into topology matrix and propose conditions for maximal topology matrix (MTM). Moreover, we propose a generalized alliance construction that derives topologies achieving DoF 1/n for n ≥ 3 by generalizing sub-alliances. A topology matrix can also be used to analyze topologies with DoF 1/n .

With the rapid development of wireless communication technologies, interference has become a key impediment in the improvement of network capacity. In practice, although dense deployment of network can effectively increase the spectrum efficiency, severe co-channel interference (CCI) is likely incurred, especially for mobile users in the overlapping areas of multiple access points, leading to serious deterioration of these users' spectral efficiency (SE). Interference steering (IS), emerging as a novel interference management (IM) scheme, can not only eliminate the impact of interference on the desired transmission, but also incur less power overhead than other IM methods. Although some works conducted flexible design of IS via power domain adaptation, the space domain flexibility has not been exploited. Moreover, existing IS works are mainly for the Z-interference channel (ZIC), while in practical use X-interference channel (XIC) is more common. To remedy the above deficiencies, this paper proposes a space-power adaptive interference steering (SPAIS) method in XIC. By adaptively adjusting the strength and spatial feature of the steering signal, SPAIS can not only effectively suppress interference imposed on the interfered receiver (Rx), but also make full use of the power of the steering signal, thus enhancing desired data transmission. Our in-depth simulation results show that compared to existing IM methods, SPAIS can significantly improve the users' SE performance.

Interference alignment (IA) and rate splitting (RS) are two promising interference processing techniques to handle the interference problems in the multi-input multi-output (MIMO) multi-cell cooperation scenario. However, under the imperfect channel state information (CSIT) in the practical systems, the poor robustness of IA has become a bottleneck of its performance gain. On the other hand, RS can provide a robust solution, yet its outage performance is limited by its SIC decoding process. To make up these two schemes' shortcomings and achieve the robust and reliable multi-cell cooperation, in this paper, we propose a novel transmission scheme, signal and interference alignment based rate splitting (SIA-RS), and provide comprehensively analyze the performance under CSIT quantization error. Closed-from expressions are derived for the average sum rate, outage probability and symbol error rate (SER) and their asymptotic versions in high signal-to-noise ratio (SNR) region. Due to signal alignment and common message, there exists certain correlation between the key variables. The nested finite weighted sum of independent and correlated Erlang random variables is used to approximate the exact expressions of performance. The relationship between “splitting” and “alignment” is revealed via the analytical derivation and numerical simulation. The simulation results show that the proposed SIA-RS achieves the best average sum rate compared with conventional IA and RS schemes, indicate that alignment would further reduce the outage probability, and suggest to the separate modulation schemes for common and private messages in terms of SER.

Deep learning (DL) based autoencoder (AE) has been proposed recently as a promising, and potentially disruptive approach to design the physical layer of beyond-5G communication systems. Compared to a traditional communication system with a multiple-block structure, the DL based AE approach provides a new paradigm to physical layer design with a pure data-driven and end-to-end learning based solution. In this article, we address the dynamic interference in a multi-user Gaussian interference channel. We show that standard constellation are not optimal for this context, in particular, for a high interference condition. We propose a novel adaptive DL based AE to overcome this problem. With our approach, dynamic interference can be learned and predicted, which updates the learning processing for the decoder. Compared to other machine learning approaches, our method does not rely on a fixed training function, but is adaptive and applicable to practical systems. In comparison with the conventional system using n-psk or n-QAM modulation schemes with zero force (ZF) and minimum mean square error (MMSE) equalizer, the proposed adaptive deep learning (ADL) based AE demonstrates a significant achievable BER in the presence of interference, especially in strong and very strong interference scenarios. The proposed approach has laid the foundation of enabling adaptable constellation for 5G and beyond communication systems, where dynamic and heterogeneous network conditions are envisaged.

The two-user Gaussian interference channel (G-IC) is revisited, with a particular focus on practically amenable discrete input signalling and treating interference as noise (TIN) receivers. The corresponding deterministic interference channel (D-IC) is first investigated and coding schemes that can achieve the entire capacity region of the D-IC under TIN are proposed. These schemes are then systematically translated into multi-layer superposition coding schemes based on purely discrete inputs for the real-valued G-IC. Our analysis shows that the proposed scheme is able to achieve the entire capacity region to within a constant gap for all channel parameters. To the best of our knowledge, this is the first constant-gap result under purely discrete signalling and TIN for the entire capacity region and all the interference regimes. Furthermore, the approach is extended to obtain coding schemes based on discrete inputs for the complex-valued G-IC. For such a scenario, the minimum distance and the achievable rate of the proposed scheme under TIN are analyzed, which takes into account the effects of random phase rotations introduced by the channels. Simulation results show that our scheme is capable of approaching the capacity region of the complex-valued G-IC and significantly outperforms Gaussian signalling with TIN in various interference regimes. [ABSTRACT FROM AUTHOR]

The wide utilization of frequency spectrum sharing causes problems such as interference as the result of active links' effects on each other. One of the proposed methods to decrease the interference in device-to-device networks is information-theoretic links scheduling (ITLinQ), where it schedules links by using information theory optimally conditions. In this paper, the idea of simultaneous using frequency sharing and reliable full-duplex relaying (FDR) is proposed. Although FDR causes self-interference when the same frequency spectrum is used for reception and transmission at the relays simultaneously, it is shown that the proposed system can increase the transmission rate and coverage area. Moreover, a relay selection scheme to select relays, and a power allocation mechanism between sources and relays to allocate appropriate power are presented. This is investigated by an analytical study to validate the superiority of the proposed method over the ITLinQ scheme in improving the total rate in the network. In addition, the simulation results confirm that by using FDR with the ITLinQ scheme, the better achievable sum-rate of users is obtained. [ABSTRACT FROM AUTHOR]

In this paper, we consider a wireless-powered two-way communication, called transmit-harvest-respond, with co-channel interference. The two-way communication considered here comprises three steps: i) transmitters send data signals, ii) receivers decode information and harvest energy simultaneously from the received signals using a policy of time switching (TS) or power splitting (PS), and iii) receivers transmit responses back to transmitters using this harvested energy. We aim to find the transmit power and energy harvesting ratios that maximize the sum rate of the forward links while ensuring a minimum rate requirement for each backward link. Due to the non-convexity and NP hardness of the optimization problem considered here, we first derive suboptimal solutions using an iterative algorithm (IA) on the basis of asymptotic strong duality. In view of the high computation time of the IA, we then design an efficient deep neural network (DNN) framework and novel training strategy as a means of combining supervised and unsupervised training. Specifically, DNNs are pre-trained using the suboptimal solutions obtained by the IA in a supervised manner, as a means of initialization; further training is then applied to DNNs using a well-designed loss function in an unsupervised manner to enhance performance. Simulation results reveal that the pre-training technique using IA solutions is beneficial for improving the performance of the DNN. The proposed hybrid scheme thus achieves near-optimal performances with a lower computation time, compared with the use of IA or DNN alone. [ABSTRACT FROM AUTHOR]

Recent progress in the understanding of the behavior of the interference channel has led to valuable insights: first, discrete signaling has been discovered to have tangible benefits in the presence of interference, especially when one does not wish to decode the interfering signal, i.e., the interference is treated as noise, and second, the capacity of the interference channel as a function of the interference link gains is now understood to be highly irregular, i.e., non-monotonic and discontinuous. This work addresses these two issues in an integrated and interdisciplinary manner: it utilizes discrete signaling to approach the capacity of the interference channel by developing lower bounds on the mutual information under discrete modulation and treating interference as noise, subject to an outage set, and addresses the issue of sensitivity to link gains with a liquid metal reconfigurable antenna to avoid the aforementioned outage sets. Simulations illustrate the effectiveness of our approach. [ABSTRACT FROM AUTHOR]

We study the covert communication over $K$ -user-pair discrete memoryless interference channels (DM-ICs) with a warden. It is assumed that the warden’s channel output distribution induced by $K$ “off” input symbols, which are sent when no communication occurs, is not a convex combination of those induced by any other combination of input symbols (otherwise, the square-root law does not hold). We derive the exact covert capacity region and show that a simple point-to-point based scheme with treating interference as noise is optimal. In addition, we analyze the secret key length required for the reliable and covert communication with the desired rates, and present a channel condition where a secret key between each user pair is unnecessary. The results are extended to the Gaussian case and the case with multiple wardens. [ABSTRACT FROM AUTHOR]

In wireless networks, link strengths are often affected by some topological factors such as propagation path loss, shadowing and inter-cell interference. Thus, different users in the network might experience different link strengths. In this work we consider a K-user asymmetric interference channel, where the channel gains of the links connected to Receiver k are scaled with √Pαk, k = 1,2, ⋅⋅⋅ , K , for 0 < α1 ≤ α2 ≤ ⋅⋅⋅ ≤ αK. For this setting, we show that the optimal sum generalized degrees-of-freedom (GDoF) is characterized as dsum = ∑k=1K αk + αK− αK−1/2 which matches the existing result d∑ = K/2 when α1 = α2 = ⋅⋅⋅ = αK = 1. The achievability is based on multi-layer interference alignment, where different interference alignment sub-schemes are designed in different layers associated with specific power levels, and successive decoding is applied at the receivers. While the converse for the symmetric case only requires bounding the sum degrees-of-freedom (DoF) for selected two users, the converse for this asymmetric case involves bounding the weighted sum GDoF for selected J + 2 users, with corresponding weights (2J, 2J−1, ⋅⋅⋅ , 22, 21), a geometric sequence with common ratio 2, for the first J users and with corresponding weights (1, 1) for the last two users, for J ∈ {1,2, ⋅⋅⋅ , ⌈log K/2⌉. [ABSTRACT FROM AUTHOR]

In this paper, we propose one‐bit feedback‐based distributed beamforming (DBF) techniques for simultaneous wireless information and power transfer in interference channels where the information transfer and power transfer networks coexist in the same frequency spectrum band. In a power transfer network, multiple distributed energy transmission nodes transmit their energy signals to a single energy receiving node capable of harvesting wireless radio frequency energy. Here, by considering the Internet‐of‐Things sensor network, the energy harvesting/information decoding receivers (ERx/IRx) can report their status (which may include the received signal strength, interference, and channel state information) through one‐bit feedback channels. To maximize the amount of energy transferred to the ERx and simultaneously minimize the interference to the IRx, we developed a DBF technique based on one‐bit feedback from the ERx/IRx without sharing the information among distributed transmit nodes. Finally, the proposed DBF algorithm in the interference channel is verified through the simulations and also implemented in real time by using GNU radio and universal software radio peripheral. [ABSTRACT FROM AUTHOR]

This paper explores how degrees of freedom (DoF) results from wireless networks can be translated into capacity or linear capacity results for their finite field counterparts that arise in network coding applications. The main insight is that scalar (SISO) finite field channels over \(\mathbb pn are analogous to n × n vector (MIMO) channels in the wireless setting, but with an important distinction - there is additional structure due to finite field arithmetic, which enforces commutativity of matrix multiplication and limits the channel diversity to n, making these channels similar to diagonal channels in the wireless setting. Within the limits imposed by the channel structure, the DoF optimal precoding solutions for wireless networks can be translated into capacity or linear capacity optimal solutions for their finite field counterparts. This is shown through the study of capacity of the 2-user X channel and linear capacity of the 3-user interference channel. Besides bringing the insights from wireless networks into network coding applications, the study of finite field networks over pn also touches upon important open problems in wireless networks (finite SNR, finite diversity scenarios) through interesting parallels between p and SNR, and n and diversity. © 2014 IEEE.

This paper considers a Gaussian interference channel (IC), where two transmitters send two messages to two receivers respectively. By utilizing the in-band full-duplex (FD) amplify-and-forward (AF) scheme, transmitter cooperations are deployed to enhance the system performance. In particular, each transmitter is capable of listening to its counterpart and then simultaneously transmits both the source signals to achieve higher power gain. Based on the proposed scheme, the equivalent channel model as well as the statistics of the accumulated residual self-interference and noise (ARIN) introduced by the transmitter cooperations and imperfect self-interference (SI) cancellation is analyzed. With the joint and single-user decoding schemes, the corresponding achievable rate regions are derived and a two-stage iterative algorithm is proposed to characterize these regions: in each step, fix the covariance matrix of the interferences and noises, and then adopt a two-step iterative semidefinite relaxation (SDR) method to optimize the two transmitters' transmission parameters, respectively. Simulation results reveal that the proposed scheme can significantly improve the achievable rate under several channel conditions.

Interference alignment (IA) is regarded as an important physical-layer interference management technique. Most research contributions on IA were focused on the analysis of its achievable spectral efficiency, either from the degrees of freedom or from the capacity perspective. Meanwhile, high energy-efficiency (EE) has become a key requirement for next-generation wireless communications. Hence, we focus our attention on the EE of IA in the fully connected K-user interference channel, where each user has either a single antenna or multiple antennas. We consider both perfect and imperfect channel state information scenarios. New insights into the achievable EE of IA are obtained by investigating the impact of different precoding matrices, the number of users, the number of antennas, symbol extension values, channel estimation accuracy, total transmit power, and power allocation schemes. In particular, we demonstrate that in the single-antenna-user case, the IA schemes relying on unequal power allocation achieves higher EE than their equal power allocation counterparts. However, in the scenario where each user is equipped with multiple antennas, equal power allocation achieves higher EE than unequal power allocation for the IA. Furthermore, using non-uniform precoding-matrix-generating vector w is not necessarily beneficial for improving the achievable EE of IA. We also find that the EE performance of IA with smaller symbol extension values is higher than that with larger symbol extension values, the achievable EE of IA decays with the increase of the total transmit power, the EE performance of IA degrades as the channel estimation accuracy becomes low and that having a larger number of transmit/receive antennas on each user achieves a higher EE in IA.

This paper optimizes the energy efficiency performance by joint time and beamforming weights allocation in a wireless body area network (WBAN), which consists of a multi-antenna access point (AP), in particular, the AP intends to broadcast radio frequency (RF) power to several single-antenna on-body sensors and receives physiological information from them synchronously. Different path loss and shadowing values are considered in each sensor's channel model to be more practical. In this paper, maximal ratio transmission (MRT) technique and zero-forcing (ZF) technique are applied in RF energy transmission and information decoding, respectively. Considering the signal-to-inference-plus-noise ratio (SINR) threshold of each sensor, the total rate-energy ratio is formulated as a nonconvex optimization problem in each time block, we transform the nonconvex optimization problem into a convex optimization problem and solved it by convex optimization tools. We investigate the energy efficiency performance and reliability performance of the optimal solutions, and numerical results demonstrate the effectiveness of the optimal solutions.

Interference alignment (IA) adjusts signaling scheme such that all interfering signals are squeezed in interference subspace, and obtains the maximum degrees of freedom in an interference channel. However, IA mostly achieves its performance via infinite extension of the channel, which is a major challenge in practical systems. In this paper, we schedule part of interference to be strong and achieve perfect IA within limited number of channel extensions. A single-hop 3 user single antenna interference channel (IFC) is considered and it is shown that one of the interfering signal streams needs to be strong so that perfect IA is feasible. Practical implementation of the proposed scheme is discussed in detail for the case of two extensions of the 3 user single antenna IFC. [ABSTRACT FROM AUTHOR]

In this article, we consider the joint optimization of transmit power and power splitting ratio to maximize the energy efficiency in a simultaneous wireless information and power transfer based interference channel, in which receivers use a power splitting policy to harvest energy from a wireless signal. We propose an optimization-based iterative algorithm (O-IA) from well-known optimization techniques as a comparative scheme, and also devise a neural network based learning algorithm (NN-LA) to deal with nonconvexity caused by cochannel interference among multiple nodes. Through simulations, we provide a comparative study of the two approaches in terms of energy efficiency and time complexity. In particular, we find that NN-LA achieves a near-optimal energy efficiency, whereas its time complexity is significantly reduced, in comparison with O-IA. [ABSTRACT FROM AUTHOR]

Abstract—In this letter, we characterize the degrees of freedom (DoF) of theK≥3user Gaussian interference network with a cognitive helper where each node is equipped with only one antenna. Specifically, each user sends one independent message to its corresponding receiver through its own antenna and via the help of the cognitive helper. For this network, we show that the sum DoF value is outer bounded by(K + 1)/2whenKis odd andK2/(2(K + 1))whenKis even, respectively. The new DoF outer bounds are derived based on the fact that collaboration among users does not decrease the capacity region and increasing the number of users does not increase the capacity per user. In addition, we provide a new achievable scheme to achieve a total of(K + 1)/2DoF for anyK≥3. Thus, the exact DoF value of the network is characterized with the total DoF given as(K + 1)/2, wheneverKis odd. The new achievable scheme is based on interference neutralization and asymptotic interference alignment.

We explore 5  network communication problems where the possibility of interference alignment, and consequently the total number of degrees of freedom (DoF) with channel uncertainty at the transmitters,  are unknown. These problems share the common property that in each case the best known outer bounds are essentially robust to channel uncertainty and represent the outcome with interference alignment, but the best inner bounds --- in some cases conjectured to be optimal ---   predict a total collapse of DoF, thus indicating the infeasibility of interference alignment under channel uncertainty at transmitters. Our main contribution is to introduce the idea of blind interference alignment. Specifically, we show that even with no knowledge of channel coefficient values at the transmitters, the knowledge of the channels' coherence structure can be exploited to achieve interference alignment. In each case, we show that under certain heterogeneous block fading models, i.e., when certain users experience smaller coherence time/bandwidth than others,  the transmitters are able to align interference without the knowledge of channel coefficient values.

A downlink beamforming design is proposed in multicell multi-input single-output downlink networks with limited backhaul signaling. In the proposed scheme, the beamforming vector at each base station (BS) is designed to minimize the sum of its weighted generating interference with local channel state information and the aid of information exchange between the BSs. The generating-interference weight coefficients are designed in pursuit of increasing the sum rate. Simulation results show that the proposed scheme outperforms the existing scheme in the mid-to-high signal-to-noise ratio regime even with much reduced amount of information exchange via backhaul.

In this paper, capacity bounds for the two-user interference channels with cooperative receivers via conferencing links of finite capacities are investigated. Capacity results known for these communication scenarios are limited to a very few special cases of the one-sided channels. One of the challenges in obtaining capacity limits of such cooperative networks is how to establish efficient capacity outer bounds for them. In this paper, by applying new techniques, novel capacity outer bounds are established for the interference channels with conferencing receivers. Using the outer bounds, several new capacity results are proved for interesting channels with unidirectional cooperation in strong and mixed interference regimes. A conferencing link (between receivers) may be utilized to provide a receiver with “information about its desired signal” or “information about its interfering signal”. It is demonstrated that both strategies can be helpful to achieve capacity. Finally, for the case of Gaussian interference channel with conferencing receivers, it is argued that our outer bound is strictly tighter than the previous one derived by Wang and Tse. [ABSTRACT FROM AUTHOR]

So-called improper complex signals have been shown to be beneficial in the single-antenna two-user Gaussian interference channel under the assumptions that all input signals are Gaussian and that we treat interference as noise (TIN). This result has been obtained under a restriction to pure strategies without time-sharing, and it was extended to the case where the rates, but not the transmit powers, may be averaged over several transmit strategies. In this paper, we drop such restrictions and discuss the most general case of coded time-sharing, where both the rates and the powers may be averaged. Since coded time-sharing can in general not be expressed by means of a convex hull of the rate region, we have to account for the possibility of time-sharing already during the optimization of the transmit strategy. By means of a novel channel enhancement argument, we prove a surprising result: proper signals are optimal if coded time-sharing is allowed. In addition to establishing this result, we present an algorithm to compute the corresponding achievable rate region. [ABSTRACT FROM AUTHOR]