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Power control is becoming increasingly essential for the fifth-generation (5G) and beyond systems. An example use-case, among others, is the unmanned-aerial-vehicle (UAV) communications where the nearly line-of-sight (LoS) radio channels may result in very low signal-to-interference-plus-noise ratios (SINRs). The authors in (Chiang et al., 2007) proposed to efficiently and reliably solve this kind of non-convex problem via a series of geometrical programmings (GPs) using condensation approximation. However, it is only applicable for a small-scale network with several communication pairs and practically infeasible with more (e.g., tens of) nodes to be jointly optimized. We therefore in this paper aim to provide new insights into this problem. By properly introducing auxiliary variables, the problem is transformed to an equivalent form which is simpler and more intuitive for condensation. A novel condensation method with linear complexity is also proposed based on the form. The enhancements make the GP-based power control feasible for both small- and especially large-scale networks that are common in 5G and beyond. The algorithm is verified via simulations. A preliminary case study of uplink UAV communications also shows the potential of the algorithm. [ABSTRACT FROM AUTHOR]

In multi-user downlink small cell networks, cooperative resource allocation (RA) within a small cell cluster is a key technique to enhance network capacity. However, capacity-maximizing RA in frequency-selective fading channels requires global channel state information (CSI) of users within a small cell cluster, which makes it infeasible in practical networks with limited direct link capacity. To circumvent this global CSI assumption, most of the existing studies on RA have been based on several CSI assumptions such as local CSI and local CSI at the transmitters (CSIT). Nevertheless, cost functions with local CSI or local CSIT in the literature rely on heuristic formulations, because the sum-rate cannot be computed if without global CSI. In this paper, we propose a deep reinforcement learning-based RA algorithm to maximize the sum-rate for any given limited information on instantaneous CSI or sum-rate at the previous period. The proposed scheme is not restricted to certain CSI assumptions, but attempts to find the best RA for any given information such as quantized local CSI and quantized local CSIT; thus, it is applicable to any given direct link capacity. The proposed algorithm is self-adaptive in time-varying channels, since it is not divided into training and test phases. We modify the target neural network (TNN) scheme to enhance the sum-rate and the convergence speed. Numerical simulations confirm that: i) the proposed algorithm outperforms the conventional algorithms even under the same CSI assumption such as local CSI and local CSIT; ii) a flexible trade-off between the amount of CSI and the sum-rate is realizable in practical systems. [ABSTRACT FROM AUTHOR]

In the heterogeneous networks (HetNets), the downlink-uplink decoupled (DUDE) user association is a very promising technology that can employ different association strategies for uplink and downlink. The DUDE mode has great potential for improving system capacity and frequency efficiency. However, interference between the DUDE user equipment (DUDE UE), macro user equipment (MUE) and small user equipment (SUE) still needs to be considered and properly tackled. Therefore, this paper presents an optimization scheme of frequency allocation and power control to improve the communication quality of users. Firstly, the multiple region frequency allocation (MRFA) scheme is proposed for non-uniform user distribution to alleviate interference among users and allocate spectrum resources equally to dense users. This scheme is modelled as a sub-region partition problem with maximum sum rate, which can disperse densely distributed users to different sub-bands for transmission. Secondly, the convergent power control (CPC) algorithm is considered to improve the transmission performance of each user. It can converge the transmit power to the optimal transmit power for reducing interference among co-channel users when there is no disturbance in the network. The CPC algorithm can also make the signal-to-interference-plus-noise-ratio (SINR) of each active user always greater than the target threshold until the end of communication by dynamically adjusting the transmit power of users. Simulation results show that the joint scheme of MRFA and CPC achieves a higher system throughput and better user performance than existing frequency allocation schemes or power control schemes. [ABSTRACT FROM AUTHOR]

Layered transmission is studied in this paper to accommodate various device capabilities and channel conditions. The problem of efficiently broadcasting a common set of layer-encoded data from a single base station to multiple users over quasi-static fading channels is examined. Considering the maximum tolerable delay, a block-level utility is defined in terms of average probability of successful reception of each layer. Based on non-orthogonal multiple access scheme, we propose a complete algorithm to jointly optimize power allocation and transmission rates. By utilizing the idea of opportunistic scheduling, we first derive the success probability of one single layer in homogeneous scenarios, then extend the results to heterogeneous scenarios where users are subject to different channel statistics. The multi-layer optimization is realized by iteratively repeating the single-layer operations. Starting from the base layer, we first choose an optimal subset of users as the corresponding target group, then apply the single-layer allocation process until no power resources are left. Computer simulation and discussion confirm that our method maximizes the block-level utility at the same time balances the tradeoff between multiuser diversity gain and multicast gain. [ABSTRACT FROM AUTHOR]

In this paper, we propose a new beam allocation strategy aiming to maximize the average successful tracking probability (ASTP) of a single user time-varying millimeter-wave MIMO system. In contrast to most existing works that employ one transmitting-receiving (Tx-Rx) beam pair once only, we investigate a more general framework, where the Tx-Rx beam pairs are allowed to be used repeatedly in each beam training period to improve the received signal powers in specific directions. In the case of orthogonal Tx-Rx beam pairs, a power-based estimator is employed to track the time-varying AoA and AoD of the channel, and the resulting training beam pair sequence design problem is formulated as an integer nonlinear programming (I-NLP) problem. By dividing the feasible region into a set of subregions, the formulated I-NLP is decomposed into a series of concave sub I-NLPs, which can be solved by recursively invoking a nonlinear branch-and-bound algorithm. To reduce the computational cost, we relax the integer constraints of each sub I-NLP and obtain a low-complexity solution via solving the Karush-Kuhn-Tucker conditions of their relaxed problems. For the case when the Tx-Rx beam pairs are overlapped in the angular space, we estimate the updated AoA and AoD via an orthogonal matching pursuit (OMP) algorithm. Moreover, since no explicit expression for the ASTP exists for the OMP-based estimator, we derive a closed-form lower bound of the ASTP, based on which a favorable beam pair allocation strategy can be obtained. Numerical results demonstrate the superiority of the proposed beam allocation strategy over existing benchmarks. [ABSTRACT FROM AUTHOR]

In this paper, a power allocation space-time coding scheme based on the self-coding property of residual loop interference (RLI) for two-way full-duplex (FD) amplify-and-forward (AF) relaying networks is proposed. The relay and direct link cooperate to forward the received signals to destination node, forming the self-coded distributed space-time code, in which the relay performs self-coding using the RLI after implementing some self-interference cancellation techniques. Then, an iterative power allocation algorithm is presented. With the proposed power allocation algorithm, both amplifying factor and transmit powers of terminal nodes are jointly optimized to maximize the smaller signal-to-interference-plus-noise ratio (SINR) of terminal nodes under a total power constraint when estimated instantaneous channel state information is available. We theoretically demonstrate the benefits of the proposed power allocation algorithm compared to equal power allocation. Simulation results show that the proposed scheme achieves significant improvement in performance compared with existing schemes for FD relaying systems and half-duplex relaying systems. [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]

This paper explores the problem of joint mode selection, spectrum management, power control, and interference mitigation for device-to-device (D2D) communication underlaying a Long Term Evolution-Advanced (LTE-A) network. We consider a dynamic mode selection scenario, in which the modes (D2D or cellular) of the devices depend on optimal allocations. To improve the quality of service (QoS) for the users, the optimization objective in a corresponding problem is formulated in terms of buffer size of user equipments (UEs), which is estimated based on buffer status information collected by the UEs. The realizations of a resource allocation approach presented in the paper include its real-time and non-real-time implementations, as well as two modifications applicable to a standard LTE-Direct (LTE-D) network. Performance of the proposed algorithms has been evaluated using the OPNET-based simulations. All algorithms show improved performance in terms of mean packet end-to-end delay when compared to most relevant schemes proposed earlier. [ABSTRACT FROM AUTHOR]

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]

In this paper, we consider the coordinated multipoint (CoMP) transmission design for the downlink cloud radio access network (Cloud-RAN). Our design aims to optimize the set of remote radio heads (RRHs) serving each user and the precoding and transmission power to minimize the total transmission power while maintaining the fronthaul capacity and users' quality-of-service (QoS) constraints. The fronthaul capacity constraint involves a nonconvex and discontinuous function that renders the optimal exhaustive search method unaffordable for large networks. To address this challenge, we propose two low-complexity algorithms. The first pricing-based algorithm solves the underlying problem through iteratively tackling a related pricing problem while appropriately updating the pricing parameter. In the second iterative linear-relaxed algorithm, we directly address the fronthaul constraint function by iteratively approximating it with a suitable linear form using a conjugate function and solving the corresponding convex problem. For performance evaluation, we also compare our proposed algorithms with two existing algorithms in the literature. Finally, extensive numerical results are presented, which illustrate the convergence of our proposed algorithms and confirm that our algorithms significantly outperform the state-of-the-art existing algorithms. [ABSTRACT FROM PUBLISHER]

Direct device-to-device (D2D) links are proposed as a possible enabler for vehicle-to-vehicle (V2V) communications, where the incurred intracell interference and the stringent latency and reliability requirements are challenging issues. In this paper, we investigate the radio resource management problem for D2D-based V2V communication. First, we analyze and transform the latency and reliability requirements of V2V communication into optimization constraints that are computable using only the slowly varying channel information. This transformation opens up the possibility of extending certain existing D2D techniques to cater to V2V communication. Second, we propose a problem formulation that fulfills the different requirements of V2V communication and traditional cellular communication. Moreover, a Separate resOurce bLock and powEr allocatioN (SOLEN) algorithm is proposed to solve this problem. Finally, simulations are presented to evaluate different schemes, which illustrate the necessity of careful design when extending D2D methods to V2V communication, as well as show promising performance of the proposed SOLEN algorithm. [ABSTRACT FROM AUTHOR]

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]

This paper considers the transmission of scheduling information in orthogonal frequency-division multiple-access (OFDMA)-based cellular communication systems such as the Third-Generation Partnership Project (3GPP) long-term evolution (LTE). These systems provide efficient usage of radio resources by allowing users to be dynamically scheduled in both frequency and time. This requires considerable amounts of scheduling information to be sent to the users. This paper compares two basic transmission strategies: transmitting a separate scheduling message to each user versus broadcasting a joint scheduling message to all users. Different scheduling granularities are considered, as are different scheduling algorithms. The schemes are evaluated in the context of the LTE downlink using multiuser system simulations, assuming a full-buffer situation. The results show that separate transmission of the scheduling information requires a slightly lower overhead than joint broadcasting when proportional fair scheduling is employed and the users are spread out over the cell area. The results also indicate that the scheduling granularity standardized for LTE provides a good tradeoff between scheduling granularity and overhead. [ABSTRACT FROM PUBLISHER]

Interference alignment (IA) is a promising technique for interference management and can be applied to spectrum sharing in cognitive radio (CR) networks. However, the sum rate may fall short of the theoretical maximum, particularly at low signal-to-noise ratio (SNR), and the quality of service (QoS) of the primary user (PU) may not be guaranteed. In addition, power allocation (PA) in IA-based CR networks is largely ignored, which can further improve its performance. Thus, in this paper, PA in IA-based CR networks is studied. To guarantee the QoS requirement of the PU, its minimal transmitted power is derived. Then, we propose three PA algorithms to maximize the throughput of secondary users (SUs), the energy efficiency (EE) of the network, and the requirements of SUs, respectively, while guaranteeing the QoS of the PU. To reduce the complexity, the closed-form solutions of these algorithms are further studied in detail. The outage probability of the PU according to its rate threshold is also derived to analyze the performance of these algorithms. Moreover, we propose a transmission-mode adaptation scheme to further improve the PU's performance when its QoS requirement cannot be guaranteed at low SNR, and it can be easily combined with the proposed PA algorithms. Simulation results are presented to show the effectiveness of the proposed adaptive PA algorithms for IA-based CR networks. [ABSTRACT FROM AUTHOR]

In this paper, we study the power allocation problem in a multiuser cooperative network, where users help each other's transmissions to an access point. We use iterative double auction (IDA) game to model the interaction among the users and the access point. In each iteration of this game, the users first submit bids for buying other users' power and asks for selling its own power, and then, the access point determines the power allocation based on users' bids and asks. We propose a distributed algorithm for the implementation of the IDA-based power allocation. We also show that the proposed algorithm achieves a weighted sum signal-to-noise ratio (sum-SNR)-optimal solution. Simulation results are conducted to verify the performance of the proposed algorithm. [ABSTRACT FROM PUBLISHER]

This paper deals with joint source and relay beamforming (BF) design for an amplify-and-forward (AF) multiantenna multirelay network. Considering that the channel state information (CSI) from relays to a destination is imperfect, we aim to maximize the worst-case received signal-to-noise ratio (SNR). The associated optimization problem is then solved in two steps. In the first step, by fixing the source BF vector, a semi-closed-form solution of the relay BF matrices is obtained, up to a power-allocation factor. In the second step, the global optimal source BF vector is obtained based on the polyblock outer approximation (PA) algorithm. We also propose two low-complexity methods for obtaining the source BF vector, which differ in their complexity and performances. The optimal joint source and relay BF solution obtained by the proposed algorithms serves as the benchmark for evaluating the existing schemes and the proposed low-complexity methods. Simulation results show that the proposed robust design can significantly reduce the sensitivity of the channel uncertainty to the system performance. [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]

In this paper, altruistic coalition formation in cooperative relay networks is studied. The communication is performed over two phases, the broadcasting phase and the cooperation phase. In the broadcasting phase, each node broadcasts its signal in its time-slot, while in the cooperation phase, all the nodes within their coalitions simultaneously relay each others' signals. A distributed merge-and-split algorithm is proposed to allow nodes to form coalitions and improve their total achievable rate. Moreover, the impact of different power allocation criteria is studied, where the sum-of-rates maximizing power allocation is shown to promote altruistic coalition formation and results in the largest coalitions among the different power allocation criteria. Finally, the proposed algorithm is compared with centralized power allocation and coalition formation, and shown to yield a good tradeoff between network sum-rate and computational complexity. [ABSTRACT FROM AUTHOR]

In this paper, we study a transmit beamforming technique for multiple-input–multiple-output (MIMO) downlink multicast systems. Assuming that channel state information (CSI) is available at a transmitter, we try to maximize the lowest transmit data rate among users for a given transmit power level. Since finding the optimal beamforming vector requires prohibitively high computational complexity, we propose a reduced complexity scheme by applying greedy vector search and a simple power-allocation (PA) algorithm. Simulation results show that the proposed scheme outperforms the conventional semidefinite relaxation (SDR) technique and provides a performance quite close to the optimal performance with much reduced complexity. [ABSTRACT FROM PUBLISHER]

Channel estimation error results in severe performance deterioration in wireless networks. In this paper, we study the impact of imperfect channel estimation (ICE) on the outage performance of bidirectional relaying where the two sources have asymmetric traffic requirements (ATRs). In particular, we focus on the amplify-and-forward (AF) relay-assisted multiple-access broadcast (MABC) protocol, i.e., MABC-AF, which has received much attention due to its high spectrum efficiency and low complexity. In the single-relay scenario, we derive exact and generalized closed-form expressions for system outage probability, which indicate that the system outage is determined by the unidirectional outage in the case of highly asymmetric traffic patterns. For more insights into our approach, the closed-form asymptotic expressions are also evaluated, manifesting the interesting error floor (EF) phenomenon due to ICE. Using these analytical results, we further develop a robust and practical optimum power-allocation (OPA) algorithm that minimizes the system outage probability under aggregate and individual node power constraints. In the multirelay scenario, by taking into account the traffic knowledge, a novel relay selection criterion is proposed for asymmetric MABC-AF, followed by its impact on the system outage probability in the presence of ICE. Numerical results validate the accuracy of our analytical results and highlight the effect of the proposed OPA algorithm under various traffic requirements and channel estimation errors. Furthermore, the results also show that the proposed relay selection criterion is superior to the classical max–min criterion with ATRs, and the performance improvement becomes remarkable as channel estimation error increases. [ABSTRACT FROM PUBLISHER]

Joint channel and rate allocation with power minimization in orthogonal frequency-division multiple access (OFDMA) has attracted extensive attention. Most of the research has dealt with the development of suboptimal but low-complexity algorithms. In this paper, the contributions comprise new insights from revisiting tractability aspects of computing the optimum solution. Previous complexity analyses have been limited by assumptions of fixed power on each subcarrier or power-rate functions that locally grow arbitrarily fast. The analysis under the former assumption does not generalize to problem tractability with variable power, whereas the latter assumption prohibits the result from being applicable to well-behaved power-rate functions. As the first contribution, we overcome the previous limitations by rigorously proving the problem's NP-hardness for the representative logarithmic rate function. Next, we extend the proof to reach a much stronger result, namely, that the problem remains NP-hard, even if the channels allocated to each user are restricted to be a consecutive block with given size. We also prove that, under these restrictions, there is a special case with polynomial-time tractability. Then, we treat the problem class where the channels can be partitioned into an arbitrarily large but constant number of groups, each having uniform gain for every individual user. For this problem class, we present a polynomial-time algorithm and provide its optimality guarantee. In addition, we prove that the recognition of this class is polynomial-time solvable. [ABSTRACT FROM PUBLISHER]

Intercell interference is the major limitation to the performance of future cellular systems. Despite the joint detection and joint transmission techniques aiming at interference cancelation which suffer from the limited possible cooperation among the nodes, power allocation is a promising approach for optimizing the system performance. If the interference is treated as noise, the power allocation optimization problem aiming at maximizing the sum rate with a total power constraint is nonconvex and up to now an open problem. In the present paper, the solution is found by reformulating the nonconvex objective function of the sum rate as a difference of two concave functions. A globally optimum power allocation is found by applying a branch-and-bound algorithm to the new formulation. In principle, the algorithm partitions the feasible region recursively into subregions where for every subregion the objective function is upper and lower bounded. For each subregion, a linear program is applied for estimating the upper bound of the sum rate which is derived from a convex maximization formulation of the problem with piecewise linearly approximated constraints. The performance is investigated by system-level simulations. The results show that the proposed algorithm outperforms the known conventional suboptimum schemes. Furthermore, it is shown that the algorithm asymptotically converges to a globally optimum power allocation. [ABSTRACT FROM PUBLISHER]

In this paper, we investigate the problem of fair resource allocation based on the Nash bargaining solution (NBS) over wireless amplify-and-forward (AF) relay networks in which variable-rate users and constant-rate users coexist. Via the dual decomposition method, a distributed algorithm, including relay selection, relay power allocation, and rate adaptation, is proposed. When the number of users is large and/or the rate constraints are stringent, the problem becomes infeasible, and admission control is necessary. We formulate the admission control and fair resource allocation problem as a two-stage optimization problem, aiming to first find the maximal number of users the network can support with guaranteed rates and then allocate resources based on NBS for the admitted users. Since the problem is combinatorially hard, we transform it into an equivalent one-stage optimization problem, which can be solved by existing methods but with a higher computational complexity. To further reduce the computational complexity, a suboptimal algorithm is then developed. Simulations verify the convergence and fairness of the distributed algorithm. Simulations also show that the suboptimal algorithm achieves an approximate optimal performance but with a significant running time reduction. [ABSTRACT FROM PUBLISHER]

In this paper, the average rate maximization problem given transmission power constraints in a decode-and-forward (DF) relay network over slow fading channels is studied. For the problem, either a long- or short-term power constraint is imposed on the source and relays. In each combination of the power constraints at the source and relays, the original optimization problem is decomposed into subproblems, each corresponding to a specific channel gain realization. For each subproblem, a fast algorithm with closed-form solutions is provided. The case with additional peak power spectrum density constraints is also investigated. Numerical results are presented to demonstrate the effectiveness of the algorithms. [ABSTRACT FROM PUBLISHER]

Subcarrier allocation, power allocation, and partner selection (PS) algorithms are examined for amplify-and-forward (AF) cooperative multicarrier systems. Consider a network that consists of multiple cooperative pairs that employ a two-phase cooperation scheme, where the users first transmit their own messages on each of their subcarriers in phase I and either retransmit their own messages or relay the messages of their partners in phase II. In general, subcarrier allocation and partner selection problems are known to be intractable for systems with large number of users and subcarriers. In this paper, we propose a suboptimal but mathematically tractable approach to address these issues and show its effectiveness compared with existing algorithms. This approach is done by considering the power allocation over a relaxed problem formulation, where each subcarrier is allowed to retransmit or relay for all other subcarriers in phase II over virtually orthogonal channels. The power allocation in this case is a convex optimization problem and, thus, can efficiently be solved. Based on the results of the relaxed power allocation problem, an efficient subcarrier allocation algorithm for the original problem can then be derived by allowing subcarriers to forward the data that are allocated the most power. The results also yield a necessary and sufficient condition on when cooperation is helpful among two users. A partner selection algorithm is then devised based on the criterion given in the condition. With the proposed subcarrier allocation scheme, an iterative procedure is then adopted for resource allocation in the two phases. The complexity of the proposed algorithm is discussed, and its efficacy is demonstrated through numerical simulations. [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]

In this paper, low-complexity joint power assignment algorithms are developed for multi-source multi-destination relay networks where multiple sources share a common relay that forwards all received signals simultaneously to destinations. In particular, we consider the following power optimization strategies: (i) Minimization of the total transmission power of the sources and the relay under the constraint that the signal-to-interference-plus-noise ratio (SINR) requirement of each source-destination pair is satisfied, and (ii) Maximization of the minimum SINR among all source-destination pairs subject to any given total power budget. Both optimization problems involve K power variables, where K is the number of source-destination pairs in the network, and an exhaustive search is prohibitive for large K. In this work, we develop a methodology that allows us to obtain an asymptotically tight approximation of the SINR and reformulate the original optimization problems to single-variable optimization problems, which can be easily solved by numerical search of the single variable. Then, the corresponding optimal transmission power at each source and relay can be calculated directly. The proposed optimization schemes are scalable and lead to power assignment algorithms that exhibit the same optimization complexity for any number (K) of source-destination pairs in the network. Moreover, we apply the methodology that we developed to solve a related max-min SINR based optimization problem in which we determine power assignment for the sources and the relay to maximize the minimum SINR among all source-destination pairs subject to any given total power budget. Extensive numerical studies illustrate and validate our theoretical developments. [ABSTRACT FROM AUTHOR]

We analyze the sum rate performance in multicell single-hop networks where access points are allowed to cooperate in terms of a joint resource allocation. The resource allocation policies considered here combine power control and user scheduling. Although promising from a conceptual point of view, the optimization of the sum of per-link rates hinges on tough issues such as computational complexity and the requirement for heavy receiver-to-transmitter and cell-to-cell channel information feedback. In this paper, however, we show that simple distributed algorithms can scale optimally in terms of rates, when the number of users per cell U is allowed to grow large. We use extreme value theory to provide scaling laws for upper and lower bounds for the network sum-rate (sum of single user rates over all cells), corresponding to zero-interference and worst-case interference scenarios. We show that the scaling is either dominated by path loss statistics or by small-scale fading, depending on the regime and user location scenario. A surprising result is that the well known \log \log U rate behavior exhibited in i.i.d. fading channels with maximum rate schedulers is transformed into a \log U behavior when path loss is accounted for. Additionally, by showing that upper and lower rate bounds behave in fact identically, asymptotically, our results suggest, remarkably, that the impact of multicell interference on the rate (in terms of scaling) actually vanishes asymptotically, when appropriate resource allocation policies are used. [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]

Cooperative communications have received considerable attention for wireless multimedia applications as a technique to improve reliability and service coverage. To apply such cooperation over slow fading channels, we consider exploiting the spatial diversity in multiple relay networks. In this paper, we focus on amplify-and-forward relaying schemes, namely, orthogonal amplify-and-forward, selective relaying, and distributed beamforming, for the cooperative wireless multimedia transmission. Furthermore, the successive refinement source coding is exploited such that the multimedia signal is encoded into multiple layers and the quality of its reconstruction at the receiver is improved when more layers are received correctly. We study two strategies for the layered source transmission, namely, progressive transmission and superposition coding. With our developed framework, we propose suboptimal resource allocation algorithms to efficiently assign rate, power, and channel uses to different layers so as to maximize the quality of the multimedia signal reconstructed at the receiver. The proposed optimization methodology is simple and only requires the knowledge of the channel statistics compared to the existing algorithms. As a result, the receiver only requires to feedback the determined rate, power, and channel uses to the transmitter whenever the channel statistics or the layered source transmission strategy changes. [ABSTRACT FROM PUBLISHER]

This paper introduces a joint bit loading and power allocation algorithm for systems combining bit-interleaved coded modulation (BICM) with multicarrier transmission. The proposed algorithm maximizes the mutual information, so it can be regarded as a generalization of mercury/waterfilling policy that incorporates bit loading. The followed approach relies on irregular modulation and power to cast the problem in the framework of convex optimization. This allows to derive the optimum solution without resorting to greedy algorithms, embedding the bit loading in the definition of an equivalent constellation such that the complexity increase with respect to mercury/waterfilling is negligible. While irregular modulation plays a key role in algorithm definition, it is proved that only a few subcarriers employ it and it is shown that a practical low complexity algorithm can be obtained with minimal losses that does not use irregular modulation. [ABSTRACT FROM PUBLISHER]