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This paper investigates the dynamic access point (AP) clustering and precoding problem in the downlink of user-centric virtual cell networks. The goal is to maximize the weighted sum spectral efficiency (SE) while satisfying the power constraints and AP clustering constraints in adjacent time slots (TSs). By adopting the random walk mobility to model the mobile user equipments’ movement behaviors, we consider dynamic and time-varying channel conditions. Therefore, the weighted sum SE maximization programming takes the form of discrete-time sequence of mixed-integer non-convex optimization problems. In this paper, we propose to solve this sequential problem in two stages. In the first stage, a dynamic AP clustering approach based on discrete particle swarm optimization is developed. This approach takes the advantage of the channel correlation by exploiting the relationship between AP clustering solutions in adjacent TSs to improve the SE performance and reduce complexity. In the second stage, given the AP clustering solution obtained in the first stage, a distributed precoding algorithm is devised via applying the weighted minimum mean square error method. By combining these two stages, we propose a novel dynamic AP clustering and precoding algorithm (DAPC-Pre). The effectiveness of the proposed DAPC-Pre algorithm is verified by the simulation results. In particular, the proposed algorithm converges fast and significantly outperforms benchmark algorithms in terms of sum SE under different dynamic environments. [ABSTRACT FROM AUTHOR]

Dynamic network architectures (DNAs) have been developed under the assumption that some terminals can be converted into temporary access points (APs) anytime when connected to the Internet. In this paper, we consider the problem of assigning a group of users to a set of potential APs with the aim to maximize the downlink system throughput of DNA networks, subject to total transmit power and users’ quality of service (QoS) constraints. In our first method, we relax the integer optimization variables to be continuous. The resulting non-convex continuous optimization problem is solved using successive convex approximation framework to arrive at a sequence of second-order cone programs (SOCPs). In the next method, the selection process is viewed as finding a sparsity constrained solution to our problem of sum rate maximization. It is demonstrated in numerical results that while the first approach has better data rates for dense networks, the sparsity oriented method has a superior speed of convergence. Moreover, for the scenarios considered, in addition to comprehensively outperforming some well-known approaches, our algorithms yield data rates close to those obtained by branch and bound method. [ABSTRACT FROM AUTHOR]

Motivated by the work of Dahrouj and Yu in applying the Han-Kobayashi transmission strategy for mitigating the intercell interference in a multi-cell multi-user multiple-input single-output interference network (MISO IN), this paper considers splitting messages into private and common parts in a multi-cell multi-user MIMO IN. Specifically, the covariances of the private messages and common messages are designed to optimize either the sum rate or the minimal rate. The common messages and private messages are decoded in sequence using successive decoding. This paper shows how these difficult optimization problems can be adequately solved by means of d.c. (difference of concave functions) optimization over a simple convex set. Numerical and simulation results also reveal the great advantage of our proposed solutions for various types of INs. In particular, the proposed solutions are shown to outperform the algorithm developed by Dahrouj and Yu for the simpler case of the MISO IN. [ABSTRACT FROM PUBLISHER]

In this paper, we tackle the problem of multi-cell resource scheduling, where the objective is to maximize the weighted sum-rate through inter-cell interference coordination (ICIC). The blanking method is used to mitigate the inter-cell interference, where a resource is either used with a predetermined transmit power or not used at all, i.e., blanked. This problem is known to be strongly NP-hard, which means that it is not only hard to solve in polynomial time, but it is also hard to find an approximation algorithm with guaranteed optimality gap. In this work, we identify special scenarios where a polynomial-time algorithm can be constructed to solve this problem with theoretical guarantees. In particular, we define a dominant interference environment, in which for each user the received power from each interferer is significantly greater than the aggregate received power from all other weaker interferers. We show that the originally strongly NP-hard problem can be tightly relaxed to a linear programming problem in a dominant interference environment. Consequently, we propose a polynomial time distributed algorithm that is not only guaranteed to be tight in a dominant interference environment, but which also computes an upper bound on the optimality gap without additional computational complexity. The proposed scheme is based on the primal-decomposition method, where the problem is divided into a master-problem and multiple subproblems. We solve the master-problem iteratively using the projected-subgradient method. We also show that each subproblem has a special network flow structure. By exploiting this network structure, each subproblem is solved using the network-based optimization methods, which significantly reduces the complexity in comparison to the general-purpose convex or linear optimization methods. In comparison with baseline schemes, simulation results of the International Mobile Telecommunications-Advanced (IMT-Advanced) scenarios show that the proposed scheme achieves higher gains in aggregate throughput, cell-edge throughput, and outage probability. [ABSTRACT FROM PUBLISHER]

In this paper, we investigate the linear transceiver optimization for multiple-inputmultiple-output (MIMO) interference networks, where multiple pairs of multi-antenna source and destination nodes communicate simultaneously. Different from most of existing works, we jointly consider three critical issues of the linear transceiver optimization for MIMO interference networks based on multi-objective optimization theory, i.e., signal transmission, energy and security. Specifically, using the modified weighted Tchebycheff method, we investigate three kinds of multi-objective optimization problems (MOOPs): 1) sum mean square error minimization and harvested energy maximization; 2) transmit power minimization and energy harvesting efficiency maximization; 3) transmit power minimization, energy harvesting efficiency maximization, and physical layer security. Based on the Charnes–Cooper transformation and penalty function method, the formulated MOOPs are transformed into convex optimization problems and thus can be effectively solved. The resulting Pareto optimal solutions set reveals the complicated but important relationships among these involved single objective optimization problems, which are usually individually investigated in the literature. Finally, numerical simulation results demonstrate the performance advantages of the proposed algorithm and corroborate the theoretical analysis. [ABSTRACT FROM PUBLISHER]

Large-Scale Antenna System (LSAS) has played an important role in the emerging fifth-generation mobile systems (5G) due to its potential for excellent spectral efficiency. However, it may cause a mass of pilot overhead that is not conducive to the application of LSAS in massive Machine-Type Communications (mMTC), one of three typical traffic modes of 5G. In this paper, we present a pilot-efficient scheduling strategy for mMTC systems, in which the Base Stations (BS) are equipped with large-scale antennas, from a cross-layer perspective. Our scheme can not only schedule the massive devices to access the spectrum, but also allocate the BS’ power without the need for much pilot overhead. More particularly, the users allowed to access the spectrum can be selected based on their queue state information without any channel estimations, while the power allocation only needs the channel estimations for scheduled users. We shall show the optimality of the presented policy based on the Lyapunov optimization theory. To solve the Lyapunov optimization problem, we present a low complexity two-layer iteration algorithm for more practical purposes. Simulation results demonstrate the substantial gain of our presented method over existing scheduling protocols of massive MIMO. [ABSTRACT FROM AUTHOR]

Electronically steerable parasitic array radiator (ESPAR) technology enables the implementation of antenna arrays of a number of elements with a single radio frequency (RF) source. Two approaches to achieve stable transmission using an ESPAR antenna (EA) are to increase the self-resistance of an EA or to transmit signals closely approximating the actual signals that keep the EA stable. Both approaches did not take into account the impact of limited power on an EA transmission, which is a practical constraint on power amplifier design. We propose a new transmission scheme to enable an EA to provide stable multiple antenna functionality taking into account the instantaneous total power requirement. This problem is formulated as a non-convex optimization problem and it is solved analytically by coordinate transformation and geometric analysis. The optimal approximate signals are obtained using root finding and interior-point algorithms. Moreover, it is shown through simulations that our proposed scheme achieves similar symbol error rate performance to that of the standard multiple antenna transmitter with multiple RF chains under power limited single-user and multi-user transmission scenarios. Furthermore, it is shown that increasing the self-resistance of an EA to achieve stability is highly power inefficient. [ABSTRACT FROM PUBLISHER]

Existing minimum-symbol-error-rate equalizers were derived based on the symbol-error-rate objective function. Due to the complexity of the objective function the derivation is not straightforward. In this paper we present a new approach to derive the minimum-symbol-error-rate adaptive equalizers. The problem is formulated as minimizing the norm between two subsequent parameter vectors under the constraint of symbol-error-rate minimization. The constrained optimization problem then is solved with the Lagrange multiplier method, which results in an adaptive algorithm with normalization. Simulation results show that the proposed algorithm outperforms the existing adaptive minimum-symbol-error-rate equalizer in convergence speed and steady-state performance. [ABSTRACT FROM AUTHOR]

In this paper we consider orthogonal frequency division multiplexing (OFDM) based cognitive radio systems that operate as secondary users (SUs) in a spectrum overlay approach and study joint spectral shaping and power control subject to specified target signal-to-interference+noise ratio (SINR) as a constrained optimization problem. We discuss necessary and sufficient conditions for the optimal solution of this problem where the spectrum overlay constraints are satisfied and the specified target SINR value is achieved with minimum transmit power. We also present an algorithm that incrementally adapts the OFDM transmitter to reach the optimal solution and study the convergence speed of the proposed algorithm through simulations. [ABSTRACT FROM PUBLISHER]

The joint base matrix ${{{{B}}_{{\mathrm{J}}}}}$ of the joint source-channel coding (JSCC) system based on double protograph low-density parity-check (DP-LDPC) codes consists of four components, namely, the source code ${{{{B}}_{{\mathrm{s}}}}}$ , the channel code ${{{{B}}_{{\mathrm{c}}}}}$ , the type-1 connection edge ${{{{B}}_{{ \textit {L1}}}}}$ and the type-2 connection edge ${{{{B}}_{{ \textit {L2}}}}}$ , each having a non-negligible influence on the system performance. Different from the traditional component-specific design approach, we propose a joint design and optimization algorithm based on the idea of multi-objective differential evolution (MODE). Specifically, we consider the optimization of the DP-LDPC JSCC system through joint design of three components ${{{{B}}_{{\mathrm{s}}}}}$ , ${{{{B}}_{{\mathrm{c}}}}}$ , ${{{{B}}_{{ \textit {L1}}}}}$ and all four components ${{{{B}}_{{\mathrm{s}}}}}$ , ${{{{B}}_{{\mathrm{c}}}}}$ , ${{{\mathbf{B}}_{{ \textit {L1}}}}}$ , ${{{{B}}_{{ \textit {L2}}}}}$ , respectively. The proposed algorithm has low search complexity due to the reduction in size and element value of base matrices. The joint protograph extrinsic information transfer (JPEXIT) analyses and the simulation results demonstrate that the resulting JSCC system is free from a high error floor, requires fewer number of iterations for reaching the same bit error rate (BER) and achieves significant coding gains as compared to the state-of-the-art. Our DP-LDPC JSCC system is also shown to outperform its separation-based counterpart by a wide margin. [ABSTRACT FROM AUTHOR]

Natural type selection was originally proposed by Zamir and Rose for universal single description coding. In this paper, we generalize this principle to universal multiple description coding (MDC). Two schemes based on random codebooks are proposed: one is of fixed distortion and the other is of fixed weight. Their operational sum-rate-distortion functions are derived, which coincide with the EGC (El Gamal-Cover) sum-rate bound if the parameters of the schemes are optimized. It is also shown that in both schemes the joint type of reconstruction codewords can be used to improve the rate-distortion (R-D) performance. Based on our theoretical results, a practical universal scheme is proposed by leveraging the MDC methods based on low-density generator matrix (LDGM) codes. The performance of this scheme is compared experimentally with the EGC bound, which shows its effectiveness. [ABSTRACT FROM AUTHOR]

We consider channels affected by intersymbol interference with reduced-complexity, mutual information optimized, channel-shortening detection. For such settings, we optimize the transmit filter, taking into consideration the reduced receiver complexity constraint. As figure of merit, we consider the achievable information rate of the entire system and with functional analysis, we establish a general form of the optimal transmit filter, which can then be optimized by standard numerical methods. As a corollary to our main result, we obtain some insight of the behavior of the standard waterfilling algorithm for intersymbol interference channels. With only some minor changes, the general form we derive can be applied to multiple-input multiple-output channels with intersymbol interference. To illuminate the practical use of our results, we provide applications of our theoretical results by deriving the optimal shaping pulse of a linear modulation transmitted over a bandlimited additive white Gaussian noise channel which has possible applications in the faster-than-Nyquist/time packing technique. [ABSTRACT FROM PUBLISHER]

The Shannon code is a very simple suboptimal scheme for computing prefix-free codelengths for a given memoryless source. A recursive version of the well-known Shannon code (RSh) is investigated in this paper. It has a redundancy which never exceeds that of the Shannon code. Unlike the Huffman code, the RSh code and its variations can be directly applied to sources with an infinitely countable alphabet. The average redundancy, taken over the set of all n-tuple distributions, is considered as a criterion for code comparisons. For n>40, the average redundancy is approximately 0.14 bits for the RSh code, compared to approximately 0.5 and 0.03 bits for the Shannon and Huffman codes, respectively. For large n, a constrained version of the RSh code, called CRSh, has an average redundancy of only 0.07 bits for unsorted symbol probabilities. If the symbol probabilities are sorted in descending order, then a very simple modification of the RSh algorithm results in a code which is near-optimal from the average redundancy point of view. [ABSTRACT FROM AUTHOR]

This paper discusses a special type of multi-user communication scenario, in which users' utilities are linearly impacted by their competitors' actions. First, we explicitly characterize the Nash equilibrium and Pareto boundary of the achievable utility region. Second, the price of anarchy incurred by the non-collaborative Nash strategy is quantified. Third, to improve the performance in the non-cooperative scenarios, we investigate the properties of an alternative solution concept named conjectural equilibrium, in which individual users compensate for their lack of information by forming internal beliefs about their competitors. The global convergence of the best response and Jacobi update dynamics that achieve various conjectural equilibria is analyzed. It is shown that the Pareto boundaries of the investigated linearly coupled games can be sustained as stable conjectural equilibria if the belief functions are properly initialized. The investigated models apply to a variety of realistic applications encountered in the multiple access design, including wireless random access and flow control. [ABSTRACT FROM AUTHOR]

This article investigates the downlink communications of intelligent reflecting surface (IRS) assisted non-orthogonal multiple access (NOMA) systems. To maximize the system throughput, we formulate a joint optimization problem over the channel assignment, decoding order of NOMA users, power allocation, and reflection coefficients. The formulated problem is proved to be NP-hard. To tackle this problem, a three-step novel resource allocation algorithm is proposed. Firstly, the channel assignment problem is solved by a many-to-one matching algorithm. Secondly, by considering the IRS reflection coefficients design, a low-complexity decoding order optimization algorithm is proposed. Thirdly, given a channel assignment and decoding order, a joint optimization algorithm is proposed for solving the joint power allocation and reflection coefficient design problem. Numerical results illustrate that: i) with the aid of IRS, the proposed IRS-NOMA system outperforms the conventional NOMA system without the IRS in terms of system throughput; ii) the proposed IRS-NOMA system achieves higher system throughput than the IRS assisted orthogonal multiple access (IRS-OMA) systems; iii) simulation results show that the performance gains of the IRS-NOMA and the IRS-OMA systems can be enhanced via carefully choosing the location of the IRS. [ABSTRACT FROM AUTHOR]

In code-division multiplexing, the real/complex-valued sequence (signature) that maximizes the pre-detection signal-to-interference-plus-noise ratio (SINR) at the output of the maximum-SINR filter is simply the minimum-eigenvalue eigenvector of the disturbance autocorrelation matrix. SINR sequence optimization over a finite alphabet, however, is NP-hard in the sequence length. In this paper, we derive new easy to calculate upper and lower bounds on the SINR of the optimal binary and quaternary sequences that can serve as benchmarking tools for any suggested suboptimal designs. [ABSTRACT FROM AUTHOR]

This paper presents a joint subcarrier and power allocation algorithm for cooperative multiuser orthogonal frequency division multiplexing (MU-OFDM) cognitive radio (CR) systems. By simplifying a three-node relay network to a two-node network with the equivalent channel gain, we formulate the resource allocation problem for a cooperative MU-OFDM CR system in a similar form to that for a non-cooperative MU-OFDM CR system. With this problem formulation, a rule is derived for performing subcarrier assignment operations under a given power distribution, where the sum capacity optimization is alternated between the subcarrier and power to form a joint subcarrier and power allocation algorithm. To simplify the high-complexity optimal power allocation algorithm (i.e., the iterative partitioned water-filling algorithm) for the joint scheme, we further develop a simple method for updating the water level when each subcarrier is assigned. Simulation results show that, with or without cooperative transmission, using the proposed method to update the water level achieves a system capacity close to that of the joint subcarrier and power allocation algorithm with the optimal power distribution. In addition, if the relay node is at the midpoint between the source and destination, the system capacity can reach its maximum value. [ABSTRACT FROM AUTHOR]

In this paper, we consider beamforming (BF) for cooperative networks with one multi—antenna source, multiple multi—antenna amplify—and—forward (AF) relays, and one single—antenna destination. The source BF vector and the AF—BF matrices at the relays are optimized for maximization of the signal—to—noise ratio (SNR) at the destination under three different power constraints. In particular, we consider individual relay power constraints, a joint relay power constraint, and a joint power constraint for the source and the relays. We solve the associated optimization problems in two stages. In the first stage, we find the optimal AF—BF matrices for a given BF vector at the source. For the cases of individual and joint relay power constraints, closed—form solutions for the AF—BF matrices are provided, respectively. Furthermore, for the case of a joint source—relay power constraint, the AF—BF matrices are obtained in closed form up to a scalar power allocation factor and an efficient numerical algorithm for the power allocation between the source and the relays is given. In the second stage, the optimal source BF vectors are computed. Thereby, we show that for the joint relay and the joint source—relay power constraints, the resulting problem can be transformed into a non—convex polynomial programming problem which allows for an exact solution for small—scale networks. For large—scale networks and networks with individual relay power constraints, we propose efficient suboptimal optimization methods for the source BF vector. Simulation results show the benefits of having multiple antennas at the source and/or multiple multi—antenna relays and illustrate the performance differences introduced by the three different power constraints. [ABSTRACT FROM AUTHOR]

In this paper, we consider the problem of resource allocation in the DFT-Spread-OFDMA (DFT-S-OFDMA) uplink. We show that the resource allocation problem can be formulated as a set packing problem, which in general is NP-hard. We propose polynomial-time message-passing based algorithms, one of which is guaranteed to yield a solution that is within a constant fraction of the optimal solution and is also asymptotically optimal in the limit as the number of subcarriers in the system goes to infinity. The message-passing based algorithm is also extended to solve the resource allocation problem over a multi-cell uplink in a distributed fashion. Our algorithms account for finite input alphabets and non-ideal practical outer codes. Extensive simulations are performed to assess the performance of the proposed algorithms and it is shown that they yield near-optimal solutions at a low complexity and with a low memory requirement. [ABSTRACT FROM PUBLISHER]

This paper considers the optimization of a class of joint source-channel codes described by finite-state encoders (FSEs) generating variable-length codes. It focuses on FSEs associated to joint source-channel integer arithmetic codes, which are uniquely decodable codes by design. An efficient method for computing the free distance of such codes using Dijkstra's algorithm is proposed. To facilitate the search for codes with good distance properties, FSEs are organized within a tree structure, which allows the use of efficient branch-and-prune techniques avoiding a search of the whole tree. [ABSTRACT FROM PUBLISHER]

This article investigates the problem of distributed channel selection in opportunistic spectrum access (OSA) networks with partially overlapping channels (POC) using a game-theoretic learning algorithm. Compared with traditional non-overlapping channels (NOC), POC can increase the full-range spectrum utilization, mitigate interference and improve the network throughput. However, most existing POC approaches are centralized, which are not suitable for distributed OSA networks. We formulate the POC selection problem as an interference mitigation game. We prove that the game has at least one pure strategy NE point and the best pure strategy NE point minimizes the aggregate interference in the network. We characterize the achievable performance of the game by presenting an upper bound for aggregate interference of all NE points. In addition, we propose a simultaneous uncoupled learning algorithm with heterogeneous exploration rates to achieve the pure strategy NE points of the game. Simulation results show that the heterogeneous exploration rates lead to faster convergence speed and the throughput improvement gain of the proposed POC approach over traditional NOC approach is significant. Also, the proposed uncoupled learning algorithm achieves satisfactory performance when compared with existing coupled and uncoupled algorithms. [ABSTRACT FROM AUTHOR]

Several instantaneous optimization strategies for rate and/or power allocation in the coded V-BLAST are studied analytically. Outage probabilities and system capacities of these strategies in a spatial multiplexing system are compared under generic settings. The conventional waterfilling algorithm is shown to be suboptimal for the coded V-BLAST and a new algorithm ("fractional water-filling") is proposed, which simultaneously maximizes the system capacity and minimizes the outage probability. Closed-form performance analysis of the considered algorithms is given, and the fractional water-filling algorithm is shown to attain the full MIMO channel diversity, significantly outperforming other strategies. Many of the results also apply to generic multi-stream transmission systems (e.g. spatial multiplexing on the channel eigenmodes, OFDM) or the systems relying on successive interference cancelation (multi-user detection, channel equalization). [ABSTRACT FROM AUTHOR]