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Deep neural networks (DNNs) have been widely applied for classification tasks in wireless communications such as link scheduling, user association and base station (BS) muting. When training a DNN using datasets with highly-skewed class distribution where most data belong to a few majority classes, learning performance for minority classes will degrade since the imbalanced data forces the training process to be biased towards the majority classes. In many wireless problems, multiple decisions for classification are made jointly, say deciding which BSs in a network should be muted to avoid interference. In this paper, we employ DNN to learn an optimal predictive interference coordination policy, and strive to avoid biased learning in such a multi-label multi-class classification problem. The major contribution is proposing a training method to encourage fairness among classes by minimizing the maximal cost of decisions among classes, which is converted into a problem to optimize the weighting factors on the training cost of each class. We provide a gradient descent algorithm to optimize the weighting factors and the model parameters of the DNN by alternative updates. Simulation results show that the training method can improve the learning performance for the minority class and can achieve higher network utility than existing training methods. [ABSTRACT FROM AUTHOR]

This paper considers a method for improving physical layer security of wireless networks with full-duplex radio. In particular, fast algorithms are developed to compute power allocations in subcarriers, subject to power and rate constraints, to maximize the secrecy capacity of a three-node network consisting of a source, a full-duplex destination, and an eavesdropper. A residual level of radio self-interference channel is considered. The optimal power allocation at the destination is found to be significant especially when its power budget is high. Also studied in this paper are a network with multiple full-duplex destinations and another network with multiple sources. Using the algorithms developed in this paper, we are able to show that a multiuser strategy that optimizes the power distributions among the users (in terms of either the sources or the destinations) can yield a substantial gain of secrecy capacity over a single-user strategy. [ABSTRACT FROM PUBLISHER]

In this paper, we investigate the multiple input multiple output (MIMO)-non-orthogonal multiple access (NOMA) transmission with the aid of location information. We first consider two users separated in both the distance and angle domains. With different access distances, NOMA could be used to serve the near-user and the far-user simultaneously, whereas spatial division multiple access (SDMA) would be applied if the two users have largely-separated angles of departure (AOD) that guarantees spatial orthogonality. Comparing the ergodic sum rates of these two multiple access (MA) schemes, we first characterize the preferable MA regions in the angle-distance plane. Analytical expression of the region boundary between NOMA and SDMA is derived. Moreover, NOMA-preferable regions are expressed in terms of the maximum distance difference and the minimum angle difference between the two users, respectively. On basis of these results, we further propose a location-based low-complexity user pairing algorithm for the general multiuser scenario. Numerical results confirm the accuracy of the derived region boundaries, and the simulations show that the proposed user pairing algorithm can effectively improve the resource utilization rate, compared to the conventional MA and user pairing schemes. [ABSTRACT FROM AUTHOR]

Hardware non-idealities are among the main performance restrictions for upcoming wireless communication systems. Asymmetric hardware distortions (HWD) happen when the impairments of the I/Q branches are correlated or imbalanced, which in turn generate improper additive interference at the receiver side. When the interference is improper, as well as in other interference-limited scenarios, improper Gaussian signaling (IGS) has been shown to provide rate and/or power efficiency benefits. In this paper, we investigate the rate benefits of IGS in a two-user interference channel (IC) with additive asymmetric HWD when interference is treated as noise. We propose two iterative algorithms to optimize the parameters of the improper transmit signals. We first rewrite the rate region as an pseudo-signal-to-interference-plus-noise-ratio (PSINR) region and employ majorization minimization and fractional programming to find a suboptimal solution for the achievable user rates. Then, we propose a simplified algorithm based on a separate optimization of the powers and complementary variances of the users, which exhibits lower computational complexity. We show that IGS can improve the performance of the two-user IC with additive HWD. Our proposed algorithms outperform proper Gaussian signaling and competing IGS algorithms in the literature that do not consider asymmetric HWD. [ABSTRACT FROM AUTHOR]

Sparse code multiple access (SCMA) is one type of non-orthogonal multiple access techniques for the fifth-generation wireless communication networks. Existing multiuser detection algorithms for SCMA uplink systems are based on the assumption that signals are in synchronization. However, asynchrony cannot be neglected due to the propagation delays in practical environments. In this paper, we present three detection algorithms for uplink asynchronous SCMA systems, namely belief propagation message passing algorithm, belief propagation with equal gain combining message passing algorithm, and partial phase-coherent algorithms. Simulation results show that our proposed asynchronous detection algorithms are able to offer descent bit error rate performances, with only a small performances loss compared to the synchronous case. Moreover, our proposed algorithms converge well in the uplink asynchronous SCMA systems. [ABSTRACT FROM AUTHOR]

By blending the concepts of non-orthogonal multiple access (NOMA) and orthogonal frequency division multiplexing, in this paper, a novel hybrid scheme is conceived for supporting diverse services in future wireless systems. Motivating to maximize energy efficiency (EE), the joint resource management of user clustering (UC) and power allocation is investigated for the downlink hybrid NOMA systems. Under two different power consumption cases, the optimal resource allocation (Opt-RA) algorithm is developed with the help of converting the original mixed integer non-linear programming (MINLP) problem to the tractable decoupled problems. For practical implementation, the heuristic resource allocation (Heur-RA) algorithm is also proposed, and it includes a low-complexity UC algorithm based on the candidate search-and-allocation approach. Our simulation results show that, both the Opt-RA and Heur-RA algorithms achieve significantly higher EE performance than other existing algorithms. Further, the results also prove that, the hybrid NOMA conceived is able to exploit the advantages of NOMA scheme, and is superior to conventional orthogonal multiple access (OMA) in terms of EE, as well as achieving higher flexibility for system configuration than NOMA. [ABSTRACT FROM AUTHOR]

As an important complement and enhancement technique for cellular communication systems, the device-to-device (D2D) technique has exhibited a number of benefits in terms of, for instance, offering an improved spectral efficiency, enabling a reduced transmit power and providing an enhanced channel capacity, etc. However, a severe interference may be imposed on the conventional cellular users by the activated D2D links, thus, substantially eroding the performance of the former. In this paper, we propose an interference limited area (ILA) based D2D-management scheme along with an appropriately designed power control algorithm for mitigating the above-mentioned interference. We first derive the closed-form expressions of the average coverage probability for both the cellular and D2D links under the proposed ILA scheme, followed by proposing a resource-allocation algorithm subject to a power control mechanism. Numerical results show that the proposed ILA scheme is capable of outperforming the conventional scheme in terms of both the coverage probability (i.e., for both cellular and D2D links) and the sum data rate, particularly in the scenarios with a high D2D density and a high signal-to-interference-plus-noise ratio. [ABSTRACT FROM AUTHOR]

Ultradense heterogeneous networks (HetNets) are emerging as an inevitable approach to tackle the capacity crunch in cellular networks. However, imbalanced load among small and macrocells and poor resource utilization as a consequence in HetNets remains a long-standing problem. This paper addresses this problem by presenting a solution for maximization of coverage and capacity while minimizing load imbalance among macro and small cells. Most recent studies on the topic focus on either optimization of coverage, capacity or load, or a combination of two of these three intertwined objectives. We formulate the optimization problem as a function of two hard parameters namely antenna tilt and transmit power, and a soft parameter, cell individual offset, that affect the coverage, capacity, and load directly. The resulting solution is a combination of the otherwise conflicting coverage and capacity optimization (CCO) and load balancing (LB) self-organizing network (SON) functions. In the presented joint CCO-LB solution, a conflict free operation of CCO and LB is ensured by designing a novel load aware user association methodology and resolving the effects of shadowing on coverage probability using stochastic approximation. The problem is proven to be nonconvex and is solved using genetic algorithm, sequential quadratic programming, and pattern search algorithms. The proposed CCO-LB solution is compared against two recently proposed CCO and CCO-LB solutions in the literature. Results show that the proposed solution can yield significant gain in terms of throughput, spectral efficiency, and load distribution. [ABSTRACT FROM AUTHOR]

This paper considers a heavy loaded full-duplex (FD) small cell consisting of one FD base station and multiple half-duplex users where the number of subchannels is less than the number of uplink and downlink users. With the objective of maximizing the total sum-rate, a joint power allocation, and user pairing problem is formulated. To solve this problem, a novel decomposition method is proposed, which decouples the power allocation and user pairing problem into two subproblems. It is proven that the optimal solution of subproblems is the optimal solution of the original problem. Then, optimal algorithms are developed for the two subproblems. Numerical results show that the proposed algorithm achieves better performance compared with the baselines of the greedy algorithm and random pairing algorithm. [ABSTRACT FROM AUTHOR]

Traffic offloading via device-to-device communications is expected to play a major role to meet the exponential data traffic growth in wireless networks. In this paper, we focus on the problem of user capacity maximization in ultra-dense heterogeneous networks with device-to-device cooperation, where a large number of users in a given geographical area request common data content, such as video on demand streaming, with strict quality of service guarantees. We aim at finding the best strategy for delivering the content either over long range connectivity from the access points or short range connectivity from peer mobile terminals while meeting a target rate per served user. We formulate the multiuser resource management problem as an optimization problem including traffic offloading and channel allocation considering a very high user density reaching up to 1.75 users per square meter. We extend the system model to include mobile terminals acting as content owners by having the content cached locally, in order to further enhance the network capacity and coverage. We then propose an iterative resource management solution, which first solves the optimization subproblem of traffic offloading with orthogonal channels, and then optimally allocates channels to the transmitters with minimized interference due to channel reuse. We also propose efficient suboptimal hierarchical tree-based algorithms that operate in real time with dynamic and fast solutions for ultra-dense networks. For performance evaluation, we consider a realistic scenario consisting of a stadium topology with thousands of mobile terminals active simultaneously. We generate results as a function of a wide range of system parameters, and demonstrate that the proposed algorithms achieve near-optimal performance with notably low time complexity. [ABSTRACT FROM AUTHOR]

Cognitive small-cell networks have great potential in improving spectrum efficiency and mitigating inter-cell interference. Comprehensively classifying the channel states in cognitive small-cell networks is important for efficiently reusing the spectrum bands that are licensed to a macrocell. In this paper, we investigate channel state classification in cognitive small-cell networks with multilevels of transmission powers, including occupation detection of spectrum bands and transmission power classification of a macrocell base station (MBS). Specifically, two scenarios including a priori known signaling features and unknown signaling features are both studied. For the former scenario, we propose an optimal spectrum sensing and power classification algorithm, based on coherent classification, to achieve accurate sensing performance by fully exploiting the inherent information of the signaling features. Optimal sensing threshold and decision regions are derived for detecting and classifying the transmission power of the MBS. For the scenario without signaling features, a generic spectrum sensing and power classification algorithm is proposed based on noncoherent classification with low implementation complexity. A new performance metric, i.e., classification probability, is introduced to comprehensively evaluate the classification capability of the proposed algorithms. Finally, extensive simulations are provided to verify the proposed algorithms. [ABSTRACT FROM AUTHOR]

Multiple linear constraint water filling problem (MLWFP) is solved innovatively in this paper, in which powers allotted to the $T$ resources of the transmitting user maximize the capacity of the transmitting user while controlling the interference to all the surrounded users of the wireless network. MLWFP is solved previously using iterative algorithms that take more number of iterations, thus, increasing the computational complexity. Unlike prior algorithms, the uniqueness of the proposed algorithm is in detecting the tight bounds for the number of nonzero powers $S$. Subsequently, $S$ is calculated from its bounds. This makes it possible for the proposed algorithm to compute $S$ nonzero powers alone (using the closed-form solution), whereas all the prior algorithms find powers iteratively for all $T$ ( $>$ $S$ ) resources. Thus, the worst case computational complexity of the proposed algorithm is reduced manyfold and is considerably lower than that of the prior algorithms by a factor of $O(T^2)$. Simulation results justify the analysis of the proposed algorithm. [ABSTRACT FROM AUTHOR]

To achieve spectral-efficient massive access in future wireless networks, this paper proposes a comprehensive fully non-orthogonal communication framework. First, we design a fully non-orthogonal communication scheme which consists of non-orthogonal channel estimation and non-orthogonal multiple access. Then, we analyze the performance of the proposed fully non-orthogonal communication, and derive a tight lower bound on the spectral efficiency in terms of key system parameters and channel conditions. Meanwhile, several novel insights are provided on spectral efficiency via asymptotic analysis in three important cases, i.e., a large number of base station (BS) antennas, a high BS transmit power, and perfect channel state information (CSI) at the BS. Finally, we optimize the performance of the proposed fully non-orthogonal communication and present two simple but efficient optimization algorithms for maximizing the weighted sum of spectral efficiency. Extensive simulation results validate the effectiveness of the proposed schemes. [ABSTRACT FROM AUTHOR]

To efficiently trade off system sum rate and link fairness, power allocation in wireless spectrum-sharing networks often concentrates upon proportional fairness. The corresponding problem has been proved to be convex for a single tone but NP-hard for more than two tones. However, in the two-tone case, the complexity of the problem for achieving proportional fairness has not been addressed yet. In this paper, we prove that the issue of proportional-fairness optimization for the two-tone situation is NP-hard by reducing the problem of finding the maximum independent set in an undirected graph to it. Moreover, a computationally efficient algorithm is proposed to provide an efficient suboptimal solution. Simulation results are presented to illustrate the effectiveness of our proposal. [ABSTRACT FROM PUBLISHER]

This paper uses a spatial Aloha model to describe a distributed autonomous wireless network in which a group of transmit–receive pairs (users) shares a common collision channel via slotted-Aloha-like random access. The objective of this study is to develop an intelligent algorithm to be embedded into the transceivers so that all users know how to self-tune their medium access probability (MAP) to achieve overall Pareto optimality in terms of network throughput under spatial reuse while maintaining network stability. While the optimal solution requires each user to have complete information about the network, our proposed algorithm only requires users to have local information. The fundamental of our algorithm is that the users will first self-organize into a number of nonoverlapping neighborhoods, and the user with the maximum node degree in each neighborhood is elected as the local leader (LL). Each LL then adjusts its MAP according to a parameter R, which indicates the radio intensity level in its neighboring region, whereas the remaining users in the neighborhood simply follow the same MAP value. We show that by ensuring R \le \text{2} at the LLs, the stability of the entire network can be assured, even when each user only has partial network information. For practical implementation, we propose each LL to use R=\text{2}$ as the constant reference signal to its built-in proportional and integral controller. The settings of the control parameters are discussed, and we validate through simulations that the proposed method is able to achieve close-to-Pareto-front throughput. [ABSTRACT FROM PUBLISHER]

Recent advances in loop interference cancelation techniques enable full-duplex relaying (FDR) systems, which transmit and receive simultaneously in the same band with high spectrum efficiency. Meanwhile, wireless virtualization has attracted a lot of attention from both academia and industry because it can provide more flexibility, diversity, and other benefits in the process of wireless network design, construction, operation, and management. In this paper, we introduce the idea of wireless virtualization into FDR cellular networks. Then, the problem of energy-aware virtual resource management is formulated as a three-stage Stackelberg game. The subgame perfect equilibrium for each stage is analyzed. In addition, the interplays of the three-stage game are discussed, and an iterative algorithm is proposed to obtain the Stackelberg equilibrium solution. Simulation results are presented to show the effectiveness of the proposed scheme. [ABSTRACT FROM PUBLISHER]

We present in this paper a novel proactive interference avoidance scheme for mobile-to-mobile (M2M) communication as an underlay in Long-Term Evolution (LTE) downlink networks. The proposed scheme introduces an intelligent user-selective resource allocation algorithm that aims to proactively avoid interference between M2M pairs and LTE regular users while achieving a satisfactory link budget for both M2M and LTE regular links. The proposed resource allocation algorithm is based on a novel low-complexity interference estimation approach that requires only channel quality indicator (CQI) information (CSI). Since the CQI reporting mechanism has been already implemented in current LTE systems, the proposed algorithm is indeed practical and standard compatible, compared with the existing works that require perfect knowledge of the channel state information (CSI). In this research, achievable data throughput with LTE-compliant adaptive modulation and coding has been adopted as the optimization measurement, rather than the information-theoretic capacity. Therefore, the performance of the proposed scheme is practically realizable in LTE communication systems. Since the performance of the proposed scheme highly relies on the accuracy of CQI feedback, we also carry out performance analysis to evaluate the impact of CQI estimation error and delay. The simulation results show that the total achievable data throughput for LTE networks is dramatically enhanced by M2M communication with this proposed interference avoidance resource allocation scheme. [ABSTRACT FROM PUBLISHER]

The increasing densification of telecommunications mobile networks raises the compelling problem of exposure to electromagnetic fields (EMF). In this paper, we address the particular concept of heterogeneous networks, where small cells (SCs) are deployed to build up a second layer to the existing macrocell network. SC deployment is shown to be particularly effective in reducing EMF exposure, in addition to capacity enhancement. Previous studies on EMF have focused on the uplink (UL) and downlink (DL) components separately, although they are strongly correlated. Relying on the definition of the exposure index (EI) from the European FP7 Lexnet project, we evaluate the impact on EMF exposure due to SC deployment. Guidelines for EMF-efficient deployment of SCs are first presented and evaluated. Then, a new enhanced intercell interference coordination (eICIC) algorithm using UL and DL metrics to optimize capacity and exposure to EMF is proposed. Static and self-optimizing dynamic implementations of the solution are considered, using a utility function related to the sojourn time of the users. Time-domain and frequency-domain self-optimized eICIC are compared in terms of EMF exposure reduction and capacity enhancement. The eICIC algorithms are combined with a self-optimized coverage range expansion function leading to further reduction of the EI. The proposed solution for optimizing both capacity and exposure to EMF is compared with a capacity-driven solution based on the DL metrics only. Simulation results illustrate the performance gains obtained for the different SC deployment scenarios. [ABSTRACT FROM PUBLISHER]

In heterogeneous networks (HetNets), the load between macrocell base stations (MBSs) and small-cell base stations (SBSs) is imbalanced due to their different transmission powers and locations. This load imbalance significantly impacts system performance and affects the experience of mobile users (MUs) with different priorities. In this paper, we aim to distributively optimize the user association in HetNets with various user priorities to solve the load balancing problem. Since the user association is a binary matching problem, which is NP-hard, we propose a distributed belief propagation (BP) algorithm to approach the optimal solution. We first develop a factor graph model, using the network topology, to represent this user association problem. With this factor graph, we propose a novel distributed BP algorithm by adopting the proportional fairness as the objective. Next, we theoretically prove the existence of the fixed point in our BP algorithm. To be more practical, we develop an approximation method to significantly reduce the computational and communication complexity of the BP algorithm. Furthermore, we analyze some properties of the factor graph relevant to the performance of the BP algorithm using the stochastic geometry. Simulation results show that 1) the proposed BP algorithm well approaches the optimal system performance and achieves a much better performance compared with other association schemes and that 2) the analytical results on the average degree distribution and sparsity of the factor graph match with those obtained from the Monte Carlo simulations. [ABSTRACT FROM PUBLISHER]

In this paper, we study the energy-efficient resource allocation problem for device-to-device (D2D) communication underlaying cellular networks, which aims to maximize the minimum weighted energy efficiency of D2D links while guaranteeing minimum data rates for cellular links. We first characterize the optimal power allocation of the cellular links to transform the original resource allocation problem into the joint subchannel and power allocation problem for D2D links. We then propose three resource allocation algorithms with different complexity levels, namely, dual-based, branch-and-bound (BnB), and relaxation-based rounding (RBR) algorithms. While the dual-based algorithm solves the problem by using the dual decomposition method, the BnB and RBR algorithms tackle the problem by employing the relaxation approach. We establish the strong performance guarantees for the proposed algorithms through theoretical analysis. Extensive numerical studies demonstrate that the proposed algorithms achieve superior performance and significantly outperform a conventional algorithm. [ABSTRACT FROM PUBLISHER]

The cross-layer utility maximization problem, which is subject to stability constraints for a multicommodity wireless network where all links share the same number of orthogonal channels, is considered in this paper. We assume a time-slotted network, where the channel gains randomly change from one slot to another. The optimal cross-layer network control policy can be decomposed into the folloing three subproblems: 1) flow control; 2) next-hop routing and in -node scheduling; and 3) power and rate control, which is also known as resource allocation (RA). These subproblems span the layers from the physical layer to the transport layer. In every time slot, a network controller decides the amount of each commodity data admitted to the network layer, schedules different commodities over the network's links, and controls the power and rate allocated to every link in every channel. To fully exploit the available multichannel diversity, we consider the general case, where multiple links can be activated in the same channel during the same time slot, and the interference is controlled solely through power and rate control. Unfortunately, the RA subproblem is not yet amendable to a convex formulation, and in fact, it is NP-hard. The main contribution of this paper is to develop efficient RA algorithms for multicommodity multichannel wireless networks by applying complementary geometric programming and homotopy methods to analyze the quantitative impact of gains that can be achieved at the network layer in terms of end-to-end rates and network congestion by incorporating different RA algorithms. Although the global optimality of the solution cannot be guaranteed, the numerical results show that the proposed algorithms perform close to the (exponentially complex) optimal solution methods. Moreover, they efficiently exploit the available multichannel diversity, which provides significant gains at the network layer in terms of end-to-end rates and network congestion. In addition, the assessment of the improvement in performance due to the use of multiuser detectors at the receivers is provided. [ABSTRACT FROM PUBLISHER]

In wireless network resource allocation, the radio power control problems are often solved by fixed point algorithms. Although these algorithms give feasible problem solutions, such solutions often lack notion of problem optimality. This paper reconsiders well-known fixed-point algorithms, such as those with standard and type-II standard interference functions, and investigates the conditions under which they give optimal solutions. The optimality is established by the recently proposed fast-Lipschitz optimization framework. To apply such a framework, the analysis is performed by a logarithmic transformation of variables that gives tractable fast-Lipschitz problems. It is shown how the logarithmic problem constraints are contractive by the standard or type-II standard assumptions on the power control problem, and how sets of cost functions fulfill the fast-Lipschitz qualifying conditions. The analysis on nonmonotonic interference function allows establishing a new qualifying condition for fast-Lipschitz optimization. The results are illustrated by considering power control problems with standard interference function, problems with type-II standard interference functions, and a case of subhomogeneous power control problems. Given the generality of fast-Lipschitz optimization compared to traditional methods for resource allocation, it is concluded that such an optimization may help to determine the optimality of many resource allocation problems in wireless networks. [ABSTRACT FROM AUTHOR]

In an interference limited network, joint power and admission control (JPAC) aims at supporting a maximum number of links at their specified signal-to-interference-plus-noise ratio (SINR) targets while using minimum total transmission power. Various convex approximation deflation approaches have been developed for the JPAC problem. In this paper, we propose an effective polynomial time non-convex approximation deflation approach for solving the problem. The approach is based on the non-convex \ell q~(0< q< 1) approximation of an equivalent sparse \ell 0 reformulation of the JPAC problem. We show that, for any instance of the JPAC problem, there exists a \bar q\in (0,1) such that it can be exactly solved by solving its \ell q approximation problem with any q\in (0, \bar q]. We also show that finding the global solution of the \ell q approximation problem is NP-hard. Then, we propose a potential reduction interior-point algorithm, which can return an \epsilon -KKT solution of the NP-hard \ell q approximation problem in polynomial time. The returned solution can be used to check the simultaneous supportability of all links in the network and to guide an iterative link removal procedure, resulting in the polynomial time non-convex approximation deflation approach for the JPAC problem. Numerical simulations show that the proposed approach outperforms the existing convex approximation approaches in terms of the number of supported links and the total transmission power, particularly exhibiting a quite good performance in selecting which subset of links to support. [ABSTRACT FROM PUBLISHER]

Radar signal design in a spectrally crowded environment is currently a challenge due to the increasing requests for spectrum from both military sensing applications and civilian wireless services. The goal of this paper is to improve a previously devised algorithm for the synthesis of optimized radar waveforms fulfilling spectral compatibility with overlaid licensed radiators. The new technique achieves an enhanced spectral coexistence with the surrounding electromagnetic environment through a suitable modulation of the transmitted waveform energy, which was kept fixed at the maximum level in the previously devised algorithm. At the analysis stage, the waveform performance is studied in terms of trade-off among the achievable Signal to Interference Plus Noise Ratio (SINR), spectral shape, and the resulting Autocorrelation Function (ACF), also in situations where the previous technique cannot be applied. [ABSTRACT FROM AUTHOR]

In this paper, we consider the problem of power control for sum-rate maximization on multiple interfering links [transmitter–receiver pairs (Tx–RX)] under a sum power constraint. We consider a single-frequency network, where all pairs are operating in the same frequency band, thereby creating interference with each other. We study the power-allocation problem for sum-rate maximization with and without quality-of-service (QoS) requirements on individual links. When the objective is only sum-rate maximization without QoS guarantees, we develop an analytic solution to decide optimal power allocation for a two-TX–RX-pair problem. We also develop a low-complexity iterative algorithm for a three-TX–RX-pair problem. For a generic N>\3 TX–RX pair problem, we develop two low-complexity suboptimal power-allocation algorithms. The first algorithm is based on the idea of making clusters of two or three TX–RX pairs and then leveraging the power-allocation results obtained for the two- and three-TX–RX-pair problems. The second algorithm is developed by using a high signal-to-interference-plus-noise ratio (SINR) approximation, and this algorithm can be also implemented in a distributed manner by individual TXs. We then consider the same problem but with additional QoS guarantees for individual links. We again develop an analytic solution for the two-TX–RX-pair problem and a distributed algorithm for N>\2 TX–RX pairs. [ABSTRACT FROM PUBLISHER]

We consider the uplink transmission in multiuser wireless systems with multiple single-antenna transmitting users and a multiantenna receiver. We address two problems in this paper. The first problem is user admission, i.e., given a large number of users, how to admit the maximum number of users that can simultaneously satisfy quality of service (QoS) and power constraints. The second problem is how to distribute the data among the users when they are allowed to share data before transmission. The aim is to minimize the total data exchange cost. Such a problem is called user clustering. We formulate those problems into sparsity-maximization problems, which are NP-hard. Inspired by compressive sensing techniques, we propose a common framework to tackle those problems by first applying the \ell1 -norm relaxation and then solving them with convex optimization methods. Simulations show that the proposed algorithms achieve excellent performance. For user admission, the numbers of admitted users by the proposed algorithms are close to the optimum numbers of admitted users obtained by exhaustive search (ES) . For user clustering, the total data exchange cost is reduced by more than 10% after only a few iterations. When the QoS requirement is low, the user data exchange can be avoided using the proposed method, which achieves the optimum result obtained by ES. [ABSTRACT FROM PUBLISHER]

Cognitive radio (CR) is currently one of the most promising solutions for designing spectrum-efficient wireless communication systems. In this paper, we present a greedy cognitive access policy that handles spectrum sensing with the tools of image processing. By means of the Wigner–Ville transform (WVT), we are able to analyze the time and frequency features of the received interference, and represent them on a time–frequency grid as an image in which brightness reveals the spectrum occupancy. According to sensed interference, each secondary user autonomously tries to access the grid following a multiple-division principle, i.e., by properly shaping the signal in the code, time, and frequency domains. In detail, this is realized by processing the WVT to derive the shaping mask (and the matching algorithm) that allows the filling up of the “time spectrum holes.” Numerical simulations and a test-bed implementation show how the proposed scheme outperforms selected state-of-the-art approaches while still presenting an affordable computational cost. [ABSTRACT FROM AUTHOR]

In this paper, we investigate the problem of distributed channel selection for interference mitigation in a canonical communication network. The channel is assumed time-varying, and the active user set is considered dynamically variable due to the specific service requirement. This problem is formulated as an exact potential game, and the optimality property of the solution to this problem is first analyzed. Then, we design a low-complexity fully distributed no-regret learning algorithm for channel adaptation in a dynamic environment, where each active player can independently and automatically update its action with no information exchange. The proposed algorithm is proven to converge to a set of correlated equilibria with a probability of 1. Finally, we conduct simulations to demonstrate that the proposed algorithm achieves near-optimal performance for interference mitigation in dynamic environments. [ABSTRACT FROM AUTHOR]

One of the key challenges in wireless networking is allocating the available radio resources to maximize key service delivery parameters such as the aggregate throughput and the multimedia quality of experience (QoE). We propose a novel and effective scheduling policy that allocates resource blocks, such that interference power is shifted toward capacity-saturated users, while improving the throughput of unsaturated users. The highlight of this paper is that the proposed scheme can dramatically improve the performance of cells that have a high discrepancy in its signal-to-noise ratio (SNR) distribution, which is typical in urban areas. The results show that a free-lunch (FL) solution is possible, whereby for negligible performance degradation in the saturated users, a large improvement in the nonsaturated users can be obtained. However, on average, the number of FL user pairings is low. By relaxing the degradation constraints, the non-FL (NFL) solution can yield a greater multiuser throughput gain. Motivated by a surge in mobile multimedia traffic, we further demonstrate that the proposed scheduling may have a profound impact on both energy efficiency and QoE of multimedia service delivery. [ABSTRACT FROM PUBLISHER]

Device-to-device (D2D) communications bring significant benefits to mobile multimedia services in local areas. However, these potential advantages hinge on intelligent resource sharing between potential D2D pairs and cellular users. In this paper, we study the problem of energy-efficient uplink resource sharing over mobile D2D multimedia communications underlaying cellular networks with multiple potential D2D pairs and cellular users. We first construct a novel analytical model of energy efficiency for different sharing modes, which takes into account quality-of-service (QoS) requirements and the spectrum utilization of each user. Then, we formulate the energy-efficient resource sharing problem as a nontransferable coalition formation game, with the characteristic function that accounts for the gains in terms of energy efficiency and the costs in terms of mutual interference. Moreover, we develop a distributed coalition formation algorithm based on the merge-and-split rule and the Pareto order. The distributed solution is characterized through novel stability notions and can be adapted to user mobility. From it, we obtain the energy-efficient sharing strategy on joint mode selection, uplink reusing allocation, and power management. Extensive simulation results are provided to demonstrate the effectiveness of our proposed game model and algorithm. [ABSTRACT FROM PUBLISHER]

To increase frequency efficiency in cellular communication networks, this paper describes a cell coloring based distributed frequency allocation approach (C-DFA) for all kinds of cellular networks. C-DFA has high computational efficiency and is simpler to realize than other distributed approaches. Building on C-DFA, a distributed dynamic fractional frequency allocation (DDFFA) algorithm is designed for IEEE 802.16j supported Relay Based Cellular Networks (RBCN). It is shown that: a) C-DFA can better realize frequency efficiency and network resilience compared to centralized traditional distributed frequency allocation approach. b) DDFFA can significantly increase frequency efficiency to provide high capacity and throughput to the RBCN though at the cost of extra computation and BS-BS communication. The evaluations and analysis are based on moderate and high user congestion RBCN scenarios. [ABSTRACT FROM AUTHOR]

In this paper, we propose two cross-layer algorithms, namely, the Power-optimal Scheduling Algorithm (PSA) and the Throughput-optimal Scheduling Algorithm (TSA), to minimize energy consumption and to maximize throughput, respectively, in multihop wireless networks. Our algorithms guarantee a flow-based minimum data rate and jointly integrate congestion control, power allocation, routing, and link rate scheduling. Different from traditional algorithms that assume infinite buffers, the proposed algorithms deterministically upper bound the flow-based packet queue length and thus can be employed in multihop networks with finite buffers. In addition, the algorithms achieve a power expenditure/throughput “\epsilon-close” to the optimal value, with a tradeoff of order O(\1/\epsilon) in buffer size. The average end-to-end delay upper bound can also be derived from the finite buffer property. Finally, numerical results are presented to show the performance of the two algorithms with different system parameters. [ABSTRACT FROM PUBLISHER]

Most slot assignment algorithms that are proposed for spatial time-division multiple-access (STDMA) mesh networks use a simplified transmission model that does not consider the time-varying fading characteristic of a wireless channel. In addition, these algorithms are usually link based, and scheduling in each slot is fixed and done for links. When the scheduled link is in deep fade or has no traffic, the slot that is given to that link will be wasted. In this paper, we present a node-based slot assignment scheme in which scheduling in each slot is done for nodes and not links. Because a node has multiple links, it can exploit multiuser diversity, i.e., different links have different traffic and fading conditions, to achieve much better performance. [ABSTRACT FROM PUBLISHER]

Femto base stations (FBSs) are cost-effective choices for wireless service providers (WSPs) to provide better indoor coverage and capacity in cellular networks. With the introduction of femtocells, spectrum allocation among macro base stations (MBSs) and numerous FBSs becomes challenging. WSPs will allocate resources among hierarchical infrastructures to mitigate mutual interference and maximize WSPs' revenue from service provisioning. In this paper, we propose a channel allocation and service provision framework to help WSPs allocate resources and increase revenue. The spectrum allocation and revenue maximization problem is formulated as a convex problem, and a centralized method is proposed to efficiently solve it. We also propose a distributed algorithm called LD (based on Lagrangian decomposition) to achieve a near-optimal solution with low computational and communication complexity, which can be used when the system is on a large scale. The simulation results show that our scheme can significantly improve the system's revenue. [ABSTRACT FROM PUBLISHER]

In this paper, we propose an iterative interference alignment (IA) algorithm for MIMO cellular networks with partial connectivity, which is induced by heterogeneous path losses and spatial correlation. Such systems impose several key technical challenges in the IA algorithm design, namely the overlapping between the direct and interfering links due to the MIMO cellular topology as well as how to exploit the partial connectivity. We shall address these challenges and propose a three stage IA algorithm. As illustration, we analyze the achievable degree of freedom (DoF) of the proposed algorithm for a symmetric partially connected MIMO cellular network. We show that there is significant DoF gain compared with conventional IA algorithms due to partial connectivity. The derived DoF bound is also backward compatible with that achieved on fully connected K-pair MIMO interference channels. [ABSTRACT FROM PUBLISHER]

Interference alignment (IA) has been recognized as a promising technique to obtain large multiplexing gain in multiple-input–multiple-output (MIMO) interference channels. Most existing IA schemes require global channel state information (CSI) at the transmitter to design precoding vectors and, thus, result in significant capacity overhead in the feedback link. To reduce the feedback overhead, in this paper, we investigate the IA scheme employing differential CSI feedback over time-correlated MIMO channels. Specifically, we analyze the achievable sum-rate performance using differential feedback and derive the minimum differential feedback rate to achieve the maximum multiplexing gain. We also provide the upper bound of the sum-rate performance loss due to the limited differential feedback rate. Finally, the analytical results are verified by simulations in a practical IA scheme with differential CSI feedback using Lloyd's quantization algorithm. [ABSTRACT FROM PUBLISHER]

This paper investigates transmit antenna selection for linearly precoded multiuser multiple-input–multiple-output (MU-MIMO) systems. First, in some precoded single-user MIMO systems, using all transmit antennas does not always lead to the best performance due to ill-conditioned channel matrices. This condition motivates us to investigate whether a similar result can be obtained in MU-MIMO systems. Based on the derived analytical results, we found that, for a given number of transmit antennas, decreasing the number of active transmit antennas [number of radio frequency (RF) units] always degrades system performance in the linearly precoded MU-MIMO systems. However, in practical systems, RF units are expensive. To reduce the hardware cost, antenna selection is usually used to reduce the number of RF units. Thus, we further analyze the performance loss due to transmit antenna selection (TAS). These analytical results provide good design references for using TAS in practical systems. Moreover, based on the analytical results, we proposed several simple TAS algorithms for linearly precoded MU-MIMO systems. Complexity analysis and simulation results show that the computational complexity of the proposed algorithms can significantly be reduced, whereas the performance is still comparable with the optimal selection scheme. As a result, the analyzed results enable us to better understand how TAS affects the MU-MIMO systems. In addition, the proposed algorithms make TAS more feasible to be used in practical systems. [ABSTRACT FROM PUBLISHER]

In wireless communication systems with multiple users, the near–far effect and the cochannel interferences decrease the spectral efficiency and system performance. An efficient known method to combat these destructive effects is to properly adjust the transmit powers. A practical and efficient way for power control (PC) in cellular communication systems is the autonomous distributed PC (DPC). In this method, the transmit power of each link is independently modified based on the received signal-to-interference-plus-noise ratio (SINR) reported from the other side of that link. It is known that in a perfect scenario where the reported SINRs are exact, this algorithm converges to the optimum transmit powers. However, in the case of erroneous SINR reports, the transmit powers deviate from their optimum values. This paper addresses the destructive effect of inaccurate SINR reports on the performance of a DPC algorithm. Then, independent variable step-sizes that update the transmit powers are proposed, and convergence of the proposed DPC algorithm to the optimum solution of the original problem is analytically shown. Additionally, a protocol to adaptively tune the step-sizes in our algorithm, which improves the performance of PC in the presence of inaccurate SINR reports, is proposed. Computer simulations are used to study the performance of the proposed method in terms of both the speed of converges and the steady-state accuracy of link qualities. [ABSTRACT FROM PUBLISHER]

In downlink multiuser multiple-input–multiple-output (MU-MIMO) systems, users are practically heterogeneous. However, many existing user-scheduling algorithms are designed with an implicit assumption that users are homogeneous. In this paper, we revisit the problem by exploring the characteristics of heterogeneous users from a subspace viewpoint. With an objective of minimizing interference nonorthogonality among users, three new angular-based user-scheduling criteria are proposed. While the first criterion is heuristically determined by identifying the incapability of the largest principal angle to characterize subspace correlation and, hence, interference nonorthogonality between users, the second and third criteria are derived by using the sum rate capacity bounds with block diagonalization and the change in capacity by adding a new user into an existing user subset, respectively. Aiming to capture fairness among heterogeneous users while maintaining multiuser diversity gain, two new hybrid user-scheduling algorithms, whose computational complexities are only linearly proportional to the number of users, are proposed. We show by simulations the effectiveness of our proposed user-scheduling criteria and algorithms with respect to those commonly used in a homogeneous environment. [ABSTRACT FROM PUBLISHER]

In this paper, we consider a wireless amplify-and-forward relay network with one relay node and multiple source-destination pairs/users and propose a pricing framework that enables the relay to set prices to maximize either its revenue or any desirable system utility. Specifically, depending on the quality of the received signals, the relay sets prices and correspondingly charges the users utilizing its resources for their transmissions. The price is determined in such a way that the relay's revenue or system utility is maximized. Given the specified price, the users competitively employ the relay node to forward their signals. We model each user as a rational player, which aims at maximizing its own net utility through power allocation, and analyze the competition among the users within the framework of noncooperative game theory. It is shown that, in the game played by the users, there always exists a unique pure Nash equilibrium point that can be achieved through distributed iterations. Next, subject to the availability of complete information about the users at the relay, we propose a low-complexity uniform pricing algorithm and an optimal differentiated pricing algorithm, in which the relay either charges the users at a suboptimal uniform price or charges different users at different prices. We also show that, by applying the differentiated pricing algorithm that enforces the users to transmit at certain power levels, any system utility can be maximized. Extensive simulations are conducted to quantify the performance of the proposed methods. [ABSTRACT FROM AUTHOR]

A constantly available control channel facilitates control message exchange and spectrum coordination in cognitive radio (CR) ad hoc networks. When a dedicated control channel is unavailable, a control channel must be dynamically allocated in licensed channels and vacated for the presence of primary users (PUs). As a result, the establishment of such a control channel is a challenge. In this paper, an efficient recovery control channel (ERCC) design is proposed to address this challenge. This heuristic and distributed design approach is essentially based on the observed spectrum homogeneity in a neighborhood. By adaptively updating a list of channels commonly available to neighbors, each secondary user is able to efficiently establish new control channels among neighbors in response to PU activity changes. Therefore, a virtually “always on” control channel robust to PU activity can be realized by the proposed method. The contributions are summarized as follows: 1) The proposed method efficiently recovers control channels from PU activity changes and maintains network connectivity. 2) It extends the control channel coverage to facilitate broadcast and reduce control overhead and delay. 3) It minimizes the interference with PUs. Simulation results show that the proposed solution outperforms the classic group- and sequence-based solutions in the responsiveness to rapidly changing PU activity and the maintenance of connectivity. Furthermore, the increase in control channel coverage and the allocation of the highest quality channels to control channels can be well balanced with reliability and scalability in various network scenarios. [ABSTRACT FROM PUBLISHER]

In multihop ad hoc networks over 802.11, the real bandwidth-delay product (BDP) cannot reach a value as large as in wired networks due to contention through wireless shared channels. As a result, conventional congestion-control algorithms cause an overshooting window problem and result in poor throughput in ad hoc networks. In this paper, we split the real round-trip time (RTT) into two parts: 1) congestion RTT and 2) contention RTT. We reveal that the contention RTT has nothing to do with the BDP and that the BDP is determined by only the congestion RTT if a link with the worst contention status does not lead to link breakage. An inadequate use of contention RTT causes a Transmission Control Protocol (TCP) congestion window overshooting problem. We propose a novel mechanism called congestion window adaptation through contention detection, which provides a more accurate method of estimating the contention status. Based on this mechanism, we present a congestion window adaptation method to limit the window size from overshooting. Simulation results show that our proposed mechanism outperforms the conventional TCP and the enhanced mechanisms called TCP with contention control and TCP with a maximum window. [ABSTRACT FROM PUBLISHER]

Distributed power control over interference limited network has received an increasing intensity of interest over the past few years. Distributed solutions (like the iterative water-filling, gradient projection, etc.) have been intensively investigated under quasi-static channels. However, as such distributed solutions involve iterative updating and explicit message passing, it is unrealistic to assume that the wireless channel remains unchanged during the iterations. Unfortunately, the behavior of those distributed solutions under time-varying channels is in general unknown. In this paper, we shall investigate the distributed scaled gradient projection algorithm (DSGPA) in a K pairs multicarrier interference network under a finite-state Markov channel (FSMC) model. We shall analyze the convergence property as well as tracking performance of the proposed DSGPA. Our analysis shows that the proposed DSGPA converges to a limit region rather than a single point under the FSMC model. We also show that the order of growth of the tracking errors is given by \cal O(1 / \barN), where \barN is the average sojourn time of the FSMC. Based on the analysis, we shall derive the tracking error optimal scaling matrices via Markov decision process modeling. We shall show that the tracking error optimal scaling matrices can be implemented distributively at each transmitter. The numerical results show the superior performance of the proposed DSGPA over three baseline schemes, such as the gradient projection algorithm with a constant stepsize. [ABSTRACT FROM PUBLISHER]

In this paper, we extend an existing heuristic algorithm for fixed-frequency assignment in cellular communication networks in order to account for the effects of adjacent channel interference (Act). Our algorithm utilizes estimations of carrier-to-interference ratio instead of binary channel separation constraints. The extended algorithm avoids Overoptimistic frequency assignments that do not guarantee interference-free operation environments. Its cost is the increased computational complexity. We estimate the upper bound of the algorithm's complexity when ACI is taken into account. Simulations demonstrate that despite the theoretical high increase in complexity, the algorithm is suitable even for a quasi-dynamic frequency assignment scheme in practical situations. The algorithm enhances the spectrum utilization in cellular networks while reducing harmful interference. [ABSTRACT FROM AUTHOR]

In this paper, we investigate the problem of flow routing and fair bandwidth allocation under interference constraints for multihop wireless networks. We first develop a novel isotonic routing metric, RI^3M, considering the influence of interflow and intraflow interference. The isotonicity of the routing metric is proved using virtual network decomposition. Second, in order to ensure QoS, an interference-aware max-min fair bandwidth allocation algorithm, LMX:M^3F, is proposed where multiple paths (determined by using the routing metric) coexist for each user to the base station. In order to solve the algorithm, we develop an optimization formulation that is modeled as a multicommodity flow problem where the lexicographically largest bandwidth allocation vector is found among all optimal allocation vectors while considering constraints of interference on the flows. We compare our RI^3M routing metric and LMX:M^3F bandwidth allocation algorithm with various interference-based routing metrics and interference-aware bandwidth allocation algorithms established in the literature. We show that RI^3M and LMX:M^3F succeed in improving network performance in terms of delay, packet loss ratio, and bandwidth usage. [ABSTRACT FROM AUTHOR]