The recently proposed orthogonal delay-Doppler division multiplexing (ODDM) modulation, which is a delay-Doppler (DD) domain multi-carrier (DDMC) modulation scheme based on the DD domain orthogonal pulse (DDOP), is studied. We first revisit the linear time-varying (LTV) channel model for the wireless channel, and review the conventional multi-carrier (MC) modulation schemes and their design guidelines for both linear time-invariant (LTI) and LTV channels. We then focus on the representation of the LTV channel in an equivalent sampled DD (ESDD) domain, and propose an impulse-function-based transmission strategy for the ESDD channel. Next, we take an in-depth look into the DDOP and show that it achieves orthogonality with respect to the fine time and frequency resolutions in the ESDD domain thusbehaves likean impulse function. This allows us to unveil the unique input-output relation of the resultant ODDM modulation over the ESDD channel. We point out that the conventional MC modulation design guidelines based on the Weyl-Heisenberg (WH) frame theory can be relaxed without compromising its orthogonality or violating the WH frame theory. More specifically, for a practical communication system with bandwidth and duration constraints, MC modulation signals can be designed considering so-calledlocal or sufficient (bi)orthogonality,which refers to the (bi)orthogonality among a WH subset for the MC signal within a specific bandwidth and duration. This is different from the conventional MC modulation waveform design guidelines (such as for orthogonal frequency division multiplexing and orthogonal time frequency space) based on the global (bi)orthogonality, which is the (bi)orthogonality among a WHfull setcorresponding to the MC signal occupying the entire TF domain. This novel design guideline could potentially open up opportunities for developing future waveforms required by new applications such as communication systems associated with high delay and/or Doppler shifts, as well as integrated sensing and communications.

This paper proposes an approach that leverages multimodal data by integrating visual images with radio frequency (RF) pilots to optimize user association and beamforming in a downlink wireless cellular network under a max-min fairness criterion. Traditional methods typically optimize wireless system parameters based on channel state information (CSI). However, obtaining accurate CSI requires extensive pilot transmissions, which lead to increased overhead and latency. Moreover, the optimization of user association and beamforming is a discrete and non-convex optimization problem, which is challenging to solve analytically. In this paper, we propose to incorporate visual camera data in addition to the RF pilots to perform the joint optimization of user association and beamforming. The visual image data help enhance channel awareness, thereby reducing the dependency on extensive pilot transmissions for system optimization. We employ a learning-based approach based on using first a detection neural network that estimates user locations from images, and subsequently two graph neural networks (GNNs) that extract features for system optimization based on the location information and the received pilots, respectively. Then, a multimodal GNN is constructed to integrate the features for the joint optimization user association and beamforming. Simulation results demonstrate that the proposed method achieves superior performance, while having low computational complexity and being interpretable and generalizable, making it an effective solution as compared to traditional methods based only on RF pilots.

This paper studies an uplink dual-functional sensing and communication system aided by a reconfigurable intelligent surface (RIS), whose reflection pattern is optimally configured to trade-off sensing and communication functionalities. Specifically, the Bayesian Cramér-Rao lower bound (BCRLB) for estimating the azimuth angle of a sensing user is minimized while ensuring the signal-to-interference-plus-noise ratio constraints for communication users. We show that this problem can be formulated as a novel fractionally constrained fractional programming (FCFP) problem. To deal with this highly nontrivial problem, we extend a quadratic transform technique, originally proposed to handle optimization problems containing fractional structures only in objectives, to the scenario where the constraints also include ratios. First, we consider the case where the fading coefficient is known. Using the quadratic transform, the FCFP problem can be turned into a sequence of subproblems that are convex except for the constant-modulus constraints which can be tackled using a penalty-based approach. To further reduce the computational complexity, we leverage the constant-modulus conditions and propose a novel linear transform. This new transform enables the FCFP problem to be turned into a sequence of linear programming (LP) subproblems, which can be solved with linear complexity in the dimension of reflecting elements. Then, we consider the case where the fading coefficient is unknown. A modified BCRLB is used to make the problem more tractable, and the proposed quadratic transform-based algorithm is used to solve the problem. Numerical results unveil nontrivial and effective reflection patterns that can be synthesized by the RIS to facilitate both communication and sensing functionalities.

This paper presents a novel optimization framework for solving active sensing problems in wireless communications, in which a base station equipped with massive multiple-input multiple-output (MIMO) and a limited number of radio-frequency chains aims to estimate the channel parameters of a sensing target. Specifically, the receive beamforming matrix at the BS is designed sequentially through optimizing the Bayesian Cramér-Rao bound (B-CRB) metric at each sensing stage, while satisfying a rank constraint and that the receive beamformers must be implementable by analog phase shifters. The proposed approach tackles this B-CRB minimization problem in the Lagrangian dual domain. This dual optimization approach has the advantage of reducing the dimension of the search space from the number of antenna elements to the number of channel parameters, which is typically much smaller for sparse mmWave channels. We propose efficient numerical methods for obtaining the primal solution from the dual and subsequentially setting the phase shifts in each active sensing stage based on this approach. Finally, we demonstrate the benefits of the proposed approach as compared to existing beamforming strategies.

Adopting low-resolution hardware at transceivers in multi-input multi-output (MIMO) sensing systems can substantially reduce hardware costs and power consumption. This motivates us to study MIMO sensing systems with hardware constraints, specifically phase-only analog transmit antennas and low-resolution receive antennas. This paper adopts a Bayesian approach and aims to design low-complexity algorithms for the MIMO sensing beamforming problem while leveraging prior information about the target at each sensing stage. We formulate the problem of minimizing the Bayesian Cramér-Rao lower bound (BCRLB) for estimating a parameter of interest, and show that it has the structure of a weighted sum-of-ratios problem. For the case where the phase shifters at transmit antennas are continuous, we propose a novel linear transform that can transform a fractional function into a linear function. In this way, the original problem is turned into a sequence of sub-problems that can be solved in closed-form in each step with linear complexity in the number of antennas, making the iterative optimization process highly efficient. When the phase shifters are discrete, we propose a penalty-based convex-hull relaxation algorithm, which provides better performance than directly quantizing the solution of the continuous case, but at the cost of increased computational complexity. Numerical results demonstrate the effectiveness of the proposed algorithms.

Recent advancements in video diffusion models have established a strong foundation for developing world models with practical applications. The next challenge lies in exploring how an agent can leverage these foundation models to understand, interact with, and plan within observed environments. This requires adding more controllability to the model, transforming it into a versatile game engine capable of dynamic manipulation and control. To address this, we investigated three key conditioning factors: camera, context frame, and text, identifying limitations in current model designs. Specifically, the fusion of camera embeddings with video features leads to camera control being influenced by those features. Additionally, while textual information compensates for necessary spatiotemporal structures, it often intrudes into already observed parts of the scene. To tackle these issues, we designed the Spacetime Epipolar Attention Layer, which ensures that egomotion generated by the model strictly aligns with the camera’s movement through rigid constraints. Moreover, we propose the CI2V-adapter, which uses camera information to better determine whether to prioritize textual or visual embeddings, thereby alleviating the issue of textual intrusion into observed areas. Through extensive experiments, we demonstrate that our new model EgoSim achieves excellent results on both the RealEstate and newly repurposed Epic-Field datasets. For more results, please refer to https://egosim.github.io/EgoSim/.

This paper presents a novel technique to design receive beamformers for on-grid angle-of-arrival (AoA) estimation in large-scale multiple-input multiple-output systems using channel codes. Specifically, the receive beamformers are designed so that the measurement model is effectively transformed to a Gaussian channel whose inputs are codewords in a channel code, with each codeword corresponding to a different AoA on the grid. Assuming that the number of antennas is larger than the desired angle resolution in the grid, the AoAs can be recovered by leveraging a suitable decoder on the resulting equivalent channel. The performance of the proposed method is derived in terms of the performance of the underlying channel code. Simulations results demonstrate the advantage of the proposed approach compared to existing beamforming strategies.

This paper considers the coded downlink massive random access problem in which a base-station (BS) aims to communicate descriptions of the sources$\left(X_{1}, \cdots, X_{k}\right)$to a randomly activated subset of$k$users, among a large pool of$n$potential users, via a common message in the downlink. Assuming that the downlink channel is noiseless, this paper investigates the lossy source coding setting where upon receiving the common message from the BS, each active user aims to recover a reconstruction$\hat{X}_{i}$of their intended source$X_{i}$, such that the expected distortion between$\left(\hat{X}_{1}, \cdots, \hat{X}_{k}\right)$and$\left(X_{1}, \cdots, X_{k}\right)$is less than$D$. In this paper, we show that a previously proposed lossless coding strategy and its corresponding codebook construction for exchangeable sources, the urn codebook, can be extended to the lossy source coding setting using the Poisson functional representation. With this coding strategy, we show that for exchangeable sources$\left(X_{1}, \cdots, X_{k}\right)$, a common message length of$R(D)$bits plus an overhead of$O(k)$bits, independent of$n$, is achievable, where$R(D)$is the rate-distortion function for compressing$\left(X_{1}, \cdots, X_{k}\right)$. If the sources are i.i.d., this overhead can be reduced to$O(\log (k))$bits.

Compared to the generations up to 4G, whose main focus was on broadband and coverage aspects, 5G has expanded the scope of wireless cellular systems toward embracing two new types of connectivity: massive machine-type communications (mMTCs) and ultrareliable low-latency communications (URLLCs). This article discusses the possible evolution of these two types of connectivity within the umbrella of 6G wireless systems. This article consists of three parts. The first part deals with the connectivity for a massive number of devices. While mMTC research in 5G predominantly focuses on the problem of uncoordinated access in the uplink for a large number of devices, the traffic patterns in 6G may become more symmetric, leading to closed-loop massive connectivity. One of the drivers for this type of traffic pattern is distributed/decentralized learning and inference. The second part of this article discusses the evolution of wireless connectivity for critical services. While latency and reliability are tightly coupled in 5G, 6G will support a variety of safety-critical control applications with different types of timing requirements, as evidenced by the emergence of metrics related to information freshness and information value. In addition, ensuring ultrahigh reliability for safety-critical control applications requires modeling and estimation of the tail statistics of the wireless channel, queue length, and delay. The fulfillment of these stringent requirements calls for the development of novel artificial intelligence (AI)-based techniques, incorporating optimization theory, explainable AI (XAI), generative AI, and digital twins (DTs). The third part analyzes the coexistence of massive connectivity and critical services. Specifically, we consider scenarios in which a massive number of devices need to support traffic patterns of mixed criticality. This is followed by a discussion about the management of wireless resources shared by services with different criticality.

The two-user broadcast channel (BC) with receivers connected by bidirectional cooperation links of finite capacities, known as conferencing decoders, is considered. A novel capacity region outer bound is established based on multiple applications of the Csiszár-Körner identity. Achievable rate regions are derived by using Marton’s coding as the transmission scheme, together with different combinations of decode-and-forward and quantize-bin-and-forward strategies at the receivers. It is shown that the outer bound coincides with the achievable rate region for a new class of semi-deterministic BCs with degraded message sets; for this class of channels, one-round cooperation is sufficient to achieve the capacity. Capacity result is also derived for a class of more capable semi-deterministic BCs with both common and private messages and one-sided conferencing. For the Gaussian BC with conferencing decoders, if the noises at the decoders are perfectly correlated (i.e., the correlation is either 1 or -1), the new outer bound yields exact capacity region for two cases: i) BC with degraded message sets; ii) BC with one-sided conferencing from the weaker receiver to the stronger receiver. When the noises have arbitrary correlation, the outer bound is shown to be within half a bit from the capacity region for these same two cases. Finally, for the general Gaussian BC, a one-sided cooperation scheme based on decode-and-forward from the stronger receiver to the weaker receiver is shown to achieve the capacity region to within 1/2 log(2/1-|λ|) bits, where λ is the noise correlation. An interesting implication of these results is that for a Gaussian BC with perfectly negatively correlated noises and conferencing decoders with finite cooperation link capacities, it is possible to achieve a strictly positive rate using only an infinitesimal amount of transmit power.

This paper considers a massive connectivity setting in which a base-station (BS) aims to communicate sources (X1, · · · ,Xk) to a randomly activated subset ofkusers, among a large pool ofnusers, via a common message in the downlink. Although the identities of thekactive users are assumed to be known at the BS, each active user only knows whether itself is active and does not know the identities of the other active users. A naive coding strategy is to transmit the sources alongside the identities of the users for which the source information is intended. This requiresH(X1, · · · ,Xk) +klog(n) bits, because the cost of specifying the identity of one out ofnusers is log(n) bits. For largen, this overhead can be significant. This paper shows that it is possible to develop coding techniques that eliminate the dependency of the overhead onn, if the source distribution follows certain symmetry. Specifically, if the source distribution is independently and identically distributed (i.i.d.) then the overhead can be reduced to at mostO(log(k)) bits, and in case of uniform i.i.d. sources, the overhead can be further reduced toO(1) bits. For sources that follow a more general exchangeable distribution, the overhead is at mostO(k)bits, and in case of finite-alphabet exchangeable sources, the overhead can be further reduced toO(log(k)) bits. The downlink massive random access problem is closely connected to the study of finite exchangeable sequences. The proposed coding strategy allows bounds on the Kullback-Leibler (KL) divergence between finite exchangeable distributions and i.i.d. mixture distributions to be developed, and gives a new KL divergence version of the finite de Finetti theorem which is scaling optimal.

This paper studies a beam tracking problem in which an access point (AP), in collaboration with a reconfigurable intelligent surface (RIS), dynamically adjusts its downlink beamformers and the reflection pattern at the RIS in order to maintain reliable communications with multiple mobile user equipments (UEs). Specifically, the mobile UEs send uplink pilots to the AP periodically during the channel sensing intervals, the AP then adaptively configures the beamformers and the RIS reflection coefficients for subsequent data transmission based on the received pilots. This is an active sensing problem, because channel sensing involves configuring the RIS coefficients during the pilot stage and the optimal sensing strategy should exploit the trajectory of channel state information (CSI) from previously received pilots. Analytical solution to such an active sensing problem is very challenging. In this paper, we propose a deep learning framework utilizing a recurrent neural network (RNN) to automatically summarize the time-varying CSI obtained from the periodically received pilots into state vectors. These state vectors are then mapped to the AP beamformers and RIS reflection coefficients for subsequent downlink data transmissions, as well as the RIS reflection coefficients for the next round of uplink channel sensing. The mappings from the state vectors to the downlink beamformers and the RIS reflection coefficients for both channel sensing and downlink data transmission are performed using graph neural networks (GNNs) to account for the interference among the UEs. Simulations demonstrate significant and interpretable performance improvement of the proposed approach over the existing data-driven methods with nonadaptive channel sensing schemes.

This paper explores the design of beamforming codebooks for the base station (BS) and for the reconfigurable intelligent surfaces (RISs) in an active sensing scheme for uplink localization, in which the mobile user transmits a sequence of pilots to the BS through reflection at the RISs, and the BS and the RISs are adaptively configured by carefully choosing BS beamforming codeword and RIS codewords from their respective codebooks in a sequential manner to progressively focus onto the user. Most existing codebook designs for RIS are not tailored for active sensing, by which we mean the choice of the next codeword should depend on the measurements made so far, and the sequence of codewords should dynamically focus reflection toward the user. Moreover, most existing codeword selection methods rely on exhaustive search in beam training to identify the codeword with the highest signal-to-noise ratio (SNR), thus incurring substantial pilot overhead as the size of the codebook scales. This paper proposes a learning-based approach for codebook construction and for codeword selection for active sensing. The proposed learning approach aims to locate a target in the service area by recursively selecting a sequence of BS beamforming codewords and RIS codewords from the respective codebooks as more measurements become available without exhaustive beam training. The codebook design and the codeword selection fuse key ideas from the vector quantized variational autoencoder (VQ-VAE) and the long short-term memory (LSTM) network to learn respectively the discrete function space of the codebook and the temporal dependencies between measurements.

This paper studies an uplink dual-functional sensing and communication system assisted by an active or passive reconfigurable intelligent surface (RIS), whose reflection pattern is optimally configured to trade off sensing and communication functionalities. Specifically, the Bayesian Cramér-Rao lower bound (BCRLB) for sensing is minimized under the quality-of-service (QoS) communication constraints. We show that this problem can be formulated as a fractionally constrained fractional programming (FCFP) problem for which a quadratic transform, originally proposed for the sum-of-ratio fractional programs, can be used to decouple the numerators and denominators in both the objective function and the constraints. In this way, the FCFP is turned into a sequence of sub-problems that are convex except for the constant-modulus amplitude constraints which can be dealt with using a penalty-based method. Numerical results unveil nontrivial beamforming reflection patterns that the RIS can be configured to generate in order to facilitate both sensing and communications. The results demonstrate the effectiveness of the proposed algorithm.

This paper presents a novel optimization framework for beamforming design in integrated sensing and communication systems where a base station seeks to minimize the Bayesian Cramer-Rao bound of a sensing problem while satisfying quality of service constraints for the communication users. Prior approaches formulate the design problem as a semidefinite program for which acquiring a beamforming solution is computationally expensive. In this work, we show that the computational burden can be considerably alleviated. To achieve this, we transform the design problem to a tractable form that not only provides a new understanding of Cramer-Rao bound optimization, but also allows for an uplink-downlink duality relation to be developed. Such a duality result gives rise to an efficient algorithm that enables the beamforming design problem to be solved at a much lower complexity as compared to the-state-of-the-art methods.

This paper studies the rate-distortion-perception (RDP) tradeoff for a memoryless source model in the asymptotic limit of large block-lengths. The perception measure is based on a divergence between the distributions of the source and reconstruction sequences conditioned on the encoder output, first proposed by Mentzer et al. We consider the case when there is no shared randomness between the encoder and the decoder. For the case of discrete memoryless sources we derive a single-letter characterization of the RDP function, in contrast to the marginal-distribution metric case (introduced by Blau and Michaeli), whose RDP characterization remains open when there is no shared randomness. The achievability scheme is based on lossy source coding with a posterior reference map. For the case of continuous valued sources under squared error distortion measure and squared quadratic Wasserstein perception measure we also derive a single-letter characterization and show that a noise-adding mechanism at the decoder suffices to achieve the optimal representation. Interestingly, the RDP function characterized for the case of zero perception loss coincides with that of the marginal metric and further zero perception loss can be achieved with a 3-dB penalty in minimum distortion. Finally we specialize to the case of Gaussian sources, and derive the RDP function for Gaussian vector case and propose a waterfilling like solution. We also partially characterize the RDP function for a mixture of Gaussian vector sources.

The paper considers a communication constrained distributed hypothesis testing problem in which the transmitter sends a message about its local observation to the receiver, and the receiver tries to decide whether or not its own observation is independent of the observation at the transmitter. We analyze the problem in the one-shot setting and derive an achievability region under both the fixed-length and the variable-length communication constraints. Novel information-theoretic tools, including the generalized Poisson matching lemma and the strong functional representation lemma, are applied. It is shown that the proposed one-shot schemes, when applied to the asymptotic case, recover the optimal fixed-length and variable-length type-II error exponents for testing against independence.

Mathematical optimization is now widely regarded as an indispensable modeling and solution tool for the design of wireless communications systems. While optimization has played a significant role in the revolutionary progress in wireless communication and networking technologies from 1G to 5G and onto the future 6G, the innovations in wireless technologies have also substantially transformed the nature of the underlying mathematical optimization problems upon which the system designs are based and have sparked significant innovations in the development of methodologies to understand, to analyze, and to solve those problems. In this paper, we provide a comprehensive survey of recent advances in mathematical optimization theory and algorithms for wireless communication system design. We begin by illustrating common features of mathematical optimization problems arising in wireless communication system design. We discuss various scenarios and use cases and their associated mathematical structures from an optimization perspective. We then provide an overview of recently developed optimization techniques in areas ranging from nonconvex optimization, global optimization, and integer programming, to distributed optimization and learning-based optimization. The key to successful solution of mathematical optimization problems is in carefully choosing or developing suitable algorithms (or neural network architectures) that can exploit the underlying problem structure. We conclude the paper by identifying several open research challenges and outlining future research directions.

This paper investigates the information theoretic limit of a reconfigurable intelligent surface (RIS) aided communication scenario in which the RIS and the transmitter either jointly or independently send information to the receiver. The RIS is an emerging technology that uses a large number of passive reflective elements with adjustable phases to intelligently reflect the transmit signal to the intended receiver. While most previous studies of the RIS focus on its ability to beamform and to boost the received signal-to-noise ratio (SNR), this paper shows that if the information data stream is also available at the RIS and can be modulated through the adjustable phases at the RIS, significant improvement in the degree-of-freedom (DoF) of the overall channel is possible. For example, for an RIS system in which the signals are reflected from a transmitter with$M$antennas to a receiver with$K$antennas through an RIS with$N$reflective elements, assuming no direct path between the transmitter and the receiver, joint transmission of the transmitter and the RIS can achieve a DoF of$\min \left({M+\frac {N}{2}-\frac {1}{2},N,K}\right)$as compared to the DoF of$\min (M,K)$for the conventional multiple-input multiple-output (MIMO) channel. This result is obtained by establishing a connection between the RIS system and the MIMO channel with phase noise and by using results for characterizing the information dimension under projection. The result is further extended to the case with a direct path between the transmitter and the receiver, and also to the multiple access scenario, in which the transmitter and the RIS send independent information. Finally, this paper proposes a symbol-level precoding approach for modulating data through the phases of the RIS, and provides numerical simulation results to verify the theoretical DoF results.

This paper studies the rate-distortion-perception (RDP) tradeoff for a memoryless source model in the asymptotic limit of large block-lengths. The perception measure is based on a divergence between the distributions of the source and reconstruction sequences conditioned on the encoder output, first proposed by Mentzer et al. We consider the case when there is no shared randomness between the encoder and the decoder and derive a single-letter characterization of the RDP function, for the case of discrete memoryless sources. This is in contrast to the marginal-distribution metric case (introduced by Blau and Michaeli), whose RDP characterization remains open when there is no shared randomness. The achievability scheme is based on lossy source coding with a posterior reference map. For the case of continuous valued sources under the squared error distortion measure and the squared quadratic Wasserstein perception measure, we also derive a single-letter characterization and show that the decoder can be restricted to a noise-adding mechanism. Interestingly, the RDP function characterized for the case of zero perception loss coincides with that of the marginal metric, and further zero perception loss can be achieved with a 3-dB penalty in minimum distortion. Finally we specialize to the case of Gaussian sources, and derive the RDP function for Gaussian vector case and propose a reverse water-filling type solution. We also partially characterize the RDP function for a mixture of Gaussian vector sources.

This paper focuses on the covariance-based activity detection problem in a multi-cell massive multiple-input multiple-output (MIMO) system. In this system, active devices transmit their signature sequences to multiple base stations (BSs), and the BSs cooperatively detect the active devices based on the received signals. While the scaling law for the covariance-based activity detection in the single-cell scenario has been extensively analyzed in the literature, this paper aims to analyze the scaling law for the covariance-based activity detection in the multi-cell massive MIMO system. Specifically, this paper demonstrates a quadratic scaling law in the multi-cell system, under the assumption that the path-loss exponent of the fading channel$\gamma \gt 2$. This finding shows that, in the multi-cell massive MIMO system, the maximum number of active devices that can be correctly detected in each cell increases quadratically with the length of the signature sequence and decreases logarithmically with the number of cells (as the number of antennas tends to infinity). Moreover, in addition to analyzing the scaling law for the signature sequences randomly and uniformly distributed on a sphere, the paper also establishes the scaling law for signature sequences based on a finite alphabet, which are easier to generate and store. Finally, this paper proposes two efficient accelerated coordinate descent (CD) algorithms with a convergence guarantee for solving the device activity detection problem. The first algorithm reduces the complexity of CD by using an inexact coordinate update strategy. The second algorithm avoids unnecessary computations of CD by using an active set selection strategy. Simulation results show that the proposed algorithms exhibit excellent performance in terms of computational efficiency and detection error probability.

This paper investigates the fronthaul compression problem in a user-centric cloud radio access network, in which single-antenna users are served by a central processor (CP) cooperatively via a cluster of remote radio heads (RRHs). To satisfy the fronthaul capacity constraint, this paper proposes a transform-compress-forward scheme, which consists of well-designed transformation matrices and uniform quantizers. The transformation matrices perform dimension reduction in the uplink and dimension expansion in the downlink. To reduce the communication overhead for designing the transformation matrices, this paper further proposes a deep learning framework to first learn a suboptimal transformation matrix at each RRH based on the local channel state information (CSI), and then to refine it iteratively. To facilitate the refinement process, we propose an efficient signaling scheme that only requires the transmission of low-dimensional effective CSI and its gradient between the CP and RRH, and further, a meta-learning based gated recurrent unit network to reduce the number of signaling transmission rounds. For the sum-rate maximization problem, simulation results show that the proposed two-stage neural network can perform close to the fully cooperative global CSI based benchmark with significantly reduced communication overhead for both the uplink and the downlink. Moreover, using the first stage alone can already outperform the existing local CSI based benchmark.

We consider a multi-user multi-server mobile edge computing (MEC) system, in which users arrive on a network randomly over time and generate computation tasks, which will be computed either locally on their own computing devices or be offloaded to one of the MEC servers. Under such a dynamic network environment, we propose a novel task offloading policy based on hybrid online–offline learning, which can efficiently reduce the overall computation delay and energy consumption only with information available at nearest MEC servers from each user. We provide a practical signaling and learning framework that can train deep neural networks for both online and offline learning and can adjust its offloading policy based on the queuing status of each MEC server and network dynamics. Numerical results demonstrate that the proposed scheme significantly reduces the average computation delay for a broad class of network environments compared to the conventional offloading methods. It is further shown that the proposed hybrid online–offline learning framework can be extended to a general cost function reflecting both delay- and energy-dependent metrics.

Grant-free access is a key enabler for connecting wireless devices with low latency and low signaling overhead in massive machine-type communications (mMTC). For massive grant-free access, user-specific signatures are uniquely assigned to mMTC devices. In this paper, we first derive a sufficient condition for the successful identification of active devices through maximum likelihood (ML) estimation in massive grant-free access. The condition is represented by the coherence of a signature sequence matrix containing the signatures of all devices. Then, we present a design framework of non-orthogonal signature sequences in a deterministic fashion. The design principle relies on unimodular masking sequences with low correlation, which are applied as masking sequences to the columns of the discrete Fourier transform (DFT) matrix. For example constructions, we use four polyphase masking sequences represented by characters over finite fields. Leveraging algebraic techniques, we show that the signature sequence matrix of proposed non-orthogonal sequences has theoretically bounded low coherence. Simulation results demonstrate that the deterministic non-orthogonal signatures achieve the excellent performance of joint activity and data detection by ML- and approximate message passing (AMP)-based algorithms for massive grant-free access in mMTC.

This paper addresses an uplink localization problem in which a base station (BS) aims to locate a remote user with the help of reconfigurable intelligent surfaces (RISs). We propose a strategy in which the user transmits pilots sequentially and the BS adaptively adjusts the sensing vectors, including the BS beamforming vector and multiple RIS reflection coefficients based on the observations already made, to eventually produce an estimated user position. This is a challenging active sensing problem for which finding an optimal solution involves searching through a complicated functional space whose dimension increases with the number of measurements. We show that the long short-term memory (LSTM) network can be used to exploit the latent temporal correlation between measurements to automatically construct scalable state vectors. Subsequently, the state vector is mapped to the sensing vectors for the next time frame via a deep neural network (DNN). A final DNN is used to map the state vector to the estimated user position. Numerical result illustrates the advantage of the active sensing design as compared to non-active sensing methods. The proposed solution produces interpretable results and is generalizable in the number of sensing stages. Remarkably, we show that a network with one BS and multiple RISs can outperform a comparable setting with multiple BSs.

This paper studies a beam tracking problem in a reconfigurable intelligent surface (RIS)-assisted communication system, in which a single antenna access point (AP) tracks a single-antenna mobile user equipment (UE) through actively reconfiguring the RIS. To maintain beam alignment over time, the mobile UE periodically sends a sequence of pilots to the AP in the uplink, and the AP updates the RIS reflection coefficients for both the subsequent downlink data transmission and uplink pilot reception stages in a sequential fashion. This is an active sensing problem which is analytically intractable. This paper proposes a deep learning framework to solve this problem. We use a neural network architecture based on long short-term memory (LSTM) in which the LSTM cell automatically summarizes the time-varying channel information based on periodically received pilots into a state vector, and the state vector is mapped to the RIS reflection coefficients for subsequent downlink data transmission and uplink pilot reception using two additional deep neural networks (DNNs). Simulation results show that this proposed active sensing approach is able to maintain beam alignment much more efficiently than traditional data-driven methods based only on channel statistics.

This paper studies the covariance based activity detection problem in a multi-cell massive multiple-input multiple-output (MIMO) system, where the active devices transmit their signature sequences to multiple base stations (BSs), and the BSs cooperatively detect the active devices based on the received signals. The scaling law of covariance based activity detection in the single-cell scenario has been thoroughly analyzed in the literature. This paper aims to analyze the scaling law of covariance based activity detection in the multi-cell massive MIMO system. In particular, this paper shows a quadratic scaling law in the multi-cell system under the assumption that the exponent in the classical path-loss model is greater than 2, which demonstrates that in the multi-cell MIMO system the maximum number of active devices that can be correctly detected in each cell increases quadratically with the length of the signature sequence and decreases logarithmically with the number of cells (as the number of antennas tends to infinity). This paper also characterizes the distribution of the estimation error in the multi-cell scenario.

This paper investigates the uplink signal dimension reduction problem for a user-centric cloud radio access network, in which each single-antenna user communicates with the central processor (CP) through a cluster of remote radio heads (RRHs). To reduce the fronthaul traffic, each RRH applies a compression matrix to reduce the dimension of the received signal before relaying it to the CP. However, the optimal design of the compression matrices requires significant communication overhead for transmitting the high-dimensional channel state information (CSI) matrices from the RRHs to the CP. To address this issue, this paper proposes a deep learning framework to first learn a sub-optimal compression matrix at each RRH based on the local CSI, then iteratively refine the learned compression matrix using a meta-learning-based gradient method. To reduce the communication cost for CSI sharing and gradients transmission, this paper proposes an efficient signaling scheme that only requires the transmission of low-dimensional effective CSI and its gradient between the CP and each RRH. Furthermore, a meta-learning-based gated recurrent unit (GRU) network is proposed to reduce the number of signaling transmission rounds. For the sum-rate maximization problem, simulation results show that the proposed two-stage neural network can perform closely to the fully cooperative global CSI-based benchmark with significantly reduced communication overhead. Moreover, using the first stage alone can already outperform the existing local CSI-based benchmark.

This paper addresses an uplink localization problem in which the base station (BS) aims to locate a remote user with the aid of reconfigurable intelligent surface (RIS). This paper proposes a strategy in which the user transmits pilots over multiple time frames, and the BS adaptively adjusts the RIS reflection coefficients based on the observations already received so far in order to produce an accurate estimate of the user location at the end. This is a challenging active sensing problem for which finding an optimal solution involves a search through a complicated functional space whose dimension increases with the number of measurements. In this paper, we show that the long short-term memory (LSTM) network can be used to exploit the latent temporal correlation between measurements to automatically construct scalable information vectors (called hidden state) based on the measurements. Subsequently, the state vector can be mapped to the RIS configuration for the next time frame in a codebook-free fashion via a deep neural network (DNN). After all the measurements have been received, a final DNN can be used to map the LSTM cell state to the estimated user equipment (UE) position. Numerical result shows that the proposed active RIS design results in lower localization error as compared to existing active and nonactive methods. The proposed solution produces interpretable results and is generalizable to early stopping in the sequence of sensing stages.

The two-user Gaussian broadcast channel (BC) with correlated noises and with decoders connected by cooperative links of finite capacities (known as conferencing decoders) is considered. A novel outer bound on the capacity region is established. For the channel with fully correlated noises (i.e., the noise correlation is either 1 or -1), the new outer bound yields exact capacity region for two cases: 1) BCs with degraded message sets; 2) BCs with one-sided conferencing from the weaker receiver to the stronger receiver. For these two cases, it is also shown that the outer bound is within half bits to the capacity region for arbitrary noise correlation. Furthermore, for the Gaussian BC with arbitrary noise correlation λ, we show that regardless of the capacities of conferencing links, a one-sided cooperative scheme (from the stronger user to the weaker one) based on decode-and-forward is sufficient to achieve the capacity region to within $\frac{1}{2}\log \left( {\frac{2}{{1 - |\lambda |}}} \right)$ bits.

This paper considers a massive connectivity scenario in which a base-station (BS) aims to communicate k individual sources (X1, ⋯ , Xk) to a random subset of k users among a large pool of n users via a common downlink message. The identities of the k active users are known at the BS, but each active user only knows whether it is active itself and does not know the identities of the other active users. The naive coding strategy of transmitting the source messages together with the indices of the users for which the messages are intended would require a rate of H(X1, ⋯ , Xk) + k log(n) bits. This paper shows that if the sources are jointly distributed according to an exchangeable distribution, better coding techniques can be used to eliminate the dependency of the overhead on log(n). Specifically, if the sources are independently and identically distributed (i.i.d.) or are i.i.d. mixture, then the overhead can be reduced to O(log(H(X1, ⋯ , Xk))) or at most O(log(k)) bits. The overhead can be further reduced to O(1) if the source distribution is uniform over its support. For a general exchangeable source not necessarily i.i.d. nor i.i.d. mixture, an overhead of O(k + log(k + H(X1, ⋯, Xk))) bits is achievable; if the source distribution has finite support, the overhead can be further reduced to O(log(k)). Moreover, for exchangeable distributions that are extendable, the rate can be further improved.

The two-user broadcast channel (BC) with decoders connected by cooperative links of given capacities (known as conferencing decoders) is considered. A novel outer bound on the capacity region is established. This outer bound is derived using multiple applications of the Csiszár-Körner identity. A new achievable rate region for the channel is also presented which is derived by applying Marton’s coding as the transmission scheme, and quantize-bin-and-forward at one receiver and decode-and-forward at the other receiver as cooperative strategy. It is proved that the outer bound coincides with the achievable region for a class of semi-deterministic BCs with degraded message sets. This is the first capacity result for the two-user BC with bidirectional conferencing decoders. This result demonstrates that a one-round cooperation scheme is sufficient to achieve capacity for this class of semi-deterministic BCs with degraded message set. A capacity result is also derived for a new class of more capable semi-deterministic BCs with both common and private messages and one-sided conferencing.

We study causal, low-latency, sequential video compression when the output is subjected to both a mean squared-error (MSE) distortion loss as well as a perception loss to target realism. Motivated by prior approaches, we consider two different perception loss functions (PLFs). The first, PLF-JD, considers the joint distribution (JD) of all the video frames up to the current one, while the second metric, PLF-FMD, considers the framewise marginal distributions (FMD) between the source and reconstruction. Using information theoretic analysis and deep-learning based experiments, we demonstrate that the choice of PLF can have a significant effect on the reconstruction, especially at low-bit rates. In particular, while the reconstruction based on PLF-JD can better preserve the temporal correlation across frames, it also imposes a significant penalty in distortion compared to PLF-FMD and further makes it more difficult to recover from errors made in the earlier output frames. Although the choice of PLF decisively affects reconstruction quality, we also demonstrate that it may not be essential to commit to a particular PLF during encoding and the choice of PLF can be delegated to the decoder. In particular, encoded representations generated by training a system to minimize the MSE (without requiring either PLF) can be {\em near universal} and can generate close to optimal reconstructions for either choice of PLF at the decoder. We validate our results using (one-shot) information-theoretic analysis, detailed study of the rate-distortion-perception tradeoff of the Gauss-Markov source model as well as deep-learning based experiments on moving MNIST and KTH datasets.

Federated learning obtains the global model through the cooperative training of each participating device while protecting data privacy. However, the huge communication cost caused by each client sending complete model update becomes an important problem for its wide application. Model sparsification and quantization are practical solutions to reduce single-round uplink communication, respectively. However, when the compression ratio increases, (1) the current model sparsification methods based on fine granularity by uploading significant gradients are difficult to effectively select important parameters by using thresholds; (2) the unified bit-width quantization of parameters of layers with different importance in the model will lead to greater information loss. In this paper, we propose a joint compression framework based on functional structure sparsity and hybrid quantization sensing to compress the communication cost to more than two orders of magnitude. First, we proposed to take filters as sparse granularity through the visual analysis of parameter updating rules in the training process, and searched for filters with strong representational ability among filters with different representational abilities in the convolution layer. Second, we propose an adaptive layer-sensing quantization method according to the parameter distribution of each layer, which assigns different bit widths to different layers in the model after structural sparsification. Finally, we design a model aggregation and update scheme based on the above joint compression framework, which reduces the error caused by compression through model reconstruction and parameter reuse. Experiments show that our proposed framework compress single communication to more than two orders of magnitude in different tasks while ensuring the convergence speed and final model performance.

Channel acquisition is one of the main challenges for the deployment of reconfigurable intelligent surface (RIS) aided communication systems. This is because an RIS has a large number of reflective elements, which are passive devices with no active transmitting/receiving abilities. In this paper, we study the channel estimation problem for the RIS aided multi-user millimeter-wave (mmWave) multi-input multi-output (MIMO) system. Specifically, we propose a novel channel estimation protocol for the above system to estimate the cascaded channels, which are the products of the channels from the base station (BS) to the RIS and from the RIS to the users. Further, since the cascaded channels are typically sparse, this allows us to formulate the channel estimation problem as a sparse recovery problem using compressive sensing (CS) techniques, thereby allowing the channels to be estimated with less training overhead. Moreover, the sparse channel matrices of the cascaded channels of all users have a common block sparsity structure due to the common channel between the BS and the RIS. To take advantage of the common sparsity pattern, we propose a two-step multi-user joint channel estimation procedure. In the first step, we make use of the common column-block sparsity and project the received signals onto the common column subspace. In the second step, we make use of the row-block sparsity of the projected signals and propose a multi-user joint sparse matrix recovery algorithm that takes into account the common channel between the BS and the RIS.

This paper proposes a paradigm of uncertainty injection for training deep learning model to solve robust optimization problems. The majority of existing studies on deep learning focus on the model learning capability, while assuming the quality and accuracy of the inputs data can be guaranteed. However, in realistic applications of deep learning for solving optimization problems, the accuracy of inputs, which are the problem parameters in this case, plays a large role. This is because, in many situations, it is often costly or sometime impossible to obtain the problem parameters accurately, and correspondingly, it is highly desirable to develop learning algorithms that can account for the uncertainties in the input and produce solutions that are robust against these uncertainties. This paper presents a novel uncertainty injection scheme for training machine learning models that are capable of implicitly accounting for the uncertainties and producing statistically robust solutions. We further identify the wireless communications as an application field where uncertainties are prevalent in problem parameters such as the channel coefficients. We show the effectiveness of the proposed training scheme in two applications: the robust power loading for multiuser multiple-input-multiple-output (MIMO) downlink transmissions; and the robust power control for device-to-device (D2D) networks.

Recent works in video prediction have mainly focused on passive forecasting and low-level action-conditional pre-diction, which sidesteps the learning of interaction between agents and objects. We introduce the task of semantic action-conditional video prediction, which uses semantic action labels to describe those interactions and can be regarded as an inverse problem of action recognition. The challenge of this new task primarily lies in how to effectively inform the model of semantic action information. Inspired by the idea of Mixture of Experts, we embody each abstract label by a structured combination of various visual concept learn-ers and propose a novel video prediction model, Modular Action Concept Network (MAC). Our method is evaluated on two newly designed synthetic datasets, CLEVR-Building- Blocks and Sapien-Kitchen, and one real-world dataset called Tower-Creation. Extensive experiments demonstrate that MAC can correctly condition on given instructions and generate corresponding future frames without need of bounding boxes. We further show that the trained model can make out-of-distribution generalization, be quickly adapted to new object categories and exploit its learnt features for object detection, showing the progression towards higher-level cognitive abilities. More visualizations can be found at http://www.pair.toronto.edu/mac/.

Neural networks have been widely utilized for wireless communication optimizations. In most of the literature, a dedicated neural network is trained for each specific optimization problem. However, under many scenarios, several distinct objectives are worth optimizing on the same wireless environment. Instead of exhaustively training a new model for every objective, it is more efficient to exploit the correlations between these objectives to train models with shared model parameters and feature representations. In the deep learning literature, transfer learning has been proposed to encourage knowledge transfer among models solving correlated problems. Unlike a majority of transfer learning applications where the high level features are relatively easy to locate in the neural networks, this paper considers wireless communication problems, in which it is much more difficult to identify high level features transferable to correlated tasks. To address this issue, this paper proposes to add an additional reconstruction loss when training the model. This new loss is for reconstructing the problem inputs starting from a selected neural network hidden layer. This approach encourages the features learnt to be general and descriptive about the inputs, instead of being solely responsible for minimizing the specific task-based loss. When a new objective is to be optimized, these features can be readily used for transfer learning. Simulation results in device-to-device wireless network power allocation optimization suggest that the proposed approach is highly efficient in data and model complexity, resilient to over-fitting, and supports competitive optimization performances.

This paper proposes a data-driven architecture for zero-delay lossy source coding with side information (i.e., Wyner-Ziv coding) for sources with memory. The overall architecture involves designing suitable filters at the encoder and the decoder and performing fixed-rate scalar quantization followed by one-dimensional binning of quantization indices. Unlike previous work, which uses an exhaustive search to optimize the system parameters, this paper proposes a lower-complexity data-driven method that does not require a priori knowledge of source and side information statistics. The main ingredients of the proposed approach include modeling the quantization process by an additive quantization noise process, modeling the modulo operation by a continuous approximation, and approximating the decoding process by a softmin function, which makes the system amenable to training using stochastic gradient descent. Experimental results on Gauss-Markov sources with different memory orders demonstrate that our proposed system can match the performance of systems optimized using an exhaustive search.

Reconfigurable intelligent surface (RIS) is capable of intelligently manipulating the phases of the incident electromagnetic wave to improve the wireless propagation environment between the base station (BS) and the users. This paper addresses the joint user scheduling, RIS configuration, and BS beamforming problem in an RIS-assisted downlink network with limited pilot overhead. We show that graph neural networks (GNN) with permutation invariance and equivariance properties can be used to appropriately schedule users and to design RIS configurations to achieve high overall throughput while accounting for fairness among the users. As compared to the conventional methodology of first estimating the channels then optimizing the user schedule, RIS configuration and the beamformers, this paper shows that an optimized user schedule can be obtained directly from a very short set of pilots using a GNN, then the RIS configuration can be optimized using a second GNN, and finally BS beamformers can be designed based on the overall effective channel. Numerical results show that the proposed approach can utilize received pilots more efficiently than conventional channel estimation based approach.

This paper considers a massive random access scenario in which a small set of k users out of a large number of n potential users are active at any given time, and a central base-station wishes to send a common message to the active users in order to label them into a finite number of categories. Specifically, given c possible categories, the base-station wishes to send label ℓ to a set of kℓusers, where ℓ ∈ {1, …, c} and $\sum\nolimits_{\ell = 1}^c {{k_\ell } = k} $. Assuming that n, k1, …, kcare fixed, we ask: what is the minimum rate of the common message that the base-station needs to send so that the correct label is received at each of the k active users? This paper shows that instead of a conventional scheme of listing the indices of the users followed by their labels, which requires a common message rate of $k\left( {\log (n) + H\left( {\frac{{{k_1}}}{k}, \ldots ,\frac{{{k_c}}}{k}} \right)} \right)$ bits, it is possible to construct a fixed-length common message code with a rate of just $kH\left( {\frac{{{k_1}}}{k}, \ldots ,\frac{{{k_c}}}{k}} \right)$ bits plus a term that scales in n as O(log log(n)) for fixed k1, …, kc, where H(•) is the entropy of a probability distribution. If a variable-length code is permitted, the minimum common message rate is characterized as $kH\left( {\frac{{{k_1}}}{k}, \ldots ,\frac{{{k_c}}}{k}} \right) + O(1)$ bits, with no dependence on n. Finally, if k1, …, kcdeviate from the values for which the common message is designed, an additional cost per user equal to a Kullback-Leibler divergence term would be incurred.

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.

This paper proposes a deep learning approach to a class of active sensing problems in wireless communications in which an agent sequentially interacts with an environment over a predetermined number of time frames to gather information in order to perform a sensing or actuation task for maximizing some utility function. In such an active learning setting, the agent needs to design an adaptive sensing strategy sequentially based on the observations made so far. To tackle such a challenging problem in which the dimension of historical observations increases over time, we propose to use a long short-term memory (LSTM) network to exploit the temporal correlations in the sequence of observations and to map each observation to a fixed-size state information vector. We then use a deep neural network (DNN) to map the LSTM state at each time frame to the design of the next measurement step. Finally, we employ another DNN to map the final LSTM state to the desired solution. We investigate the performance of the proposed framework for adaptive channel sensing problems in wireless communications. In particular, we consider the adaptive beamforming problem for mmWave beam alignment and the adaptive reconfigurable intelligent surface sensing problem for reflection alignment. Numerical results demonstrate that the proposed deep active sensing strategy outperforms the existing adaptive or nonadaptive sensing schemes.

Massive machine-type communications protocols have typically been designed under the assumption that coordination between users requires significant communication overhead and is thus impractical. Recent progress in efficient activity detection and collision-free scheduling, however, indicates that the cost of coordination can be much less than the naive scheme for scheduling. This work considers a scenario in which a massive number of devices with sporadic traffic seek to access a massive multiple-input multiple-output (MIMO) base-station (BS) and explores an approach in which device activity detection is followed by a single common feedback broadcast message, which is used both to schedule the active users to different transmission slots and to assign orthogonal pilots to the users for channel estimation. The proposed coordinated communication scheme is compared to two prevalent contention-based schemes: coded pilot access, which is based on the principle of coded slotted ALOHA, and an approximate message passing scheme for joint user activity detection and channel estimation. Numerical results indicate that scheduled massive access provides significant gains in the number of successful transmissions per slot and in sum rate, due to the reduced interference, at only a small cost of feedback.

This paper considers a reconfigurable intelligent surface (RIS)-aided millimeter wave (mmWave) downlink communication system where hybrid analog-digital beamforming is employed at the base station (BS). We formulate a power minimization problem by jointly optimizing hybrid beamforming at the BS and the response matrix at the RIS, under the signal-to-interference-plus-noise ratio (SINR) constraints at all users. The problem is highly challenging to solve due to the non-convex SINR constraints as well as the unit-modulus phase shift constraints for both the RIS reflection coefficients and the analog beamformer. A two-layer penalty-based algorithm is proposed to decouple variables in SINR constraints, and manifold optimization is adopted to handle the non-convex unit-modulus constraints. We also propose a low-complexity sequential optimization method, which optimizes the RIS reflection coefficients, the analog beamformer, and the digital beamformer sequentially without iteration. Furthermore, the relationship between the power minimization problem and the max-min fairness (MMF) problem is discussed. Simulation results show that the proposed penalty-based algorithm outperforms the state-of-the-art semidefinite relaxation (SDR)-based algorithm. Results also demonstrate that the RIS plays an important role in the power reduction.

This paper considers a massive random access problem in which a large number of sporadically active devices wish to communicate with a base station (BS) equipped with massive multiple-input multiple-output (MIMO) antennas. Each device is preassigned a unique signature sequence, and the BS identifies the active devices by detecting which sequences are transmitted. This device activity detection problem can be formulated as a maximum likelihood estimation (MLE) problem for which the sample covariance matrix of the received signal is a sufficient statistic. The goal of this paper is to characterize the feasible set of problem parameters under which this covariance based approach is able to successfully recover the device activities in the massive MIMO regime. Through an analysis of the asymptotic behaviors of MLE via its associated Fisher information matrix, this paper derives a necessary and sufficient condition on the Fisher information matrix to ensure a vanishing probability of detection error as the number of antennas goes to infinity, based on which a numerical phase transition analysis is obtained. This condition is also examined from a perspective of covariance matching, which relates the phase transition analysis to a recently derived scaling law. Further, we provide a characterization of the distribution of the estimation error in MLE, based on which the error probabilities in device activity detection can be accurately predicted. Finally, this paper studies a random access scheme with joint device activity and data detection and analyzes its performance in a similar way.

This paper proposes a deep learning approach to channel sensing and downlink hybrid beamforming for massive multiple-input multiple-output systems operating in the time division duplex mode and employing either single-carrier or multicarrier transmission. The conventional precoding design involves a two-step process of first estimating the high-dimensional channel, then designing the precoders based on such estimate. This two-step process is, however, not necessarily optimal. This paper shows that by using a learning approach to design the analog sensing and the hybrid downlink precoders directly from the received pilots without the intermediate high-dimensional channel estimation, the overall system performance can be significantly improved. Training a neural network to design the analog and digital precoders simultaneously is, however, difficult. Further, such an approach is not generalizable to systems with different number of users. In this paper, we develop a simplified and generalizable approach that learns the uplink sensing matrix and downlink analog precoder using a deep neural network that decomposes on a per-user basis, then designs the digital precoder based on the estimated low-dimensional equivalent channel. Numerical comparisons show that the proposed methodology results in significantly less training overhead and leads to an architecture that generalizes to various system settings.

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 proposes a novel and scalable reinforcement learning approach for routing in ad-hoc wireless networks. In most previous reinforcement learning based routing methods, the links in the network are assumed to be fixed, and a different agent is trained for each transmission node — this limits scalability and generalizability. In this paper, we account for the inherent signal-to-interference-plus-noise ratio (SINR) in the physical layer and propose a more scalable approach in which a single agent is associated with each flow and is trained using a novel reward definition and according to the physical-layer characteristics of the environment. This allows a highly effective routing strategy based on the geographic locations of the nodes in the ad-hoc network. The proposed deep reinforcement learning strategy is capable of accounting for the mutual interference between the links and is capable of producing highly effective routing solutions over the entire network in a scalable manner.

This paper proposes a deep learning approach to the adaptive and sequential beamforming design problem for the initial access phase in a mmWave environment with a single-path channel model. In particular, for a single-user scenario where the problem is equivalent to designing the sequence of sensing beamformers to learn the angle of arrival (AoA) of the dominant path, we propose a novel deep neural network (DNN) that designs a sequence of adaptive sensing vectors based on the available information so far at the base station (BS). By recognizing that the posterior distribution of the AoA provides sufficient statistic for solving the initial access problem, we consider the AoA posterior distribution as the main component of the input to the proposed DNN for designing the adaptive beamforming strategy. However, computing the AoA posterior distribution can be computationally challenging when the fading coefficient is unknown. To address this issue, this paper proposes to use the minimum mean squared error (MMSE) estimate of the fading coefficient to compute an approximation of the posterior distribution. Numerical results demonstrate that as compared to the existing adaptive beamforming schemes utilizing predesigned hierarchical codebooks, the proposed deep learning-based adaptive beamforming achieves a higher AoA detection performance.

This paper proposes a computationally efficient algorithm to solve the joint data and activity detection problem for massive random access with massive multiple-input multiple-output (MIMO). The BS acquires the active devices and their data by detecting the transmitted preassigned nonorthogonal signature sequences. This paper employs a covariance based approach that formulates the detection problem as a maximum likelihood estimation (MLE) problem. To efficiently solve the problem, this paper designs a novel iterative algorithm with low complexity in the regime where the device activity pattern is sparse - a key feature that existing algorithmic designs have not previously exploited for reducing complexity. Specifically, at each iteration, the proposed algorithm focuses on only a small subset of all potential sequences, namely the active set, which contains a few most likely active sequences (i.e., transmitted sequences by all active devices), and performs the detection for the sequences in the active set. The active set is carefully selected at each iteration based on the current detection result and the first-order optimality condition of the MLE problem. Simulation results show that the proposed active set algorithm enjoys significantly better computational efficiency (in terms of the CPU time) than the state-of-the-art algorithms.

This paper investigates the information theoretical limit of a reconfigurable intelligent surface (RIS) aided communication scenario in which the RIS and the transmitter jointly send information to the receiver. The RIS is an emerging technology that uses a large number of passive reflective elements with adjustable phases to intelligently reflect the transmit signal to the intended receiver. While most previous studies of the RIS focus on its ability to beamform and to boost the received signal-to-noise ratio (SNR), this paper shows that if the information data stream is available both at the transmitter and the RIS and the phases at the RIS can be used to modulate data, then the multiplexing gain of the overall channel can potentially be significantly enhanced. Specifically, we show that in a multiple-input multiple-output (MIMO) channel with$M$transmit antennas and$K$receive antennas, a RIS with$N$reflective elements can improve the multiplexing gain from min(M, K) to min(M + N/2 - 1/2,$N$, K). This result is obtained by establishing a connection between the RIS system and the MIMO channel with phase noises and using results for characterizing the information dimension under projection.

Massive access, also known as massive connectivity or massive machine-type communication (mMTC), is one of the main use cases of the fifth-generation (5G) and beyond 5G (B5G) wireless networks. A typical application of massive access is the cellular Internet of Things (IoT). Different from conventional human-type communication, massive access aims at realizing efficient and reliable communications for a massive number of IoT devices. Hence, the main characteristics of massive access include low power, massive connectivity, and broad coverage, which require new concepts, theories, and paradigms for the design of next-generation cellular networks. This paper presents a comprehensive survey of massive access design for B5G wireless networks. Specifically, we provide a detailed review of massive access from the perspectives of theory, protocols, techniques, coverage, energy, and security. Furthermore, several future research directions and challenges are identified.

Intelligent reflecting surface (IRS), which consists of a large number of tunable reflective elements, is capable of enhancing the wireless propagation environment in a cellular network by intelligently reflecting the electromagnetic waves from the base-station (BS) toward the users. The optimal tuning of the phase shifters at the IRS is, however, a challenging problem, because due to the passive nature of reflective elements, it is difficult to directly measure the channels between the IRS, the BS, and the users. Instead of following the traditional paradigm of first estimating the channels then optimizing the system parameters, this paper advocates a machine learning approach capable of directly optimizing both the beamformers at the BS and the reflective coefficients at the IRS based on a system objective. This is achieved by using a deep neural network to parameterize the mapping from the received pilots (plus any additional information, such as the user locations) to an optimized system configuration, and by adopting a permutation invariant/equivariant graph neural network (GNN) architecture to capture the interactions among the different users in the cellular network. Simulation results show that the proposed implicit channel estimation based approach is generalizable, can be interpreted, and can efficiently learn to maximize a sum-rate or minimum-rate objective from a much fewer number of pilots than the traditional explicit channel estimation based approaches.

This paper proposes a deep learning approach to the adaptive and sequential beamforming design problem for the initial access phase in a mmWave environment with a single-path channel. For a single-user scenario where the problem is equivalent to designing the sequence of sensing beamformers to learn the angle of arrival (AoA) of the dominant path, we propose a novel deep neural network (DNN) that designs the adaptive sensing vectors sequentially based on the available information so far at the base station (BS). By recognizing that the AoA posterior distribution is a sufficient statistic for solving the initial access problem, we use the posterior distribution as the input to the proposed DNN for designing the adaptive sensing strategy. However, computing the posterior distribution can be computationally challenging when the channel fading coefficient is unknown. To address this issue, this paper proposes to use an estimate of the fading coefficient to compute an approximation of the posterior distribution. Further, this paper shows that the proposed DNN can deal with practical beamforming constraints such as the constant modulus constraint. Numerical results demonstrate that compared to the existing adaptive and non-adaptive beamforming schemes, the proposed DNN-based adaptive sensing strategy achieves a significantly better AoA acquisition performance.

This paper proposes a novel scalable reinforcement learning approach for simultaneous routing and spectrum access in wireless ad-hoc networks. In most previous works on reinforcement learning for network optimization, the network topology is assumed to be fixed, and a different agent is trained for each transmission node—this limits scalability and generalizability. Further, routing and spectrum access are typically treated as separate tasks. Moreover, the optimization objective is usually a cumulative metric along the route, e.g., number of hops or delay. In this paper, we account for the physical-layer signal-to-interference-plus-noise ratio (SINR) in a wireless network and further show thatbottleneckobjective such as the minimum SINR along the route can also be optimized effectively using reinforcement learning. Specifically, we propose a scalable approach in which a single agent is associated with each flow and makes routing and spectrum access decisions as it moves along the frontier nodes. The agent is trained according to the physical-layer characteristics of the environment using a novel rewarding scheme based on the Monte Carlo estimation of the future bottleneck SINR. It learns to avoid interference by intelligently making joint routing and spectrum allocation decisions based on the geographical location information of the neighbouring nodes.

Consider a massive random access scenario in which a small set of$k$active users out of a large number of$n$potential users need to be scheduled in$b\ge k$slots. What is the minimum common feedback to the users needed to ensure that scheduling is collision-free? Instead of a naive scheme of listing the indices of the$k$active users in the order in which they should transmit, at a cost of$k\log (n)$bits, this paper shows that for the case of$b=k$, the rate of the minimum fixed-length common feedback code scales only as$k \log (e)$bits, plus an additive term that scales in$n$as$\Theta (\log \log (n))$for fixed$k$. If a variable-length code can be used, assuming uniform activity among the users, the minimum average common feedback rate still requires$k \log (e)$bits, but the dependence on$n$can be reduced to$O(1)$. When$b>k$, the number of feedback bits needed for collision-free scheduling can be significantly further reduced. Moreover, a similar scaling on the minimum feedback rate is derived for the case of scheduling$m$users per slot, when$k \le mb$. The problem of constructing a minimum collision-free feedback scheduling code is connected to that of constructing a perfect hashing family, which allows practical feedback scheduling codes to be constructed from perfect hashing algorithms.

Uplink-downlink duality refers to the fact that under a sum-power constraint, the capacity regions of a Gaussian multiple-access channel and a Gaussian broadcast channel with Hermitian transposed channel matrices are identical. This paper generalizes this result to a cooperative cellular network, in which remote access-points are deployed as relays in serving the users under the coordination of a central processor (CP). In this model, the users and the relays are connected over noisy wireless links, while the relays and the CP are connected over noiseless but rate-limited fronthaul links. Based on a Lagrangian technique, this paper establishes a duality relationship between such a multiple-access relay channel and broadcast relay channel, under the assumption that the relays use compression-based strategies. Specifically, we show that under the same total transmit power constraint and individual fronthaul rate constraints, the achievable rate regions of the Gaussian multiple-access and broadcast relay channels are identical, when either independent compression or Wyner-Ziv and multivariate compression strategies are used. The key observations are that if the beamforming vectors at the relays are fixed, the sum-power minimization problems under the achievable rate and fronthaul constraints in both the uplink and the downlink can be transformed into either a linear programming or a semidefinite programming problem depending on the compression technique, and that the uplink and downlink problems are Lagrangian duals of each other. Moreover, the dual variables corresponding to the downlink rate constraints become the uplink powers; the dual variables corresponding to the downlink fronthaul constraints become the uplink quantization noises. This duality relationship enables an efficient algorithm for optimizing the downlink transmission and relaying strategies based on the uplink.

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.

We propose a novel joint activity, channel and data estimation (JACDE) scheme for multiple-input multiple-output (MIMO) systems. The contribution aims to allow significant overhead reduction of MIMO systems by enabling grant-free access, while maintaining moderate throughput per user. To that end, we extend the conventional MIMO transmission framework so as to incorporate activity detection capability without resorting to spreading informative data symbols, in contrast with related work which typically relies on signal spreading. Our method leverages a Bayesian message passing scheme based on Gaussian approximation, which jointly performs active user detection (AUD), channel estimation (CE), and multi-user detection (MUD), incorporating also a well-structured low-coherence pilot design based on frame theory, which mitigates pilot contamination, and finally complemented with a detector empowered by bilinear message passing. The efficacy of the resulting JACDE-based grant-free access scheme in the cell-free MIMO system setup compliant with fifth generation (5G) new radio (NR) orthogonal frequency-division multiplexing (OFDM) signaling is demonstrated by simulation results. The results are shown to outperform the current state-of-the-art and approach the performance of an idealized (genie-aided) scheme in which user activity and channel coefficients are perfectly known.

This paper introduces a wireless communication protocol for industrial control systems that uses channel quality awareness to dynamically create network-device cooperation and assist the nodes in momentary poor channel conditions. To that point, channel state information is used to identify nodes with strong and weak channel conditions. We show that strong nodes in the network are best to be served in a single-hop transmission with transmission rate adapted to their instantaneous channel conditions. Meanwhile, the remainder of time-frequency resources is used to serve the nodes with weak channel condition using a two-hop transmission with cooperative communication among all the nodes to meet the target reliability in their communication with the controller. We formulate the achievable multi-user and multi-antenna diversity gain in the low-latency regime, and propose a new scheme for exploiting those on-demand, in favor of reliability and efficiency. The proposed transmission scheme is therefore dubbed adaptive network-device cooperation (ANDCoop), since it is able to adaptively allocate cooperation resources while enjoying the multi-user diversity gain of the network. We formulate the optimization problem of associating nodes to each group and dividing resources between the two groups. Numerical solutions show significant improvement in spectral efficiency and system reliability compared to the existing schemes in the literature. System design incorporating the proposed transmission strategy can thus reduce infrastructure cost for future private wireless networks.

This paper shows that deep neural network (DNN) can be used for efficient and distributed channel estimation, quantization, feedback, and downlink multiuser precoding for a frequency-division duplex massive multiple-input multiple-output system in which a base station (BS) serves multiple mobile users, but with rate-limited feedback from the users to the BS. A key observation is that the multiuser channel estimation and feedback problem can be thought of as a distributed source coding problem. In contrast to the traditional approach where the channel state information (CSI) is estimated and quantized at each user independently, this paper shows that a joint design of pilots and a new DNN architecture, which maps the received pilots directly into feedback bits at the user side then maps the feedback bits from all the users directly into the precoding matrix at the BS, can significantly improve the overall performance. This paper further proposes robust design strategies with respect to channel parameters and also a generalizable DNN architecture for varying number of users and number of feedback bits. Numerical results show that the DNN-based approach with short pilot sequences and very limited feedback overhead can already approach the performance of conventional linear precoding schemes with full CSI.

Intelligent reflecting surface (IRS), consisting of massive number of tunable reflective elements, is capable of boosting spectral efficiency between a base station (BS) and a user by intelligently tuning the phase shifters at the IRS according to the channel state information (CSI). However, due to the large number of passive elements which cannot transmit and receive signals, acquisition of CSI for IRS is a practically challenging task. Instead of using the received pilots to estimate the channels explicitly, this paper shows that it is possible to learn the effective IRS reflection pattern and beamforming at the BS directly based on the received pilots. This is achieved by parameterizing the mapping from the received pilots to the optimal configuration of IRS and the beamforming matrix at the BS by properly tuning a deep neural network using unsupervised training. Simulation results indicate that the proposed neural network can efficiently learn to maximize the system sum rate from much fewer received pilots as compared to the traditional channel estimation based solutions.

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.

This paper proposes an autoencoder-based symbol-level precoding (SLP) scheme for a massive multiple-input multiple-output (MIMO) system operating in a limited-scattering environment. By recognizing that only imperfect channel state information (CSI) is available in practice, the goal of the proposed approach is to design the down-link SLP system robust to such imperfect CSI. Toward this goal, this paper leverages the concept of autoencoder wherein the end-to-end communications system is modeled by a deep neural network. By end-to-end training the proposed autoencoder, this paper shows that the downlink symbol-level precoder as well as the receivers' decision rule can be jointly designed in ways that are robust to channel uncertainty. Moreover, this paper introduces a novel two-step training procedure to design a robust precoding scheme for conventional modulations such as quadrature amplitude modulation (QAM) and phase shift keying (PSK). Numerical results indicate that the proposed autoencoder-based framework, either trained by the end-to-end approach in which the receive constellation is a design variable or by the proposed two-step training approach with QAM constellation, can efficiently design a SLP scheme for massive MIMO system which is robust to channel uncertainty.

This paper considers a massive random access scenario where a small random set of k active users out of a larger number of n total potential users seek to transmit data to a base station. Specifically, we examine an approach in which the base station first determines the set of active users based on an uplink pilot phase, then broadcasts a common feedback message to all the active users for the scheduling of their subsequent data transmissions. Our main question is: What is the minimum amount of common feedback needed to schedule k users in k slots while completely avoiding collisions? Instead of a naive scheme of using k log(n) feedback bits, this paper presents upper and lower bounds to show that the minimum number of required common feedback bits scales linearly in k, plus an additive term that scales only as Θ(log log(n)). The achievability proof is based on a random coding argument. We further connect the problem of constructing a minimal length feedback code to that of finding a minimal set of complete k-partite subgraphs that form an edge covering of a k-uniform complete hypergraph with n vertices. Moreover, the problem is also equivalent to that of finding a minimal perfect hashing family, thus allowing leveraging the explicit perfect hashing code constructions for achieving collision-free massive random access.

Edge machine learning can deliver low-latency and private artificial intelligent (AI) services for mobile devices by leveraging computation and storage resources at the network edge. This article presents an energy-efficient edge processing framework to execute deep learning inference tasks at the edge computing nodes whose wireless connections to mobile devices are prone to channel uncertainties. Aimed at minimizing the sum of computation and transmission power consumption with probabilistic Quality-of-Service (QoS) constraints, we formulate the joint inference tasking and the downlink beamforming problem that is characterized by a group sparse objective function. We provide a statistical learning-based robust optimization approach to approximate the highly intractable probabilistic-QoS constraints by nonconvex quadratic constraints, which are further reformulated as matrix inequalities with a rank-one constraint via matrix lifting. We design a reweighted power minimization approach by iteratively reweighted ℓ1minimization with difference-of-convex-functions (DC) regularization and updating weights, where the reweighted approach is adopted for enhancing group sparsity whereas the DC regularization is designed for inducing rank-one solutions. The numerical results demonstrate that the proposed approach outperforms other state-of-the-art approaches.

A deep neural network (DNN) based power control method that aims at solving the non-convex optimization problem of maximizing the sum rate of a fading multi-user interference channel is proposed. Towards this end, we first present PCNet, which is a multi-layer fully connected neural network that is specifically designed for the power control problem. A key challenge in training a DNN for the power control problem is the lack of ground truth, i.e., the optimal power allocation is unknown. To address this issue, PCNet leverages the unsupervised learning strategy and directly maximizes the sum rate in the training phase. We then present PCNet+, which enhances the generalization capacity of PCNet by incorporating noise power as an input to the network. Observing that a single PCNet(+) does not universally outperform the existing solutions, we further propose ePCNet(+), a network ensemble with multiple PCNets(+) trained independently. Simulation results show that for the standard symmetric K -user Gaussian interference channel, the proposed methods can outperform state-of-the-art power control solutions under a variety of system configurations. Furthermore, the performance improvement of ePCNet comes with a reduced computational complexity.

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.

This paper aims to provide an optimization framework for coded caching that accounts for various heterogeneous aspects of practical systems. An optimization theoretic perspective on the seminal work on the fundamental limits of caching by Maddah-Ali and Niesen is first developed, whereas it is proved that the coded caching scheme presented in that work is the optimal scheme among a large, non-trivial family of possible caching schemes. The optimization framework is then used to develop a coded caching scheme capable of handling simultaneous non-uniform file length, non-uniform file popularity, and non-uniform user cache size. Although the resulting full optimization problem scales exponentially with the problem size, this paper shows that tractable simplifications of the problem that scale as a polynomial function of the problem size can still perform well compared to the original problem. By considering these heterogeneities both individually and in conjunction with one another, evidence of the effect of their interactions and influence on optimal cache content is obtained.

Network virtualization is a key enabler for 5G systems to support the expected use cases of vertical markets. In this context, we study the joint optimal deployment of Virtual Network Functions (VNFs) and allocation of computational resources in a hybrid cloud infrastructure by taking the requirements of the 5G services and the characteristics of the cloud architecture into consideration. The resulting mixed-integer problem is reformulated as an integer linear problem, which can be solved by using a standard solver. Our results underline the advantages of a hybrid infrastructure over a standard cloud radio access network consisting only of a central cloud, and show that the proposed mechanism to deploy VNF chains leads to high resource utilization efficiency and large gains in terms of the number of supported VNF chains. To deal with the computational complexity of optimizing a large number of clouds and VNF chains, we propose a simple low-complexity heuristic that attempts to find a feasible VNF deployment solution with a limited number of functional splits. Numerical results indicate that the performance of the proposed heuristic is close to the optimal one when the edge clouds are well dimensioned with respect to the computational requirements of the 5G services.

For massive machine-type communications, centralized control may incur a prohibitively high overhead. Grant-free non-orthogonal multiple access (NOMA) provides possible solutions, yet poses new challenges for efficient receiver design. In this paper, we develop a joint user identification, channel estimation, and signal detection (JUICESD) algorithm. We divide the detection scheme into two modules: slot-wise multi-user detection (SMD) and combined signal and channel estimation (CSCE). SMD is designed to decouple the transmissions of different users by leveraging the approximate message passing (AMP) algorithms, and CSCE is designed to deal with the nonlinear coupling of activity state, channel coefficient and transmit signal of each user separately. To address the problem that the exact calculation of the messages exchanged within CSCE and between the two modules is complicated due to phase ambiguity issues, this paper proposes a rotationally invariant Gaussian mixture (RIGM) model, and develops an efficient JUICESD-RIGM algorithm. JUICESD-RIGM achieves a performance close to JUICESD with a much lower complexity. Capitalizing on the feature of RIGM, we further analyze the performance of JUICESD-RIGM with state evolution techniques. Numerical results demonstrate that the proposed algorithms achieve a significant performance improvement over the existing alternatives, and the derived state evolution method predicts the system performance accurately.

Optimizing the sum-log-utility for the downlink of multi-frequency band, multiuser, multiantenna networks requires joint solutions to the associated beamforming and user scheduling problems through the use of cloud radio access network (CRAN) architecture; optimizing such a network is, however, non-convex and NP-hard. In this paper, we present a novel iterative beamforming and scheduling strategy based on fractional programming and the Hungarian algorithm. The beamforming strategy allows us to iteratively maximize the chosen objective function in a fashion similar to block coordinate ascent. Furthermore, based on the crucial insight that, in the downlink, the interference pattern remains fixed for a given set of beamforming weights, we use the Hungarian algorithm as an efficient approach to optimally schedule users for the given set of beamforming weights. Specifically, this approach allows us to select the best subset of users (amongst the larger set of all available users). Our simulation results show that, in terms of average sum-log-utility, as well as sum-rate, the proposed scheme substantially outperforms both the state-of-the-art multicell weighted minimum mean-squared error (WMMSE) and greedy proportionally fair WMMSE schemes, as well as standard interior-point and sequential quadratic solvers. Importantly, our proposed scheme is also far more computationally efficient than the multicell WMMSE scheme.

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.

Network densification and millimeter-wave technologies are key enablers to fulfill the capacity and data rate requirements of the fifth generation (5G) of mobile networks. In this context, designing low-complexity policies with local observations, yet able to adapt the user association with respect to the global network state and to the network dynamics is a challenge. In fact, the frameworks proposed in literature require continuous access to global network information and to recompute the association when the radio environment changes. With the complexity associated to such an approach, these solutions are not well suited to dense 5G networks. In this paper, we address this issue by designing a scalable and flexible algorithm for user association based on multi-agent reinforcement learning. In this approach, users act as independent agents that, based on their local observations only, learn to autonomously coordinate their actions in order to optimize the network sum-rate. Since there is no direct information exchange among the agents, we also limit the signaling overhead. Simulation results show that the proposed algorithm is able to adapt to (fast) changes of radio environment, thus providing large sum-rate gain in comparison to state-of-the-art solutions.

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.

The orthogonal matching pursuit (OMP) algorithm is a commonly used algorithm for recovering K-sparse signals x ∈ ℝnfrom linear model y = Ax, where A ∈ ℝm×nis a sensing matrix. A fundamental question in the performance analysis of OMP is the characterization of the probability that it can exactly recover x for random matrix A. Although in many practical applications, in addition to the sparsity, x usually also has some additional property (for example, the nonzero entries of x independently and identically follow the Gaussian distribution), none of existing analysis uses these properties to answer the above question. In this paper, we first show that the prior distribution information of x can be used to provide an upper bound on ||x||21/||x||22, and then explore the bound to develop a better lower bound on the probability of exact recovery with OMP in K iterations. Simulation tests are presented to illustrate the superiority of the new bound.

This paper considers a grant-free random access scenario for massive machine-type communications (mMTC) in which the devices are sporadically active with small payloads. Each active device transmits the identification information as well as the data symbol by selecting a sequence from a pre-assigned sequence set, and the base-station (BS) detects both the device activity and the data by detecting which sequences are transmitted. This paper makes an observation that in the massive multiple-input multiple-output (MIMO) regime, where the BS is equipped with a large number of antennas, a covariance based detection scheme that solves a maximum likelihood estimation problem is more effective than the approximate message passing (AMP) based compressed sensing approach for sequence detection. A main contribution of this paper is an analytic framework capable of accurately predicting the performance of the proposed scheme in terms of the probabilities of false alarm and missed detection. The analysis is based on the asymptotic properties of the maximum likelihood estimator under a nonstandard condition. Simulation results validate the analysis, and demonstrate that as compared to the AMP based approach, the covariance based approach achieves lower error probabilities, especially when the sequence length is short, as is often the case for low-latency mMTC.

Network virtualization is a key enabler for the 5G systems for supporting the novel use cases related to the vertical markets. In this context, we investigate the joint optimal deployment of Virtual Network Functions (VNFs) and the allocation of computational resources in a hybrid cloud infrastructure by taking into account the requirements of the 5G services and the characteristics of the cloud nodes. To achieve this goal, we analyze the relations between functional placement, computational requirements, and latency constraints, and formulate an integer linear programming problem, which can be solved by using a standard solver. Our results underline the advantages of a hybrid architecture over a standard solution with a central cloud, and show that the proposed mechanism to deploy VNFs leads to high resource utilization efficiency and large gains in terms of the number of slice chains that can be supported by the cloud-enhanced 5G networks.

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.

This paper proposes interference mitigation techniques for provisioning ultrareliable low-latency wireless communication in an industrial automation setting, where multiple transmissions from controllers to actuators interfere with each other. Channel fading and interference are key impairments in wireless communication. This paper leverages the recently proposed “Occupy CoW” protocol that efficiently exploits the broadcast opportunity and spatial diversity through a two-hop cooperative communication strategy among distributed receivers to combat deep fading, but points out that because this protocol avoids interference by frequency division orthogonal transmission, it is not scalable in terms of bandwidth required for achieving ultrareliability, when multiple controllers simultaneously communicate with multiple actuators (akin to the downlink of a multicell network). The main observation of this paper is that full frequency reuse in the first phase, together with successive decoding and cancellation of interference, can improve the performance of this strategy notably. We propose two protocols depending on whether interference cancellation or avoidance is implemented in the second phase, and show that both outperform Occupy CoW in terms of the required bandwidth and power for achieving ultrareliability at practical values of the transmit power.

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.

Sparse support recovery arises in many applications in communications and signal processing. Existing methods tackle sparse support recovery problems for a given measurement matrix, and cannot flexibly exploit the properties of sparsity patterns for improving performance. In this letter, we propose a data-driven approach to jointly design the measurement matrix and support recovery method for complex sparse signals, using auto-encoder in deep learning. The proposed architecture includes two components, an auto-encoder and a hard thresholding module. The proposed auto-encoder successfully handles complex signals using standard auto-encoder for real numbers. The proposed approach can effectively exploit properties of sparsity patterns, and is especially useful when these underlying properties do not have analytic models. In addition, the proposed approach can achieve sparse support recovery with low computational complexity. Experiments are conducted on an application example, device activity detection in grant-free massive access for massive machine type communications (mMTC). Numerical results show that the proposed approach achieves significantly better performance with much less computation time than classic methods, in the presence of extra structures in sparsity patterns.

This paper tackles the problem of millimeter-wave (mmWave) channel estimation in massive MIMO communication systems. A new Bayes-optimal channel estimator is derived using recent advances in the approximate belief propagation Bayesian inference paradigm. By leveraging the inherent sparsity of the mmWave MIMO channel in the angular domain, we recast the underlying channel estimation problem into that of reconstructing a compressible signal from a set of noisy linear measurements. Then, the generalized approximate message passing (GAMP) algorithm is used to find the entries of the unknown mmWave MIMO channel matrix. Unlike all the existing works on the same topic, we model the angular-domain channel coefficients by Laplacian distributed random variables. Furthermore, we establish the closed-form expressions for the various statistical quantities that need to be updated iteratively by GAMP. To render the proposed algorithm fully automated, we also develop an expectation-maximization (EM) based procedure that can be easily embedded within GAMP's iteration loop in order to learn all the unknown parameters of the underlying Bayesian inference problem. The computer simulations show that the proposed combined EM-GAMP algorithm under a Laplacian prior exhibits improvements both in terms of channel estimation accuracy, achievable rate, and computational complexity, as compared to the Gaussian mixture prior that has been advocated in the recent literature. In addition, it is found that the Laplacian prior speeds up the convergence time of GAMP over the entire signal-to-noise ratio range.

This paper studies the effectiveness of relaying for interference mitigation in an interference-limited communication scenario. We are motivated by the observation that in a cellular network, a relay node placed at the cell edge observes a combination of intended signal and inter-cell interference that is correlated with the received signal at a nearby destination, so a relaying link can effectively allow the antennas at the relay and at the destination to be pooled together for both signal enhancement and interference mitigation. We model this scenario by a multiple-input multiple-output (MIMO) Gaussian relay channel with a digital relay-to-destination link of finite capacity, and with correlated noise across the relay and destination antennas. Assuming a compress-and-forward strategy with Gaussian input distribution and quantization noise, we propose a coordinate ascent algorithm for obtaining a stationary point of the non-convex joint optimization of the transmit and quantization covariance matrices. For fixed input distribution, the globally optimum quantization noise covariance matrix can be found in closed-form using a transformation for the relay's observation that simultaneously diagonalizes two conditional covariance matrices by congruence. For fixed quantization, the globally optimum transmit covariance matrix can be found via convex optimization. This paper further shows that such an optimized achievable rate is within a constant additive gap of the MIMO relay channel capacity. The optimal structure of the quantization noise covariance enables a characterization of the slope of the achievable rate as a function of the relaying link capacity. Moreover, this paper shows that the improvement in spatial degrees of freedom by MIMO relaying in the presence of noise correlation is related to the aforementioned slope via a connection to the deterministic relay channel.

This paper studies the downlink of a cloud radio access network (C-RAN) in which a centralized processor (CP) communicates with mobile users through base stations (BSs) that are connected to the CP via finite-capacity fronthaul links. Information theoretically, the downlink of a C-RAN is modeled as a two-hop broadcast-relay network. Among the various transmission and relaying strategies for such model, this paper focuses on the compression strategy, in which the CP centrally encodes the signals to be broadcast jointly by the BSs, then compresses and sends these signals to the BSs through the fronthaul links. We characterize an achievable rate region for a generalized compression strategy with Marton's multicoding for broadcasting and multivariate compression for fronthaul transmission. We then compare this rate region with the distributed decode-forward (DDF) scheme, which achieves the capacity of the general relay networks to within a constant gap, and show that the difference lies in that DDF performs Marton's multicoding and multivariate compression jointly as opposed to successively as in the compression strategy. A main result of this paper is that under the assumption that the fronthaul links are subject to a sum capacity constraint, this difference is immaterial; so, for the Gaussian network, the compression strategy based on successive encoding can already achieve the capacity region of the C-RAN to within a constant gap, where the gap is independent of the channel parameters and the power constraints at the BSs. As a further result, for C-RAN under individual fronthaul constraints, this paper also establishes that the compression strategy can achieve to within a constant gap to the sum capacity.

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.

This paper considers sparse device activity detection for cellular machine-type communications with non-orthogonal signatures using the approximate message passing algorithm. This paper compares two network architectures, massive multiple-input-multiple-output (MIMO) and cooperative MIMO, in terms of their effectiveness in overcoming inter-cell interference. In the massive MIMO architecture, each base station (BS) detects only the users from its own cell while treating inter-cell interference as noise. In the cooperative MIMO architecture, each BS detects the users from neighboring cells as well; the detection results are then forwarded in the form of a log-likelihood ratio (LLR) to a central unit where final decisions are made. This paper analytically characterizes the probabilities of false alarm and missed detection for both architectures. The numerical results validate the analytic characterization and show that as the number of antennas increases, a massive MIMO system effectively drives the detection error to zero, while as the cooperation size increases, the cooperative MIMO architecture mainly improves the cell-edge user performance. Moreover, this paper studies the effect of LLR quantization to account for the finite-capacity fronthaul. The numerical simulations of a practical scenario suggest that in specific case cooperating three BSs in a cooperative MIMO system achieves about the same cell-edge detection reliability as a non-cooperative massive MIMO system with four times the number of antennas per BS.

This paper aims at the problem of time-of-flight (ToF) estimation using channel state information (CSI) obtainable from commercialized multiple-input-multiple-output orthogonal frequency-division multiplexing (MIMO-OFDM) wireless local area network (WLAN) receivers. It was often claimed that the CSI phase is contaminated with errors of known and unknown natures rendering the ToF-based positioning difficulty. To search for an answer, we take a bottom-up approach by first understanding CSI, its constituent building blocks, and the sources of error that contaminate it. We then model these effects mathematically. The correctness of these models is corroborated based on the CSI collected in extensive measurement campaign, including radiated, conducted, and chamber tests. Knowing the nature of contaminations in the CSI phase and amplitude, we proceed with introducing pre-processing methods to clean CSI from those errors and make it usable for range estimation. To check the validity of the proposed algorithms, the MUSIC super-resolution algorithm is applied to post-processed CSI to perform range estimates. The results substantiate that a median accuracy of 0.7, 0.8, and 0.9 m is achievable in a highly multipath line-of-sight environment where the transmitter and the receiver are 5, 10, and 15 m apart.

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.

Sparse device activity detection for machine-type communications has attracted increasing attention in recent studies. However, most of the previous works focus on the single-cell case. This paper studies the impact of the inter-cell interference on the device activity detection problem with non-orthogonal signatures in multi-cell systems by employing the computationally efficient approximate message passing algorithm (AMP). Specifically, this paper studies the impact of the inter-cell interference by either treating it as noise or recovering it, showing that it is always beneficial to recover the interference at each base station (BS). Two network architectures, namely BSs with large antenna arrays and network with multi-cell cooperation, are compared in terms of their effectiveness in overcoming inter-cell interference. This paper provides an analytical characterization of probabilities of false alarm and missed detection. Simulation results show that large-scale antenna array is effective in improving the performance of all users whereas cooperation is effective in improving the performance of cell-edge users. In terms of the detection performance of the 95-percentile users, simulation results under a typical network setting show that having twice as many antennas provides almost the same benefit as multi-cell cooperation.

The performance of cloud radio access networks (C-RAN) is limited by the finite capacities of the backhaullinks connecting the cloud with the base-stations (BSs). A promising approach to improving the performance of C-RAN is to augment the backhaul through BS caching, where the BSs pre-store some of the popular contents. In this paper, we first derive a multicast backhaul rate expression based on a joint cache-channel coding scheme, and show that, as compared to the uniform cache allocation, it is better to allocate larger cache sizes to the weaker BSs. Then, by leveraging the sample approximation method and the alternating direction method of multipliers, we develop an efficient algorithm to optimize the cache allocation by maximizing the BS expected file downloading rate from the cloud. Numerical results show considerable performance improvement of the optimized cache allocation scheme over heuristic schemes.

In massive MIMO aided cloud radio access network (C-RAN), plenty of remote radio heads (RRHs), each equipped with a massive MIMO array, are distributed within a specific geographical area and are connected to a centralized baseband unit (BBU) pool through fronthaul links. One major performance bottleneck in the uplink of massive MIMO aided C-RAN is that, the RRHs need to transport a huge amount of data to the BBU for baseband processings. Existing fronthaul compression methods that rely on fully-digital processing are not suitable for the massive MIMO regime due to their high implementation cost. To overcome this challenge, we propose a two-timescale hybrid analog-and-digital spatial compression scheme at RRHs to reduce the fronthaul data, where the analog filter is updated at a slow timescale according to the channel statistics to achieve massive MIMO array gain, and the digital filter is updated at a fast timescale according to the instantaneous effective channel state information (CSI) to achieve spatial multiplexing gain. Such a design can alleviate the performance bottleneck of limited fronthaul with reduced hardware cost and power consumption, and is more robust to the CSI delay. We propose an online algorithm for the two-timescale non-convex optimization of analog and digital filters. Simulations verify the advantages of the proposed scheme over state-of-the-art baseline schemes.

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.

Full-duplex (FD) has emerged as a disruptive solution for improving the achievable spectral efficiency (SE), thanks to the recent major breakthroughs in self-interference (SI) mitigation. The FD versus half-duplex (HD) SE gain, in the context of cellular networks, is however largely limited by the mutual interference (MI) between the downlink (DL) and uplink (UL). A potential remedy for tackling the MI bottleneck is through cooperative communications. This paper provides a stochastic analysis of FD enabled cloud radio access network (CRAN) with finite user- centric cooperative clusters. Contrary to the most existing theoretical studies of C-RAN, we explicitly take into consideration non-isotropic fading channel conditions, and finite-capacity fronthaul links. Accordingly, we develop analytical expressions for the FD C-RAN DL and UL SEs. The results indicate that significant FD versus HD C-RAN SE gains can be achieved, particularly in the presence of sufficient- capacity fronthaul links and advanced interference cancellation capabilities.

The performance of cloud radio access network (C-RAN) is limited by the finite capacities of the backhaul links connecting the centralized processor (CP) with the base-stations (BSs), especially when the backhaul is implemented in a wireless medium. This paper proposes the use of wireless multicast together with BS caching, where the BSs pre-store the contents of popular files, to augment the backhaul of C-RAN. For a downlink C-RAN consisting of a single cluster of BSs and wireless backhaul, this paper studies the optimal cache size allocation strategy among the BSs and the optimal multicast beamforming transmission strategy at the CP such that the user's requested messages are delivered from the CP to the BSs in the most efficient way. We first state a multicast backhaul rate expression based on a joint cache-channel coding scheme, which implies that larger cache sizes should be allocated to the BSs with weaker channels. We then formulate a two-timescale joint cache size allocation and beamforming design problem, where the cache is optimized offline based on the long-term channel statistical information, while the beamformer is designed during the file delivery phase based on the instantaneous channel state information. By leveraging the sample approximation method and the alternating direction method of multipliers, we develop efficient algorithms for optimizing the cache size allocation among the BSs, and quantify how much more caches should be allocated to the weaker BSs. We further consider the case with multiple files having different popularities and show that it is in general not optimal to entirely cache the most popular files first. Numerical results show considerable performance improvement of the optimized cache size allocation scheme over the uniform allocation and other heuristic schemes.

The integration of local device-to-device (D2D) communications and cellular connections has been intensively studied to satisfy co-existing D2D and cellular communication demand. In future cellular networks, there will be numerous standby users possessing D2D communication capabilities in close proximity to each other. Considering that these standby users do not necessarily request D2D communications all the time, in this paper we propose a hybrid D2D-cellular scheme to make use of these standby users and to improve the rate performance for cellular users. More specifically, through D2D links, a virtual antenna array can be formed by sharing antennas across different terminals to realize the diversity gain of MIMO channels. This paper considers the use of millimeter wave links to enable high data rate D2D communications. We then design an orthogonal D2D multiple access protocol and formulate the optimization problem of joint cellular and D2D resource allocation for downlink transmissions using the proposed scheme. We obtain a closed-form solution for D2D resource allocation, which reveals useful insights for practical system design. Numerical results from extensive system-level simulations demonstrate that the rate performance of cellular users is significantly improved.

A base-station (BS) equipped with multiple antennas can use its spatial dimensions in three different ways: 1) to serve multiple users, thereby achieving a multiplexing gain; 2) to provide spatial diversity in order to improve user rates; and 3) to null interference in neighboring cells. This paper answers the following question: What is the optimal balance between these three competing benefits? We answer this question in the context of the downlink of a cellular network, where multi-antenna BSs serve multiple single-antenna users using zero-forcing beamforming with equal power assignment, while nulling interference at a subset of out-of-cell users. Any remaining spatial dimensions provide transmit diversity for the scheduled users. Utilizing tools from stochastic geometry, we show that, surprisingly, to maximize the per-BS ergodic sum rate, with an optimal allocation of spatial resources, interference nulling does not provide a tangible benefit. The strategy of avoiding inter-cell interference nulling, reserving some fraction of spatial resources for multiplexing, and using the rest to provide diversity, is already close-to-optimal in terms of the sum-rate. However, interference nulling does bring significant benefit to cell-edge users, particularly when adopting a range-adaptive nulling strategy where the size of the cooperating BS cluster is increased for cell-edge users.

This paper studies transmission strategies for the downlink of a cloud radio access network, in which the base stations are connected to a centralized cloud computing-based processor with digital fronthaul or backhaul links. We provide a system-level performance comparison of two fundamentally different strategies, namely, the data-sharing strategy and the compression strategy, which differ in the way the fronthaul/backhaul is utilized. It is observed that the performance of both strategies depends crucially on the available fronthaul or backhaul capacity. When the fronthaul/backhaul capacity is low, the data-sharing strategy performs better, while under moderate-to-high fronthaul/backhaul capacity, the compression strategy is superior. Using insights from such a comparison, we propose a novel hybrid strategy, combining the data-sharing and compression strategies, which allows for better control over the fronthaul/backhaul capacity utilization. An optimization framework for the hybrid strategy is proposed. Numerical evidence demonstrates the performance gain of the hybrid strategy.

This paper provides an analytical performance characterization of both the uplink (UL) and downlink (DL) user-centric network multiple-input multiple-output (MIMO) systems, where a cooperating base station (BS) cluster is formed for each user individually and the clusters for different users may overlap. In this model, cooperating BSs (each equipped with multiple antennas) jointly perform zero-forcing beamforming to the set of single-antenna users associated with them. As compared with a baseline network MIMO system with disjoint BS clusters, the effect of user-centric clustering is that it improves signal strength in both the UL and DL, while reducing cluster-edge interference in the DL. This paper quantifies these effects by assuming that BSs and users form Poisson point processes and by further approximating both the signal and interference powers using Gamma distributions of appropriate parameters. We show that BS cooperation provides significant gain as compared to single-cell processing for both the UL and DL, but the advantage of user-centric clustering over the baseline disjoint clustering system is significant for the DL cluster-edge users only. Although the analytic results are derived with the assumption of perfect channel state information and infinite backhaul between the cooperating BSs, they nevertheless provide architectural insight into the design of the future cooperative cellular networks.

Caching at base stations (BSs) is a promising approach for supporting the tremendous traffic growth of content delivery over future small-cell wireless networks with limited backhaul. This paper considers exploiting spatial caching diversity (i.e., caching different subsets of popular content files at neighboring BSs) that can greatly improve the cache hit probability, thereby leading to better overall system performance. A key issue in exploiting spatial caching diversity is that the cached content may not be located at the nearest BS, which means that to access such content, a user needs to overcome strong interference from the nearby BSs; this significantly limits the gain of spatial caching diversity. In this paper, we consider a joint design of frequency reuse and caching, such that the benefit of an improved cache hit probability induced by spatial caching diversity and the benefit of interference coordination induced by frequency reuse can be achieved simultaneously. We obtain a closed-form characterization of the approximate successful transmission probability for the proposed scheme and analyze the impact of key operating parameters on the performance. We design a low-complexity algorithm to optimize the frequency reuse factor and the cache storage allocation. Simulations show that the proposed scheme achieves a higher successful transmission probability than existing caching schemes.

As an indispensable use case for the 5G wireless systems on the roadmap, ultra-reliable and low-latency communications (URLLC) is a crucial requirement for the coming era of wireless industrial automation. The key performance indicators for URLLC stand in sharp contrast to the requirements of enhanced mobile broadband: low-latency and ultra-reliability are paramount but high data rates are often not required. This paper aims to develop communication techniques for making a paradigm shift from the conventional human-type broadband communications to the emerging machine-type URLLC. One fundamental task for URLLC is to deliver short commands from a controller to a group of actuators within the stringent delay requirement and with high reliability. Motivated by the factory automation setting in which the tasks are assigned to groups of devices that work in close proximity to each other and can thus form clusters of reliable device-to-device (D2D) networks, this paper proposes a novel two-phase transmission protocol for achieving URLLC. In the first phase, within the latency requirement, the multi-antenna base station (BS) combines the messages of all devices within each group together and multicasts them to the corresponding groups; messages for different groups are spatially multiplexed. In the second phase, the devices that have decoded the messages successfully, herein defined as the leaders, help relay the messages to the other devices in their groups. Under this protocol, we design an innovative leader selection-based beamforming strategy at the BS by utilizing the sparse optimization technique. The proposed strategy leads to a desired sparsity pattern in user activity with at least one leader being able to decode its message in each group in the first phase, thus ensuring full utilization of the reliability enhancing D2D transmissions in the second phase. Simulation results are provided to show that the proposed two-phase transmission protocol considerably improves the reliability of the entire system within the stringent latency requirement as compared with existing schemes for URLLC.

Full-duplex (FD) wireless has emerged as a disruptive communications paradigm for enhancing the achievable spectral efficiency (SE), thanks to the recent major breakthroughs in self-interference mitigation. The FD versus half-duplex (HD) SE gain in cellular networks is, however, largely limited by the mutual-interference (MI) between the downlink (DL) and the uplink (UL). A potential remedy for tackling the MI bottleneck is through cooperative communications. This paper provides a stochastic design and analysis of FD enabled cloud radio access network (C-RAN) under the Poisson point process-based abstraction model of multi-antenna radio units and user equipments. We consider different network- and user-centric approaches toward the formation of finite clusters in the C-RAN. Contrary to most existing studies, we explicitly take into consideration non-isotropic fading channel conditions and finite-capacity fronthaul links. Accordingly, upper-bound expressions for the C-RAN DL and UL SEs, involving the statistics of all intended and interfering signals, are derived. The performance of the FD C-RAN is investigated through the proposed theoretical framework and Monte-Carlo simulations. According to simulations using parameters of a state-of-the-art system, significant FD versus HD C-RAN SE gains can be achieved in the presence of advanced interference cancellation capabilities and sufficient-capacity fronthaul links.

User activity detection is a central problem in massive device communication scenarios in which an access point needs to detect active devices among large number of potential devices each transmitting sporadically. By exploiting sparsity in user activity, the detection problem can be formulated as a compressed sensing problem, thereby allowing the use of computationally efficient approximate message passing (AMP) algorithm for activity detection. This paper proposes an AMP-based user activity detector that accounts for the statistics of device geographic locations in a cellular network. The proposed scheme is based on a minimum mean squared error (MMSE) denoiser designed for specific wireless channel fading and path-loss distributions. This paper further provides an analytic characterization of the false alarm versus missed detection probabilities using state evolution for AMP. Simulation results show significantly improved detection threshold for the channel-aware denoiser as compared to standard soft threshold based AMP.

This paper studies a single-cell uplink massive device communication scenario in which a large number of single-antenna devices are connected to the base station (BS), but user traffic is sporadic so that at a given coherence interval, only a subset of users are active. For such a system, active user detection and channel estimation are key issues. To accommodate many simultaneously active users, this paper studies an asymptotic regime where the BS is equipped with a large number of antennas. A grant-free two-phase access scheme is adopted where user activity detection and channel estimation are performed in the first phase, and data is transmitted in the second phase. Our main contributions are as follows. First, this paper shows that despite the non-orthogonality of pilot sequences (which is necessary for accommodating a large number of potential devices), in the asymptotic massive multiple-input multiple-output (MIMO) regime, both the missed detection and false alarm probabilities can be made to go to zero by utilizing compressed sensing techniques that exploit sparsity in user activities. Further, this paper shows that despite the guaranteed success in user activity detection, the non-orthogonality of pilot sequences nevertheless can cause significantly larger channel estimation error as compared to the conventional massive MIMO system, thus lowering the overall achievable transmission rate. This paper quantifies the cost due to device detection and channel estimation and illustrates its effect on the optimal pilot length for massive device connectivity.

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 paper explores the benefits of deploying multi-antenna half-duplex amplify-and-forward shared relays at the cell-edge to assist the downlink transmission in a multiple-input multiple-output wireless cellular network. We design the relay node to provide extra spatial dimensions to multiple receivers at the same time for interference mitigation and signal enhancement. This paper proposes an efficient algorithm to solve the non-convex problem of jointly optimizing the transmit beamforming and relay combining matrices to a stationary point by extending the celebrated weighted minimum mean squared error (WMMSE) algorithm. We show that the optimized relaying strategy can significantly improve the long-term average rates of cell-edge users in a cellular network, even after accounting for the extra bandwidth required for halfduplex relaying.

Hybrid analog and digital beamforming is a promising candidate for large-scale millimeter wave (mmWave) multiple-input multiple-output (MIMO) systems because of its ability to significantly reduce the hardware complexity of the conventional fully digital beamforming schemes while being capable of approaching the performance of fully digital schemes. Most of the prior work on hybrid beamforming considers frequency-flat channels. However, broadband mmWave systems are frequency-selective. In broadband systems, it is desirable to design common analog beamformer for the entire band while employing different digital (baseband) beamformers in different frequency sub-bands. This paper considers the hybrid beamforming design for systems with orthogonal frequency division multiplexing modulation. First, for a single-user MIMO (SU-MIMO) system where the hybrid beamforming architecture is employed at both transmitter and receiver, we show that hybrid beamforming with a small number of radio frequency (RF) chains can asymptotically approach the performance of fully digital beamforming for a sufficiently large number of transceiver antennas due to the sparse nature of the mmWave channels. For systems with a practical number of antennas, we then propose a unified heuristic design for two different hybrid beamforming structures, the fully connected and the partially connected structures, to maximize the overall spectral efficiency of an mmWave MIMO system. Numerical results are provided to show that the proposed algorithm outperforms the existing hybrid beamforming methods, and for the fully connected architecture, the proposed algorithm can achieve spectral efficiency very close to that of the optimal fully digital beamforming but with much fewer RF chains. Second, for the multiuser multiple-input single-output case, we propose a heuristic hybrid percoding design to maximize the weighted sum rate in the downlink and show numerically that the proposed algorithm with practical number of RF chains can already approach the performance of fully digital beamforming.

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 design of the routing strategy over the fronthaul network and the transmission strategy over the wireless network in a downlink cloud radio access network (C-RAN), in which the remote radio heads (RRHs) are connected to the central processor (CP) via multi-hop routers. The data-sharing strategy is adopted, where the CP multicasts each user's data to all the RRHs serving this user via the multihop fronthaul network, which then cooperatively serve the users through joint beamforming. Such a setting naturally provides an opportunity for applying the technique of network coding to efficiently reduce the multicast traffic in the fronthaul network. A novel cross-layer optimization framework is then investigated, where the RRH's beamforming vectors as well as the user- RRH association in the physical-layer, and the network coding design in the network-layer are jointly optimized to maximize the throughput of C- RAN subject to fronthaul link capacity constraints. This paper proposes a two-stage algorithm to solve this problem using the techniques of sparse optimization and successive convex approximation. Simulation results are provided to verify the effectiveness of the proposed cross-layer design in the downlink multi- hop C-RAN.

This paper considers the optimal placement of content in cache-enabled base-stations (BSs) for reducing backhaul traffic in a densely deployed wireless access network. By caching popular files, users requesting these files can be served directly by their associated BSs without needing to fetch content from the core network. This paper makes an observation that a real network consists of distinct classes of users with different file preferences, so jointly optimizing cache placement and user-BS association can result in significant benefit. This paper considers such a joint optimization problem for achieving an optimized tradeoff between load balancing and backhaul saving, while accounting for both the physical layer wireless propagation characteristics and the finite cache size at the BSs. By proposing a numerical algorithm that iteratively optimizes the content placement policy for fixed user-association and optimizes the user association policy for fixed content placement, with a goal of maximizing a backhaul-aware proportional fairness network utility, this paper shows that placing similar content at strategically located BSs can result in significant backhaul saving without sacrificing as much in user access rates.

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.

Uplink-downlink duality refers to the fact that the Gaussian broadcast channel has the same capacity region as the dual Gaussian multiple-access channel under the same sum-power constraint. This paper investigates a similar duality relationship between the uplink and downlink of a cloud radio access network (C-RAN), where a central processor (CP) cooperatively serves multiple mobile users through multiple remote radio heads (RRHs) connected to the CP with finite-capacity fronthaul links. The uplink of such a C-RAN model corresponds to a multiple-access relay channel; the downlink corresponds to a broadcast relay channel. This paper considers compression-based relay strategies in both uplink and downlink C-RAN, where the quantization noise levels are functions of the fronthaul link capacities. If the fronthaul capacities are infinite, the conventional uplink-downlink duality applies. The main result of this paper is that even when the fronthaul capacities are finite, duality continues to hold for the case where independent compression is applied across each RRH in the sense that when the transmission and compression designs are jointly optimized, the achievable rate regions of the uplink and downlink remain identical under the same sum-power and individual fronthaul capacity constraints. As an application of the duality result, the power minimization problem in downlink C-RAN can be efficiently solved based on its uplink counterpart.

This paper studies the energy efficiency of the cloud radio access network (C-RAN), specifically focusing on two fundamental and different downlink transmission strategies, namely the data-sharing strategy and the compression strategy. In the data-sharing strategy, the backhaul links connecting the central processor (CP) and the base-stations (BSs) are used to carry user messages-each user's messages are sent to multiple BSs; the BSs locally form the beamforming vectors then cooperatively transmit the messages to the user. In the compression strategy, the user messages are precoded centrally at the CP, which forwards a compressed version of the analog beamformed signals to the BSs for cooperative transmission. This paper compares the energy efficiencies of the two strategies by formulating an optimization problem of minimizing the total network power consumption subject to user target rate constraints, where the total network power includes the BS transmission power, BS activation power, and load-dependent backhaul power. To tackle the discrete and nonconvex nature of the optimization problems, we utilize the techniques of reweighted ℓ1minimization and successive convex approximation to devise provably convergent algorithms. Our main finding is that both the optimized data-sharing and compression strategies in C-RAN achieve much higher energy efficiency as compared to the nonoptimized coordinated multipoint transmission, but their comparative effectiveness in energy saving depends on the user target rate. At low user target rate, data-sharing consumes less total power than compression; however, as the user target rate increases, the backhaul power consumption for data-sharing increases significantly leading to better energy efficiency of compression at the high user rate regime.

The emergence of interference alignment (IA) as a degrees-of-freedom optimal strategy motivates the need to investigate whether IA can be leveraged to aid conventional network utility maximization algorithms, which are typically only capable of finding locally optimal solutions. To test the usefulness of IA in this context, this paper proposes a two-stage optimization framework for the downlink of a G -cell multi-antenna network with K users/cell. The first stage of the proposed framework focuses on nulling interference from a set of dominant interferers using IA, while the second stage optimizes transmit and receive beamformers to maximize a network-wide utility using the IA solution as the initial condition. Further, this paper establishes a set of new feasibility results for partial IA that can be used to guide the number of dominant interferers to be nulled in the first stage. Through simulations on specific topologies of a cluster of base stations, it is observed that the impact of IA depends on the choice of the utility function and the presence of out-of-cluster interference. In the absence of out-of-cluster interference, the proposed framework outperforms straightforward optimization when maximizing the minimum rate, while providing marginal gains when maximizing sum rate. However, the benefit of IA is greatly diminished in the presence of significant out-of-cluster interference.

This paper investigates the compress-and-forward scheme for an uplink cloud radio access network (C-RAN) model, where multi-antenna base stations (BSs) are connected to a cloud-computing-based central processor (CP) via capacity-limited fronthaul links. The BSs compress the received signals with Wyner-Ziv coding and send the representation bits to the CP; the CP performs the decoding of all the users' messages. Under this setup, this paper makes progress toward the optimal structure of the fronthaul compression and CP decoding strategies for the compress-and-forward scheme in the C-RAN. On the CP decoding strategy design, this paper shows that under a sum fronthaul capacity constraint, a generalized successive decoding strategy of the quantization and user message codewords that allows arbitrary interleaved order at the CP achieves the same rate region as the optimal joint decoding. Furthermore, it is shown that a practical strategy of successively decoding the quantization codewords first, then the user messages, achieves the same maximum sum rate as joint decoding under individual fronthaul constraints. On the joint optimization of user transmission and BS quantization strategies, this paper shows that if the input distributions are assumed to be Gaussian, then under joint decoding, the optimal quantization scheme for maximizing the achievable rate region is Gaussian. Moreover, Gaussian input and Gaussian quantization with joint decoding achieve to within a constant gap of the capacity region of the Gaussian multiple-input multiple-output (MIMO) uplink C-RAN model. Finally, this paper addresses the computational aspect of optimizing uplink MIMO C-RAN by showing that under fixed Gaussian input, the sum rate maximization problem over the Gaussian quantization noise covariance matrices can be formulated as convex optimization problems, thereby facilitating its efficient solution.

This paper presents a content-centric transmission design in a cloud radio access network by incorporating multicasting and caching. Users requesting the same content form a multicast group and are served by a same cluster of base stations (BSs) cooperatively. Each BS has a local cache, and it acquires the requested contents either from its local cache or from the central processor via backhaul links. We investigate the dynamic content-centric BS clustering and multicast beamforming with respect to both channel condition and caching status. We first formulate a mixed-integer nonlinear programming problem of minimizing the weighted sum of backhaul cost and transmit power under the quality-of-service constraint for each multicast group. Theoretical analysis reveals that all the BSs caching a requested content can be included in the BS cluster of this content, regardless of the channel conditions. Then, we reformulate an equivalent sparse multicast beamforming (SBF) problem. By adopting smoothed ℓ0-norm approximation and other techniques, the SBF problem is transformed into the difference of convex programs and effectively solved using the convex-concave procedure algorithms. Simulation results demonstrate significant advantage of the proposed content-centric transmission. The effects of heuristic caching strategies are also evaluated.

Multicast transmission and wireless caching are effective ways of reducing air and backhaul traffic load in wireless networks. This paper proposes to incorporate these two key ideas for content-centric transmission in a cloud radio access network (RAN) where multiple base stations (BSs) are connected to a central processor (CP) via finite-capacity backhaul links. Each BS has a cache with finite storage size and is equipped with multiple antennas. The BSs cooperatively transmit contents, either stored in the local cache or fetched from the CP, to multiple users in the network. Users requesting a same content form a multicast group and are served by a same cluster of BSs cooperatively using multicast beamforming. Assuming fixed cache placement, this paper investigates the joint design of multicast beamforming and content-centric BS clustering by formulating an optimization problem of minimizing the total network cost under the quality-of-service (QoS) constraints for each multicast group. The network cost involves both the transmission power and the backhaul cost. We model the backhaul cost using the mixed ℓ0/ℓ2-norm of beamforming vectors. To solve this non-convex problem, we first approximate it using the semidefinite relaxation (SDR) method and concave smooth functions. We then propose a difference of convex functions (DC) programming algorithm to obtain suboptimal solutions and show the connection of three smooth functions. Simulation results validate the advantage of multicasting and show the effects of different cache size and caching policies in cloud RAN.

Large-scale multiple-input multiple-output (MIMO) systems enable high spectral efficiency by employing large antenna arrays at both the transmitter and the receiver of a wireless communication link. In traditional MIMO systems, full digital beamforming is done at the baseband; one distinct radio-frequency (RF) chain is required for each antenna, which for large-scale MIMO systems can be prohibitive from either cost or power consumption point of view. This paper considers a two-stage hybrid beamforming structure to reduce the number of RF chains for large-scale MIMO systems. The overall beamforming matrix consists of analog RF beamforming implemented using phase shifters and baseband digital beamforming of much smaller dimension. This paper considers precoder and receiver design for maximizing the spectral efficiency when the hybrid structure is used at both the transmitter and the receiver. On the theoretical front, bounds on the minimum number of transmit and receive RF chains that are required to realize the theoretical capacity of the large-scale MIMO system are presented. It is shown that the hybrid structure can achieve the same performance as the fully-digital beamforming scheme if the number of RF chains at each end is greater than or equal to twice the number of data streams. On the practical design front, this paper proposes a heuristic hybrid beamforming design strategy for the critical case where the number of RF chains is equal to the number of data streams, and shows that the performance of the proposed hybrid beamforming design can achieve spectral efficiency close to that of the fully-digital solution.

This paper considers a novel use of device-to-device link for cooperative communication wherein a nearby user terminal acts as a relay in enabling both signal enhancement and common interference rejection at the intended destination. Assuming Gaussian transmission and Gaussian compress-and-forward relaying strategy for the multiple-input multiple-output (MIMO) relay channel with a finite-capacity out-of-band relay-destination link and with arbitrarily correlated noises, this paper proposes a coordinate ascent approach for iteratively optimizing the transmit covariance matrix at the source and the quantization noise covariance matrix at the relay. We show that the optimization of quantization noise covariance matrix under fixed input can be solved in closed form using a simultaneous diagonalization approach, while the optimization of transmit covariance matrix under fixed quantization can be cast as a convex optimization problem. This paper further introduces the concept of antenna pooling and illustrates the importance of accounting for the noise correlation across the user terminals due to common interference. We show that the optimized transmission and device-to-device relaying strategies that take advantage of the noise correlation can significantly improve the user throughput in a cellular environment by enabling interference rejection across the user terminals.

This paper investigates the compress-and-forward scheme for an uplink cloud radio access network (C-RAN) model, where multi-antenna base-stations (BSs) are connected to a cloudcomputing based central processor (CP) via capacity-limited fronthaul links. The BSs perform Wyner-Ziv coding to compress and send the received signals to the CP; the CP performs either joint decoding of both the quantization codewords and the user messages at the same time, or the more practical successive decoding of the quantization codewords first, then the user messages. Under this setup, this paper makes progress toward the optimization of the fronthaul compression scheme by proving two results. First, it is shown that if the input distributions are assumed to be Gaussian, then under joint decoding, the optimal Wyner-Ziv quantization scheme for maximizing the achievable rate region is Gaussian. Second, for fixed Gaussian input, under a sum fronthaul capacity constraint and assuming Gaussian quantization, this paper shows that successive decoding and joint decoding achieve the same maximum sum rate. In this case, the optimization of Gaussian quantization noise covariance matrices for maximizing sum rate can be formulated as a convex optimization problem, therefore can be solved efficiently.

The joint user association and spectrum allocation problem is studied for multi-tier heterogeneous networks (HetNets) in both downlink and uplink in the interference-limited regime. Users are associated with base-stations (BSs) based on the biased downlink received power. Spectrum is either shared or orthogonally partitioned among the tiers. This paper models the placement of BSs in different tiers as spatial point processes and adopts stochastic geometry to derive the theoretical mean proportionally fair utility of the network based on the coverage rate. By formulating and solving the network utility maximization problem, the optimal user association bias factors and spectrum partition ratios are analytically obtained for the multi-tier network. The resulting analysis reveals that the downlink and uplink user associations do not have to be symmetric. For uplink under spectrum sharing, if all tiers have the same target signal-to-interference ratio (SIR), distance-based user association is shown to be optimal under a variety of path loss and power control settings. For both downlink and uplink, under orthogonal spectrum partition, it is shown that the optimal proportion of spectrum allocated to each tier should match the proportion of users associated with that tier. Simulations validate the analytical results. Under typical system parameters, simulation results suggest that spectrum partition performs better for downlink in terms of utility, while spectrum sharing performs better for uplink with power control.

This paper investigates the symmetric degrees of freedom (DoF) of multiple-input multiple-output (MIMO) cellular networks with G cells and K users per cell, having N antennas at each base station and M antennas at each user. In particular, we investigate techniques for achievability that are based on either decomposition with asymptotic interference alignment or linear beamforming schemes and show that there are distinct regimes of (G,K,M,N) , where one outperforms the other. We first note that both one-sided and two-sided decomposition with asymptotic interference alignment achieve the same DoF. We then establish specific antenna configurations under which the DoF achieved using decomposition-based schemes is optimal by deriving a set of outer bounds on the symmetric DoF. Using these results, we completely characterize the optimal DoF of any G-cell network with single-antenna users. For linear beamforming schemes, we first focus on small networks and propose a structured approach to linear beamforming based on a notion called packing ratios. Packing ratio describes the interference footprint or shadow cast by a set of transmit beamformers and enables us to identify the underlying structures for aligning interference. Such a structured beamforming design can be shown to achieve the optimal spatially normalized DoF (sDoF) of two-cell two-user/cell network and the two-cell three-user/cell network. For larger networks, we develop an unstructured approach to linear interference alignment, where transmit beamformers are designed to satisfy conditions for interference alignment without explicitly identifying the underlying structures for interference alignment. The main numerical insight of this paper is that such an approach appears to be capable of achieving the optimal sDoF for MIMO cellular networks in regimes where linear beamforming dominates asymptotic decomposition, and a significant portion of sDoF elsewhere. Remarkably, polynomial identity test appears to play a key role in identifying the boundary of the achievable sDoF region in the former case.

This paper proposes a linear beamforming strategy for interference alignment in multiple-input multiple-output (MIMO) cellular networks. In particular, we consider a network consisting of G mutually interfering cells with K users/cell, having N antennas at each base station (BS) and M antennas at each user - a (G, K, M, N) network. We develop an unstructured approach to designing linear beamformers for interference alignment where transmit beamformers are designed to satisfy conditions for interference alignment without explicitly identifying the underlying structures for alignment. Specifically, the transmit beamformers in the uplink are required to satisfy a certain number of random linear vector equations in order to constrain the number of dimensions occupied by interference at each BS. The conceptual simplicity and the fact that no customization to a given network is needed makes this method applicable to a broad class of cellular networks. The key observation made in this paper is that such an approach appears to be capable of achieving the optimal DoF for MIMO cellular networks in regimes where linear beamforming dominates asymptotic decomposition-based schemes for interference alignment, and a significant portion of the DoF elsewhere. Remarkably, polynomial identity test plays a key role in identifying the scope and limitations of such a technique.

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 studies the uplink of a cloud radio access network (C-RAN) where the cell sites are connected to a cloud-computing-based central processor (CP) with noiseless backhaul links with finite capacities. We employ a simple compress-and-forward scheme in which the base stations (BSs) quantize the received signals and send the quantized signals to the CP using either distributed Wyner-Ziv coding or single-user compression. The CP first decodes the quantization codewords and then decodes the user messages as if the remote users and the cloud center form a virtual multiple-access channel (VMAC). This paper formulates the problem of optimizing the quantization noise levels for weighted sum rate maximization under a sum backhaul capacity constraint. We propose an alternating convex optimization approach to find a local optimum solution to the problem efficiently, and more importantly, to establish that setting the quantization noise levels to be proportional to the background noise levels is near optimal for sum-rate maximization when the signal-to-quantization-noise-ratio (SQNR) is high. In addition, with Wyner-Ziv coding, the approximate quantization noise level is shown to achieve the sum-capacity of the uplink C-RAN model to within a constant gap. With single-user compression, a similar constant-gap result is obtained under a diagonal dominant channel condition. These results lead to an efficient algorithm for allocating the backhaul capacities in C-RAN. The performance of the proposed scheme is evaluated for practical multicell and heterogeneous networks. It is shown that multicell processing with optimized quantization noise levels across the BSs can significantly improve the performance of wireless cellular networks.

This paper studies the downlink spectral efficiency of distributed antenna system (DAS) where antenna ports are distributed as a Poisson point process (PPP), while assuming channel state information is not available at the transmitter and each antenna has an individual power constraint. We first consider the case with a single user per cell and analyze regular DAS with fixed cell boundaries, and study both blanket transmission where the user is served by all the antenna ports within each cell, and selective transmission where only the closest antenna port to the user within each cell is selected. We derive efficiently computable spectral efficiency expressions as a function of the user location, and show the limitation of blanket transmission by establishing that the cell-edge spectral efficiency under blanket transmission is upper bounded by a constant. Further, from a network perspective, we also model users as a PPP and assume a time-division multiple-access (TDMA) scheme, and give analytical expressions for and compare the average spectral efficiencies of regular DAS and user-centric DAS where no fixed cell boundaries exist. We validate our models with simulation, and show that selective transmission outperforms blanket transmission for regular DAS, and user-centric DAS with selective transmission achieves a higher spectral efficiency averaged over the network than regular DAS.

This paper considers a downlink multicell cooperation model in which the base-stations (BSs) are connected to a central processor (CP) via rate-limited backhaul links. A user-centric clustering model is adopted where each scheduled user is cooperatively served by a cluster of BSs, and the serving BSs for different users may overlap. This paper formulates an optimal joint clustering and beamforming design problem in which each user dynamically forms a sparse network-wide beamforming vector whose non-zero entries correspond to the serving BSs. Specifically, we assume a fixed signal-to-interference-and-noise ratio (SINR) constraint for each user, and investigate the optimal tradeoff between the sum transmit power and the sum backhaul capacity needed to form the cooperating clusters. Intuitively, larger cooperation size leads to lower transmit power, because interference can be mitigated through cooperation, but it also leads to higher sum backhaul, because user data needs to be made available to more BSs. Motivated by the compressive sensing literature, this paper formulates the sparse beamforming problem as an ℓ0-norm optimization problem, then uses the iterative reweighted ℓ1heuristic to find a solution. A key observation of this paper is that the reweighting can be done on the ℓ2-norm square of the beamformers (i.e., the power) at the BSs. This gives rise to a weighted power minimization problem over the entire network, which can be solved using the uplink-downlink duality technique with low computational complexity. This paper further proposes judicious choice of the weights, and shows that the new algorithm can provide a better tradeoff between the sum power and the sum backhaul capacity in the high SINR regime than previous algorithms.

This paper considers the joint optimization of frequency reuse and base-station (BS) bias for user association in downlink heterogeneous networks for load balancing and intercell interference management. To make the analysis tractable, we assume that BSs are randomly deployed as point processes in multiple tiers, where BSs in each tier have different transmission powers and spatial densities. A utility maximization framework is formulated based on the user coverage rate, which is a function of the different BS biases for user association and different frequency reuse factors across BS tiers. Compared to previous works where the bias levels are heuristically determined and full reuse is adopted, we quantitatively compute the optimal user association bias and obtain the closed-form solution of the optimal frequency reuse. Interestingly, we find that the optimal bias and the optimal reuse factor of each BS tier have an inversely proportional relationship. Further, we also propose an iterative method for optimizing these two factors. In contrast to system-level optimization solutions based on specific channel realization and network topology, our approach is off-line and is useful for deriving deployment insights. Numerical results show that optimizing user association and frequency reuse for multi-tier heterogeneous networks can effectively improve cell-edge user rate performance and utility.

In this paper we investigate the spatially-normalized degrees of freedom (sDoF) of 2-cell, multiple-input multiple-output (MIMO) cellular networks with three users per cell having M antennas at each user and N antennas at each base-station. We characterize the optimal sDoF/user for all values of M and N and show that the optimal sDoF is a piecewise linear function, with either M or N being the bottleneck. We assume all channels to be generic, and establish achievability through linear transmit beamforming strategies. We introduce the notion of packing ratio that describes the interference footprint or shadow cast by a set of transmit beamformers. Through this notion, we reinterpret the alternating behavior of the optimal sDoF and attribute it to the availability of sets of transmit beamformers with certain packing ratios. We also derive a new DoF outer bound when 5 over 9 ≤ M over N ≤ 3 over 4.

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.

This paper proposes an algorithm to compute the transmit beamformers for linear interference alignment for the MIMO interference channel and the MIMO interfering multiple-access/broadcast channel without symbol extensions. We first formulate the interference alignment problem as a rank minimization problem with linear constraints, then approximate the matrix rank by the nuclear norm. We further propose the use of an iterative reweighted nuclear norm approach and show that adaptive reweighting can significantly improve the algorithm's ability to find aligned beamformers. Simulation results show that the proposed algorithm is able to provide more interference-free dimensions and also converges faster than a previously proposed rank-constrained rank-minimization approach for interference alignment.

This paper studies the ergodic capacity of a multicell distributed antenna system (DAS), where remote antenna ports are spread within each cell to cooperatively transmit to user terminals. Unlike most prior studies which assume the antenna ports to be deployed at fixed locations, this paper assumes the antenna ports to be distributed as a spatial Poisson point process (PPP) to account for the fact that in practice the antenna ports are randomly placed to cover wherever the dead spots are. We first model DAS within each cell as a downlink multiple-input single-output (MISO) channel with per-antenna power constraint while accounting for inter-cell (inter-cluster) interference. Two DAS layouts are considered: the “regular” layout where the antenna ports are randomly deployed within regular cellular boundary to serve a given user, and the “user-centric” layout where the antenna ports are distributed over a wide area and the users choose the surrounding antenna ports to form a “virtual cell” as its own serving antenna subset. Using the tool of stochastic geometry, we analytically derive efficiently computable ergodic capacity expressions for the two layouts of DAS. Using these expressions, the cell-edge capacity of DAS under the regular layout is shown to be upper-bounded by α/2, where α is the pathloss exponent. Numerical results show that the proposed analytical model can accurately model the first layout, and can well approximate the second layout when the serving radius of users is not large. Compared to the traditional cellular system where all antennas are co-located at the cell center, DAS has better cell-edge performance. Further, the user-centric DAS has higher capacity than the DAS under regular layout.

This paper characterizes the spatially-normalized degrees of freedom of a 2-cell, 2-user/cell MIMO cellular networks with M antennas at each user and N antennas at each base-station. We show that the optimal DoF is a piecewise linear function, with either M or N being the bottleneck. Denoting the ratio M/N as γ, we show that the network has redundant dimensions in both M and N when γ ϵ {1/2,1} and that the network has no redundancy when γ ϵ {1/4, 2/3, 3/2}. We also show that not all proper systems are feasible and that the only set of feasible proper systems that lie on the proper-improper boundary are those with γ ϵ {1/4, 2/3, 3/2}. We make comparisons between the DoF achievable using strategies such as time sharing between users or cells and discuss their implications on user scheduling in such networks.

This paper investigates an uplink multi-cell processing (MCP) model where the cell sites are linked to a central processor (CP) via noiseless backhaul links with limited capacity. A simple compress-and-forward scheme is employed, where the base-stations (BSs) quantize the received signals and send the quantized signals to the CP using distributed Wyner-Ziv compression. The CP decodes the quantization codewords first, then decodes the user messages as if the users and the CP form a virtual multiple-access channel. This paper formulates the problem of maximizing the overall sum rate under a sum backhaul constraint for such a setting. It is shown that setting the quantization noise levels to be uniform across the BSs maximizes the achievable sum rate under high signal-to-noise ratio (SNR). Further, for general SNR a low-complexity fixed-point iteration algorithm is proposed to optimize the quantization noise levels. This paper further shows that with uniform quantization noise levels, the compress-and-forward scheme with Wyner-Ziv compression already achieves a sum rate that is within a constant gap to the sum capacity of the uplink MCP model. The gap depends linearly on the number of BSs in the network but is independent of the SNR and the channel matrix.

This paper proposes an efficient two-stage channel quantization and feedback scheme for the downlink limited-feedback network multiple-input multiple-output (MIMO) system. In the first stage, the users report their best set of base-station antenna and physical resource block combinations, and the base-stations schedule the best user for each antenna in each resource block. The scheduled users are then polled in the second stage to feedback their quantized channel vectors. This paper proposes an analytical framework to show that, under a total feedback budget of B bits, the number of bits assigned to the second feedback stage should scale as log B, and in quantizing channel vectors from different base-stations, each user should allocate feedback bits in proportion to the channel magnitudes in dB scale. Under these optimized bit allocations, the overall sum rate of the system is shown to scale double-logarithmically with B, linearly with the total number of antennas, and logarithmically with transmit power, thus achieving both multiuser diversity and spatial multiplexing gains under limited feedback. Finally, realistic wireless propagation model of an urban small-cell deployment is used to show that the proposed scheme can approach the performance of a network MIMO system with full channel state information with only modest amount of channel feedback.

This paper studies an uplink multicell joint processing model in which the base-stations are connected to a centralized processing server via rate-limited digital backhaul links. We propose a simple scheme that performs Wyner-Ziv compress-and-forward relaying on a per-base-station basis followed by successive interference cancellation (SIC) at the central processor. The proposed scheme has a significantly reduced complexity as compared to joint decoding, resulting in an easily computable achievable rate region. Although suboptimal in general, this paper shows that the proposed per-base-station SIC scheme can achieve the sum capacity of a class of Wyner cellular model to within a constant gap. This paper also establishes that in order to achieve to within a constant gap to the maximum SIC rate with infinite backhaul, the limited-backhaul system must have backhaul capacities that scale logarithmically with the signal-to-interference-and-noise ratios (SINRs) at the base-stations. Further, this paper studies the optimal backhaul rate allocation problem for the per-base-station SIC model with a total backhaul capacity constraint, and shows that the sum-rate maximizing allocation should also have individual backhaul rates that scale logarithmically with the SINR at each base-station. Finally, the proposed per-base-station SIC scheme is evaluated in a practical multicell network to quantify the performance gain brought by multicell processing.

The two-user Gaussian interference channel with a shared out-of-band relay is considered. The relay observes a linear combination of the source signals and broadcasts a common message to the two destinations, through a perfect link of fixed limited rateR0bits per channel use. The out-of-band nature of the relay is reflected by the fact that the common relay message does not interfere with the received signal at the two destinations. A general achievable rate is established, along with upper bounds on the capacity region for the Gaussian case. ForR0values below a certain threshold, which depends on channel parameters, in achievable rates asymptotically in regimes where joint a two-for-one gain is achievable by the capacity region of this channel is determined in this paper to within a constant gap of Δ = 1.95 bits. We identify interference regimes where a two-for-one gain in achievable rates is possible for every bit relayed, up to a constant approximation error. Instrumental to these results is a carefully designed quantize-and-forward type of relay strategy along with a joint decoding scheme employed at destination ends. Further, we also study successive decoding strategies with optimal decoding order (corresponding to the order at which common, private, and relay messages are decoded), and identify interference regimes where with an optimal decoding order, successive decoding may also achieve two-for-one gains similar to joint decoding; yet, in general, successive decoding produces unbounded loss asymptotically when compared to joint decoding.

This paper studies the capacity region of aK-user cyclic Gaussian interference channel, where thekth user interferes with only the (k-1)th user (modK) in the network. Inspired by the work of Etkin, Tse, and Wang, who derived a capacity region outer bound for the two-user Gaussian interference channel and proved that a simple Han-Kobayashi power-splitting scheme can achieve to within one bit of the capacity region for all values of channel parameters, this paper shows that a similar strategy also achieves the capacity region of theK-user cyclic interference channel to within a constant gap in the weak interference regime. Specifically, for theK-user cyclic Gaussian interference channel, a compact representation of the Han-Kobayashi achievable rate region using Fourier-Motzkin elimination is first derived; a capacity region outer bound is then established. It is shown that the Etkin-Tse-Wang power-splitting strategy gives a constant gap of at most 2 bits in the weak interference regime. For the special three-user case, this gap can be sharpened to 1 ½ bits by time-sharing of several different strategies. The capacity result of theK-user cyclic Gaussian interference channel in the strong interference regime is also given. Further, based on the capacity results, this paper studies the generalized degrees of freedom (GDoF) of the symmetric cyclic interference channel. It is shown that the GDoF of the symmetric capacity is the same as that of the classic two-user interference channel, no matter how many users are in the network.

This paper studies incremental relay strategies for a two-user Gaussian relay-interference channel with an in-band-reception and out-of-band-transmission relay, where the link between the relay and the two receivers is modelled as a degraded broadcast channel. It is shown that generalized hash-and-forward (GHF) can achieve the capacity region of this channel to within a constant number of bits in a certain weak-relay regime, where the transmitter-to-relay link gains are not unboundedly stronger than the interference links between the transmitters and the receivers. The GHF relaying strategy is ideally suited for the broadcasting relay because it can be implemented in an incremental fashion, i.e., the relay message to one receiver is a degraded version of the message to the other receiver. A generalized-degree-of-freedom (GDoF) analysis in the high signal-to-noise ratio (SNR) regime reveals that in the symmetric channel setting, each common relay bit can improve the sum rate roughly by either one bit or two bits asymptotically depending on the operating regime, and the rate gain can be interpreted as coming solely from the improvement of the common messages rate, or alternatively in the very weak interference regime as solely coming from the rate improvement of the private messages. Further, this paper studies an asymmetric case in which the relay has only a single link to one of the destinations. It is shown that with only one relay-destination link, the approximate capacity region can be established for a larger regime of channel parameters. Further, from a GDoF point of view, the sum-capacity gain due to the relay can now be thought as coming from either signal relaying only or interference forwarding only.

The mitigation of intercell interference is an importance issue for current and next-generation wireless cellular networks where frequencies are aggressively reused and hierarchical cellular structures may heavily overlap. The paper examines the benefit of coordinating transmission strategies and resource allocation schemes across multiple base-stations for interference mitigation. Two different wireless cellular architectures are studied: a multicell network where base-stations coordinate in their transmission strategies, and a mixed macrocell and femtocell/picocell deployment with coordination among macro and femto/pico base-stations. For both scenarios, this paper proposes a heuristic joint proportionally fair scheduling, spatial multiplexing, and power spectrum adaptation algorithm that coordinates multiple base-stations with an objective of optimizing the overall network utility. The proposed scheme optimizes the user schedule, transmit and receive beamforming vectors, and transmit power spectra jointly, while taking into consideration both the intercell and intracell interference and the fairness among the users. System-level simulation results show that coordination at the transmission strategy and resource allocation level can already significantly improve the overall network throughput as compared to a conventional network design with fixed transmit power and per-cell zero-forcing beamforming.

Complexity and hardware constraints are two essential considerations in applying interference mitigation techniques to practical wireless systems. This paper considers a practical wireless backhaul network composed of several access nodes (AN), each serving several remote terminals (RT), and where the transmit frame structure at each AN is comprised of multiple zones, with different RTs scheduled on different zones. The objective of this paper is to design power control strategies to mitigation inter-AN interference in the downlink. Unlike prior studies, this paper adopts a practical constraint whereby every AN maintains the same power level across the different zones within one transmitted frame. The main advantage of imposing this new constraint, called the one-power-zone (OPZ) constraint in this paper, is that for a class of scheduling policies under which the number of zones assigned to each RT is fixed, the power optimization and the scheduling subproblems are decoupled under the OPZ constraint. This allows the design of efficient power control methods independent of scheduling. Further, it also simplifies the design of radio-frequency (RF) front-end. The main contribution of this paper is a set of efficient algorithms to solve this constrained power control problem based on an iterative function evaluation technique. The proposed algorithms have low computational complexity, and can be implemented in a distributed fashion. Some of these algorithms can be further implemented asynchronously at each AN.

The placement of the access points (APs) has a significant impact on the wireless system performance, especially for irregular heterogeneous networks with hierarchical APs such as macro/micro base-stations, pico-stations, and femto-stations. Traditional system modeling and optimization are based on the regular 2D hexagonal cellular topology and a set of predefined large-scale propagation models, which is highly abstract and may be inaccurate. This paper considers the AP placement optimization problem in realistic deployment environments, where radio-wave propagation characteristics are accurately modeled using ray-tracing techniques. Toward this end, this paper proposes a novel concept of area proportional fairness utility for the entire network under a given user geographic distribution, and proposes an iterative method to optimize the placement of the APs for utility improvement while taking into account the mutual interference between the APs. The significant benefit of the placement optimization is shown in a numerical experiment conducted in a realistic Chicago downtown topology, where placement optimization is shown to improve the sum rate by up to 40%.

This paper studies an uplink multicell joint processing model in which the base-stations are connected to a centralized processing server via rate-limited digital backhaul links. Unlike previous studies where the centralized processor jointly decodes all the source messages from all base-stations, this paper proposes a suboptimal achievability scheme in which the Wyner-Ziv compress-and-forward relaying technique is employed on a per-base-station basis, but successive interference cancellation (SIC) is used at the central processor to mitigate multicell interference. This results in an achievable rate region that is easily computable, in contrast to the joint processing schemes in which the rate regions can only be characterized by exponential number of rate constraints. Under the per-base-station SIC framework, this paper further studies the impact of the limited-capacity backhaul links on the achievable rates and establishes that in order to achieve to within constant number of bits to the maximal SIC rate with infinite-capacity backhaul, the backhaul capacity must scale logarithmically with the signal-to-interference-and-noise ratio (SINR) at each base-station. Finally, this paper studies the optimal backhaul rate allocation problem for an uplink multicell joint processing model with a total backhaul capacity constraint. The analysis reveals that the optimal strategy that maximizes the overall sum rate should also scale with the log of the SINR at each base-station.

This paper proposes an efficient two-stage beamforming and scheduling algorithm for the limited-feedback cooperative multi-point (CoMP) systems. The system includes multiple base-stations cooperatively transmitting data to a pool of users, which share a rate-limited feedback channel for sending back the channel state information (CSI). The feedback mechanism is divided into two stages that are used separately for scheduling and beamforming. In the first stage, the users report their best channel gain from all the base-station antennas and the basestations schedule the best user for each of their antennas. The scheduled users are then polled in the second stage to feedback their quantized channel vectors. The paper proposes an analytical framework to derive the bit allocation between the two feedback stages and the bit allocation for quantizing each user's CSI. For a total number of feedback bits B, it is shown that the number of bits assigned to the second feedback stage should scale as log B. Furthermore, in quantizing channel vectors from different base-stations, each user should allocate its feedback budget in proportion to the logarithm of the corresponding channel gains. These bit allocation are then used to show that the overall system performance scales double-logarithmically with B and logarithmically with the transmit SNR. The paper further presents several numerical results to show that, in comparison with other beamforming-scheduling algorithms in the literature, the proposed scheme provides a consistent improvement in downlink sum rate and network utility. Such improvements, in particular, are achieved in spite of a significant reduction in the beamforming-scheduling computational complexity, which makes the proposed scheme an attractive solution for practical system implementations.

Base-station (BS) cooperation in wireless cellular networks offers a promising approach for interference mitigation. However, the implementation of practical network multi-input multi-output (MIMO) system also faces the challenge of high capacity cost for sharing the user data over the backhaul connections. This paper considers a downlink multi-cell orthogonal frequency-division multiple-access (OFDMA) network where the capacities of the backhaul links between the BSs are limited, and extends the single-antenna BS multi-cell system model considered in our previous work to the multiple-antenna BS case. The BSs use zero-forcing precoding to spatially multiplex multiple users within each cell and to pre-subtract the interference from cooperating BSs that share user data with them. An iterative algorithm that maximizes the downlink network utility is proposed. The algorithm iteratively selects the cooperation links, schedules the users, and optimizes the precoding coefficients and the power spectra for each frequency tone. Numerical results suggest that the use of dynamic cooperation link selection can provide a better trade-off between the downlink sum-rate gain and the backhaul capacity than the earlier fixed link-selection algorithm.

One of the major challenges in deploying femtocells is the management of interference between neighboring femtocells and between the femtocells and the macrocells. This paper explores the use of opportunistic multiuser detection for interference mitigation in the downlink of an orthogonal frequency division multiple access (OFDMA) femtocell network. In particular, we focus on the use of joint decoding (JD) at the receiver, where a macro or femto user may jointly decode both the desired message and the message from a selected interfering user in order to achieve a higher overall transmission rate. It is shown that to take the full advantage of opportunistic multiuser detection, the selection of JD pairs needs to be jointly optimized with scheduling, power allocation, and rate adaptation. This paper adopts a network utility maximization framework and proposes an iterative algorithm for such a joint optimization across the network. Simulation results show that multiuser detection can significantly benefit the femto-users, while maintaining the performance of macro-users. Further, although the lower-complexity successive interference cancellation (SIC) scheme can already reap significant benefit of multiuser detection, JD can further improve upon SIC.

This paper examines the shared relay architecture for the wireless cellular network, where instead of deploying multiple separate relays within each cell sector, a single relay with multiple antennas is placed at the cell edge and is shared by multiple sectors. The advantage of shared relaying is that the joint processing of signals at the relay enables the mitigation of intercell interference. To maximize the benefit of shared relaying, the resource allocation and the scheduling of users among adjacent cell sectors need to be optimized jointly. Based on this motivation, this paper formulates a network utility maximization problem for the shared relay system that considers the practical wireless backhaul constraint of matching the relay-to-user rate demand with the base-station-to-relay rate supply using a set of pricing variables. In addition, zero-forcing beamforming is used at the shared relay to separate users spatially; multiple users are scheduled in the frequency domain to maximize frequency reuse. A heuristic but efficient scheduling and resource allocation algorithm is proposed accordingly. System-level simulations quantify the effectiveness of the proposed approach, and show that the incorporation of the shared relay can improve the overall network performance and in particular significantly increase the throughput of cell edge users as compared to separate relaying.

This paper studies a Gaussian Z-interference channel with a rate-limited digital relay link from one receiver to another. Achievable rate regions are derived based on a combination of a Han–Kobayashi common-private power-splitting technique and either a compress-and-forward relay strategy or a decode-and-forward strategy for interference subtraction at the other end. For the Gaussian Z-interference channel with a digital link from the interference-free receiver to the interfered receiver, the capacity region is established in the strong interference regime; an achievable rate region is established in the weak interference regime. In the weak interference regime, the decode-and-forward strategy is shown to be asymptotically sum-capacity achieving in the high signal-to-noise ratio and high interference-to-noise ratio limit. In this case, each relay bit asymptotically improves the sum capacity by one bit. For the Gaussian Z-interference channel with a digital link from the interfered receiver to the interference-free receiver, the capacity region is established in the strong interference regime; achievable rate regions are established in the moderately strong and weak interference regimes. In addition, the asymptotic sum capacity is established in the limit of a large relay link rate. In this case, the sum capacity improvement due to the digital link is bounded by half a bit when the interference link is weaker than a certain threshold, but the sum capacity improvement becomes unbounded when the interference link is strong.

This paper proposes an efficient two-stage limited feedback beamforming and scheduling scheme for multiple antenna cellular communication systems. The system model includes a base-station with M antennas and a large pool of users with a total feedback rate of B bits per fading block. The feedback process is divided into two stages. In the first stage, the users measure their channel gains from each antenna and feedback the index of the antenna with the highest channel gain along with the gain itself. Based on this information, the base station schedules M users with the highest channel gains from its M antennas and polls those users for explicit quantization of their vector channels in the second stage. Based on these quantized channels, the base-station then forms zero-forcing beamforming vectors for downlink transmission. This paper presents an approximate analysis for the proposed scheme which is used to optimize the bit allocation between the two feedback stages. It is shown that for a total number of feedback bits B, the number of feedback bits assigned to the second stage, B2, should scale as M(M-1) log(SNR × B). In particular, the fraction B2/B behaves as logB/B in the asymptotic regime where B → ∞. Further, the approximate downlink sum rate is shown to scale as M log SNR + M log log B, suggesting that both multiuser multiplexing and multiuser diversity gains are realized. As the numerical results verify, the proposed feedback scheme, in spite of its low complexity, performs very close to the more complicated beamforming and scheduling schemes in the literature and in fact outperforms such schemes in the high-SNR regime.

This papers considers the downlink of a wireless multicell downlink orthogonal frequency division multiple-access (OFDMA) system where neighboring base-stations (BSs) can jointly encode and transmit data signals to their users using zero-forcing (ZF) precoding in each frequency tone, but only a limited backhaul-capacity is available for each BS to share its user's data streams with the neighboring BSs. A numerical algorithm is proposed to maximize the network-wide utility of this system subject to backhaul capacity constraints. The proposed algorithm first selects a subset of frequency tones for each BS to share its user's data streams with the neighboring BSs and then, jointly schedules users and adapts the precoding coefficients and the power spectra of the BSs to effectively utilize the limited backhaul capacity. Numerical results show that using the proposed algorithm, the gain in downlink sum-rate per cell can be made to scale linearly with the available backhaul capacity per BS between the no-cooperation and the full-cooperation scenarios.

The mitigation of intercell interference is a central issue for future-generation wireless cellular networks where frequencies are reused aggressively and where hierarchical cellular structures may heavily overlap. The paper examines the benefit of coordinating transmission strategies and resource allocation schemes across multiple cells for interference mitigation. For a multicell network serving multiple users per cell sectors and where both the base-stations and the remote users are equipped with multiple antennas, this paper proposes a joint proportionally fair scheduling, spatial multiplexing, and power spectrum adaptation method that coordinates multiple base-stations with an objective of optimizing the overall network utility. The proposed scheme optimizes the user schedule, transmit and receive beamforming vectors, and transmit power spectra jointly, while taking into consideration both the intercell and intracell interference and the fairness among the users. The proposed system is shown to significantly improve the overall network throughput while maintaining fairness as compared to a conventional network with per-cell zero-forcing beamforming and with fixed transmit power spectrum. The proposed system goes toward the vision of a fully coordinated multicell network, whereby transmission strategies and resource allocation schemes (rather than transmit signals) are coordinated across the base-stations as a first step.

This paper studies the capacity region of a K-user cyclic Gaussian interference channel, where the kth user interferes with only the (k-1)th user (mod K) in the network. Inspired by the work of Etkin, Tse and Wang, which derived a capacity region outer bound for the two-user Gaussian interference channel and proved that a simple Han-Kobayashi power splitting scheme can achieve to within one bit of the capacity region for all values of channel parameters, this paper shows that a similar strategy also achieves the capacity region for the K-user cyclic interference channel to within a constant gap in the weak interference regime. Specifically, a compact representation of the Han-Kobayashi achievable rate region using Fourier-Motzkin elimination is first derived, a capacity region outer bound is then established. It is shown that the Etkin-Tse-Wang power splitting strategy gives a constant gap of at most two bits (or one bit per dimension) in the weak interference regime. Finally, the capacity result of the K-user cyclic Gaussian interference channel in the strong interference regime is also given.

This paper addresses the design of optimal and near-optimal detectors in an interference channel with fading and with additive white Gaussian noise (AWGN), where the transmitters employ discrete modulation schemes as in practical communication scenarios. The conventional detectors typically either ignore the interference or successively detect and then cancel the interference, assuming that the desired signal and/or the interference are Gaussian. This paper quantifies the significant performance gain that can be obtained if the detectors explicitly take into account the modulation formats of the desired and the interference signals. This paper first describes the optimal maximum-likelihood (ML) detector that minimizes the probability of detection error for a given modulation scheme, and the joint minimum-distance (MD) detector, which is a lower-complexity approximation of the ML detector. It is then demonstrated by analysis and by simulation that in an AWGN channel, while interference-ignorant and successive interference cancellation detectors are both prone to error floors, the optimal ML and joint MD detectors are not. This paper further analyzes the performance of joint detection in a Rayleigh fading environment. It is demonstrated that the joint detector can achieve symbol error rates that have the same dependence on the received signal-to-noise ratio (SNR) as if the channel were interference free. Thus, the performance of joint detection is fundamentally limited by the SNR rather than the signal-to-interference ratio (SIR). Moreover, the joint detector enables the use of transmit diversity schemes to achieve the same diversity order as in the absence of interference. These results show that the use of interference-aware detectors can significantly alleviate the effect of interference thereby improving the achievable rates and the reliability of future wireless systems.

Conventional wireless cellular systems treat out-of-cell interference as noise. This paper proposes methods and examines the benefit of designing decodable interference signals, whereby a transmitter may split its message into a common and a private part, and the common message may be decoded and subtracted by users in adjacent cells. This paper considers a downlink scenario, where the base-stations are equipped with multiple antennas, the mobile users are equipped with a single antenna, and multiple users are active simultaneously via spatial multiplexing. The network optimization problem consists of jointly determining the appropriate users in adjacent cells for rate splitting, the optimal transmit beamformers for common and private messages, and the optimal common-private rates to maximize the minimum achievable rate across the users. This paper shows that for fixed user selection and fixed common-private rate splitting, the optimization of transmit beamformers can be solved using a semidefinite programming (SDP) relaxation approach. Further, it is shown that for the case where the network consists of two message-splitting pairs, SDP relaxation is tight, i.e., beamforming is optimal. Finally, this paper proposes a heuristic user-selection and rate splitting strategy to characterize the performance improvement for cell-edge users due to common-message decoding.

This paper studies the structure of the channel quantization codebook for multiuser MISO systems with limited channel state information at the base-station. The problem is cast in the form of minimizing the sum power subject to the worst-case SINR constraints over spherical channel uncertainty regions. This paper adopts a zero-forcing approach for beamforming vectors design, and uses a robust optimization technique via semideflnite programming (SDP) for power control as the benchmark performance measure. We then present an alternative less complex and practically feasible method for computing the power values and present sufficient conditions on the uncertainty radius so that the resulting sum power remains close to the SDP solution. The proposed conditions guarantee that the interference caused by the channel uncertainties can be effectively controlled. Based on these conditions, we study the structure of the channel quantization codebooks and show that the quantization codebook has a product form that involves spatially uniform quantization of the channel direction, and independent channel magnitude quantization which is uniform in dB scale. The structural insight obtained by our analysis also gives a bit-sharing law for dividing the quantization bits between the two codebooks. We finally show that the total number of quantization bits should increase as log(SINRtarget) as the target SINR increases.

This paper addresses the design of optimal and near-optimal detectors for a practical interference channel scenario where the transmitters employ discrete modulation schemes. The conventional detectors, which either ignore the interference or successively detect then cancel the interference, typically assume that the desired signal and/or the interference are Gaussian. This paper proposes detectors that explicitly take into account the modulation formats of both the desired signal and the interference. The optimal maximum-likelihood (ML) detector that minimizes the probability of detection error for a given set of modulation schemes is derived first. A joint minimum-distance detector (MD) is then presented as a low-complexity approximation of the optimal ML detector. It is demonstrated by analysis and by simulation that the proposed detectors can significantly outperform their conventional counterparts. In particular, while the interference-ignorant and the successive interference cancellation detectors are both prone to error floors, the proposed optimal ML and joint MD detectors are not.

Conventional multicell wireless systems operate with out-of-cell interference treated as noise - interference detection is infeasible as intercell interference is typically weak. This paper considers the benefit of designing decodable interference signals by allowing common-private message splitting at the transmitter and common message decoding by users in adjacent cells. In particular, we consider a downlink scenario, where base-stations are equipped with multiple transmit antennas, the remote users are equipped with a single antenna, and multiple remote users are active simultaneously via spatial division multiplexing. We solve a network optimization problem of jointly determining the appropriate users in adjacent cells for rate splitting, the optimal beamforming vectors for both common and private messages, and the optimal common-private rates to minimize the total transmit power across the base-stations subject to service rate requirements for remote users. We observe that for fixed user selection and fixed common-private rate splitting, the optimization of beamforming vectors can be performed using a semidefinite programming approach. Further, this paper proposes a heuristic user-selection and rate splitting strategy to maximize the benefit of common message decoding. Simulation results show that common message decoding can significantly improve both the total transmit power and the feasibility region for cell-edge users.

A communication model is considered in which the classic two-user Gaussian interference channel is augmented by noiseless rate-limited digital conferencing links between the transmitters. We propose a partial zero-forcing precoding strategy based on a shared-private rate splitting scheme at the transmitter, in which each transmitter communicates part of its message to the other transmitter, and subsequently partially pre-subtracts the interfering signal using a zero-forcing precoder. We prove an outer bound and show that the proposed strategy is asymptotically sum-capacity achieving in a very weak interference regime, where both the signal-to-noise ratio (SNR) and the interference-to-noise ratio (INR) go to infinity while their ratio in dB scale is kept fixed. In this case, every cooperation bit results in one-bit gain in sum capacity. We also consider a different asymptotic regime where the transmit power constraints and the channel gains are fixed while the noise powers go down to zero. In this case, if one compares with the achievable sum rate with interference treated as noise, one cooperation bit can in fact result in more than one-bit gain in achievable sum rate.

This paper presents an overview of the theory and currently known techniques for multi-cell MIMO (multiple input multiple output) cooperation in wireless networks. In dense networks where interference emerges as the key capacity-limiting factor, multi-cell cooperation can dramatically improve the system performance. Remarkably, such techniques literally exploit inter-cell interference by allowing the user data to be jointly processed by several interfering base stations, thus mimicking the benefits of a large virtual MIMO array. Multi-cell MIMO cooperation concepts are examined from different perspectives, including an examination of the fundamental information-theoretic limits, a review of the coding and signal processing algorithmic developments, and, going beyond that, consideration of very practical issues related to scalability and system-level integration. A few promising and quite fundamental research avenues are also suggested.

The one tutorial paper and eight contributed papers in this special issue focus on cooperative communications in MIMO cellular networks.

The optimal performance-complexity tradeoff for error-correcting codes at rates strictly below the Shannon limit is a central question in coding theory. This paper proposes a numerical approach for the minimization of decoding complexity for long-block-length irregular low-density parity-check (LDPC) codes. The proposed design methodology is applicable to any binary-input memoryless symmetric channel and any iterative message-passing decoding algorithm with a parallel-update schedule. A key feature of the proposed optimization method is a new complexity measure that incorporates both the number of operations required to carry out a single decoding iteration and the number of iterations required for convergence. This paper shows that the proposed complexity measure can be accurately estimated from a density-evolution and extrinsic-information transfer chart analysis of the code. A sufficient condition is presented for convexity of the complexity measure in the variable edge-degree distribution; when it is not satisfied, numerical experiments nevertheless suggest that the local minimum is unique. The results presented herein show that when the decoding complexity is constrained, the complexity-optimized codes significantly outperform threshold-optimized codes at long block lengths, within the ensemble of irregular codes.

In a conventional wireless cellular system, signal processing is performed on a per-cell basis; out-of-cell interference is treated as background noise. This paper considers the benefit of coordinating base-stations across multiple cells in a multi-antenna beamforming system, where multiple base-stations may jointly optimize their respective beamformers to improve the overall system performance. Consider a multicell downlink scenario where base-stations are equipped with multiple transmit antennas employing either linear beamforming or nonlinear dirty-paper coding, and where remote users are equipped with a single antenna each, but where multiple remote users may be active simultaneously in each cell. This paper focuses on the design criteria of minimizing either the total weighted transmitted power or the maximum per-antenna power across the base-stations subject to signal-to-interference-and-noise-ratio (SINR) constraints at the remote users. The main contribution of the paper is an efficient algorithm for finding the joint globally optimal beamformers across all base-stations. The proposed algorithm is based on a generalization of uplink-downlink duality to the multicell setting using the Lagrangian duality theory. An important feature is that it naturally leads to a distributed implementation in time-division duplex (TDD) systems. Simulation results suggest that coordinating the beamforming vectors alone already provide appreciable performance improvements as compared to the conventional per-cell optimized network.

This paper investigates the joint design and optimization of the power control and beamforming codebooks for the single-user multiple-input single-output (MISO) wireless systems with a rate-limited feedback link. The problem is cast in the form of minimizing the outage probability subject to the transmit power constraint and cardinality constraints on the beamforming and power codebooks. We show that by appropriately choosing and fixing the beamforming codebook and optimizing the power codebook for that beamforming codebook, it is possible to achieve a performance very close to the optimal joint optimization. Further, this paper investigates the optimal tradeoffs between beamforming and power codebook sizes for different number of feedback bits and transmit antennas. Given a target outage probability, our results provide the optimal codebook sizes independent of the target rate. As the outage probability decreases, we show that the optimal joint design should use fewer feedback bits for beamforming and more feedback bits for power control. The jointly optimized beamforming and power control modules combine the power gain of beamforming and diversity gain of power control, which enable it to approach the performance of the system with perfect channel state information as the feedback link capacity increases to infinity - something that is not possible with beamforming or power control alone.

This paper aims to quantify the performance improvement due to the use of fixed relays in the uplink of a wireless cellular network. Consider an orthogonal frequency division multiplex (OFDM) based cellular network in which each cell consists of a base station, multiple mobile users, and a number of relays. The questions of which frequency tones each link should use, whether the mobile station should communicate directly to the base station or though a relay, and how much power should be allocated on each frequency tone, form a simultaneous routing, frequency planning, and power allocation problem. This paper presents a dual decomposition approach to this problem and illustrates that while the use of relays does not necessarily increase the total cell throughput, it significantly improves the minimum common rate achievable across all the mobile users. Thus, the main benefit for deploying relays is in the improvement in fairness, rather than the total throughput.

This paper characterizes the capacity of a class of modular additive noise relay channels, in which the relay observes a corrupted version of the noise and has a separate channel to the destination. The capacity is shown to be strictly below the cut-set bound in general and achievable using a quantize-and-forward strategy at the relay. This result confirms a previous conjecture on the capacity of channels with rate-limited side information at the receiver for this particular class of modulo-sum channels. This paper also considers a more general setting in which the relay is capable of conveying noncausal rate-limited side information about the noise to both the transmitter and the receiver. The capacity is characterized for the case where the channel is binary symmetric with a crossover probability 1/2. In this case, the rates available for conveying side information to the transmitter and to the receiver can be traded with each other arbitrarily-the capacity is a function of the sum of the two rates.

This paper proposes a relaying strategy for the multiple-relay network in which each relay decodes a selection of transmitted messages by other transmitting terminals, and forwards parities of the decoded codewords. This protocol improves the previously known achievable rate of the decode-and-forward (DF) strategy for multirelay networks by allowing relays to decode only a selection of messages from relays with strong links to it. Hence, each relay may have several choices as to which messages to decode, and for a given network many different parity forwarding protocols may exist. A tree structure is devised to characterize a class of parity forwarding protocols for an arbitrary multirelay network. Based on this tree structure, closed-form expressions for the achievable rates of these DF schemes are derived. It is shown that parity forwarding is capacity achieving for new forms of degraded relay networks.

We consider the problem of beamforming codebook design for limited feedback half-duplex multiple-input multiple output (MIMO) amplify-and-forward (AF) relay system. In the first part of the paper, the direct link between the source and the destination is ignored. Assuming perfect channel state information (CSI), we show that the source and the relay should map their signals to the dominant right singular vectors of the source-relay and relay-destination channels. For the limited feedback scenario, we prove the appropriateness of Grassmannian codebooks as the source and relay beamforming codebooks based on the distributions of the optimal source and relay beamforming vectors. In the second part of the paper, the direct link is considered in the problem model. Assuming perfect CSI, we derive the optimization problem that identifies the optimal source beamforming vector and show that the solution to this problem is uniformly distributed on the unit sphere for independent and identically distributed (i.i.d) Rayleigh channels. For the limited feedback scenario, we justify the appropriateness of Grassmannian codebooks for quantizing the optimal source beamforming vector based on its distribution. Finally, a modified quantization scheme is presented, which introduces a negligible penalty in the system performance but significantly reduces the required number of feedback bits.

This paper proposes a cross-layer optimization framework for the wireless sensor networks. In a wireless sensor network, each sensor makes a local observation of the underlying physical phenomenon and sends a quantized version of the observation to a central location via wireless links. As the sensor observations are often partial and correlated, the network performance is a complicated and nonseparable function of individual data rates at each sensor. In addition, due to the shared nature of wireless medium, nearby transmissions often interfere with each other. Thus, the traditional "bit-pipe" model for network link capacity no longer holds. This paper deals with the joint optimization of source quantization, routing, and power control in a wireless sensor network. We follow a separate source and channel coding approach and show that the overall network optimization problem can be naturally decomposed into a source coding subproblem at the application layer and a wireless power control subproblem at the physical layer. The interfaces between the layers are precisely the dual optimization variables. In addition, we introduce a novel source coding model at the application layer, which allows the efficient design of practical source quantization schemes at each sensor. Finally, we propose a dual algorithm for the overall network optimization problem. The dual algorithm, when combined with a column- generation method, allows an efficient solution for the overall network optimization problem.

This paper characterizes the capacity of a class of modulo additive noise relay channels, in which the relay observes a corrupted version of the noise and has a separate channel to the destination. The capacity is shown to be strictly below the cut-set bound in general and achievable using a quantize-and- forward strategy at the relay. This result confirms a conjecture by Ahlswede and Han about the capacity of channels with rate limited state information at the destination for this particular class of channels.

This paper presents a structured characterization of a class of decode-and-forward (DF) strategies for arbitrary multirelay networks. In contrast to conventional DF strategies in which each relay transmits the bin index of the source message only, the proposed generalized DF strategies allow each relay to decode a selection of messages from other nodes and forward bin indices for them. A tree structure is utilized to characterize the dependencies of messages on each other. Based on this tree structure, closed-form expressions for the achievable rates of these DF schemes are derived. The proposed DF strategy improves the previous multirelay DF rates and gives the capacity of new forms of degraded multirelay networks.

This paper considers a wireless cooperative cellular data network with a base station and many subscribers in which the subscribers have the ability to relay information for each other to improve the overall network performance. For a wireless network operating in a frequency-selective slow-fading environment, the choices of relay node, relay strategy, and the allocation of power and bandwidth for each user are important design parameters. The design challenge is compounded further by the need to take user traffic demands into consideration. This paper proposes a centralized utility maximization framework for such a network. We show that for a cellular system employing orthogonal frequency-division multiple-access (OFDMA), the optimization of physical-layer transmission strategies can be done efficiently by introducing a set of pricing variables as weighting factors. The proposed solution incorporates both user traffic demands and the physical channel realizations in a cross-layer design that not only allocates power and bandwidth optimally for each user, but also selects the best relay node and best relay strategy (i.e. decode-and-forward vs. amplify-and-forward) for each source-destination pair

This paper develops a practical design method for implementing Tomlinson-Harashima precoding (THP) in a downlink channel with multiple antennas at the transmitter and a single antenna at each receiver. A two-step design process is proposed for minimizing the total transmit power while satisfying every user's minimum data rate and maximum bit-error rate (BER) requirements. First, the BER and rate requirements are converted to "virtual rate" requirements, which account for the gap-to-capacity introduced by practical quadrature amplitude modulation (QAM) and THP. The second step is to determine the transmit covariance matrices (which specify the entire THP system) that will provide these virtual rates at the minimum total transmit power. As one of the main features in the proposed scheme, an algorithm for finding the optimal user encoding (or presubtraction) order in polynomial time is proposed. In addition, we also propose an algorithm that finds a near-optimal order, but which is much less complex. The proposed method outperforms existing zero-forcing-based THP systems in term of power efficiency

This paper describes an efficient implementation of binning for decode-and-forward (DF) in relay channels using low-density parity-check (LDPC) codes. Bilayer LDPC codes are devised to approach the theoretically promised rate of the DF relaying strategy by incorporating relay-generated parity bits in specially designed bilayer graphical code structures. While conventional LDPC codes are sensitively tuned to operate efficiently at a certain channel parameter, the proposed bilayer LDPC codes are capable of working at two different channel parameters and two different rates: that at the relay and at the destination. To analyze the performance of bilayer LDPC codes, bilayer density evolution is devised as an extension of the standard density evolution algorithm. Based on bilayer density evolution, a design methodology is developed for the bilayer codes in which the degree distribution is iteratively improved using linear programming. Further, in order to approach to the theoretical DF rate for a wide range of channel parameters, this paper proposes two different forms of bilayer codes: the bilayer-expurgated and bilayer-lengthened codes. It is demonstrated that the rate of a properly designed bilayer LDPC code can closely approach the theoretical DF limit. Finally, it is shown that a generalized version of the proposed bilayer code construction is applicable to relay networks with multiple relays.

We address the problem of allocating transmission rates to a set of network sessions with end-to-end bandwidth and delay requirements. We give a unified convex programming formulation that captures both average and probabilistic delay requirements. Moreover, we present a distributed algorithm and establish its convergence to the global optimum of the overall rate allocation problem. In our algorithm, session sources selfishly update their rates as to maximize their individual benefit (utility minus bandwidth cost), the network partitions end-to-end delay requirements into local per-link delays, and the links adjust their prices to coordinate the sources' and network's decisions, respectively. This algorithm relies on a network utility maximization (NUM) approach, and can be viewed as a generalization of TCP and active queue management (AQM) algorithms to handle end-to-end QoS. We extend our results to deterministic delay requirements when nodes employ Packet-level Generalized Processor Sharing (PGPS) schedulers.

We address the problem of allocating transmission rates to a set of network sessions with end-to-end bandwidth and delay requirements. We give a unified convex programming formulation that captures both average and probabilistic delay requirements. Moreover, we present a distributed algorithm and establish its convergence to the global optimum of the overall rate allocation problem. In our algorithm, session sources update their rates as to maximize their individual benefit (utility minus bandwidth cost), the network partitions end-to-end delay requirements into local per-link delays, and the links adjust their prices to coordinate the sources' and network's decisions, respectively. This algorithm relies on a network utility maximization approach, and can be viewed as a generalization of TCP and queue management algorithms to handle end-to-end QoS. We also extend our results to deterministic delay requirements when nodes employ packet- level generalized processor sharing (PGPS) schedulers.

This paper proposes a distributed optimization framework for wireless multihop sensor networks base on a game theoretic approach. We show that the cross-layer optimization problem can be decomposed into two subproblems corresponding to two separate layers (the physical and the application layers) of the overall system. By modelling each subproblem as a noncooperative game, we aim to solve the nonconvex application-layer rate- allocation and physical-layer power-allocation subproblems in a distributed manner. Further, we prove the existence, uniqueness, and stability of the Nash equilibria for both games under certain sufficient conditions. Finally, we show that by using a set of dual variables as the market prices to coordinate the physical layer supply and the application layer demand, the overall optimization process strikes a right balance between the two layers in an overall cross-layer design.

This paper describes a methodology for efficient implementation of binning and block-Markov coding for the relay channel using powerful features of low-density parity-check (LDPC) codes. We devise bilayer LDPC codes to approach the theoretically promised rate of the decode-and-forward relaying strategy by incorporating relay-generated random linear paritybits in a specially designed bilayer graphical code structure. Bilayer density evolution is devised as a novel extension of the standard density evolution algorithm to analyze the performance of the proposed bilayer LDPC code. Based on this bilayer density evolution technique, an EXIT-chart-based code design method using linear programming is developed. While conventional LDPC codes are sensitively tuned to operate efficiently at a certain channel parameter, the proposed bilayer LDPC code is capable of working at two different channel parameters, the signal-to-noise ratio (SNR) at the relay and the SNR at the destination. In this paper, for specific channel parameters, it is demonstrated that a bilayer LDPC code can approach the theoretical decode-and-forward rate of the relay channel within a 0.19 dB gap to the source-relay channel capacity and a 0.34 dB gap to the relay-destination channel capacity.

This paper proposes a relaying strategy for networks with multiple relays where each relay forwards parities of decoded codewords. This parity-forwarding scheme can be thought of as a generalization of Cover and El Gamal's well-known decode-and-forward strategy for the classic three-terminal relay channel to networks with multiple relays. As compared to previous multiple-relay decode-and-forward strategies, the parity-forwarding scheme is more flexible and can achieve a higher rate. The proposed strategy can be easily applied to networks with complex topologies. We show that relay networks can be degraded in more than one way, and parity-forwarding is capacity achieving for a new form of degraded relay networks

Convex optimization methods are widely used in the design and analysis of communication systems and signal processing algorithms. This tutorial surveys some of recent progress in this area. The tutorial contains two parts. The first part gives a survey of basic concepts and main techniques in convex optimization. Special emphasis is placed on a class of conic optimization problems, including second-order cone programming and semidefinite programming. The second half of the survey gives several examples of the application of conic programming to communication problems. We give an interpretation of Lagrangian duality in a multiuser multi-antenna communication problem; we illustrate the role of semidefinite relaxation in multiuser detection problems; we review methods to formulate robust optimization problems via second-order cone programming techniques.

The optimal and distributed provisioning of high throughput in mesh networks is known as a fundamental but hard problem. The situation is exacerbated in a wireless setting due to the interference among local wireless transmissions. In this paper, we propose a cross-layer optimization framework for throughput maximization in wireless mesh networks, in which the data routing problem and the wireless medium contention problem are jointly optimized for multihop multicast. We show that the throughput maximization problem can be decomposed into two subproblems: a data routing subproblem at the network layer, and a power control subproblem at the physical layer with a set of Lagrangian dual variables coordinating interlayer coupling. Various effective solutions are discussed for each subproblem. We emphasize the network coding technique for multicast routing and a game theoretic method for interference management, for which efficient and distributed solutions are derived and illustrated. Finally, we show that the proposed framework can be extended to take into account physical-layer wireless multicast in mesh networks

Crosstalk is a major issue in modern digital subscriber line (DSL) systems such as ADSL and VDSL. Static spectrum management, which is the traditional way of ensuring spectral compatibility, employs spectral masks that can be overly conservative and lead to poor performance. This paper presents a centralized algorithm for optimal spectrum balancing in DSL. The algorithm uses the dual decomposition method to optimize spectra in an efficient and computationally tractable way. The algorithm shows significant performance gains over existing dynamics spectrum management (DSM) techniques, e.g., in one of the cases studied, the proposed centralized algorithm leads to a factor-of-four increase in data rate over the distributed DSM algorithm iterative waterfilling.

In this letter, we investigate the performance of constant-power waterfilling algorithms for the intersymbol interference channel and for the independent identically distributed fading channel where a constant power level is used across a properly chosen subset of subchannels. A rigorous performance analysis that upper bounds the maximum difference between the achievable rate under constant-power waterfilling and that under true waterfilling is given. In particular, it is shown that for the Rayleigh fading channel, the spectral efficiency loss due to constant-power waterfilling is at most 0.266 b/s/Hz. Furthermore, the performance bound allows a very-low-complexity, logarithm-free, power-adaptation algorithm to be developed. Theoretical worst-case analysis and simulation show that the approximate waterfilling scheme is very close to the optimum.

The design and optimization of multicarrier communications systems often involve a maximization of the total throughput subject to system resource constraints. The optimization problem is numerically difficult to solve when the problem does not have a convexity structure. This paper makes progress toward solving optimization problems of this type by showing that under a certain condition called the time-sharing condition, the duality gap of the optimization problem is always zero, regardless of the convexity of the objective function. Further, we show that the time-sharing condition is satisfied for practical multiuser spectrum optimization problems in multicarrier systems in the limit as the number of carriers goes to infinity. This result leads to efficient numerical algorithms that solve the nonconvex problem in the dual domain. We show that the recently proposed optimal spectrum balancing algorithm for digital subscriber lines can be interpreted as a dual algorithm. This new interpretation gives rise to more efficient dual update methods. It also suggests ways in which the dual objective may be evaluated approximately, further improving the numerical efficiency of the algorithm. We propose a low-complexity iterative spectrum balancing algorithm based on these ideas, and show that the new algorithm achieves near-optimal performance in many practical situations.

This letter investigates the structure of the optimal spatial multiplex scheme in a multiuser multiantenna wireless fading environment. Based on a sum-capacity criterion, this letter shows that the optimal transmission strategy in an uplink or downlink channel with n antennas at the base-station involves more than n users at the same time. In particular, when remote users are equipped with m antennas each, the maximum number of data streams is shown to be upper bounded by n2, with each user transmitting or receiving up to m2data streams. This gives a dimension-counting interpretation for multiuser diversity. Multiple antennas at the base-station increases the total number of dimensions, thus allowing more users to transmit and receive at the same time. By contrast, multiple antennas at the remote terminal allow a single user to occupy multiple dimensions, which increases its transmission rate, but also has the potential effect of precluding simultaneous transmission by other users

The sum capacity of a Gaussian vector broadcast channel is the saddle point of a minimax Gaussian mutual information expression where the maximization is over the set of transmit covariance matrices subject to a power constraint and the minimization is over the set of noise covariance matrices subject to a diagonal constraint. This sum capacity result has been proved using two different methods, one based on decision-feedback equalization and the other based on a duality between uplink and downlink channels. This paper illustrates the connection between the two approaches by establishing that uplink-downlink duality is equivalent to Lagrangian duality in minimax optimization. This minimax Lagrangian duality relation allows the optimal transmit covariance and the least-favorable-noise covariance matrices in a Gaussian vector broadcast channel to be characterized in terms of the dual variables. In particular, it reveals that the least favorable noise is not unique. Further, the new Lagrangian interpretation of uplink-downlink duality allows the duality relation to be generalized to Gaussian vector broadcast channels with arbitrary linear constraints. However, duality depends critically on the linearity of input constraints. Duality breaks down when the input constraint is an arbitrary convex constraint. This shows that the minimax representation of the broadcast channel sum capacity is more general than the uplink-downlink duality representation.

A numerical algorithm for the computation of sum capacity for the Gaussian vector broadcast channel is proposed. The sum capacity computation relies on a duality between the Gaussian vector broadcast channel and the sum-power constrained Gaussian multiple-access channel. The numerical algorithm is based on a Lagrangian dual decomposition technique and it uses a modified iterative water-filling approach for the Gaussian multiple-access channel. The algorithm converges to the sum capacity globally and efficiently.

This paper presents a joint multiuser detection and optimal spectrum balancing algorithm for heavily unbalanced crosstalk channels in digital subscriber line systems. To ensure detection of strong crosstalk only, the set of tones subject to multiuser detection must be chosen carefully. The problem of tone selection is highly coupled with the transmit power spectra and thus the optimal solution requires the two problems to be solved jointly. This paper makes use of the idea of dual decomposition to solve the above problem. By minimizing the Lagrangian dual of the primal problem, the joint tone selection and optimal spectral balancing problem can be solved globally and efficiently. Simulations show considerable bit rate increase when multiuser detection is performed with optimal spectrum balancing.

This paper investigates the multiuser spectrum optimization problem for digital subscriber lines. We propose an iterative and low-complexity spectrum optimization technique that improves upon the recently proposed optimal spectrum balancing (OSB) algorithm. In the optimal spectrum balancing algorithm, the Lagrange multipliers are used to decouple the constrained optimization problem into a series of per-tone unconstrained optimisation problems. However, each per-tone problem still has a computational complexity that is exponential in the number of users. This paper proposes an iterative algorithm for the per-tone optimization problem to further reduce the computational complexity of spectrum balancing. The essential idea resembles that of iterative water-filling. In each step of the algorithm, each individual user iteratively optimizes the joint objective function with a fixed set of Lagrange multipliers. The new algorithm has a computational complexity that is polynomial in the number of users. Simulation results show that the new algorithm has a near-optimal performance.

This paper presents an optimization framework for a wireless sensor network in which each sensor plays a dual role of sensing the environment and relaying the sensor information. The design of such a network involves two distinct aspects. First, as the observations of the underlying environment are often correlated, distributive source coding methods have the potential to greatly improve the efficiency of the sensor operation. Thus, information theoretical source coding methods are useful in the application layer. Second, as each sensor must send information individually to a central processor, routing and power allocation in the network and physical layers are also important issues. The main focus of this paper is an optimization framework that jointly solves the source coding, routing and power allocation problems in such a network. The main insight is the following: the joint optimization problem for a sensor network, when solved in the dual domain, provides a natural separation between the application layer, the network layer and the physical layer. The interface between the layers is precisely the dual optimization variables. The crucial observation that makes this possible is that the underlying source coding problem in the application layer and the channel coding problem in the physical layer can always be made convex via time-division or frequency-division multiplexing. Convexification in time or frequency enables dual algorithms to reach the global optimum of the overall network optimization problem efficiently.

Practical implementation of random binning is one of the key challenges in achieving the largest available rate regions for many multiuser channels. This paper explores the use of low-density parity-check (LDPC) like codes for a particular kind of deterministic broadcast channel called the Blackwell channel and illustrates that random linear codes can be used to construct practical binning schemes at rates close to the capacity region of the Blackwell channel. The key ingredient is an encoding algorithm known as "survey propagation" which is a generalization of the well-known belief propagation algorithm for LDPC codes. Survey propagation has been previously devised for a class of constraint satisfaction problems called K-SAT. This paper shows that the encoding problem for the Blackwell channel contains the same features as the constraint satisfaction problem and that the survey propagation algorithm, when concatenated with an outer error correcting code, works well at rates close to the Blackwell channel capacity region

The complexity-rate tradeoff for error-correcting codes below the Shannon limit is a central question in coding theory. This paper makes progress in this area by presenting a joint numerical optimization of rate and decoding complexity for low-density parity-check codes. The focus of this paper is on the binary symmetric channel and on a class of decoding algorithms for which an exact extrinsic information transfer (EXIT) chart analysis is possible. This class of decoding algorithms includes the Gallager decoding algorithm B. The main feature of the optimization method is a complexity measure based on the EXIT chart that accurately estimates the number of iterations required for the decoding algorithm to reach a target error rate. Under a fixed check-degree distribution, it is shown that the proposed complexity measure is a convex function of the variable-degree distribution in a region of interest. This allows us to numerically characterize the complexity-rate tradeoff. We show that for the Gallager B decoding algorithm on binary symmetric channels, the optimization procedure can produce complexity savings of 30-40% as compared to the conventional code design method

This paper deals with the design and analysis of low-density parity-check (LDPC) codes for the Slepian-Wolf problem. The main contribution is a code design method based on a density evolution (DE) analysis for the cases where multiple LDPC codes are simultaneously decoded at the decoder. Good source codes are designed both for memoryless sources and sources with Markov memory. Further, simultaneous decoding is generalized to the case of source splitting, which allows non-corner points of the Slepian-Wolf region to be achieved even for sources with equiprobable marginal distributions

This paper studies the combination of practical trellis and convolution codes with Tomlinson-Harashima precoding (THP) for the presubtraction of multiuser interference that is known at the transmitter but not known at the receiver. It is well known that a straightforward application of THP suffers power, modulo, and shaping losses. This paper proposes generalizations of THP that recover some of these losses. At a high signal-to-noise ratio (SNR), the precoding loss is dominated by the shaping loss, which is about 1.53 dB. To recover shaping loss, a trellis-shaping technique is developed that takes into account the knowledge of a noncausal interfering sequence, rather than just the instantaneous interference. At rates of 2 and 3 bits per transmission, trellis shaping is shown to be able to recover almost all of the 1.53-dB shaping loss. At a low SNR, the precoding loss is dominated by power and modulo losses, which can be as large as 3-4 dB. To recover these losses, a technique that incorporates partial interference presubtraction (PIP) within convolutional decoding is developed. At rates of 0.5 and 0.25 bits per transmission, PIP is able to recover 1-1.5 dB of the power loss. For intermediate SNR channels, a combination of the two schemes is shown to recover both power and shaping losses.

This work considers a Gaussian multi-antenna broadcast channel with individual power constraints on each antenna, rather than the usual sum power constraint over all antennas. Per-antenna power constraints are more realistic because in practical implementations each antenna has its own power amplifier. The main contribution of this paper is a new derivation of the duality result for this class of broadcast channels that allows the input optimization problem to be solved efficiently. Specifically, we show that uplink-downlink duality is equivalent to Lagrangian duality in minimax optimization, and the dual multiple-access problem has a much lower computational complexity than the original problem. This duality applies to the entire capacity region. Further, we derive a novel application of Newton's method for the dual minimax problem that finds an optimal search direction for both the minimization and the maximization problems at the same time. This new computational method is much more efficient than the previous iterative water-filling-based algorithms and it is applicable to the entire capacity region. Finally, we show that the previous QR-based precoding method can be easily modified to accommodate the per-antenna constraint.

The design and optimization of orthogonal frequency division multiplex (OFDM) systems typically take the following form. The design objective is to maximize the total data rate which is the sum of individual rates in each frequency tone. The design constraints are usually linear constraints imposed across all tones. The paper shows that, regardless of whether the objective and the constraints are convex, the duality gap for this class of problems is always zero in the limit as the number of frequency tones goes to infinity. As the dual problem typically decouples into many smaller per-tone problems, solving the dual problem is much more efficient. This observation leads to an efficient method to find the global optimum of non-convex optimization problems for the OFDM system. Multiuser optimal power allocation, optimal frequency planning and optimal low-complexity crosstalk cancellation for vectored DSL are used to illustrate this point.

Crosstalk is a major issue in modern DSL systems such as ADSL and VDSL. Static spectrum management, the traditional way of ensuring spectral compatibility, employs spectral masks which can be overly conservative and lead to poor performance. In this paper we present a centralized algorithm for optimal spectrum management (OSM) in DSL. The algorithm uses a dual decomposition to solve the spectrum management problem in an efficient and computationally tractable way. The algorithm shows significant performance gains over existing DSM techniques, e.g. in a downstream ADSL scenario the centralized OSM algorithm can outperform a distributed DSM algorithm such as iterative waterfilling by up to 135%.

This paper establishes a connection between the uplink-downlink duality of the Gaussian vector multiple-access channel and broadcast channel and the Lagrangian duality in minimax optimization. This new minimax duality allows the optimal transmit covariance matrix and the least-favorable noise for the broadcast channel to be characterized in terms of the dual variables. Further, it allows uplink-downlink duality to be generalized to broadcast channels with arbitrary linear constraints. In particular, it shows that the dual of a broadcast channel with individual per-antenna power constraint is a multiple-access channel with a diagonal uncertain noise.

This work presents numerical algorithms for the computation of the capacity for channels with noncausal transmitter side information (the Gel'fand-Pinsker problem) and the rate-distortion function for source coding with decoder side information (the Wyner-Ziv problem). The algorithms are based on the reformulation of the mutual information expressions in terms of Shannon strategies.

This paper characterizes the sum capacity of a class of potentially nondegraded Gaussian vector broadcast channels where a single transmitter with multiple transmit terminals sends independent information to multiple receivers. Coordination is allowed among the transmit terminals, but not among the receive terminals. The sum capacity is shown to be a saddle-point of a Gaussian mutual information game, where a signal player chooses a transmit covariance matrix to maximize the mutual information and a fictitious noise player chooses a noise correlation to minimize the mutual information. The sum capacity is achieved using a precoding strategy for Gaussian channels with additive side information noncausally known at the transmitter. The optimal precoding structure is shown to correspond to a decision-feedback equalizer that decomposes the broadcast channel into a series of single-user channels with interference pre-subtracted at the transmitter.

This paper proposes an efficient numerical algorithm to compute the optimal input distribution that maximizes the sum capacity of a Gaussian multiple-access channel with vector inputs and a vector output. The numerical algorithm has an iterative water-filling interpretation. The algorithm converges from any starting point, and it reaches within 1/2 nats per user per output dimension from the sum capacity after just one iteration. The characterization of sum capacity also allows an upper bound and a lower bound for the entire capacity region to be derived.

This paper considers the optimal uplink transmission strategy that achieves the sum-capacity in a multiuser multi-antenna wireless system. Assuming an independent identically distributed block-fading model with transmitter channel side information, beamforming for each remote user is shown to be necessary for achieving sum-capacity when there is a large number of users in the system. This result stands even in the case where each user is equipped with a large number of transmit antennas, and it can be readily extended to channels with intersymbol interference if an orthogonal frequency division multiplexing modulation is assumed. This result is obtained by deriving a rank bound on the transmit covariance matrices, and it suggests that all users should cooperate by each user using only a small portion of available dimensions. Based on the result, a suboptimal transmit scheme is proposed for the situation where only partial channel side information is available at each transmitter. Simulations show that the suboptimal scheme is not only able to achieve a sum rate very close to the capacity, but also insensitive to channel estimation error.

The paper studies the optimal spatial multiplexing scheme in a downlink multiple-antenna environment with perfect transmitter and receiver channel knowledge. Using recent results on the sum capacity of the Gaussian vector broadcast channel, the optimal number of precoded data streams in a downlink channel is characterized. The main result is the following: the sum-capacity achieving transmission strategy in a random downlink channel with n transmit antennas at the base-station and K receivers each equipped with m antennas involves between n and 1/2 n(n+1) data streams in total, with each user receiving between m and 1/2 m(m+1) data streams. This gives a dimension counting interpretation for multiuser diversity. In particular, it shows that the throughput maximizing transmission strategy in a downlink channel with n transmit antennas should involve between n and 1/2 n(n+1) active users at any time.

The data that is transmitted in DSL system is subject to corruption by impulse noise, i.e., noise bursts of high energy that interfere with the transmitted symbols. As DSL data rates increase the crosstalk mitigation techniques become more sophisticated, impulse noise limits service in terms of rate or delay. Because of the highly non-stationary nature of impulse noise, a combination of interleaving and Reed-Solomon coding is currently used to shield systems from noise burst. This paper presents a modified impulse noise protection algorithm that takes advantage of the improved performance of Reed-Solomon codes when the location of the impaired bytes is known. Without changing the structure of the encoder or the interleaver, it is shown that the delay, or equivalently the overhead due to forward error correction coding, can be reduced without compromising the immunity of the system to impulses. A DMT-VDSL system is used as a particular example of the improvement achieved using byte-erasure.

A significant portion of the end-to-end delay in high-rate DSL systems is due to the impulse noise protection scheme employed in order to shield those systems against random, non-stationary noise bursts of high energy that appear on the copper lines. Systems are protected from impulse noise using a combination of interleaving and Reed-Solomon codes. In order to lower the end-to-end delay without reducing the data rate that is available to the user, one needs to decrease the interleaver depth. This paper presents a way to achieve this reduction without compromising neither the robustness to noise bursts nor the data rate of the system. The proposed algorithm relies on the inner code used by many DSL systems and uses the metric provided by the inner code decoder at the receiver. A DMT-VDSL system is used as a particular example of the achieved reduction of the end-to-end delay.

The paper presents a numerical study of the impact of impulse noise on asymmetric digital subscriber lines (ADSL). Methods for simulating the effect of impulse disturbances on a discrete multitone system are first presented, and actual measured noise bursts are then used for the simulations as if they were deterministic signals, in order to characterize their effects on ADSL systems. It is shown that, while a combination of coding, interleaving, and 6-dB margin is adequate in protecting ADSL systems from isolated impulses, an impulse train with long duration can cause a significant number of error bits in the system. In this case, a tradeoff among the number of error seconds, the maximum reach, and the coding delay must be made.

This paper considers the multiuser power control problem in a frequency-selective interference channel. The interference channel is modeled as a noncooperative game, and the existence and uniqueness of a Nash equilibrium are established for a two-player version of the game. An iterative water-filling algorithm is proposed to efficiently reach the Nash equilibrium. The iterative water-filling algorithm can be implemented distributively without the need for centralized control. It implicitly takes into account the loop transfer functions and cross couplings, and it reaches a competitively optimal power allocation by offering an opportunity for loops to negotiate the best use of power and frequency with each other. When applied to the upstream power backoff problem in very-high bit-rate digital subscriber lines and the downstream spectral compatibility problem in asymmetric digital subscriber lines, the new power control algorithm is found to give a significant performance improvement when compared with existing methods.

This paper proposes a numerical method for characterizing the rate region achievable with frequency-division multiple access (FDMA) for a Gaussian multiple-access channel with intersymbol interference. The frequency spectrum is divided into discrete frequency bins and the discrete bin-assignment problem is shown to have a convex relaxation, making it tractable to numerical optimization algorithms. A practical low-complexity algorithm for the two-user case is also proposed. The algorithm is based on the observation that the optimal frequency partition has a two-band structure when the two channels are identical or when the signal-to-noise ratio is high. The simulation result shows that the algorithm performs well in other cases as well. The FDMA-capacity algorithm is used to devise the optimal frequency-division duplex plan for very-high-speed digital subscriber lines.

This paper considers the vector Gaussian broadcast channel where a single transmitter with multiple antennas sends independent information to multiple receivers. An achievable rate region is derived by decomposing the broadcast channel into a series of single-user channels with non-causal side information. The side information may be completely pre-subtracted using precoding techniques. A practical trellis precoding method is presented. Trellis precoding can be viewed as a generalization of the Tomlinson-Harashima(1971, 1969) precoder. By taking into account the entire non-causal side-information sequence, a trellis precoder gives an additional shaping gain up to 1.53 dB compared to a Tomlinson precoder.

This paper investigates optimal power control in a frequency selective multiuser interference network. The power control problem is modeled as a non-cooperative game. The existence and uniqueness of a Nash equilibrium in the game is established, and an iterative water-filling algorithm is proposed to reach the Nash equilibrium efficiently. It is shown that the Nash equilibrium point corresponds to a competitively optimal power allocation in the interference network. Based on this result, an adaptive power control algorithm for upstream VDSL power back-off is developed. The power control algorithm takes into account the loop transfer functions and cross-couplings, and allows the loops to negotiate the best use of power and frequency. This new algorithm is found to have a substantial performance improvement when compared to current methods.

This paper derives a rigorous performance bound for the constant-power water-filling algorithm for ISI channels with multicarrier modulation and for i.i.d. fading channels with adaptive modulation. Based on the performance bound, a very-low complexity logarithm-free power allocation algorithm is proposed. Theoretical worst-case analysis and simulation show that the approximate water-filling scheme is close to optimal.

This paper characterizes the optimal power control method for maximum sum capacity in a multiple access fading channel with multiple transmitter and receiver antennas when perfect channel side information is available at both the transmitters and the receiver. The profound benefit of multi-antenna diversity is demonstrated by a dimension counting argument. The optimal power allocation strategy in a system with n transmit antennas for each user and m receive antennas is a combination of successive cancellation and a TDMA-like scheme where in each time slot the rank of the transmit signals r/sub k/ for all users must satisfy /spl Sigma//sub k/r/sub k/(r/sub k/+1)/spl les/m(m+1). Thus, the total number of users that are allowed to transmit simultaneously is constrained by the number of receiver antennas. Receiver diversity increases the total number of dimensions thus allowing more users to transmit at the same time. By contrast, transmitter diversity allows a single user to occupy multiple dimensions as to benefit its own transmission, thus having the effect of precluding simultaneous transmission by other users.

This paper studies the decorrelation property of the constituent codes in space-time turbo codes (STTCs), where different constituent codes are transmitted over different antennas. We show that the time correlation between constituent codes falls of with respect to STTC block length N as 1//spl radic/N for large N, and hence goes to zero as N/spl rarr//spl infin/. We then use this result to obtain analytical expressions for the frame error rate (FER) of STTCs in flat fading. The decorrelation property of STTCs helps explain the exceptional performance of STTCs on slowly varying flat fading channels.

This paper proposes a numerical method for characterizing the achievable rate region for a Gaussian multiple access channel with ISI under the frequency division multiple access restriction. The frequency spectrum is divided into discrete frequency bins and the discrete bin assignment problem is shown to have a convex programming relaxation, making it tractable to numerical algorithms. The run-time complexity may be further reduced in the two-user case if the two channels are identical, or if the signal-to-noise ratio is high.

Previous research has shown that the capacity of a multiple antenna system grows linearly with increasing number of antennas for rich-scattering environments. However, this is not true for wireless channels with a small number of independent paths. To overcome this problem, this paper investigates the possibility of exploiting the multiuser dimension with and without channel side information at the transmitter. First, the single user capacity per antenna is shown to converge to zero with increasing number of antennas for channels with a finite number of independent paths. Then multiuser capacity per antenna at the limit is shown to be positive. Simulation results are presented for a single user system and a multiuser uplink system.