Sum Rate Maximization Problem Research Articles

Delay-Optimal Joint Processing in Computation-Constrained Fog Radio Access Networks

With fog radio access networks (F-RANs), the computation capability is provided in the physical proximity of the users, which can significantly lower the delay and mitigate the heavy traffic over backhaul links. As the number of remote radio head (RRH) increases, the computational complexity becomes a severe issue and it is necessary to group the RRHs into multiple clusters. In this paper, we optimize the joint processing strategy, including the RRH clustering and the RRH-server matching to minimize the delay for F-RANs. We model the delay-optimal joint processing problem in computation-constrained F-RANs as a Markov decision process (MDP) problem. By deriving the optimality condition of this MDP, we obtain a per-slot weighted sum rate maximization problem, in which the RRH clustering and the RRH-server matching are solved jointly. Specifically, we transform the weighted sum rate maximization problem into a combinatorial auction problem (CAP). Since the CAP is NP-hard, we greedily obtain an initial solution of CAP and gradually improve the performance by adopting local α-good improvement algorithm based on the weighted independent set problem (WISP). Furthermore, we theoretically derive the bound of the performance of the proposed algorithm.

Generalized Signal-Space Alignment Based Physical-Layer Network Coding for Distributed MIMO Systems

We study an uplink distributed multiple-input-multiple-output (D-MIMO) system, where multiple users are served by multiple base stations (BSs) connected to a common central unit (CU). We propose a generalized signal-space alignment (SSA)-based physical-layer network coding (PNC) scheme, termed G-SSA-PNC. In G-SSA-PNC, the multi-antenna users encode their messages by using nested lattice coding, linearly precode the nested lattice codewords for SSA, and then broadcast to the multi-antenna BSs. The BSs linearly post-process the received signals to extract signals at certain aligned directions. PNC decoding is applied to those extracted signals to generate network-coded message combinations, and the decoded message combinations are then forwarded to the CU for user messages decoding. Compared with the original SSA-PNC scheme, our scheme is different in the following three aspects. First, the G-SSA-PNC employs an individual shaping lattice for nested lattice coding at each source, as in contrast to the use of a common shaping lattice in original SSA-PNC. Second, we derive the feasibility condition of PNC decoding under the new nested lattice coding scheme and show that the SSA precoding and the nested lattice coding needs to be jointly designed to meet the feasibility condition. Third, we formulate a sum rate maximization problem and propose a suboptimal solution to the problem. The numerical results demonstrate the superiority of the G-SSA-PNC in performance over the benchmark schemes, including original SSA-PNC, compress-and-forward, and interference alignment.

Multi-Agent Distributed Beamforming With Improper Gaussian Signaling for MIMO Interference Broadcast Channels

For rate optimization in interference limited networks, improper Gaussian signaling has shown its ability to outperform conventional proper Gaussian signaling. In this paper, we study a weighted sum-rate maximization problem with improper Gaussian signaling for the multiple-input multiple-output interference broadcast channel. To solve this nonconvex and NP-hard problem, we propose an effective separate covariance and pseudo-covariance matrices optimization algorithm. In the covariance optimization, a weighted minimum mean square error algorithm is adopted, and in the pseudo-covariance optimization, an alternating optimization (AO) algorithm is proposed, which guarantees convergence to a stationary solution and ensures a sum-rate improvement over proper Gaussian signaling. An alternating direction method of multipliers-based multi-agent distributed algorithm is proposed to solve an AO subproblem with the globally optimal solution in a parallel and scalable fashion. The proposed scheme exhibits favorable convergence, optimality, and complexity properties for future large-scale networks. The simulation results demonstrate the superior sum-rate performance of the proposed algorithm as compared with the existing schemes with proper as well as improper Gaussian signaling under various network configurations.

Generalized Compute-Compress-and-Forward

Compute-and-forward (CF) harnesses interference in wireless communications by exploiting structured coding. The key idea of CF is to compute integer combinations of code words from multiple source nodes, rather than to decode individual code words by treating others as noise. Compute-compress-and-forward (CCF) can further enhance the network performance by introducing compression operations at receivers. In this paper, we develop a more general compression framework, termed generalized CCF (GCCF), where the compression function involves the selection of message segments over finite fields. We show that GCCF achieves a broader compression rate region than CCF. We also compare our compression rate region with the fundamental Slepian–Wolf (SW) region. We show that GCCF is optimal in the sense of achieving the minimum total compression rate. We also establish the criteria under which GCCF achieves the SW region. In addition, we consider a two-hop relay network employing the GCCF scheme. We formulate a sum-rate maximization problem and develop an approximate algorithm to solve the problem. Numerical results are presented to demonstrate the performance superiority of GCCF over CCF and other schemes.

Resource Allocation for Symbiotic Radio System With Fading Channels

In this paper, we investigate the resource allocation problem for the symbiotic radio system, in which the passive backscatter transmission is parasitic in the primary transmission, and the cooperative receiver simultaneously detects the signals transmitted from the active primary transmitter (PT) and the backscatter device (BD). In particular, channel fading is considered, and the ergodic weighted sum rate of the primary and backscatter transmissions is maximized by jointly optimizing the transmit power at the PT and the reflection coefficient at the BD under either long-term or short-term transmit-power constraint over the fading states. Two practical transmission setups are considered, where the relationship between the primary and backscatter transmissions is either commensal or parasitic. For the commensal setup, we fix the reflection coefficient as the maximum value and derive the optimal power allocation scheme based on the standard convex optimization procedure. For the parasitic setup, the weighted sum-rate maximization problem is non-convex, and we resort to the concave–convex procedure to derive a suboptimal solution. Simulation results demonstrate that in the commensal setup, the BD may realize transmission while causing no harmful interference to the primary system; in the parasitic setup, a higher backscatter data rate can be achieved through slightly sacrificing the primary transmission rate.

Joint Data-Energy Beamforming and Traffic Offloading in Cloud Radio Access Networks With Energy Harvesting-Aided D2D Communications

In this paper, a simultaneous wireless information and power transfer-based traffic offloading scheme is proposed for cloud radio access networks with energy harvesting-aided device-to-device (D2D) communications. Wherein, the traffic offloading via D2D communications is utilized to alleviate the heavy burden on the capacity-limited fronthauls, while the energy harvesting design is adopted to stimulate offloading by compensating the energy consumption at the D2D transmitters. Different from the conventional works that harvest energy from ambient radio-frequency signals, dedicated energy signals are designed to provide a more flexible wireless power supply for the D2D transmitters with the data-energy beamforming technique. However, transmitting the dedicated energy signals impairs the performances of the wireless information transfer. To achieve a better balance between the traffic offloading gains and the wireless power transfer costs, a weighted sum-rate maximization problem is formulated. Nevertheless, the binary variables introduced by the offloading decision and the constraints on the data-energy beamforming make the optimization problem non-convex. To solve this intractable problem, a layered optimization method with the iterative optimizing and the ellipsoid method is proposed. Furthermore, an algorithm with lower complexity is proposed with a separated data-energy beamforming design. Simulation results reveal that a significant sum-rate gain can be achieved via the proposed scheme.

A Machine Learning-Based Algorithm for Joint Scheduling and Power Control in Wireless Networks

Wireless network resource allocation is an important issue for designing Internet of Things systems. In this paper, we consider the problem of wireless network capacity optimization that involves issues such as flow allocation, link scheduling, and power control. We show that it can be decomposed into a linear program and a nonlinear weighted sum-rate maximization problem for power allocation. Unlike most traditional methods that iteratively search the optimal solutions of the nonlinear subproblem, we propose to directly compute approximated solutions based on machine learning techniques. Specifically, the learning systems consist of both support vector machines (SVMs) and deep belief networks (DBNs) that are trained based on offline computed optimal solutions. In the running phase, the SVMs perform classification for each link to decide whether to use maximal transmit power or be turned off. At the same time, the DBNs compute an approximation of the optimal power allocation. The two results are combined to obtain an approximated solution of the nonlinear program. Simulation results demonstrate the effectiveness of the proposed machine learning-based algorithm.

Unsupervised Machine Learning-Based User Clustering in Millimeter-Wave-NOMA Systems

Millimeter-wave non-orthogonal multiple access (mm-wave-NOMA) systems exploit the power domain for multiple accesses to further enhance the spectral efficiency. User clustering and power allocation can effectively exploit the potential of NOMA in mm-wave systems. This paper investigates the sum rate maximization problem of mm-wave-NOMA systems under the constraints of the total transmission power and users’ predefined rate requirements. The formulated optimization problem is a non-linear programming problem and, thus, is non-convex and challenging to solve, especially when the number of users becomes large. Sparked by the correlation features of the users’ channels in mm-wave-NOMA systems, we develop a K-means-based machine learning algorithm for user clustering. Moreover, for a practical dynamic scenario where the new users keep arriving in a continuous fashion, we propose a K-means-based online user clustering algorithm to reduce the computational complexity. Furthermore, to further enhance the performance of the proposed mm-wave-NOMA system, we derive the optimal power allocation policy in a closed form by exploiting the successive decoding feature. Simulation results reveal that: 1) the proposed machine learning framework enhances the performance of mm-wave-NOMA systems compared to the conventional user clustering algorithms and 2) the proposed K-means-based online user clustering algorithm provides a comparable performance to the conventional K-means algorithm and strikes a good balance between performance and computational complexity.

Price-Based Resource Allocation in Massive MIMO H-CRANs With Limited Fronthaul Capacity

In this paper, we investigate the bandwidth and power allocation problem in remote radio head cluster (RRHC)-based millimeter wave (mm-wave) massive MIMO heterogeneous cloud radio access networks with limited fronthaul capacity. The coordinated multipoint transmission is applied in each RRHC for cancelling the intra-cluster interference. To avoid the inter-tier interference, distinct bandwidths are allocated to macro base station and RRHs. Following this, we formulate a bandwidth and power allocation optimization problem to maximize the downlink weighted sum rate of the system subject to per-RRHC power and fronthaul capacity constraints, which is a non-convex optimization problem and is difficult to directly solve. Next, we fix the bandwidth allocation and the original problem can be divided into two independent optimization problems, i.e., the weighted sum rate maximization problems of MUs and RRH users, respectively. For the former, the convex optimization technique can be used to solve it. As for the latter, a two-loop iterative algorithm is proposed to deal with it. Specifically, we propose the price-based outer iteration to control the fronthaul capacity and the weighted minimum mean square error-based inner iteration to obtain the power allocation. To this end, a 1-D search method is adopted to find the optimal bandwidth allocation. Finally, numerical results are conducted to verify the effectiveness of the proposed algorithms under different parameters.

The Non-Coherent Ultra-Dense C-RAN Is Capable of Outperforming Its Coherent Counterpart at a Limited Fronthaul Capacity

The weighted sum rate maximization problem of ultra-dense cloud radio access networks (C-RANs) is considered, where realistic fronthaul capacity constraints are incorporated. To reduce the training overhead, pilot reuse is adopted and the transmit beamforming is designed to be robust to the channel estimation errors. In contrast to the conventional C-RAN where the remote radio heads (RRHs) coherently transmit their data symbols to the user, we consider their non-coherent transmission, where no strict phase synchronization is required. By exploiting the classic successive interference cancellation technique, we first derive the closed-form expressions of the individual data rates from each serving RRH to the user and the overall data rate for each user that is not related to their decoding order. Then, we adopt the reweighted l 1 -norm technique to approximate the l 0 -norm in the fronthaul capacity constraints as the weighted power constraints. A low-complexity algorithm based on a novel sequential convex approximation (SCA) algorithm is developed to solve the resultant optimization problem with convergence guarantee. A beneficial initialization method is proposed to find the initial points of the SCA algorithm. Our simulation results show that in the high fronthaul capacity regime, the coherent transmission is superior to the non-coherent one in terms of its weighted sum rate. However, significant performance gains can be achieved by the non-coherent transmission over the coherent one in the low fronthaul capacity regime, which is the case in ultradense C-RANs, where mmWave fronthaul links with stringent capacity requirements are employed.

Distributed Massive MIMO Systems With Non-Reciprocal Channels: Impacts and Robust Beamforming

Hardware calibration is essential to restore the uplink/downlink channel reciprocity for multi-user massive multiple-input multiple-output (MIMO) systems operating in a time division duplexing mode. Unfortunately, due to the associated overhead, calibration cannot be performed frequently; furthermore, any calibration procedure leaves behind a residual mismatch between the uplink and downlink channels. In this paper, we study the effects of these calibration errors on the achievable rates in the downlink of a multi-cell, multi-user, and distributed massive MIMO system. Specifically, we develop accurate, yet simple, lower-bounds on the per-user achievable rate, assuming either zero-forcing (ZF) or matched filtering (MF) are used. We also introduce a performance loss coefficient as a measure of sensitivity of the performance to the calibration errors. Using this measure, we identify the conditions under which ZF precoding is more sensitive to calibration errors than MF. Finally, we consider the robust weighted sum-rate maximization problem to mitigate the degrading effects of non-ideal calibration. Our numerical experiments show that the rate lower-bounds developed in this paper accurately quantify the impacts of non-ideal calibration on performance. Also, the proposed robust beamforming scheme improves the average sum-rate by up to 42% compared with the other available schemes.

Subcarrier and Power Allocation for the Downlink of Multicarrier NOMA Systems

This paper investigates the joint subcarrier and power allocation problem for the downlink of a multi-carrier non-orthogonal multiple access (MC-NOMA) system. A novel three-step resource allocation framework is designed to deal with the sum rate maximization problem. In Step 1, we relax the problem by assuming that each of the users can use all the subcarriers simultaneously. With this assumption, we prove the convexity of the resultant power control problem and solve it via convex programming tools to get a power vector for each user. In Step 2, we allocate the subcarriers to users by a heuristic greedy manner with the obtained power vectors in Step 1. In Step 3, the proposed power control schemes used in Step 1 are applied once more to further improve the system performance with the obtained subcarrier assignment of Step 2. To solve the maximization problem with fixed subcarrier assignments in both Step 1 and Step 3, a centralized power allocation method based on projected gradient descent algorithm and two distributed power control strategies based, respectively, on pseudo-gradient algorithm and iterative waterfilling algorithm are investigated. Numerical results show that our proposed three-step resource allocation algorithm could achieve comparable sum rate performance to the existing near-optimal solution with much lower computational complexity and outperforms power controlled OMA scheme. Besides, a tradeoff between user fairness and sum rate performance can be achieved via applying different user power constraint strategies in the proposed algorithm.

Sum-Rate Maximization Methods for Wirelessly Powered Communication Networks in Interference Channels

In this paper, we study a wireless powered communication network (WPCN) in a general $N$ -user interference channel (IFC), where a hybrid access-point (H-AP) in each cell supports its corresponding user. In this multi-cell environment, the H-AP first sends the energy signal to charge users in the downlink (DL) phase, while in the subsequent uplink (UL) phase, each user transmits its information signal to the corresponding H-AP utilizing the previously harvested energy. For the WPCN in this IFC scenario, cross-link interference occurs due to asynchronous time allocation of the DL and the UL among $N$ cells which significantly affects the overall performance. To handle the interference issue efficiently, we jointly optimize the DL and UL time allocation of each cell as well as the transmit power allocation at the H-APs and the users so that the weighted sum-rate of UL information transmission is maximized. To tackle non-convexity of the weighted sum-rate maximization problem, we propose an iterative algorithm where the time allocation and the transmit power are updated based on the weighted minimum mean square error criteria and the gradient projection method, respectively. Furthermore, we consider two simple protocols where the DL time allocation of each cell is synchronized and present resource allocation method, respectively. In simulation results, we verify that the proposed algorithm for the asynchronous protocol outperforms conventional schemes.

Power Allocation for Underlay Device-to-Device Communication Over Multiple Channels

In underlay device-to-device (D2D) communication, a D2D pair reuses the cellular spectrum, causing interference to existing cellular users. The achieved D2D rate and the added interference to cellular users need to be jointly considered for optimal resource and power allocations. Unlike most existing work which only consider the simplified scenario of assigning each D2D pair a single channel or resource block (RB), we consider multiple RBs from different cellular users can be assigned to each D2D pair. We formulate the problem of optimal power allocation over multiple RBs at the D2D transmitter to maximize the sum-rate of D2D and cellular users, under the D2D transmitter power constraint and minimum signal-to-noise-and-interference ratio (SINR) requirement at each reused RB for all affected cellular users in all cells. To further lower the required overhead in a practical setting, we consider a second optimization problem for power allocation solution to maximize the D2D rate under the same constraints as the sum-rate maximization problem. We obtain the asymptotic power solution for the sum-rate maximization and the semiclosed-form optimal power solution for the D2D rate maximization. Our proposed optimization solutions are applicable to both uplink and downlink cellular spectrum sharing as well as to mutlicell with multiple D2D pairs scenarios. Our simulation studies demonstrates the effectiveness of the two proposed methods for both uplink and downlink resource sharing, and further shed light into how the maximum rate is impacted by the system parameters such as available D2D transmit power, number of D2D pairs, minimum SINR requirements, and the cell size.

Power Games for Secure Communications in Single-Stream MIMO Interference Networks

We propose a distributed interference management method for a single-stream MIMO interference network that is tapped by an external eavesdropper. Along with its information signal, each legitimate transmitter creates a bogus signal, known as transmit-based friendly jamming (TxFJ), to confuse the eavesdropper. Although generating TxFJ protects the link from eavesdropping, it creates interference at other unintended but legitimate links. Using non-cooperative game theory, we design a distributed method for maximizing the sum of secrecy rates. Each link is a player in the game. It seeks to maximize its secrecy rate subject to a given information-rate constraint and power budget. The strategy profile of each player is to control the amount of TxFJ it generates. Because a pure non-cooperative game may not have Nash equilibria that result in (Pareto-)optimal secrecy sum-rate, we propose a modified price-based game, in which each link is penalized for generating interference on other legitimate links. Under the exact knowledge of eavesdropping channels, we show that the price-based game has a comparable secrecy sum-rate to a centralized approach. We then relax the assumption of knowledge of eavesdropping channels and leverage mixed-strategy games to provide robust solutions to the distributed secrecy sum-rate maximization problem.

Topology Adaptive Sum Rate Maximization in the Downlink of Dynamic Wireless Networks

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

Joint optimization of channel allocation and power control for cognitive radio networks with multiple constraints

With the increasing demands for wireless communication, efficiently using the spectrum resource has always been an important research topic. In this paper, the problem of N pairs of Secondary Users (SU) sharing K available channels with M Primary Users and its associated problem of optimal channel allocation and power control are studied. To investigate a joint channel and power allocation for the underlay-based Cognitive Radio Networks, a sum-rate maximization problem of the SU with consideration of Quality of Service and the constraints of interference temperature and outage probability is formulated as a Mixed Integer Nonlinear Programming problem. To solve it, the objective function is divided into two sub-optimization problems: channel allocation and power control. First of all, we propose to use the Genetic Algorithm Channel Allocation algorithm (GACA) to solve the channel allocation optimization problem and get the optimal channel allocation strategy. The power control optimization is then followed, but the problem is a fractional form of function with coupling constraints that is non-convex and cannot be solved directly with convex optimization. To this end, when the SINR is sufficiently high, we obtain optimal power control strategy by introducing Geometric Programming and auxiliary variables to convert non-convex to Convex Geometric Programming. When SINR is the medium to low value, we use an iterative algorithm known as the Single Condensation Method to solve it. Finally, through our proposed iterative algorithm, that is, Joint Optimization Algorithm (JOA), the optimal solution is obtained. Moreover, the convergence and complexity of the algorithm are analyzed. The time complexity of JOA in the worst case is $$O(N^{3} \sqrt N )$$. In order to show the generality of the channel state, in the simulation part, we design a perfect CSI optimal solution scenario and a imperfect CSI sub-optimal solution scenario. Simulation results show that the proposed algorithm can achieve better performance under different CSI states.

Efficient Transmit Covariance Design in MIMO Interference Channel

This paper is concerned with the sum-rate maximization problem in multiple-input multiple-output interference channels. We design the transmit covariance matrix, associated with each transmitter to maximize the sum-rate of the network when the number of data symbols is unknown. The design problem is nonconvex and then hard to be solved. We devise a semidistributed design methodology according to majorization–minimization technique to deal with the optimization problem. This leads us to a convex quadratically constrained quadratic program at each iteration with a semiclosed form solution. The proposed method is computationally efficient and converges to stationary points of the problem. The fact that the method is based on a distributed processing framework along with relatively low computational burden ( $\mathcal{O}(\max \lbrace M,N\rbrace ^{2.3})$ per iteration with $M$ and $N$ being the number of antennas at the transmitter and receiver, respectively) for the user terminals can make it a potential candidate for practical implementations. Finally, we extend the proposed method for the case with channel estimation errors. Simulation results verify the efficiency of the proposed method, and in a typical scenario we have around 60 times lower run-time when compared to the counterparts of the method.

Enhanced Power Allocation for Sum Rate Maximization in OFDM-NOMA VLC Systems

This letter investigates the power allocation problem for the downlink of an orthogonal frequency division multiple (OFDM)-based non-orthogonal multiple access visible light communication (NOMA-VLC) system. Unlike the commonly used two-user OFDM-NOMA VLC model, in this letter, an arbitrary number of multiplexed users is considered when formulating the sum rate maximization problem. Moreover, both user-level and subcarrier-level power optimization are taken into account to improve the system performance. The corresponding theoretical analysis is presented, with the consideration of optimal decoding order and each user's required signal-to-interference-plus-noise ratio (SINR). Given a fixed power constraint on each OFDM subcarrier, we derive the optimal power ratio for an arbitrary number of multiplexed users in OFDM-NOMA VLC. Numerical results show that with a required SINR of 3 dB, the proposed enhanced power allocation algorithm has 39.59% and 40.11% sum-rate enhancement when compared to optimized fixed power allocation algorithm and gain ratio power allocation algorithm, respectively.

Optimal Beamforming and Time Allocation for Partially Wireless Powered Sensor Networks With Downlink SWIPT

Wireless powered sensor networks (WPSNs) have emerged as a key development towards the future self-sustainable Internet of Things (IoT) networks. To achieve a good balance between self-sustainability and reliability, partially WPSNs with a mixed power solution are desirable for practical applications. Specifically, most of the sensor nodes are wireless powered but the key sensor node adopts traditional wire/battery power for reliability. As a result, this paper mainly investigates optimal design for the partially WPSNs in which simultaneous wireless information and power transfer (SWIPT) is adopted in the downlink. Two scenarios with space division multiple access (SDMA) and time division multiple access (TDMA) in the uplink are considered. For both the SDMA-enabled and TDMA-enabled partially WPSNs, joint design of downlink beamforming, uplink beamforming and time allocation is investigated to maximize the uplink sum rate while guaranteeing the quality-of-service (i.e., satisfying the downlink rate constraint) at the key sensor node. After analyzing the feasibility of uplink sum rate maximization problems and the influence of the downlink rate constraint, semi-closed-form optimal solutions for both SDMA-enabled and TDMA-enabled WPSNs are proposed with guaranteed global optimality. Complexity analysis is also provided to justify the advantage of the proposed solutions in low complexity. The effectiveness and optimality of the proposed optimal solutions are finally demonstrated by simulations.