This work focuses on the optimal placement of meta-surfaces (MTSs) onto the ceiling of an industrial manufacturing workshop. In particular, we assume that a total ofMMTSs are deployed, and that there areLpossible positions for each MTS. The resulting signal-to-noise (SNR) maximization problem is difficult to tackle directly because of the coupling between the placement decisions of the different MTSs. Mathematically, we are faced with a nonlinear discrete optimization problem withLMpossible solutions. A remarkable result shown in this paper is that the above challenging problem can be efficiently solved withinO(ML2log(ML)) time. There are two key steps in developing the proposed algorithm. First, we successfully decouple the placement variables of different MTSs by introducing a continuous auxiliary variable μ; the discrete primal variables are now easy to optimize when μ is held fixed, but the optimization problem of μ is nonconvex. Second, we show that the optimization of continuous μ can be recast into a discrete optimization problem with onlyLMpossible solutions, so the optimal μ can now be readily obtained. Numerical results show that the proposed algorithm can not only guarantee a global optimum but also reach the optimal solution efficiently.

This work proposes a mixed learning-based and optimization-based approach to the weighted-sum-rates beamforming problem in a multiple-input multiple-output (MIMO) wireless network. The conventional methods, i.e., the fractional programming (FP) method and the weighted minimum mean square error (WMMSE) algorithm, can be computationally demanding for two reasons: (i) they require inverting a sequence of matrices whose sizes are proportional to the number of antennas; (ii) they require tuning a set of Lagrange multipliers to account for the power constraints. The recently proposed method called the reduced WMMSE addresses the above two issues for a single cell. In contrast, for the multicell case, another recent method called the FastFP eliminates the large matrix inversion and the Lagrange multipliers by using an improved FP technique, but the update stepsize in the FastFP can be difficult to decide. As such, we propose integrating the deep unfolding network into the FastFP for the stepsize optimization. Numerical experiments show that the proposed method is much more efficient than the learning method based on the WMMSE algorithm.

Differing from the conventional communication system paradigm that models information source as a sequence of (i.i.d. or stationary) random variables, the semantic approach aims at extracting and sending the high-level features of the content deeply contained in the source, thereby breaking the performance limits from the statistical information theory. As a pioneering work in this area, the deep learning-enabled semantic communication (DeepSC) constitutes a novel algorithmic framework based on the transformer—which is a deep learning tool widely used to process text numerically. The main goal of this work is to extend the DeepSC approach from the point-to-point link to the multiuser multiple access channel (MAC). The interuser interference has long been identified as the bottleneck of the MAC. In the classic information theory, the successive interference cancellation (SIC) scheme is a common way to mitigate interference and achieve the channel capacity. Our main contribution is to incorporate the SIC scheme into the DeepSC. As opposed to the traditional SIC that removes interference in the digital symbol domain, the proposed semantic SIC works in the domain of the semantic word embedding vectors. Furthermore, to enhance the training efficiency, we propose a pretraining scheme and a partial retraining scheme that quickly adjust the neural network parameters when new users are added to the MAC. We also modify the existing loss function to facilitate training. Finally, we present numerical experiments to demonstrate the advantage of the proposed semantic approach as compared to the existing benchmark methods.

Deep learning draws heavily on the latest progress in semantic communications. The present paper aims to examine the security aspect of this cutting-edge technique from a novel shuffling perspective. Our goal is to improve upon the conventional secure coding scheme to strike a desirable tradeoff between transmission rate and leakage rate. To be more specific, for a wiretap channel, we seek to maximize the transmission rate while minimizing the semantic error probability under the given leakage rate constraint. Toward this end, we devise a novel semantic security communication system wherein the random shuffling pattern plays the role of the shared secret key. Intuitively, the permutation of feature sequences via shuffling would distort the semantic essence of the target data to a sufficient extent so that eavesdroppers cannot access it anymore. The proposed random shuffling method also exhibits its flexibility in working for the existing semantic communication system as a plugin. Simulations demonstrate the significant advantage of the proposed method over the benchmark in boosting secure transmission, especially when channels are prone to strong noise and unpredictable fading.

Configuring intelligent surface (IS) or passive antenna array without any channel knowledge, namely blind beamforming, is a frontier research topic in the wireless communication field. Existing methods in the previous literature for blind beamforming include the RFocus and the CSM, the effectiveness of which has been demonstrated on hardware prototypes. However, this paper points out a subtle issue with these blind beamforming algorithms: the RFocus and the CSM may fail to work in the non-line-of-sight (NLoS) channel case. To address this issue, we suggest a grouping strategy that enables adaptive blind beamforming. Specifically, the reflective elements (REs) of the IS are divided into three groups; each group is configured randomly to obtain a dataset of random samples. We then extract the statistical feature of the wireless environment from the random samples, thereby coordinating phase shifts of the IS without channel acquisition. The RE grouping plays a critical role in guaranteeing performance gain in the NLoS case. In particular, if we place all the REs in the same group, the proposed algorithm would reduce to the RFocus and the CSM. We validate the advantage of the proposed blind beamforming algorithm in the real-world networks at 3.5 GHz aside from simulations.

Facing the challenges of sparsity and long tail in thousands of Mini-apps recommendation scenarios deployed on Alipay platform, there is a great need for a simple, effective, and easy-to-deploy industrial solution. To address this issue, we follow the strategy of “divide and conquer” and propose a crowd-based recommendation model by using D eterminantal P oint P rocesse s on C rowd-wise M ixture- o f- E xperts (DPPs-CMoE). Specifically, under the guidance of DPPs-based prototypical tags, the user profiling space is sequentially divided into multiple crowds, with each of them taking on a unique latent specificity; Meanwhile, by treating the modeling of crowd specificity as one of multiple tasks, a crowd-wise architecture is adopted to seamlessly unify the multiple expert networks from the overall user space and the gating network from each of independent crowd spaces. The effectiveness of the proposed method has been illustrated in the experimental results on a mini-apps recommendation scenario deployed in Alipay APPs.

The beamforming problem of intelligent reflecting surface (IRS) has been extensively considered from an optimization perspective assuming that channel state information (CSI) is available. However, the reality is that the existing prototypes seldom follow this model-based approach because channel estimation is technically difficult and costly for the network protocols and hardware to date. A recent trend is to perform beamforming blindly without channel knowledge, e.g., the so-called CSM method [1], [2]. This work looks at blind beamforming from a reinforcement learning point of view. We first show that CSM boils down to a special case of the greedy algorithm in the reinforcement learning context. We analyze the resulting cumulative regret, and further propose an upper approximation to facilitate the optimization of the exploration probability. Moreover, we show that a gradient sampling scheme can improve the efficiency of reinforcement learning as compared to the uniform sampling scheme used in CSM. Finally, we validate the performance advantage of the proposed methods in a prototype system.

Conventional data compression methods typically model the information source as an i.i.d. stochastic process, thereby establishing the fundamental limit as entropy for lossless compression and as mutual information for lossy compression. However, the source in the real world (e.g., text, music, and speech) is often statistically ill-defined because of its close connection to human perception. This work aims to exploit the semantic aspect of text as inspired by the puzzle crossword. The main idea is to only compress those semantically important words while masking the rest; the proposed decompressor can recover all the missing words automatically according to context. Experiments show that the proposed semantic approach can achieve much higher compression efficiency than the state-of-the-art semantic compression method.

This work suggests a joint optimization of the aerial intelligent reflecting surface (AIRS) placement, passive beamforming, and base station (BS) association to improve the overall data throughput and fairness across downlink heterogeneous cellular networks. Differing from the related works in the literature that just seek to maximize signal-to-interference-plus-noise ratio (SINR), the paper takes into account the load balancing between macrocells and small cells. The resulting joint optimization problem is mixed continuous-discrete and has a highly bumpy landscape, so the traditional (sub)gradient-based tools are not suited. We propose a model-free approach based on adaptive particle swarm optimization (APSO) and blind beamforming, which recovers the solution from random explorations of the solution space. Simulations show that the proposed algorithm enables balanced traffic for the coexisting macro and small cells, and thereby achieves a higher network utility than the benchmark methods.

Fractional programming (FP) arises in various communications and signal processing problems because several key quantities in the field are fractionally structured, e.g., the Cramér-Rao bound, the Fisher information, and the signal-to-interference-plus-noise ratio (SINR). A recently proposed method called the quadratic transform has been applied to the FP problems extensively. The main contributions of the present paper are two-fold. First, we investigate how fast the quadratic transform converges. To the best of our knowledge, this is the first work that analyzes the convergence rate for the quadratic transform as well as its special case the weighted minimum mean square error (WMMSE) algorithm. Second, we accelerate the existing quadratic transform via a novel use of Nesterov's extrapolation scheme [2]. Specifically, by generalizing the minorization-maximization (MM) approach in [3], we establish a nontrivial connection between the quadratic transform and the gradient projection, thereby further incorporating the gradient extrapolation into the quadratic transform to make it converge more rapidly. Moreover, the paper showcases the practical use of the accelerated quadratic transform with two frontier wireless applications: integrated sensing and communication (ISAC) and massive multiple-input multiple-output (MIMO).

The Normalized cut (NCut) problem is a fundamental and yet notoriously difficult one in the unsupervised clustering field. Because the NCut problem is fractionally structured, the fractional programming (FP) based approach has worked its way into a new frontier. However, the conventional FP techniques are insufficient: the classic Dinkelbach's transform can only deal with a single ratio and hence is limited to the two-class clustering, while the state-of-the-art quadratic transform accounts for multiple ratios but fails to convert the NCut problem to a tractable form. This work advocates a novel extension of the quadratic transform to the multidimensional ratio case, thereby recasting the fractional 0-1 NCut problem into a bipartite matching problem---which can be readily solved in an iterative manner. Furthermore, we explore the connection between the proposed multidimensional FP method and the minorization-maximization theory to verify the convergence.

Fractional programming (FP) arises in various communications and signal processing problems because several key quantities in these fields are fractionally structured, e.g., the Cramér-Rao bound, the Fisher information, and the signal-to-interference-plus-noise ratio (SINR). A recently proposed method called the quadratic transform has been applied to the FP problems extensively. The main contributions of the present paper are two-fold. First, we investigate how fast the quadratic transform converges. To the best of our knowledge, this is the first work that analyzes the convergence rate for the quadratic transform as well as its special case the weighted minimum mean square error (WMMSE) algorithm. Second, we accelerate the existing quadratic transform via a novel use of Nesterov’s extrapolation scheme. Specifically, by generalizing the minorization-maximization (MM) approach, we establish a subtle connection between the quadratic transform and the gradient projection, thereby further incorporating the gradient extrapolation into the quadratic transform to make it converge more rapidly. Moreover, the paper showcases the practical use of the accelerated quadratic transform with two frontier wireless applications: integrated sensing and communications (ISAC) and massive multiple-input multiple-output (MIMO).

Intelligent surfaces (ISs) have emerged as a key technology to empower a wide range of appealing applications for wireless networks, due to their low cost, high energy efficiency, flexibility of deployment, and capability of constructing favorable wireless channels/radio environments. Moreover, the recent advent of several new IS architectures further expanded their electromagnetic functionalities from passive reflection to active amplification, simultaneous reflection, and refraction, as well as holographic beamforming. However, the research on ISs is still in rapid progress and there have been recent technological advances in ISs and their emerging applications that are worthy of a timely review. Thus, in this article, we provide a comprehensive survey on the recent development and advances of ISs-aided wireless networks. Specifically, we start with an overview on the anticipated use cases of ISs in future wireless networks such as 6G, followed by a summary of the recent standardization activities related to ISs. Then, the main design issues of the commonly adopted reflection-based IS and their state-of-the-art solutions are presented in detail, including reflection optimization, deployment, signal modulation, wireless sensing, and integrated sensing and communications. Finally, recent progress and new challenges in advanced IS architectures are discussed to inspire future research.

Intelligent reflecting surface (IRS) is an emerging technology to enhance spatial multiplexing in wireless networks. This letter considers the discrete passive beamforming design for IRS in order to maximize the minimum signal-to-interference-plus-noise ratio (SINR) among multiple users in an IRS-assisted downlink network. The main design difficulty lies in the discrete phase-shift constraint. Differing from most existing works, this letter advocates a convex-hull relaxation of the discrete constraints which leads to a continuous reformulated problem equivalent to the original discrete problem. This letter further proposes an efficient alternating projection/proximal gradient descent and ascent algorithm for solving the reformulated problem. Simulation results show that the proposed algorithm outperforms the state-of-the-art methods significantly.

Conventional policy for configuring an intelligent reflecting surface (IRS) typically requires channel state information (CSI), thus incurring substantial overhead costs and facing incompatibility with the current network protocols. This paper proposes a blind beamforming strategy in the absence of CSI, aiming to boost the minimum signal-to-noise ratio (SNR) among all the receiver positions, namely the coverage enhancement. Although some existing works already consider the IRS-assisted coverage enhancement without CSI, they assume certain position-channel models through which the channels can be recovered from the geographic locations. In contrast, our approach solely relies on the received signal power data, not assuming any position-channel model. We examine the achievability and converse of the proposed blind beamforming method. If the IRS has N reflective elements and there are U receiver positions, then our method guarantees the minimum SNR of$\Omega (N^{2}/U)$—which is fairly close to the upper bound$O(N+N^{2}\sqrt {\ln (NU)}/\sqrt [{4}]{U})$. Aside from the simulation results, we justify the practical use of blind beamforming in a field test at 2.6 GHz. According to the real-world experiment, the proposed blind beamforming method boosts the minimum SNR across seven random positions in a conference room by 18.22 dB, while the position-based method yields a boost of 12.08 dB.

In modern commercial search engines and recommendation systems, data from multiple domains is available to jointly train the multi-domain model. Traditional methods train multi-domain models in the multi-task setting, with shared parameters to learn the similarity of multiple tasks, and task-specific parameters to learn the divergence of features, labels, and sample distributions of individual tasks. With the development of large language models, LLM can extract global domain-invariant text features that serve both search and recommendation tasks. We propose a novel framework called S&R Multi-Domain Foundation, which uses LLM to extract domain invariant features, and Aspect Gating Fusion to merge the ID feature, domain invariant text features and task-specific heterogeneous sparse features to obtain the representations of query and item. Additionally, samples from multiple search and recommendation scenarios are trained jointly with Domain Adaptive Multi-Task module to obtain the multi-domain foundation model. We apply the S&R Multi-Domain foundation model to cold start scenarios in the pretrain-finetune manner, which achieves better performance than other SOTA transfer learning methods. The S&R Multi-Domain Foundation model has been successfully deployed in Alipay Mobile Application's online services, such as content query recommendation and service card recommendation, etc.

Deep learning and natural language processing draw heavily on the recent progress in semantic communications; this paper examines the security aspect of this cutting-edge technique. Our goal is to improve upon the conventional secure coding methods to strike a superior tradeoff between transmission rate and leakage rate. Toward this end, we devise a novel semantic security communication system wherein the random shuffling pattern serves as the secret key shared. Intuitively, the permutation of words in the same text via shuffling would result in the meaning distortion of the target text to such a great extent that an eavesdropper can no longer recover the semantic truth. The proposed method can be rephrased as maximizing the transmission rate while minimizing the semantic error probability under the given leakage rate constraint. Simulations demonstrate the significant advantage of the proposed method over the benchmark in boosting secure transmission, especially when channels are prone to strong noise and unpredictable fading, can achieve up to 60% performance gain.

A major challenge with the multi-ratio Fractional Program (FP) is that the existing methods for the maximization problem typically do not work for the minimization case. We propose a novel technique called inverse quadratic transform for the sum-of-ratios minimization problem. Its main idea is to reformulate the min-FP problem in a form amenable to efficient iterative optimization. Furthermore, this transform can be readily extended to a general cost-function-of-multiple-ratios minimization problem. We also give a Majorization-Minimization (MM) interpretation of the inverse quadratic transform, showing that all those desirable properties of MM can be carried over to the new technique. Moreover, we demonstrate the application of inverse quadratic transform in minimizing the Age-of-Information (AoI) of data networks.

Weighted minimum mean squared error (WMMSE) and fractional programming (FP) constitute two common approaches to the weighted sum-rate maximization in communication system design. One subtle issue with WMMSE and FP lies in the tuning of a Lagrange multiplier for the power constraint when it comes to the multi-antenna transmission. To obtain the optimal Lagrange multiplier, we must repeatedly inverse an M × M matrix, where M is the number of transmit antennas, which incurs considerable complexity. To address the above issue, this work explores the connection of WMMSE and FP to minorization-maximization (MM), thereby modifying the two methods to get rid of the Lagrange multiplier. The proposed algorithm enables a parameter-free iterative optimization of the beamforming vectors with the power constraint enforced automatically. Numerical results demonstrate the faster convergence of the proposed beamforming method as compared to the conventional WMMSE and FP methods.

Channel acquisition is a major challenge faced by the conventional beamforming methods when dealing with multiple intelligent reflecting surfaces (IRSs), because the number of unknown channels grows exponentially with the number of IRSs. This work proposes to sidestep channel estimation and to configure the IRSs blindly based on the statistical information which is extracted from a set of random samples of the received signal power. The proposed blind beamforming method has provable performance in terms of the signal-to-noise ratio (SNR) boost. For instance, it yields a quartic SNR boost of$\Theta(N^{4})$for a double-IRS system under certain condition, where$N$is the number of reflected elements of each IRS. We remark that the above$\Theta(N^{4})$result is more sophisticated than the existing ones about the double-IRS system in the literature. Furthermore, we numerically demonstrate the advantage of the proposed blind beamforming method through prototype tests with multiple IRSs.

Channel acquisition is a major bottleneck in fully exploiting the potential of intelligent reflecting surfaces (IRSs) to improve the wireless environment. In order to bypass such difficulty, an alternative is to optimize IRS based on the received signal statistics rather than channel state information (CSI), namely blind beamforming. The two recent methods, RFocus and conditional sample mean (CSM), fall into this category, both of which have been shown highly effective in practice. Nevertheless, we find a subtle drawback with the existing blind beamforming methods that they may not work well for the non-line-of-sight (NLoS) case for two reasons. First, many more signal samples are needed when the direct propagation diminishes. Second, if the direct propagation is completely blocked then the existing blind beamforming methods cannot work whatsoever. To address this issue, we propose an adaptive strategy for blind beamforming, which guarantees an approximation ratio of the global optimum. Field tests and simulations show that the proposed blind beamforming method is much more suited for NLoS environment than the existing ones.

The unprecedented growth of mobile data traffic has fueled the deployment of artificial intelligence (AI) at the network edge, while distilling the intelligence from raw data by machine learning requires tremendous labelling effort. To overcome this challenge, wireless crowd labelling (WCL) is proposed for efficient data labelling by exploiting billions of available mobile annotators and the multicasting property of wireless channels. A WCL system is considered in this paper where unlabelled data (objects) are multicast via fading channels to different clusters of annotators for repetition labelling to improve the accuracy. Given the desired labelling accuracy, the superposition coding technique together with the repetition labelling scheme give rise to a new tradeoff between radio-and-annotator resource consumption. Building on such tradeoff, the annotator clustering and transmit power control are jointly optimized to maximize the labelling throughput (i.e., the number of labelled objects) or minimize the power consumption, resulting in NP-hard integer programming problems. To solve these problems, the optimal structure of annotator clustering is derived by exploiting the property that the power allocation for multicasting objects tends to compensate for the worst channel among the annotators in each cluster. Based on such structure, the throughput maximization problem can be recognized as a longest-path problem and solved by means of branch-and-bound, while the power minimization problem can be recasted to a shortest-path problem and solved by means of forward dynamic programming. The solution approaches can be further simplified when the channels are symmetric by merging the same nodes and cutting the identical paths in the path graph. In addition, exact polices are derived for the special cases where either the annotators or power are constrained. Last, simulation results are presented to demonstrate the performance of our proposed joint designs.

This paper proposes a blind beamforming strategy for intelligent reflecting surface (IRS), aiming to boost the signal-to-noise ratio (SNR) by coordinating phase shifts across the reflective elements in the absence of channel information. Differing from most existing approaches that first estimate channels and then optimize phase shifts, the proposed blind beamforming method explores the wireless environment by extracting statistical features directly from random samples of the received signal power, without acquiring channel station information (CSI). This new method just requires a polynomial number of random samples to provide a quadratic SNR boost in the number of reflective elements without CSI, whereas the standard random-max sampling algorithm can only achieve a linear boost under the same condition. Moreover, we interpret blind beamforming from a least-squares point of view. Field tests demonstrate the significant advantages of the proposed blind beamforming approach over the benchmark methods in enhancing wireless transmission.

This paper studies the joint device selection and power control for wireless federated learning (FL), considering both the analog downlink and over-the-air computation (AirComp)-based uplink communications between the parameter server (PS) and the terminal devices. First, we propose an AirComp-based adaptive reweighing scheme for the aggregation of local updated models, where the model aggregation weights are directly determined by the uplink transmit power values of the selected devices. Furthermore, we provide a convergence analysis for the proposed wireless FL algorithm and derive the upper bound on the expected optimality gap between the expected and optimal global loss values with respect to (w.r.t.) the selected devices and downlink and uplink transmit power values. With instantaneous channel state information (CSI), we formulate the optimality gap minimization problem, which is solved by using the semidefinite programming (SDR) technique. Numerical results reveal that our proposed wireless FL algorithm achieves close to the best performance by using the ideal FedAvg scheme with error-free model exchange and full device participation.

Recently, modeling temporal patterns of user-item interactions have attracted much attention in recommender systems. We argue that existing methods ignore the variety of temporal patterns of user behaviors. We define the subset of user behaviors that are ir- relevant to the target item as noises, which limits the performance of target-related time cycle modeling and affect the recommendation performance. In this paper, we propose Denoising Time Cycle Modeling (DiCycle), a novel approach to denoise user behaviors and select the subset of user behaviors that are highly related to the target item. DiCycle is able to explicitly model diverse time cycle patterns for recommendation. Extensive experiments are conducted on both public benchmarks and a real-world dataset, demonstrating the superior performance of DiCycle over the state-of-the-art recommendation methods.

This paper studies the joint device selection and power control scheme for wireless federated learning (FL), considering both the downlink and uplink communications between the parameter server (PS) and the terminal devices. In each round of model training, the PS first broadcasts the global model to the terminal devices in an analog fashion, and then the terminal devices perform local training and upload the updated model parameters to the PS via over-the-air computation (AirComp). First, we propose an AirComp-based adaptive reweighing scheme for the aggregation of local updated models, where the model aggregation weights are directly determined by the uplink transmit power values of the selected devices and which enables the joint learning and communication optimization simply by the device selection and power control. Furthermore, we provide a convergence analysis for the proposed wireless FL algorithm and the upper bound on the expected optimality gap between the expected and optimal global loss values is derived. With instantaneous channel state information (CSI), we formulate the optimality gap minimization problems under both the individual and sum uplink transmit power constraints, respectively, which are shown to be solved by the semidefinite programming (SDR) technique. Numerical results reveal that our proposed wireless FL algorithm achieves close to the best performance by using the idealFedAvgscheme with error-free model exchange and full device participation.

Hybrid automatic repeat request (HARQ) is a key enabler for ultrareliable communications. This paper optimizes transmit power for the initial transmission and the subsequent retransmissions of HARQ with either incremental redundancy or Chase combining, aiming to minimize the expected energy consumption given the target outage probability and the target latency. The main challenge is due to the fact that the outage probability is a complicated function of the power variables which are nested in successive convolutions. The existing works mostly use a classic upper bound to approximate the outage probability by assuming unbounded transmit power, then convert the original problem to a geometric programming (GP) problem. In contrast, we propose a novel and much tighter upper bound by taking the practical power limit into consideration. The new bound and the resulting new GP method are further extended to a broader group of channel models with various fading, multiple antennas, and multiple receivers. As shown in simulations, the GP method based on the new bound significantly outperforms the existing strategies that either fix transmit power or optimize power by the classic bounding technique.

This work pursues an optimal strategy of designing passive beamformer for intelligent reflecting surface (IRS) in order to maximize the overall channel strength. In particular, the choice of phase shift for each reflective element is restricted to$K \geq 2$discrete values. Although the resulting discrete beamforming problem is believed to be NP-hard in some prior works, the paper shows that the global optimum of the binary case with$K=2$can be achieved in linear time. For a general$K$-ary beamforming problem with$K > 2$, the state-of-the-art polynomial time algorithm is to greedily project the relaxed solution to the closest point in the constraint set. However, as shown in the paper, the performance of this greedy method cannot be guaranteed. In contrast, we propose a linear time algorithm that is capable of reaching a near-optimal solution with an approximation ratio of$(1+\cos(\pi/K))/2$, i.e., its performance is at least 75% of the global optimum for$K\geq 3$. Furthermore, inspired by the RFocus method in [1], we develop a statistic implementation of the above approximation algorithm in the absence of channel state information (CSI).

This paper aims to characterize the capacity limits of a wireless cellular network with a full-duplex (FD) base-station (BS) and half-duplex user terminals, in which three independent messages are communicated: the uplink message m1from the uplink user to the BS, the downlink message m2from the BS to the downlink user, and the device-to-device (D2D) message m3from the uplink user to the downlink user. From an information theoretical perspective, the overall network can be viewed as a generalization of the FD relay broadcast channel with a side message transmitted from the relay to the destination. We begin with a simpler case that involves the uplink and downlink transmissions of (m1, m2) only, and propose an achievable rate region based on a novel strategy that uses the BS as a FD relay to facilitate the interference cancellation at the downlink user. We also prove a new converse, which is strictly tighter than the cut-set bound, and characterize the capacity region of the scalar Gaussian FD network without a D2D message to within a constant gap. This paper further studies a general setup wherein (m1, m2, m3) are communicated simultaneously. To account for the D2D message, we incorporate Marton's broadcast coding into the previous scheme to obtain a larger achievable rate region than the existing ones in the literature. We also improve the cut-set bound by means of genie and show that by using one of the two simple rate-splitting schemes, the capacity region of the scalar Gaussian FD network with a D2D message can already be reached to within a constant gap. Finally, a generalization to the vector Gaussian channel case is discussed. Simulation results demonstrate the advantage of using the BS as relay in enhancing the throughput of the FD cellular network.

Robust optimization is an important task in wireless communications, because due to fading and feedback delay there is inherent uncertainty in channel state information in a wireless environment. This paper aims to show that a deep learning approach for network utility maximization can produce more robust solutions than the traditional model-based approach. We focus on the classic power control problem for sum-rate maximization in a wireless network with multiple interfering links. By injecting samples of random channel realizations into the unsupervised training process, the neural network is able to learn to adapt to the uncertain channel environment.

The massive sensing data generated by Internet-of-Things will provide fuel for ubiquitous artificial intelligence (AI), while tremendous labels are required for AI model training via supervised learning. To tackle this challenge, a novel framework of wireless crowd labelling is proposed that downloads data to many imperfect mobile annotators for repetition labelling by exploiting multicasting in wireless networks. The integration of the rate-distortion theory and the principle of repetition labelling gives rise to a new tradeoff between radio-and-annotator resources under a constraint on labelling accuracy. Aiming at maximizing the labelling throughput, this work focuses on optimizing the joint annotator-and-spectrum allocation (JASA). To develop an efficient solution approach, an optimal sequential annotator-clustering scheme is derived. Thereby, the optimal JASA policy can be found by an efficient tree search.

In this article, we propose a sparse hybrid combining (SHC) scheme for the uplink transmission of beamspace massive multiple-input multiple-output (MIMO) system with low-resolution analog to digital converters (LADCs), to alleviate the performance bottleneck caused by the multi-user interference and quantization noise, with reduced hardware cost and power consumption. To this end, we formulate the optimization of the proposed SHC scheme as a system energy efficiency maximization problem under some practical constraints. The resulting problem contains the highly coupled nonconvex objective function, as well as the discrete binary constraints. By exploiting some fractional programming (FP) techniques and introducing auxiliary variables, we first recast the original challenging problem into a more tractable yet equivalent form. We then develop an efficient double-loop iterative algorithm based on the penalty dual decomposition (PDD) method to find its local stationary solutions. Finally, simulation results verify the effectiveness of the proposed SHC scheme by numerical examples in terms of the achieved system energy efficiency.

Symbiotic radio (SR) is emerging as a spectrum-and energy-efficient communication paradigm for future passive Internet of Things (IoT), where some single-antenna backscatter devices, referred to as Tags, are parasitic in an active primary transmission. The primary transceiver is designed to assist both direct-link (DL) and backscatter-link (BL) communication. In multi-Tags SR systems, the transceiver designs become much more complicated due to the presence of DL and inter-Tag interference, which further poses new challenges to the availability and reliability of DL and BL transmission. To overcome these challenges, we formulate the stochastic optimization of transceiver design as the general network utility maximization problem (GUMP). The resultant problem is a stochastic multiple-ratio fractional nonconvex problem, and consequently challenging to solve. By leveraging some fractional programming techniques, we tailor a surrogate function with the specific structure and subsequently develop a batch stochastic parallel decomposition (BSPD) algorithm, which is shown to converge to stationary solutions of the GNUMP. The simulation results verify the effectiveness of the proposed algorithm by numerical examples in terms of the achieved system throughput.

Pilot contamination is a limiting factor in multicell massive multiple-input multiple-output (MIMO) systems because it can severely impair channel estimation. Prior works have suggested coordinating pilot design across cells in order to reduce the channel estimation error caused by pilot contamination. In this paper, we propose a method for coordinated pilot design using fractional programming to minimize the weighted mean squared-error (MSE) in channel estimation. In particular, we apply the recently proposed quadratic transform to the MSE expression which allows the effect of pilot contamination to be decoupled. The resulting problem reformulation enables the pilots to be optimized in closed form if they can be designed arbitrarily. When the pilots are restricted to a given set of orthogonal sequences, pilot optimization reduces to an assignment problem which can be solved by weighted bipartite matching. Furthermore, we consider the max-min fairness of data rates with orthogonal pilots and obtain an extension of the proposed method to correlated Rayleigh fading. Finally, simulations demonstrate the advantage of the proposed (orthogonal and nonorthogonal) pilot designs as compared with state-of-the-art methods in combating pilot contamination.

The massive sensing data generated by Internet-of-Things will provide fuel for ubiquitous artificial intelligence (AI), automating the operations of our society ranging from transportation to healthcare. The implementation of ubiquitous AI, however, entails labelling of an enormous amount of data prior to the training of AI models via supervised learning. To tackle this challenge, we explore a new direction called wireless crowd labelling, which involves downloading data to many imperfect mobile annotators for repetition labelling with an aim of exploiting multicasting in wireless networks. In this cross-disciplinary area, the rate-distortion theory and the principle of repetition labelling for accuracy improvement together give rise to a new tradeoff between radio-and-annotator resources under a constraint on labelling accuracy. Building on the tradeoff and aiming at maximizing the labelling throughput, this work focuses on the joint optimization of encoding rate, annotator clustering, and sub-channel allocation, which results in an NP-hard integer programming problem. To devise an efficient solution approach, we establish an optimal sequential annotator-clustering scheme based on the order of decreasing signal-to-noise ratios, thereby allowing the optimal solution to be found by an efficient tree search. This solution can be further simplified when the channels are symmetric. Alternatively, the optimization problem can be recognized as a knapsack problem, which can be efficiently solved in pseudo-polynomial time by means of dynamic programming. In addition, the optimal polices are derived for the annotator constrained and spectrum constrained cases. Last, simulation results are presented to demonstrate the significant throughput gains based on the optimal solution compared with decoupled allocation of the two types of resources.

Massive connectivity is regarded as a key requirement for future networks to support new communication paradigms, where the human-type communications coexist with machine-type communications. Owing to the limited coherence time but the huge number of potential devices, it is impossible to allocate mutually orthogonal pilot sequence for all potential devices, which may impose severe interference on the device activity detection and channel estimation. Existing nonorthogonal pilot design methods for conventional cellular network are not suitable for the massive connectivity regime. To overcome this challenge, we first formulate the pilot sequences design as an optimization problem to minimize the average mean square error (MSE) of channel estimation under the individual power constraint. The proposed optimization problem is nonconvex and highly coupled. By exploiting some approximation techniques, we convert the problem into a more tractable form and subsequently develop a distributed algorithm based on the matrix fractional programming (FP) and the alternating direction method of multipliers (ADMM) methods. Simulations validates that the proposed scheme not only achieves significant gains in channel estimation over state-of-the-art baseline schemes, but also improves the device activity detection performance.

Pilot contamination is a main limiting factor in multi-cell massive multiple-input multiple-output (MIMO) systems due to the non-orthogonality of pilot sequences which can seriously impair the channel measurement. Recent work has suggested coordinating pilot sequence design across multiple cells by choosing the sequences to minimize the channel estimation error. This paper further investigates this approach using a new optimization framework. Specifically, we reformulate the weighted minimum mean-squared error (MMSE) measure as a sum-of-functions-of-matrix-ratio program that can be efficiently solved via a matrix fractional programming approach. The proposed algorithm provides fast convergence to a stationary-point solution of the MMSE problem. Simulations demonstrate the advantage of the proposed method over alternative approaches in enhancing channel estimation accuracy.

This paper examines the achievable rate region and the converse of a full-duplex relay broadcast channel with three independent messages: from the source to the relay, from the source to the destination, and from the relay to the destination. We are motivated to study this channel, because it models a full-duplex wireless cellular network in which the uplink user also wishes to send an independent device-to-device message to the downlink users. For the discrete memoryless channel case, we incorporate Marton's broadcast coding to obtain a new achievable rate region which is larger than previous rate regions. We further propose a tighter converse than the cut-set bound. For the Gaussian scalar channel case, we show that by using one of two rate-splitting schemes depending on the channel condition, we can already achieve the capacity region of this particular relay broadcast channel to within a constant gap. The proposed scheme outperforms the benchmark methods in terms of the symmetric generalized degree-of-freedom.

The optimal scheduling of interfering links in a dense wireless network with full frequency reuse is a challenging task. The traditional method involves first estimating all the interfering channel strengths and then optimizing the scheduling based on the model. This model-based method is, however, resource intensive and computationally hard because channel estimation is expensive in dense networks; furthermore, finding even a locally optimal solution of the resulting optimization problem may be computationally complex. This paper shows that by using a deep learning approach, it is possible to bypass the channel estimation and to schedule links efficiently based solely on the geographic locations of the transmitters and the receivers due to the fact that in many propagation environments, the wireless channel strength is largely a function of the distance-dependent path-loss. This is accomplished by unsupervised training over randomly deployed networks and by using a novel neural network architecture that computes the geographic spatial convolutions of the interfering or interfered neighboring nodes along with subsequent multiple feedback stages to learn the optimum solution. The resulting neural network gives a near-optimal performance for sum-rate maximization and is capable of generalizing to larger deployment areas and to deployments of different link densities. Moreover, to provide fairness, this paper proposes a novel scheduling approach that utilizes the sum-rate optimal scheduling algorithm over judiciously chosen subsets of links for maximizing a proportional fairness objective over the network. The proposed approach shows highly competitive and generalizable network utility maximization results.

Interference management is a fundamental issue in device-to-device (D2D) communications whenever the transmitter-and-receiver pairs are located in close proximity and frequencies are fully reused, so active links may severely interfere with each other. This paper devises an optimization strategy named FPLinQ to coordinate the link scheduling decisions among the interfering links, along with power control and beamforming. The key enabler is a novel optimization method called matrix fractional programming (FP) that generalizes previous scalar and vector forms of FP in allowing multiple data streams per link. From a theoretical perspective, this paper provides a deeper understanding of FP by showing a connection to the minorization-maximization (MM) algorithm. From an application perspective, this paper shows that as compared to the existing methods for coordinating scheduling in the D2D network, such as FlashLinQ, ITLinQ, and ITLinQ+, the proposed FPLinQ approach is more general in allowing multiple antennas at both the transmitters and the receivers, and further in allowing arbitrary and multiple possible associations between the devices via matching. Numerical results show that FPLinQ significantly outperforms the previous state-of-the-art in a typical D2D communication environment.

The optimal scheduling of multiple interfering links in a densely deployed wireless network with full frequency reuse is a well-known challenging problem. The classical optimization approaches to this problem typically operate under the paradigm of first estimating all the interfering channel strengths then finding an optimum solution using the model. However, traditional scheduling methods are computationally and resource intensive, because channel estimation is expensive especially in dense networks, and further the optimization of link scheduling is typically a nonconvex problem. This paper takes a novel deep spatial learning approach to the scheduling problem. We show that it is possible to bypass the channel estimation stage altogether and to use a deep neural network to produce a near optimal schedule based solely on geographic locations of the transmitters and receivers in the network. This is accomplished by taking advantage of the recent advances in fractional programming that allows us to generate high- quality local optimum solutions to the scheduling problem for randomly deployed device-to-device networks as training data, and by using a novel neural network architecture that takes the geographic spatial convolutions of the interfering and interfered neighboring nodes as input over multiple feedback stages to learn the optimum solution.

This paper explores the information theoretical capacity limits of uplink-downlink transmissions in a wireless cellular network with full-duplex FD base station (BS) and half-duplex user terminals. We recognize the cross-channel interference between the terminals as the main capacity bottleneck, and propose novel strategies that use BS as a relay to facilitate interference cancellation. We model the FD cellular system as a two-user interference channel with an extra cross-link feedback from the uplink receiver to the downlink transmitter, and show that the feedback allows a larger achievable rate region than the conventional non-feedback schemes. This paper further provides a converse and shows that the proposed scheme achieves the capacity of the full-duplex cellular network to within a constant additive gap. Finally, this paper considers a new scenario in which the uplink terminal has additional information to transmit to the downlink terminal directly. Relaying by the BS is shown to play a crucial role in maximizing the achievable rates in this case.

Mutual interference is a key obstacle in the realistic adoption of full-duplex (FD) technique in future wireless cellular networks. Interference is a much more pressing problem for FD system than for the conventional half-duplex (HD) system, because FD allows the same time-frequency resource to be used for both uplink and downlink, thus possibly creating myriad interference between multiple transmissions throughout the network. Without proper interference control, FD may not even outperform HD in a multicell setup. The main objective of this paper is to show that coordinated scheduling and power control enables wireless cellular networks to reap significant system-level performance improvement due to FD. Toward this end, this paper utilizes fractional programming to derive a sequence of convex reformulations that allow distributed and efficient iterative optimization. Numerical results suggest that the proposed system-level interference management can provide 30-40% rate gain for an optimized FD multicell network as compared to optimized HD.

Interference management is a fundamental problem for the device-to-device (D2D) network, in which transmitter and receiver pairs may be arbitrarily located geographically with full frequency reuse, so active links may severely interfere with each other. This paper devises a new optimization strategy called FPLinQ that coordinates link scheduling decisions together with power control among the interfering links throughout the network. Scheduling and power optimization for the interference channel are challenging combinatorial and nonconvex optimization problems. This paper proposes a fractional programming (FP) approach that derives a problem reformulation whereby the optimization variables are determined analytically in each iterative step. As compared to the existing works of FlashLinQ, ITLinQ and ITLinQ+, a merit of the proposed strategy is that it does not require tuning of design parameters. FPLinQ shows significant performance advantage as compared to the benchmarks in maximizing system throughput in a typical D2D network.

This letter shows that the flexible association of possibly multiple base stations (BSs) with each user over multiple frequency bands, along with the joint optimization of BS transmit power that encourages the BSs to turn off at off-peak time, can significantly improve the performance of a downlink heterogeneous wireless cellular network. We propose a gradient projection algorithm for optimizing BS association and an iteratively reweighting scheme together with a novel proximal gradient method for optimizing power in order to find the optimal tradeoff between network utility and power consumption. Simulation results reveal significant performance improvement as compared to the conventional single-BS association.

This paper proposes a joint uplink user scheduling and beam-forming algorithm for a multiple-antenna wireless cellular network. We show that coordinated optimization across the cells can significantly alleviate intercell interference, thereby improving the cell-edge rates in a multicell network. Unlike the downlink case, coordinating uplink transmission in a multicell network is significantly more challenging, because uplink interference depends strongly on the schedule and beamformers of neighboring cells. The main contribution of this paper is the recasting of the problem in terms of sum-of-ratio programming and a subsequent quadratic reformulation which allows scheduling and beamforming to be optimized through solving a matching problem. This problem reformulation also provides a new interpretation of the well-known weighted minimum mean square error (WMMSE) algorithm. Simulation results show that the proposed approach significantly outperforms both the WMMSE algorithm and the existing uncoordinated scheduling approach.

This paper considers optimization of the user and base-station (BS) association in a wireless downlink heterogeneous cellular network under the proportional fairness criterion. We first consider the case where each BS has a single antenna and transmits at fixed power and propose a distributed price update strategy for a pricing-based user association scheme, in which the users are assigned to the BS based on the value of a utility function minus a price. The proposed price update algorithm is based on a coordinate descent method for solving the dual of the network utility maximization problem and it has a rigorous performance guarantee. The main advantage of the proposed algorithm as compared to an existing subgradient method for price update is that the proposed algorithm is independent of parameter choices and can be implemented asynchronously. Further, this paper considers the joint user association and BS power control problem and proposes an iterative dual coordinate descent and the power optimization algorithm that significantly outperforms existing approaches. Finally, this paper considers the joint user association and BS beamforming problem for the case where the BSs are equipped with multiple antennas and spatially multiplex multiple users. We incorporate dual coordinate descent with the weighted minimum mean-squared error (WMMSE) algorithm and show that it achieves nearly the same performance as a computationally more complex benchmark algorithm (which applies the WMMSE algorithm on the entire network for BS association) while avoiding excessive BS handover.

This paper considers the optimal association of remote user terminals to different cells in a heterogeneous network for load balancing. Assuming fixed transmit powers at the base-stations, we adopt a network utility maximization formulation with a proportional fairness objective and show that the downlink user association problem can be solved efficiently using a pricing approach where the prices are updated in the dual domain via coordinate descent. As compared to the previously proposed subgradient method, the proposed coordinate descent algorithm does not require the base-stations to synchronize in their price updates, while still guaranteeing convergence, which makes it particularly suitable for distributed implementation. Simulations show that the proposed method has fast convergence while achieving near-optimal solution.