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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 that bottleneck objective 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. [ABSTRACT FROM AUTHOR]

In conventional multi-user multiple-input multiple-output (MU-MIMO) systems with frequency division duplexing (FDD), channel acquisition and precoder optimization processes have been designed separately although they are highly coupled. This paper studies an end-to-end design of downlink MU-MIMO systems which include pilot sequences, limited feedback, and precoding. To address this problem, we propose a novel deep learning (DL) framework which jointly optimizes the feedback information generation at users and the precoder design at a base station (BS). Each procedure in the MU-MIMO systems is replaced by intelligently designed multiple deep neural networks (DNN) units. At the BS, a neural network generates pilot sequences and helps the users obtain accurate channel state information. At each user, the channel feedback operation is carried out in a distributed manner by an individual user DNN. Then, another BS DNN collects feedback information from the users and determines the MIMO precoding matrices. A joint training algorithm is proposed to optimize all DNN units in an end-to-end manner. In addition, a training strategy which can avoid retraining for different network sizes for a scalable design is proposed. Numerical results demonstrate the effectiveness of the proposed DL framework compared to classical optimization techniques and other conventional DNN schemes. [ABSTRACT FROM AUTHOR]

Plane spiral orbital angular momentum (PSOAM) mode-groups (MGs) and multiple-input multiple-output non-orthogonal multiple access (MIMO-NOMA) serve as two emerging techniques for achieving high spectral efficiency (SE) in the next-generation networks. In this paper, a PSOAM MGs based multi-user MIMO-NOMA system is studied, where the base station transmits data to users by utilizing the generated PSOAM beams. For such scenario, the interference between users in different PSOAM mode groups can be avoided, which leads to a significant performance enhancement. We aim to maximize the energy efficiency (EE) of the system subject to the constraints of the total transmission power and the minimum data rate. This designed optimization problem is non-convex owing to the interference among users, and hence is quite difficult to tackle directly. To solve this issue, we develop a dual layer resource allocation algorithm where the bisection method is exploited in the outer layer to obtain the optimal EE and a resource distributed iterative algorithm is exploited in the inner layer to optimize the transmit power. Besides, an alternative resource allocation algorithm with Deep Belief Networks (DBN) is proposed to cope with the requirement for low computational complexity. Simulation results verify the theoretical findings and demonstrate the proposed algorithms on the PSOAM MGs based MIMO-NOMA system can obtain a better performance comparing to the conventional MIMO-NOMA system in terms of EE. [ABSTRACT FROM AUTHOR]

In this paper, we consider an intelligent reflecting surface (IRS)-assisted cognitive radio system and maximize the secondary user (SU) rate by jointly optimizing the transmit power of secondary transmitter (ST) and the IRS’s reflect beamforming, subject to the constraints of the minimum required signal-to-interference-plus-noise ratio at the primary receiver, the ST’s maximum transmit power, and the unit modulus of the IRS reflect beamforming vector. This joint optimization problem can be solved suboptimally by the non-convex optimization techniques, which however usually require complicated mathematical transformations and are computationally intensive. To address this challenge, we propose an algorithm based on the deep deterministic policy gradient (DDPG) method. To achieve a higher learning efficiency and a lower reward variance, we propose another algorithm based on the soft actor-critic (SAC) method. In these proposed algorithms, a reward impact adjustment approach is proposed to improve their learning efficiency and stability. Simulation results show that the two proposed algorithms can achieve comparable SU rate performance with much shorter running time, as compared to the existing non-convex optimization-based benchmark algorithm, and that the proposed SAC-based algorithm learns faster and achieves a higher average reward with lower variance, as compared to the proposed DDPG-based algorithm. [ABSTRACT FROM AUTHOR]

Intelligent reflecting surface (IRS) has been regarded as a promising and revolutionary technology for future wireless communication systems owing to its capability of tailoring signal propagation environment in an energy/spectrum/ hardware-efficient manner. However, most existing studies on IRS optimizations are based on a simple and ideal reflection model that is impractical in hardware implementation, which thus leads to severe performance loss in realistic wideband/multi-band systems. To deal with this problem, in this paper we first propose a more practical and more tractable IRS reflection model that describes the difference of reflection responses for signals at different frequencies. Then, we investigate the joint transmit beamforming and IRS reflection beamforming design for an IRS-assisted multi-cell multi-band system. Both power minimization and sum-rate maximization problems are solved by exploiting popular second-order cone programming (SOCP), Riemannian manifold, minimization-majorization (MM), weighted minimum mean square error (WMMSE), and block coordinate descent (BCD) methods. Simulation results illustrate the significant performance improvement of our proposed joint transmit beamforming and reflection design algorithms based on the practical reflection model in terms of power saving and rate enhancement. [ABSTRACT FROM AUTHOR]

Caching popular contents at edge networks (such as small-cell base stations) has been proposed to deal with the ever-growing mobile traffic. At the meantime, recommendation system is able to shape user demands for further prompting caching gain. In this paper, we study a multi-antenna multi-cell edge network employing transmit beamforming with caching-aware recommendation and user association. We first establish a framework for the joint problem of beamforming, user association, content caching and recommendation to minimize the content transmission delay of mobile users, by specifying a set of necessary conditions for all four component functions of the network. The resulting optimization problem corresponds to a non-convex, multi-timescale, and mixed-integer programming problem, which is hard to handle. To deal with the difficulty in solving the joint optimization problem by the direct formulation, we equivalently decompose it into three sub-problems. Then, we develop a computationally-efficient iterative algorithm to obtain the sub-optimal solution, where the three subproblems are tackled iteratively. Simulation results are conducted to demonstrate that the proposed algorithm can obtain lower transmission delay than baseline schemes. [ABSTRACT FROM AUTHOR]

Intelligent reflecting surface (IRS) is an emerging cost-efficient technology to enhance communication performance by implementing a large number of passive reflecting elements with tunable phases in wireless systems. In this paper, we propose a general framework for the IRS-aided MIMO system designs under both single-user and multi-user setups, in which the diverse performance metrics including weighted mutual information and weighted MSE, and the realistic multiple weighted power constraint are taken into consideration. Leveraging the alternating optimization approach, the optimal IRS phase shifts are obtained in semi-closed forms. Specifically, based on the matrix-monotonic optimization theory, it is found that optimizing IRS phase shifts is essentially equivalent to tuning the eigenvalues and the corresponding eigenvectors of the MSE matrix. Then the proposed general framework is extended to a multi-user system by introducing a majorization-minimization (MM)-based method for IRS phase shift optimization. Simulation results show that our proposed optimal design brings significant enhancement on the chosen performance metric compared to the traditional MIMO systems without the IRS, and also significantly outperforms various benchmark designs in both single-user and multi-user systems. [ABSTRACT FROM AUTHOR]

This paper considers the finite-order filter design problem for the source and multiple full-duplex (FD) relays in a cooperative communication system under frequency-selective fading channels. The goal is to optimize the source and relay filters such that the end-to-end signal-to-interference-plus-noise ratio (SINR) of minimum mean-squared error decision-feedback equalizer (MMSE-DFE) can be maximized. The resultant design problem is very difficult since we need to deal with self interference (SI), inter-relay interference (IRI), and inter-symbol interference (ISI) at the same time. Novel designs are then proposed to overcome the difficulty in this work. Transforming the signals into the frequency domain and using some optimization techniques, we first theoretically derive the power spectrum of the source filter and the spectrums of the relay filters. Then, the finite-order design is developed to approach the derived spectrums. Based on the weighted least-square (WLS) criterion, the Steiglitz-McBride method is exploited to obtain the filter coefficients in finite lengths. Numerical results demonstrated that complete removal of SI may not be a good strategy; preserving a certain amount of SI at each relay instead provides better SINR performance. [ABSTRACT FROM AUTHOR]

In the present paper a closed-form framework is derived for the analysis and optimization of the coverage probability (Pcov) and of the area spectral efficiency (ASE) in long-range (LoRa) networks. The proposed framework exploits stochastic geometry tools to associate the Pcov and the ASE to the end device (ED) transmit power and to the ED density. The analysis reveals the trends of the Pcov and of the ASE curves, with respect to both of the two parameters, while the robustness of the framework holds even at the asymptotic cases. Building upon the derived framework, the analysis demonstrates that no joint global optimum exists that jointly maximizes the Pcov over both parameters, suggesting that the optimization of the Pcov must be performed separately, for the two key network parameters considered. As opposed to that, the analysis demonstrates that a set of global optima exists that jointly maximize the ASE over both parameters, and these global maxima are subsequently derived in closed form. Thus, the derived framework fully characterizes the performance of LoRa networks, while defining in closed form the optimal points of operation that can be proven of significant value, for the transceiver and network design, of practical LoRa networks. [ABSTRACT FROM AUTHOR]

In this paper, we study the outage probability minimizing problem in a two-hop cooperative relay network. To reduce outage probability, existing studies propose many schemes for relay selection and power allocation, which are usually based on the assumption of exact channel state information (CSI). However, it is difficult to obtain perfect instantaneous CSI in practical situations where channel states change rapidly, and thus traditional methods would not perform well. Considering these factors, we turn to the emerging reinforcement learning (RL) methods for solutions. RL methods do not need any prior knowledge of CSI, but use neural network for approximation and decision after interacting with communication environment. Nevertheless, conventional RL methods, including most deep reinforcement learning (DRL) methods, cannot perform well when the search space is too large. In addition, non-stationarity is a common problem when using hierarchical reinforcement learning (HRL), which is caused by the changing behavior in different hierarchies. Therefore, we first propose a DRL framework with an outage-based reward function, which is then used as a baseline. Then, we further design an HRL framework and training algorithm. By decomposing relay selection and power allocation into two hierarchical optimization objectives, and combining on- policy and off-policy methods in the HRL framework, our method successfully address the sparse reward and non-stationary problem. Simulation results reveal that compared with traditional DRL method, the proposed HRL training algorithm can converge faster and reduce the outage probability by 8% in two-hop relay network with the same outage threshold. [ABSTRACT FROM AUTHOR]

In this paper, we consider a multi-user multiple-input multiple-output (MIMO) system aided by multiple intelligent reflecting surfaces (IRSs) that are deployed to increase the coverage and, possibly, the rank of the channel. We propose an optimization algorithm to configure the IRSs, which is aimed at maximizing the network sum-rate by exploiting only the statistical characterization of the locations of the mobile users. As a consequence, the proposed approach does not require the estimation of either instantaneous channel state information (CSI) or second-order channel statistics for IRS optimization, thus significantly relaxing (or even avoiding) the need of frequently reconfiguring the IRSs, which constitutes one of the most critical issues in IRS-assisted systems. Numerical results confirm the validity of the proposed approach. It is shown, in particular, that IRS-assisted wireless systems that are optimized based on statistical position information still provide large performance gains as compared to the baseline scenarios in which no IRSs are deployed. [ABSTRACT FROM AUTHOR]

Intelligent reflecting surface (IRS) is deemed as a promising solution to improve the spectral and energy efficiency of wireless communications cost-effectively. In this paper, we consider a wireless network where multiple base stations (BSs) serve their respective users with the aid of distributed IRSs in the downlink communication. Specifically, each IRS assists in the transmission from its associated BS to user via passive beamforming, while in the meantime, it also randomly scatters the signals from other co-channel BSs, thus resulting in additional signal as well as interference paths in the network. As such, a new IRS-user/BS association problem arises pertaining to optimally balance the passive beamforming gains from all IRSs among different BS-user communication links. To address this new problem, we first derive a tractable lower bound of the average signal-to-interference-plus-noise ratio (SINR) at the receiver of each user, termed average-signal-to-average-interference-plus-noise ratio (ASAINR), based on which two ASAINR balancing problems are formulated to maximize the minimum ASAINR among all users by optimizing the IRS-user associations without and with BS transmit power control, respectively. We also characterize the scaling behavior of user ASAINRs with the increasing number of IRS reflecting elements to investigate the different effects of IRS-reflected signal versus interference power. Moreover, to solve the two ASAINR balancing problems that are both non-convex optimization problems, we propose an optimal solution to the problem without BS power control and low-complexity suboptimal solutions to both problems by applying the branch-and-bound method and exploiting new properties of the IRS-user associations, respectively. Numerical results verify our performance analysis and also demonstrate significant performance gains of the proposed solutions over benchmark schemes. [ABSTRACT FROM AUTHOR]

Fog radio access network (Fog-RAN), which pushes caching and computing capabilities to the network edge, is capable of efficiently delivering content to users by using carefully designed caching placement and content replacement algorithms. In this paper, the transmission scheme design and coding parameter optimization will be considered for coded caching in Fog-RAN, where the reliability of content delivery, i.e., content outage probability, is used as the performance metric. The problem will be formulated as a complicated multi-objective probabilistic combinatorial optimization. A novel maximum $b$ -matching approach will then be proposed to obtain the Pareto optimal solution with fairness constraint. Based on the fast message passing approach, a distributed algorithm with a low memory usage of $O(M+N)$ is also proposed, where $M$ is the number of users and $N$ is the number of fog access points (Fog-APs). Although it is usually very difficult to derive the closed-form formulas for the optimal solution, the approximation formulas of the content outage probability will also be obtained as a function of coding parameters. The asymptotic optimal coding parameters can then be obtained by defining and deriving the outage exponent region and diversity-multiplexing region. Simulation results will illustrate the accuracy of the theoretical derivations, and verify the outage performance of the proposed approach. Therefore, this paper not only proposes a practical distributed Fog-AP selection algorithm for coded caching but also provides a systematic way to evaluate and optimize the performance of Fog-RANs. [ABSTRACT FROM AUTHOR]

Technically, the security performance of generalized spatial modulation (GenSM) networks can be enhanced by dynamically adjusting the precoder allocated to the legitimate signal as communication channel varies. For this purpose, our paper proposes a secure transmission strategy upon designing both digital and analog precoders for hybrid GenSM systems, where an eavesdropper is taken into account. The concept of the hybrid GenSM system has arose to improve the spatial multiplexing (SMX) gain for remedying the shortcoming of the limited number of radio frequency chains in traditional GenSM systems. However, this may lead to a great deal of security degradation since the SMX gain of the unintended receiver will be also improved. To this end, we develop a secrecy enhancement scheme by devising both analog and digital precoders for hybrid GenSM networks. Specifically, we derive an efficiently closed-form alternative to the original secrecy rate (SR) expression for reducing the excessive computational complexity of the joint optimization problem over the hybrid precoder. Then, by using this alternative as our cost function, an iterative algorithm is proposed. In particular, we elaborately conceive a pair of concave maximization problems in order to optimize the digital and analog precoders, respectively. Our proposed strategy not only utilizes semi-positive definite relaxing technique over the analog precoder but also invokes a lower bound of the alternative to further simplify the optimization over the vectored digital precoder. Subsequently, both the convergence and computational complexity of the proposed alternating iteration algorithm are analyzed. Compared to existing designs, our proposed strategy strikes a compelling role in balancing the SR performance and complexity. Finally, our simulation results confirm the efficiency of the proposed algorithm in terms of the SR performance achieved. [ABSTRACT FROM AUTHOR]

Terahertz (THz) band has attracted considerable interest recently due to its superior high frequency and large available bandwidth. THz could act a vital part in the sixth generation (6G) mobile communication networks. In this paper, we introduce the downlink non-orthogonal multiple access (NOMA) technology into THz band small cell networks, where the total performance is optimized considering the two key enabling technologies. In order to decrease the energy consumption triggered by increasing of wireless services, we pay great attention to energy efficiency (EE) optimization and resource allocation in the THz-NOMA downlink systems by solving the subchannel assignment and power optimization. We first exploit a channel model for the THz-NOMA downlink system by using the key features of THz-NOMA networks. Then we utilize Dinkelbach-style algorithm to solve the resource allocation problem and decompose it into two subproblems. A subchannel assignment algorithm and a power optimization based on alternative direction method of multipliers (ADMM) algorithm are developed to get the solution. Finally, to embody the strengths of THz-NOMA performance, we compare our proposed schemes against the conventional schemes. Simulation results yield substantially higher EE and further prove the availability of our proposed schemes. [ABSTRACT FROM AUTHOR]

In this paper, we focus on intelligent reflecting surface (IRS) assisted multi-antenna communications with transceiver hardware impairments encountered in practice. In particular, we aim to maximize the received signal-to-noise ratio (SNR) taking into account the impact of hardware impairments, where the source transmit beamforming and the IRS reflect beamforming are jointly designed under the proposed optimization framework. To circumvent the non-convexity of the formulated design problem, we first derive a closed-form optimal solution to the source transmit beamforming. Then, for the optimization of IRS reflect beamforming, we obtain an upper bound to the optimal objective value via solving a single convex problem. A low-complexity minorization-maximization (MM) algorithm was developed to approach the upper bound. Simulation results demonstrate that the proposed beamforming design is more robust to the hardware impairments than that of the conventional SNR maximized scheme. Moreover, compared to the scenario without deploying an IRS, the performance gain brought by incorporating the hardware impairments is more evident for the IRS-aided communications. [ABSTRACT FROM AUTHOR]

This paper presents a theoretical framework to analyze the performance of an integrated unmanned aerial vehicle (UAV)-intelligent reflecting surface (IRS) relaying system in which the IRS provides an additional degree of freedom combined with the flexible deployment of full-duplex UAV to enhance communication between ground nodes. Our framework considers three different transmission modes: (i) UAV-only mode, (ii) IRS-only mode, and (iii) integrated UAV-IRS mode to achieve spectral and energy-efficient relaying. For the proposed modes, we provide exact and approximate expressions for the end-to-end outage probability, ergodic capacity, and energy efficiency (EE) in closed-form. We use the derived expressions to optimize key system parameters such as the UAV altitude and the number of elements on the IRS considering different modes. We formulate the problems in the form of fractional programming (e.g. single ratio, sum of multiple ratios or maximization-minimization of ratios) and devise optimal algorithms using quadratic transformations. Furthermore, we derive an analytic criterion to optimally select different transmission modes to maximize ergodic capacity and EE for a given number of IRS elements. Numerical results validate the derived expressions. The solutions obtained from the proposed optimization algorithms are compared with those obtained through exhaustive search. Insights are drawn related to the different communication modes, optimal number of IRS elements, and optimal UAV height. [ABSTRACT FROM AUTHOR]

This paper studies robust energy optimization for the cloud radio access network (C-RAN). The objective of this paper is to jointly minimize network power consumption through optimizing the base station (BS) mode, multi-user (MU)-BS association, and beamforming vectors given imperfect channel state information (CSI). To solve this non-trivial problem, we first transform the problem to a semi-definite programming (SDP) one using the S-lemma with the aid of the semi-definite relaxation technique, and then propose a SDP-based group sparse beamforming approach to solve it iteratively. Since the computational complexity of solving SDP problems is intractable, we propose to translate the uncertainty in the CSI to the uncertainty in its covariance matrix, and then recast the original problem as a mixed-integer second-order cone programming problem. We further propose a two-stage rank selection framework to determine the BS mode and MU-BS association separately and successively. Simulation results demonstrate the convergence of our proposed algorithms, and validate the effectiveness of the proposed algorithms in minimizing the network power consumption of the C-RAN. [ABSTRACT FROM PUBLISHER]

It is known that superposition signaling in Gaussian interference networks is capable of improving the achievable rate region. However, the problem of maximizing the rate gain offered by superposition signaling is computationally prohibitive, even in the simplest case of two-user single-input single-output interference networks. This paper examines superposition signaling for the general multiple-input multiple-output broadcast Gaussian interference networks. The problem of maximizing either the sum rate or the minimal user’s rate under superposition signaling and dirty paper coding is solved by a computationally efficient path-following procedure, which requires only a convex quadratic program for each iteration but ensures convergence at least to a locally optimal solution. Numerical results demonstrate the substantial performance advantage of the proposed approach. [ABSTRACT FROM AUTHOR]

Precoder optimization with full channel state information for finite alphabet signals over multiple-input multiple-output random channels is investigated in this paper. The precoder is represented by a product of power allocation matrix and constellation-forming matrix. There was an optimal algorithm introduced in the literature to globally maximize the channel mutual information by iteratively optimizing these two matrices. However, the computational complexity of the optimal algorithm is painfully high, especially when it is used with the high-order modulation and the high-data stream number. In this paper, we propose a novel sub-optimal low-complexity precoding algorithm and compare it with the optimal one. The new algorithm proceeds in two steps. First, the constellation-forming matrix is fixed in order to maximize the minimum Euclidean distance between the received symbols, which ensures high channel mutual information. Then, given the constellation-forming matrix, an iterative algorithm searches for the power allocation matrix that maximizes the channel mutual information. Since optimizing only one matrix instead of two, the new algorithm not only achieves a lower computational complexity but also avoids the use of initial values, which must be carefully selected for each channel and signal-to-noise ratio for fast convergence. Another advantage of the new algorithm is that the resulting precoder has a fixed form of received constellation thanks to the fixed constellation-forming matrix. This allows us to optimize the symbol mapping on the received constellation. Simulation results show that the proposed low-complexity precoder achieves error-rate performance that is close to performance of the optimal one when the conventional mapping is used. In addition, the new precoder used with optimized mapping at received constellation shows significant error-rate performance improvement. [ABSTRACT FROM AUTHOR]

In this paper, we investigate the two-way communication between two users assisted by a reconfigurable intelligent surface (RIS). The scheme that two users communicate simultaneously over Rayleigh fading channels is considered. The channels between the two users and RIS can either be reciprocal or non-reciprocal. For reciprocal channels, we determine the optimal phases at the RIS to maximize the signal-to-interference-plus-noise ratio (SINR). We then derive exact closed-form expressions for the outage probability and spectral efficiency for single-element RIS. By capitalizing the insights obtained from the single-element analysis, we introduce a gamma approximation to model the product of Rayleigh random variables which is useful for the evaluation of the performance metrics in multiple-element RIS. Asymptotic analysis shows that the outage decreases at $\left ({\log (\rho)/\rho }\right)^{L}$ rate where $L$ is the number of elements, whereas the spectral efficiency increases at $\log (\rho)$ rate at large average SINR $\rho $. For non-reciprocal channels, the minimum user SINR is targeted to be maximized. For single-element RIS, closed-form solution is derived whereas for multiple-element RIS the problem turns out to be non-convex. The latter one is solved through semidefinite programming relaxation and a proposed greedy-iterative method, which can achieve higher performance and lower computational complexity, respectively. [ABSTRACT FROM AUTHOR]

Intelligent reflecting surface (IRS) that enables the control of wireless propagation environment has recently emerged as a promising cost-effective technology for boosting the spectral and energy efficiency of future wireless communication systems. Prior works on IRS are mainly based on the ideal phase shift model assuming full signal reflection by each of its elements regardless of the phase shift, which, however, is practically difficult to realize. In contrast, we propose in this paper a practical phase shift model that captures the phase-dependent amplitude variation in the element-wise reflection design. Based on the proposed model and considering an IRS-aided multiuser system with one IRS deployed to assist in the downlink communications from a multi-antenna access point (AP) to multiple single-antenna users, we formulate an optimization problem to minimize the total transmit power at the AP by jointly designing the AP transmit beamforming and the IRS reflect beamforming, subject to the users’ individual signal-to-interference-plus-noise ratio (SINR) constraints. Iterative algorithms are proposed to find suboptimal solutions to this problem efficiently by utilizing the alternating optimization (AO) as well as penalty-based optimization techniques. Moreover, to draw essential insight, we analyze the asymptotic performance loss of the IRS-aided system that employs practical phase shifters but assumes the ideal phase shift model for beamforming optimization, as the number of IRS elements goes to infinity. Simulation results unveil substantial performance gains achieved by the proposed beamforming optimization based on the practical phase shift model as compared to the conventional ideal model. [ABSTRACT FROM AUTHOR]

In this paper, we study the problem of downlink (DL) sum rate maximization in codebook based multiuser (MU) multiple input multiple output (MIMO) systems. The user equipments (UEs) estimate the DL channels using pilot symbols sent by the access point (AP) and feedback the estimates to the AP over a control channel. We present a closed form expression for the achievable sum rate of the MU-MIMO broadcast system with codebook constrained precoding based on the estimated channels, where multiple data streams are simultaneously transmitted to all users. Next, we present novel, computationally efficient, minorization-maximization (MM) based algorithms to determine the selection of beamforming vectors and power allocation to each beam that maximizes the achievable sum rate. Our solution involves multiple uses of MM in a nested fashion. Based on this approach, we propose and contrast two algorithms, which we call the square-root-MM (SMM) and inverse-MM (IMM) algorithms. The algorithms are iterative and converge to a locally optimal beamforming vector selection and power allocation solution from any initialization. We evaluate the performance and complexity of the algorithms for various values of the system parameters, compare them with existing solutions, and provide further insights into how they can be used in system design. [ABSTRACT FROM AUTHOR]

Combining cache and heterogeneous cellular networks (HetNets) together is proposed to satisfy the increasing capacity requirements in 5G networks. In this paper, we consider the analysis and optimization of random caching in the $K$ -tier multi-antenna multi-user HetNets. We derive the expressions of the cache hit probability and the potential throughput, using tools from stochastic geometry. Limiting to the fully loaded interference-limited scenario, we obtain the closed-form exact expressions and the upper bounds of the metrics. The linear asymptotic expression of the cache hit probability in the high signal to interference and noise ratio (SINR) threshold region is also obtained. Based on analytical results, we consider the potential throughput maximization problem via optimizing the caching distribution. For a general scenario, we obtain a local optimal solution and a simple approximate asymptotic optimal solution in the high SINR threshold region. For a special case where each BS serves the largest number of users per resource block, we numerically solve a non-convex problem and propose a sub-optimal caching strategy by approximation, where a closed-form result is obtained in each iteration. Numerical simulations show that our proposed caching schemes outperform existing caching schemes. Analysis and optimization results provide insightful design guidelines for the cache-enabled multi-antenna HetNets. [ABSTRACT FROM AUTHOR]

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. [ABSTRACT FROM AUTHOR]

This paper considers the sum-rate (SR) maximization problem in downlink multi-user multiple input simgle output (MU-MISO) systems under imperfect channel state information at the transmitter (CSIT). Contrary to existing works, we consider a rather unorthodox transmission scheme. In particular, the message intended to one of the users is split into two parts: a common part which can be recovered by all users, and a private part recovered by the corresponding user. On the other hand, the rest of users receive their information through private messages. This rate-splitting (RS) approach was shown to boost the achievable degrees of freedom when CSIT errors decay with increased SNR. In this paper, the RS strategy is married with linear precoder design and optimization techniques to achieve a maximized ergodic SR (ESR) performance over the entire range of SNRs. Precoders are designed based on partial CSIT knowledge by solving a stochastic rate optimization problem using means of sample average approximation coupled with the weighted minimum mean square error approach. Numerical results show that in addition to the ESR gains, the benefits of RS also include relaxed CSIT quality requirements and enhanced achievable rate regions compared with conventional transmission with no rate-splitting. [ABSTRACT FROM PUBLISHER]

Because of its outstanding performance in extending coverage and reducing transmit power, wireless relay cooperation is deemed as one of the promising techniques to realize green-broadband communication in the future. In such relay cooperative systems, optimal power allocation not only can prolong the lifetime of users, but also is an effective way to reduce radiation in the environment. In this paper, for amplify-and-forward-relay cooperative networks, where multiple communication pairs share a common relay, we propose a novel power allocation scheme, which has considerably lower complexity compared with the existing solution schemes. The objective of this paper is to minimize sum-source-power consumption under the conditions that the transmit power of all nodes in the network is constrained and their predetermined target signal-to-noise ratios are satisfied. By providing sufficient analytical evidence, we study the optimality, convergence, and computational complexity of our proposed scheme. Through numerical simulation, we further justify the effectiveness and efficacy of our scheme compared with the existing works. [ABSTRACT FROM AUTHOR]

In next-generation digital subscriber line networks such as G.fast, employing discrete multi-tone transmission in high frequencies up to 212 MHz, the crosstalk among lines reaches very high levels. To precompensate the crosstalk in downstream transmission, QRD-based precoding has been proposed as a near-optimal dynamic spectrum management (DSM) technique. However, the performance of QRD-based precoding is greatly affected by the user encoding ordering (UEO). Since current multi-tone UEO methods are rather heuristic in the way they approach fairness, we develop, in this paper, a set of novel DSM algorithms for joint power allocation and UEO that enforce a generalized $ \alpha $ -fairness policy. Since finding the globally optimal UEO entails a combinatorial optimization problem with excessive computational complexity, an iterative algorithm is proposed which uses per-tone exhaustive searches (PTESs) and provides near-optimal approximate solutions. To further reduce the computational complexity, two suboptimal methods are suggested to replace the expensive PTESs, leading to two additional $ \alpha $ -fair DSM algorithms that are tractable for large scenarios against little performance loss. Simulations of a G.fast cable binder show that the $ \alpha $ -fair DSM algorithms achieve an efficient trade-off between fairness and performance in contrast to current UEO methods. [ABSTRACT FROM AUTHOR]

Coordinated multipoint transmission (CoMP) and soft frequency reuse (SFR) techniques are two common mechanisms that are often combined with the aim of reducing the deleterious effects caused by inter-cell interference in current and envisaged cellular networks (4G/5G). The performance of this combined mechanism has been shown to be strongly influenced by the specific choice of the parameters determining the frequency reuse settings. So far, their optimization has relied on time-consuming simulations that make the CoMP-SFR optimization a challenging issue. In an attempt to revert this situation, this paper introduces a novel analytical framework that, for a specific set of SFR parameters, allows the computation of closed-form approximations to relevant performance metrics such as average total capacity, worst-users rate, and backhaul requirements. Remarkably, these approximations exhibit a dependence on the SFR parameters that match that of the true metrics, hence allowing the optimization of the SFR-related parameters based on the proposed analytical expressions. Furthermore, and very important from a practical perspective, the availability of these closed-form expressions allows the efficient application of multi-objective optimization techniques to realistic scenarios with irregularly deployed base stations that serve to evaluate the various tradeoffs the designer is facing when optimizing multiple, and often conflictive, performance metrics. [ABSTRACT FROM AUTHOR]

The paper introduces a novel hybrid network coding scheme in a three-node two-way relaying network in a wireless fading environment. The proposed hybrid scheme is featured by an opportunistic mechanism which employs one of the two network coding schemes, namely physical-layer network coding (PNC) and digital network coding (DNC), according to the instant rate requirement and dynamic channel condition. We first develop a close-form expression for the power consumption of the proposed scheme, which is further used to formulate a series of optimization problems under both symmetric and asymmetric traffic scenarios. Solving the optimization problems yields important system parameters that determine the optimal power allocations, time split between uplink and downlink transmissions as well as between PNC and DNC, respectively. We will discuss the possible overhead in the real implementation of the proposed scheme. Extensive numerical experiments are conducted to compare the performance of the proposed hybrid scheme with the conventional PNC and DNC scheme, respectively. [ABSTRACT FROM AUTHOR]

In this paper, we investigate joint level 2 and 3 dynamic spectrum management (joint DSM 2/3) for downstream (DS) DSL. We consider a DS scenario in which users are divided into a few separate groups, where vector encoding based signal coordination can be applied in each group and spectrum coordination is possible for all users. This can be seen as a mixed interference/broadcast channel (IF/BC) scenario. In order to obtain the optimal transmitter structure, we develop a generalized duality between the vector broadcast and multiple-access channel (MAC) for scenarios in which partial signal coordination is available among users. This theory together with optimal spectrum balancing (OSB) is exploited to calculate the jointly optimal filters and transmit powers for non-linear vector dirty paper coding structures (in the form of Tomlinson-Harashima precoder (THP)) in the groups. The proposed scheme is compared to several other joint DSM 2/3 algorithms for DS DSL. Simulation results show that the proposed scheme (referred to as the IF/BC-OSB algorithm) achieves considerably higher bit rates than the other schemes. IF/BC-OSB encompasses the earlier developed BC-OSB algorithm as a special case. A simplified version of IF/BC-OSB avoiding exhaustive search with near-optimal performance is also proposed. [ABSTRACT FROM PUBLISHER]

This paper focuses on distributed linear precoding when users transmit correlated information over a fading multiple-input and multiple-output multiple access channel. The precoders are optimized in order to minimize the sum-mean square error (MSE) between the source and the estimated symbols. When sources are correlated, minimizing the sum-MSE results in a non-convex optimization problem. The precoders for an arbitrary number of users and transmit and receive antennas are thus obtained via a projected steepest-descent algorithm and a low-complexity heuristic approach. For the more restrictive case of two single-antenna users, a closed-form expression for the minimum sum-MSE precoders is derived. Moreover, for the scenario with a single receive antenna and any number of users, a solution is obtained by means of a semi-definite relaxation. Finally, we also consider precoding schemes where the precoders are decomposed into complex scalars and unit norm vectors. Simulation results show a significant improvement when source correlation is exploited at precoding, especially for low signal-to-noise ratios and when the number of receive antennas is lower than the number of transmitting nodes. [ABSTRACT FROM AUTHOR]

This paper investigates a new beamforming approach for interference exploitation, which has recently attracted interest as an alternative to conventional interference-avoidance beamforming for the downlink of multiple-input multiple-output systems. Contrary to existing interference exploitation approaches that focus on signal-to-noise ratio performance, we adopt an approach based on the detection region of the signal constellation. Focusing on quality of service, we then formulate the optimization for minimizing the error probability (EP) for the worst user, subject to power constraints. We do this by employing the knowledge of channel state information at the transmitter, along with all downlink users’ data that are readily available at the base station during downlink transmission. In this context, we also show that the detection-region-based beamforming and the worst user EP downlink beamforming are equivalent problems. Finally, we further propose a sum EPs approach and provide an analytic bound of average symbol error rate performance. Our simulations verify that the proposed techniques provide significantly improved performance over conventional downlink beamforming techniques. [ABSTRACT FROM AUTHOR]

This paper considers the problem of dynamic power allocation in the downlink of multi-cell networks, where each cell utilizes non-orthogonal multiple access (NOMA)-based resource allocation. Also, coordinated multi-point (CoMP) transmission is utilized among multiple cells to serve users experiencing severe inter-cell interference (ICI). Under this CoMP- NOMA framework, CoMP transmission is applied to a user experiencing less distinctive channel gain with multiple base stations (BSs)/cells (i.e., severe ICI-prone user) and non-CoMP transmission (i.e., transmission without any coordination among multiple BSs) is applied to a user experiencing dominating channel gain with only one BS/cell, while NOMA is utilized at each BS to schedule CoMP and non-CoMP users over the same transmission resources, i.e., time, spectrum and space. After discussing various CoMP- NOMA models for downlink power allocation in multi-cell networks, we focus on a joint transmission CoMP- NOMA (JT-CoMP-NOMA) model. For the JT-CoMP-NOMA model, an optimal joint power allocation problem is formulated and the solution is derived for each CoMP- set consisting of multiple cooperating BSs (i.e., CoMP BSs). To avoid the huge computational complexity of the joint power optimization approach, we propose a distributed power optimization approach at each cooperating BS whose optimal solution is independent of the solution of other coordinating BSs. The distributed solution for the joint power optimization problem is validated and numerical performance evaluation is carried out for the proposed CoMP- NOMA models including JT-CoMP-NOMA and coordinated scheduling CoMP- NOMA (CS-CoMP-NOMA). The obtained results reveal significant gains in spectral and energy efficiency in comparison with conventional CoMP- orthogonal multiple access (CoMP-OMA) systems. [ABSTRACT FROM AUTHOR]

Characterizing the fundamental energy efficiency (EE) performance of multiple-input–multiple-output interfering broadcast channels (MIMO-IFBC) is important for the design of green wireless system. In this paper, we propose a new network architecture proposition based on EE maximization for Multi-Cell MIMO-IFBC within the context of interference alignment (IA). Particularly, EE is maximized subject to maximum power and minimum throughput constraints. We propose two schemes to optimize EE for different signal-to-noise ratio (SNR) regions. For high-SNR operating regions, we employ a grouping-based IA scheme to jointly cancel intra- and inter-cell interferences and thus transform the MIMO-IFBC to a single-cell MIMO scenario. A gradient-based power adaptation scheme is proposed based on water-filling power adaptation and singular value decomposition to maximize EE for each cell. For moderate SNR cases, we propose an approach using dirty paper coding (DPC) with the principle of multiple access channel and broadcast channel duality to perform IA while maximizing EE in each cell. The algorithm in its dual form is solved using a subgradient method and a bisection searching scheme. Simulation results demonstrate the superior performance of the proposed schemes over several existing approaches. It also shows that interference-nulling-based IA approaches outperform hybrid DPC-IA approach in high-SNR region, and the opposite occurs in low-SNR region. [ABSTRACT FROM PUBLISHER]

Most cooperative spectrum sensing research activities focus on maximizing the primary user detection and neglect the achieved secondary system performance. This paper proposes a novel capacity-aware cooperative spectrum sensing optimization method, capable of computing the optimal values for the cooperative sensing parameters (e.g., the number of sensed samples, the number of cooperating devices, control channel bandwidth) so that the secondary system capacity is maximized. The proposed method utilizes energy detection with estimated noise power (ENP). We derive and experimentally validate the analytical models for cooperative spectrum sensing based on the ENP method. Additionally, we evaluate the performance of the proposed capacity-aware spectrum sensing described in this paper. We show that the proposed capacity-aware cooperative spectrum sensing based on noise power estimation achieves enhanced secondary system capacity compared to the previous commonly proposed sensing processes that maximize the detection performance. Moreover, the results give insight into the optimal behavior of the proposed capacity-aware cooperative spectrum sensing based on noise power estimation regarding the common cooperative spectrum sensing parameters. [ABSTRACT FROM AUTHOR]

Caching and multicasting are two promising methods to support massive content delivery in multi-tier wireless networks. In this paper, we consider a random caching and multicasting scheme with caching distributions in the two tiers as design parameters, to achieve efficient content dissemination in a two-tier large-scale cache-enabled wireless multicasting network. First, we derive tractable expressions for the successful transmission probabilities in the general region as well as the high signal-to-noise ratio (SNR) and high user density region, respectively, utilizing tools from stochastic geometry. Then, for the case of a single operator for the two tiers, we formulate the optimal joint caching design problem to maximize the successful transmission probability in the asymptotic region, which is nonconvex in general. By using the block successive approximate optimization technique, we develop an iterative algorithm, which is shown to converge to a stationary point. Next, for the case of two different operators, one for each tier, we formulate the competitive caching design game where each tier maximizes its successful transmission probability in the asymptotic region. We show that the game has a unique Nash equilibrium (NE) and adopt an iterative algorithm, which is shown to converge to the NE under a mild condition. Finally, by numerical simulations, we show that the proposed designs achieve significant gains over existing schemes. [ABSTRACT FROM AUTHOR]

To improve the spectral efficiency in long-term evolution systems, the resource blocks (RBs) are shared among different cells/base stations (BSs) resulting in interference among the cells/BSs on each RB, although all the sub-carriers (SCs) in an RB may not be used in a cell. Defining the load of a given BS per RB as the fraction of the active SCs in that RB, in this paper, we present a generalized signal-to-interference-and-noise-ratio (SINR) model for downlink users on a given RB. This model considers both the transmit powers of the BSs and the loads of the cells over that RB. Under this load-coupled SINR model, to study the feasibility of a given rate demand vector for users, we formulate an optimization problem of minimizing the total load of the BSs on the RBs. Then, for two different scenarios of feasible and infeasible demand vectors, respectively, we study the load management problem (i.e., minimizing the total load of the BSs on the RBs) and admission control problem (i.e., finding the sub-set of users with maximum cardinality whose demands can be concurrently satisfied), respectively. Our theoretical investigations, which provide guidelines for designing radio resource management methods for load-coupled OFDMA networks, are complemented through Monte Carlo simulations. [ABSTRACT FROM PUBLISHER]

We consider optimal linear precoder and decoder designs in a multi-cell multiple-input multiple-output system, where base stations and mobile users are both operating in full-duplex (FD) mode. Existing works on FD cellular systems focus on the maximization of overall throughput, which can result in unfairness between uplink and downlink channels depending on the self-interference power and inter-user interference levels. Therefore, to introduce fairness, in this paper, we consider the transmit and receive beamforming designs that maximize the harmonic-sum of signal-to-interference-plus-noise ratios (SINRs) in the uplink and downlink channels. We propose a low-complexity alternating optimization algorithm which converges to a stationary point. Moreover, in order to address practical system design aspects, we consider the transceiver design that enforces robustness against imperfect channel state information (CSI) while providing fair performance among the users. To this end, we formulate an optimization problem that maximizes the worst case SINR among all users under norm-bounded CSI errors. We devise a low-complexity iterative algorithm based on alternating optimization and semidefinite relaxation techniques. Numerical results verify the advantages of incorporating FD mode into cellular systems, and practical issues, such as CSI uncertainty and fairness performance. [ABSTRACT FROM PUBLISHER]

In this paper, we propose a general framework to study the tradeoff between energy efficiency (EE) and spectral efficiency (SE) in massive multiple-input-multiple-output-enabled heterogenous networks while ensuring proportional rate fairness among users and taking into account the backhaul capacity constraint. We aim at jointly optimizing user association, spectrum allocation, power coordination, and the number of activated antennas, which is formulated as a multi-objective optimization problem maximizing EE and SE simultaneously. With the help of weighted Tchebycheff method, it is then transformed into a single-objective optimization problem, which is a mixed-integer non-convex problem and requires unaffordable computational complexity to find the optimum. Hence, a low-complexity effective algorithm is developed based on primal decomposition, where we solve the power coordination and number of antenna optimization problem and the user association and spectrum allocation problem separately. Both theoretical analysis and numerical results demonstrate that our proposed algorithm can fast converge within several iterations and significantly improve both the EE-SE tradeoff performance and rate fairness among users compared with other algorithms. [ABSTRACT FROM AUTHOR]

Analog transmission of correlated information over Gaussian broadcast channels (BCs) using analog joint source–channel coding (JSCC) is addressed. This communication strategy has attractive advantages, such as low complexity, minimal delay, graceful degradation, and adaptation to time-varying environments. In this paper, we focus on the optimization of parametric continuous mappings that satisfy individual quality of service requirements over Gaussian BCs. This optimization is based on the balancing of the user distortions to ensure the feasibility of the resulting optimization problems. An analysis of the overall distortion corresponding to the considered mappings is carried out to design algorithms that determine the optimal values for the mappings parameters. Results show that the proposed analog JSCC scheme provides near optimal performance and an adequate balancing of the individual distortions. [ABSTRACT FROM AUTHOR]

Traditional cellular networks are moving toward heterogeneous cellular networks (HetNets) to satisfy the stringent demand for data rates and capacity. To enable the new applications in 5G, such as haptic communications, we face new challenges of achieving high availability with low latency in HetNets. In this paper, we introduce coordinated multi-point (CoMP) and carrier aggregation (CA) techniques in HetNets to guarantee the availability of all user equipment (UE), where the CoMP improves the single-path availability, and the CA enhances availability via multi-carrier gain combining. To characterize the availability, we first derive an exact closed-form expression for the availability of a random UE in a CoMP and CA-enabled HetNets. To achieve the maximum UE availability, we formulate a max-min optimization problem. To solve it, we then propose a two-step optimization algorithm (TSOA) and a joint (JTOA). The TSOA is based on heuristic algorithm for the optimal subcarrier assignment and UE association, and based on the Lagrangian dual method for the power allocation. The JTOA is based on genetic algorithm to achieve the interaction between the first step and the second step. Our results showcase the effective of our proposed JTOA, and the effective of the CoMP in availability improvement in HetNets. [ABSTRACT FROM PUBLISHER]

In this paper, we investigate a coding strategy devised to increase the throughput in hybrid ARQ (HARQ) transmission over a block fading channel. In our approach, the transmitter jointly encodes a variable number of bits for the each round of HARQ. The parameters (rates) of this joint coding can vary and may be based on the negative acknowledgment provided by the receiver or, on the past (outdated) information about the channel states. These new degrees of freedom allow us to improve the match between the codebook and the channel states experienced by the receiver. The results indicate that gains obtained using the proposed cross-packet coding strategy are particularly notable for the large values of the throughput. In this region, the conventional HARQ fails to offer throughput improvement even if the number of transmission rounds is increased. We implement the proposed cross-packet HARQ using turbo codes, where we show that the theoretically predicted throughput gains materialize in practice; the implementation challenges are also discussed. [ABSTRACT FROM PUBLISHER]

In practical scenarios with random arrival and departure of primary users (PUs), existing simultaneous sensing and transmission schemes allocated the same weight to each sample, and did not consider low signal-to-noise ratio (SNR) situations. This paper proposes an adaptive weighted sensing scheme with simultaneous transmission for dynamic PU traffic. It uses a power function based on the corresponding sampling sequence and could reveal the actual PU state in near real time. The power exponent is further adjusted to the sensing situations to achieve lowest false alarm probability under a certain detection probability constraint. Then, an analytical model considering all possible PU state transitions is developed to evaluate achievable interference, throughput, and energy efficiency. Furthermore, the optimal frame duration yielding both optimal false alarm probability and throughput is computed. After that, a fast search algorithm is proposed to track the optimal duration at an exponential convergence rate. Simulation results are provided to validate the analytical model and demonstrate the improvement in low SNR. The results indicate that the proposed scheme can achieve lower false alarm probability and higher energy efficiency over a wide SNR range than that of the existing weighting schemes, which are based on probability, geometric sequence, and equal weighting. [ABSTRACT FROM AUTHOR]

In this paper, we present a joint antenna beamforming and power allocation technique to maximize the multiuser sum rate in an underlying microcellular system which reuses the same spectrum of a macrocellular system. One challenge in this kind of hierarchical cognitive radio (HCR) systems is to manage the interference between the macrocell and the microcell. The key contribution of this paper is to develop an optimization technique for antenna beamforming that can maximize the achievable sum rate of the underlying cognitive radio (CR) microcellular system and control the interference between the macrocell and the microcell with a satisfaction level. The proposed technique optimizes the sum rate performance by maximizing its lower bound and transfers the original non-convex problem into a convex optimization problem by introducing auxiliary variables to confine the intra-user interference power among the secondary system. Next, an iterative sum rate maximization (ISM) algorithm is developed to find the beamforming weights and the allocated power for each secondary user to simultaneously maximize system sum rate, coverage, and concurrent multiuser transmission probability in the HCR system. The developed joint design methodology provides valuable insights into the design of an optimal HCR system for various numbers of users as well as cell coverage, and can quantitatively optimize the performance tradeoffs in the hierarchical multiuser CR systems for current and future wireless communication applications. [ABSTRACT FROM PUBLISHER]

In this paper, we revisit the design of Raptor codes for binary input additive white Gaussian noise channels, where we are interested in very low signal to noise ratios (SNRs). A linear programming degree distribution optimization problem is defined for Raptor codes in the low SNR regime through several approximations. We also provide an exact expression for the polynomial representation of the degree distribution with infinite maximum output node degree in the low SNR regime, which enables us to calculate the exact value of the fractions of output nodes of small degrees. A more practical degree distribution design is also proposed for Raptor codes in the low SNR regime, where we include the rate efficiency and the decoding complexity in the optimization problem, and an upper bound on the maximum rate efficiency is derived for given design parameters. Simulation results show that the Raptor code with the designed degree distributions can approach rate efficiencies larger than 0.95 in the low SNR regime. [ABSTRACT FROM PUBLISHER]

Optimal data detection of data transmitted over a linear channel can always be implemented through the Viterbi algorithm (VA). However, in many cases of interest the memory of the channel prohibits direct application of the VA. A popular and conceptually simple method in this case, studied since the early 1970s, is to first filter the received signal in order to shorten the memory of the channel, and then to apply a VA that operates with the shorter memory. We shall refer to this general concept as a channel shortening (CS) receiver. Although studied for almost four decades, an information theoretic understanding of what such a simple receiver solution is actually doing is not available. In this paper, we show that an optimized CS receiver has a direct correspondance to the chain rule of mutual information. Furthermore, we show that the tools for analyzing the ensuing achievable rates from an optimized CS receiver are precisely the same as those used for analyzing the achievable rates of a minimum mean-square-error (MMSE) receiver. [ABSTRACT FROM AUTHOR]

Increasing spectrum-efficiency (SE) as well as energy-efficiency (EE) has attracted much attention recently due to the fact that the future wireless networks need to address the issues of high throughput and low power consumption. However, the objective for optimizing SE sometimes conflicts with the one for optimizing EE, and the methods for improving EE may result in a decrease in SE. In this paper, we consider the SE-EE tradeoff for cognitive radio (CR) networks with co-operative spectrum sensing (CSS). First, we formulate the general problem, and analyze two special cases: the SE maximization problem and the EE maximization problem. The SE and EE are optimized separately via joint optimization of sensing duration and final decision threshold in CSS. Based on the solutions of the two special cases, the general problem for SE-EE tradeoff is solved. Then, we consider the tradeoff of SE and EE from two perspectives: (1) maximizing EE while satisfying SE requirement; and (2) maximizing SE while satisfying EE requirement. Efficient algorithms for sensing strategy design are proposed for each scenario. Finally, we demonstrate the effectiveness of the proposed sensing strategies and illustrate the tradeoff between SE and EE via simulations. [ABSTRACT FROM AUTHOR]

In this paper, we consider multiple-input single-output (MISO) physical layer multicasting, where several multiantenna transmitters each simultaneously multicast a common message to a distinct set of single-antenna receivers, causing interference between different multicast messages. To optimize the achievable rate of this MISO multicast interference channel, we propose iterative distributed transmit optimization algorithms that are based on interference leakage control, requiring local channel state information at each transmitter and leakage information exchange among transmitters. We, furthermore, propose extensions of existing coordinated multipoint transmission schemes that have been developed for unicast interference channels, such as signal to leakage and noise ratio beamforming, to the considered multicast system. Such methods are of importance, e.g., for future releases of LTE that will support multiantenna multicasting using MBMS/MBSFN. Numerical simulations confirm the potential of the proposed distributed transmit optimization algorithm. [ABSTRACT FROM AUTHOR]

In this paper, we investigate joint beamforming and power allocation in multicell multiple-input single-output (MISO) downlink networks. Our goal is to maximize the utility function defined as the ratio between the system weighted sum rate and the total power consumption subject to the users’ quality of service requirements and per-base-station (BS) power constraints. The considered problem is nonconvex and its objective is in a fractional form. To circumvent this problem, we first resort to an virtual uplink formulations of the the primal problem by introducing an auxiliary variable and applying the uplink-downlink duality theory. By exploiting the analytic structure of the optimal beamformers in the dual uplink problem, an efficient algorithm is then developed to solve the considered problem. Furthermore, to reduce further the exchange overhead between coordinated BSs in a large-scale antenna system, an effective coordinated power allocation solution only based on statistical channel state information is reached by deriving the asymptotic optimization problem, which is used to obtain the power allocation in a long-term timescale. Numerical results validate the effectiveness of our proposed schemes and show that both the spectral efficiency and the energy efficiency can be simultaneously improved over traditional downlink coordinated schemes, especially in the middle-high transmit power region. [ABSTRACT FROM AUTHOR]