Suboptimal Algorithm Research Articles

Novel Receive Antenna Selection Scheme for Precoding-Aided Spatial Modulation with Lattice Reduction.

In this paper, a new receive antenna subset (RAS) selection scheme is proposed for precoding-aided spatial modulation (PSM). First, a lattice reduction (LR)-based precoder is employed instead of a conventional zero-forcing (ZF) precoder. It is analytically shown that a full diversity gain can be achieved by the LR-based ZF precoder without RAS selection. Then, an optimal LR-based RAS selection criterion is derived for the over-determined LR-based PSM systems, and a suboptimal selection algorithm is additionally presented. It is also shown that optimal and suboptimal RAS selection algorithms based on LR improve the BER performance of the LR-based PSM system. Further, the overall diversity order of the over-determined LR-based PSM systems with optimal LR-based RAS selection is analyzed. Finally, diversity analysis and simulation results show that the LR-ZF-based PSM system with optimal LR-based RAS selection outperforms the conventional ZF-based PSM system with conventional optimal RAS selection.

Transmit or Backscatter: Communication Mode Selection for Narrowband IoT Systems

Backscatter communication is an energy-efficient communication technique for the Internet of things (IoT) devices. It enables data transmission by reflecting the incident radio signals. In this paper, we propose a communication mode selection scheme for IoT devices that can communicate using either active transmission or backscattering. In the active transmission mode, the IoT devices can transmit data over narrowband subcarriers using power-domain non-orthogonal multiple access (NOMA). In the backscattering mode, which operates over shorter distance than active transmission, nearby user equipment (UE) devices are used as relays. The UEs receive the backscattered signals from the IoT devices and forward them to the base station. We formulate a connection density maximization problem to select the communication mode used by each IoT device. We determine the IoT device pairing for active transmission mode with NOMA and UE-IoT device association for backscattering mode. The formulated problem is a binary integer programming problem. Although it can be solved optimally, the optimal algorithm incurs exponential computational complexity. Hence, we propose a low-complexity suboptimal algorithm to solve this problem. Results show that our proposed algorithm can enhance the connection density of narrowband IoT systems by up to 64% when compared with using single communication mode.

Subcarrier‐pair based joint resource allocation for secure cooperative OFDM system with multiple untrusted AF relays

In this paper, a cooperative orthogonal frequency division multiplexing system with multiple untrusted amplify-and-forward relays is considered. Since there exists no direct link between source and user because of the shadowing effect, a positive system secrecy rate cannot be obtained. Addressing this issue, a cooperative user-aided jamming technique to improve the secrecy performance is employed. With the aim of maximising the system secrecy rate, a joint resource allocation problem of power allocation, relay assignment and subcarrier pairing is formulated. By using the signal-to-noise ratio based approach and the Lagrange dual method, this nonconvex problem is solved efficiently. To reduce the calculation complexity, a suboptimal algorithm, which decouples the power allocation with the relay assignment and subcarrier pairing is further proposed. Numerical results are presented to demonstrate the advantage of the two proposed algorithms and the impact of relays' location and number of subcarriers on the secrecy performance. Moreover, it is shown that the secrecy performance of the proposed scheme is decreased with respect to the number of untrusted relays.

Delayed Reward Bernoulli Bandits: Optimal Policy and Predictive Meta-Algorithm PARDI

Bernoulli multi-armed bandits are a reinforcement learning model used to optimize the sequences of decisions with binary outcomes. Well-known bandit algorithms, including the optimal policy, assume that before a decision is made the outcomes of previous decisions are known. This assumption is often not satisfied in real-life scenarios. As demonstrated in this article, if decision outcomes are affected by delays, the performance of existing algorithms can be severely affected. We present the first practically applicable method to compute statistically optimal decisions in the presence of outcome delays. Our method has a predictive component abstracted out into a meta-algorithm, predictive algorithm reducing delay impact (PARDI), which significantly reduces the impact of delays on commonly used algorithms. We demonstrate empirically that PARDI-enhanced Whittle index is nearly optimal for a wide range of Bernoulli bandit parameters and delays. In a wide spectrum of experiments, it performed better than any other suboptimal algorithm, e.g., UCB1-tuned and Thompson sampling. PARDI-enhanced Whittle index can be used when computational requirements of the optimal policy are too high.

Cylindrical Massive MIMO System with Low-Complexity Angle-Based User Selection for High-Altitude Platform Stations

High-altitude platform stations (HAPSs) are recognized as a promising technology for coverage extension in the sixth generation (6G) mobile communications and beyond. The purpose of this study is to develop a HAPS system with a coverage radius of 100km and high capacity by focusing on the following two aspects: array antenna structure and user selection. HAPS systems must jointly use massive multiple-input multiple-output (mMIMO) and multiuser MIMO techniques to increase their capacity. However, the coverage achieved by a conventional planar array antenna is limited to a circular area with a radius of only tens of kilometers. A conventional semi-orthogonal user selection (SUS) scheme based on the orthogonality of channel vectors achieves high capacity, but it has high complexity. First, this paper proposes a cylindrical mMIMO system to achieve an ultra-wide coverage radius of 100km and high capacity. Second, this paper presents a novel angle-based user selection (AUS) scheme, where a user selection problem is formulated as a maximization of the minimum angular difference between users over all user groups. Finally, a low-complexity suboptimal algorithm (SA) for AUS is also proposed. Assuming an area with a 100km radius, simulation results demonstrate that the proposed cylindrical mMIMO system improves the signal-to-interference-plus-noise ratio by approx. 12dB at the boundary of the area, and it achieves approx. 1.5 times higher capacity than the conventional mMIMO which uses a planar array antenna. In addition, the results show that the proposed AUS scheme improves the lower percentiles in the system capacity distribution compared with SUS and basic random user selection. Furthermore, the computational complexity of the proposed SA is in the order of only 1/4000 that of SUS.

An Efficient Riemannian Gradient Based Algorithm for Max-Cut Problems

The max-cut problem addresses the problem of finding a cut for a graph that splits the graph into two subsets of vertices so that the number of edges between these two subsets is as large as possible. However, this problem is NP-Hard, which may be solved by suboptimal algorithms. In this paper, we propose a fast and accurate Riemannian optimization algorithm for solving the max-cut problem. To do so, we develop a gradient descent algorithm and prove its convergence. Our simulation results show that the proposed method is extremely efficient on some already-investigated graphs. Specifically, our method is on average 50 times faster than the best well-known techniques with slightly losing the performance, which is on average 0.9729 of the max-cut value of the others.

A Fast Method for the Selection of Samples in Populations with Available Genealogical Data

Optimal selection of samples in populations should provide the best coverage of sample variations for the available sampling resources. In populations with known genealogical connections, or pedigrees, this amounts to finding the set of samples with the largest sum of mutual distances in a genealogical tree. We present an optimal, and a faster sub-optimal, method for the selection of K samples from a population of N individuals. The optimal method works in time proportional to NK2, and the sub-optimal in time proportional to NK, which is more practical for large populations. The sub-optimal algorithm can process pedigrees of millions of individuals in a matter of minutes. With the real-life pedigrees, the difference in the quality of the output of the two algorithms is negligible. We provide the Python3 source codes for the two methods.

Optimal Resource Allocation Design for Large IRS-Assisted SWIPT Systems: A Scalable Optimization Framework

In this paper, we study the optimal resource allocation algorithm design for large intelligent reflecting surface (IRS)-assisted simultaneous wireless information and power transfer (SWIPT) systems. To facilitate efficient system design for large IRSs, instead of jointly optimizing all the IRS elements, we partition the IRS into several tiles and employ a scalable optimization framework comprising an offline design stage and an online optimization stage. In the offline stage, the IRS elements of each tile are jointly designed to support a set of different phase shift configurations, referred to as transmission modes, while the best transmission mode is selected from the set for each tile in the online stage. Given a transmission mode set, we aim to minimize the total base station (BS) transmit power by jointly optimizing the beamforming and the transmission mode selection policy taking into account the quality-of-service requirements of information decoding and non-linear energy harvesting receivers, respectively. Although the resource allocation algorithm design is formulated as a non-convex combinatorial optimization problem, we solve it optimally by applying the branch-and-bound (BnB) approach which entails a high computational complexity. To strike a balance between optimality and computational complexity, we also develop an efficient suboptimal algorithm capitalizing on the penalty method and successive convex approximation. Our simulation results show that the proposed designs enable considerable power savings compared to several baseline schemes. Moreover, our results reveal that by properly adjusting the numbers of tiles and transmission modes, the proposed scalable optimization framework indeed facilitates online design for large IRSs. Besides, our results confirm that the advocated physics-based model and scalable optimization framework enable a flexible trade-off between performance and complexity, which is vital for realizing the performance gains promised by large IRS-assisted communication systems in practice.

Wideband beamspace squint user grouping algorithm based on subarray collaboration

The beam direction constrained problem is one of the important issues to be solved in millimeter-wave (mmWave) wideband communications when serving multi-user with squint beams whose direction varies with frequency. In this paper, we improve the number of simultaneous users served by collaboratively transmitting squint beams among multi-subarray at the base station (BS) end in a downlink multi-user line-of-sight (LoS) scenario, and reduce the interference among co-channel squint beams by a beam domain approach. The optimization problem of maximizing the number of users served in the system by transmitting beams in the two-dimensional beamspace of the planar antenna subarray is proposed and its suboptimal algorithm is given. Finally, the feasibility of the proposed method and the performance of the proposed algorithm are verified by numerical simulations.

Resting State Functional Connectivity Analysis During General Anesthesia: A High-Density EEG Study

The depth of anesthesia monitoring is helpful to guide administrations of general anesthetics during surgical procedures,however, the conventional 2-4 channels electroencephalogram (EEG) derived monitors have their limitations in monitoring conscious states due to low spatial resolution and suboptimal algorithm. In this study, 256-channel high-density EEG signals in 24 subjects receiving three types of general anesthetics (propofol, sevoflurane and ketamine) respectively were recorded both before and after anesthesia. The raw EEG signals were preprocessed by EEGLAB 14.0. Functional connectivity (FC) analysis by traditional coherence analysis (CA) method and a novel sparse representation (SR) method. And the network parameters, average clustering coefficient (ACC) and average shortest path length (ASPL) before and after anesthesia were calculated. The results show SR method find more significant FC differences in frontal and occipital cortices, and whole brain network (p<0.05). In contrast, CA can hardly obtain consistent ASPL in the whole brain network (p>0.05). Further, ASPL calculated by SR for whole brain connections in all of three anesthesia groups increased, which can be a unified EEG biomarker of general anesthetics-induced loss of consciousness (LOC). Therefore FC analysis based on SR analysis has better performance in distinguishing anesthetic-induced LOC from awake state.

Optimal Resource Allocation for Multi-User OFDMA-URLLC MEC Systems

In this paper, we study resource allocation algorithm design for multi-user orthogonal frequency division multiple access (OFDMA) ultra-reliable low latency communication (URLLC) in mobile edge computing (MEC) systems. To meet the stringent end-to-end delay and reliability requirements of URLLC MEC systems, we propose joint uplink-downlink resource allocation and finite blocklength transmission. Furthermore, we employ a partial time overlap between the uplink and downlink frames to minimize the end-to-end delay, which introduces a new time causality constraint. The proposed resource allocation algorithm is formulated as an optimization problem for minimization of the total weighted power consumption of the network under a constraint on the number of URLLC user bits computed within the maximum allowable computation time, i.e., the end-to-end delay of a computation task. Despite the non-convexity of the formulated optimization problem, we develop a globally optimal solution using a branch-and-bound approach based on discrete monotonic optimization theory. The branch-and-bound algorithm minimizes an upper bound on the total power consumption until convergence to the globally optimal value. Furthermore, to strike a balance between computational complexity and performance, we propose two efficient suboptimal algorithms based on successive convex approximation and second-order cone techniques. Our simulation results reveal that the proposed resource allocation algorithm design facilitates URLLC in MEC systems, and yields significant power savings compared to three baseline schemes. Moreover, our simulation results show that the proposed suboptimal algorithms offer different trade-offs between performance and complexity and attain a close-to-optimal performance at comparatively low complexity.

Proportionally Fair Robust Beamforming for Multicast Multibeam Satellite Systems

We study the proportionally fair beamforming design for the downlink channel of multibeam satellite systems. Due to the channel phase uncertainty caused by oscillators and the long round trip delay, the beamforming design needs to be robust to the imperfect channel state information (CSI). We formulate an optimization problem with outage probability and per-antenna power constraints to maximize the weighted sum utility of the signal-to-interference-plus-noise (SINR) threshold to realize proportional fairness. The optimization problem is non-convex and we transformed it into the convex form through accurate approximation. We solve the optimization problem via a proposed sub-optimal algorithm. The simulation result shows that the proposed algorithm can converge within 30 iterations when the number of beams is 9. Furthermore, the proposed beamforming scheme obtains a 10% average performance gain in weighted sum utility over the robust max-min schemes in different levels of phase uncertainty.

Residential Demand Response-Based Load-Shifting Scheme to Increase Hosting Capacity in Distribution System

Increasing the use of solar photovoltaic (PV) generation in order to decarbonize the electric energy system results in many challenges. Overvoltage is one of the most common problems in distribution systems with high penetration of solar PV. Utilizing demand-side resources such as residential demand response (RDR) have the potential to alleviate this problem. To increase the solar PV hosting capacity, we propose an RDR based load-shifting scheme that utilizes the interaction between the distribution system operator (DSO) and demand-side resources. We first model a customer utility that consists of the cost of purchasing power, revenue from the subsidy, and discomfort due to load shifting. When an overvoltage problem is expected, DSO issues a local subsidy, and customers in the distribution system move their load in response. An optimization framework that minimizes the additional cost due to the subsidy while keeping the voltages in a prescribed range is proposed. Because of the non-linearity of the power flow analysis, we propose a sub-optimal algorithm to obtain a subsidy, prove the performance gap between the optimal subsidy and the subsidy obtained by the algorithm. A case study shows that the proposed RDR scheme increases the hosting capacity to almost its theoretical limit at a lower cost than the curtailment method.

Sum-Rate Optimization for IRS-Aided D2D Communication Underlaying Cellular Networks

The intelligent reflective surface (IRS)-assisted communication is recognized as a promising technology to enhance capacity and coverage of the network by controlling propagation. However, in IRS-aided device-to-device (D2D) communication underlaying cellular networks, co-channel interference is more complicated and challenging than traditional D2D communication networks due to the addition of extra reflection paths. To alleviate interference and improve sum-rate, we establish a multivariable resource optimization function based on IRS-assisted D2D communication in this paper. Then, a hybrid sub-optimal algorithm is proposed, which includes channel allocation based on game theory, power allocation and phase shift optimization. Specifically, a sub-channel allocation algorithm is introduced to maximize system sum-rate, where the problem of sub-channel allocation is formulated as a two-stage combinatorial auction model, and the approximate optimal solution is obtained by bidding and adjustment. Then, a sub-optimal solution for transmission power and phase shifts is obtained through local search and iterative optimization. Simulation results show that the proposed hybrid sub-optimal algorithm is feasible and effective. Compared to the other 5 schemes, it has best sum-rate performance and lowest power consumption.

Joint scheduling and resource allocation for federated learning in SWIPT-enabled micro UAV swarm networks

Micro-UAV swarms usually generate massive data when performing tasks. These data can be harnessed with various machine learning (ML) algorithms to improve the swarm's intelligence. To achieve this goal while protecting swarm data privacy, federated learning (FL) has been proposed as a promising enabling technology. During the model training process of FL, the UAV may face an energy scarcity issue due to the limited battery capacity. Fortunately, this issue is potential to be tackled via simultaneous wireless information and power transfer (SWIPT). However, the integration of SWIPT and FL brings new challenges to the system design that have yet to be addressed, which motivates our work. Specifically, in this paper, we consider a micro-UAV swarm network consisting of one base station (BS) and multiple UAVs, where the BS uses FL to train an ML model over the data collected by the swarm. During training, the BS broadcasts the model and energy simultaneously to the UAVs via SWIPT, and each UAV relies on its harvested and battery-stored energy to train the received model and then upload it to the BS for model aggregation. To improve the learning performance, we formulate a problem of maximizing the percentage of scheduled UAVs by jointly optimizing UAV scheduling and wireless resource allocation. The problem is a challenging mixed integer nonlinear programming problem and is NP-hard in general. By exploiting its special structure property, we develop two algorithms to achieve the optimal and suboptimal solutions, respectively. Numerical results show that the suboptimal algorithm achieves a near-optimal performance under various network setups, and significantly outperforms the existing representative baselines. considered.

MMSE-Optimal Sequential Processing for Cell-Free Massive MIMO With Radio Stripes

Cell-free massive multiple-input-multiple-output (mMIMO) is an emerging technology for beyond 5G with its promising features such as higher spectral efficiency and superior spatial diversity as compared to conventional multiple-input-multiple-output (MIMO) technology. The main working principle of cell-free mMIMO is that many distributed access points (APs) cooperate simultaneously to serve all the users within the network without creating cell boundaries. This paper considers the uplink of a cell-free mMIMO system utilizing the radio stripe network architecture with a sequential fronthaul between the APs. A novel uplink sequential processing algorithm is developed, which is proved to be optimal in both the maximum spectral efficiency (SE) and the minimum mean square error (MSE) sense. A detailed quantitative analysis of the fronthaul requirement or signaling of the proposed algorithm and its comparison with competing sub-optimal algorithms is provided. Key conclusions and implications are summarized in the form of corollaries. Based on the analytical and numerical simulation results, we conclude that the proposed scheme can significantly reduce the fronthaul signaling, without compromising the communication performance.

Fast optimal and bounded suboptimal Euclidean pathfinding

We consider optimal and suboptimal algorithms for the Euclidean Shortest Path Problem (ESPP) in two dimensions. For optimal path planning, Our approach leverages ideas from two recent works: Polyanya, a mesh-based ESPP planner which we use to represent and reason about the environment, and Compressed Path Databases (CPD), a speedup technique for pathfinding on grids and spatial networks, which we exploit to efficiently compute candidate paths, in order to construct a completely novel ESPP algorithm, End Point Search (EPS). In a range of experiments and empirical comparisons we show that: (i) the auxiliary data structures required by EPS are cheap to build and store; (ii) for optimal search, the new algorithm is faster than a range of recent ESPP planners, with speedups ranging from several factors to over one order of magnitude; (iii) for anytime search, where feasible solutions are needed fast, we report even better performance. For suboptimal path planning, we extend the CPD such that it computes and compresses first move data of a larger number of selected candidate nodes covering every point in the Euclidean space. Our approach is search-free, simultaneously fast, and returns a path within a fixed bound of the optimal solution. In a range of empirical results, we show that: (i) our approach outperforms both offline/online optimal and suboptimal ESPP algorithms proposed in the literature; (ii) our approach demonstrates excellent path quality, better than all existing suboptimal ESPP algorithms; and (iii) the approach offers flexibility by allowing a trade-off between the CPD construction cost (space and time) and the suboptimality bound.

Dynamic Programming Algorithms for Computing Optimal Knockout Tournaments

We study competitions structured as hierarchically shaped single-elimination tournaments. We define optimal tournaments by maximizing attractiveness such that the topmost players will have the chance to meet in higher stages of the tournament. We propose a dynamic programming algorithm for computing optimal tournaments and we provide its sound complexity analysis. Based on the idea of the dynamic programming approach, we also develop more efficient deterministic and stochastic sub-optimal algorithms. We present experimental results obtained with the Python implementation of all the proposed algorithms regarding the optimality of solutions and the efficiency of the running time.

Power and Rate Adaptive Pushing Over Fading Channels

Proactive caching is capable of reducing access latency and improving network throughput, thereby attracting attention from both industry and academia. However, the energy efficiency (EE) of proactive caching over fading channels has not been well studied yet. In this paper, we aim at presenting an energy-efficient content pushing policy by carefully adapting the transmit rate and power in pushing and on-demand delivery phases, while assuring delivery delay constraints. To this end, the average delay, delay-outage probability, and EE are analyzed for a general adaptive pushing policy based on the saddle point approximation. According to these results, we formulate EE maximization problems under average delay and delay-outage constraints, respectively, for two special types of adaptive pushing policies, namely, opportunistic pushing and water-filling-based pushing. Due to the high complexity of the grid search for solving the formulated problems, suboptimal algorithms are presented based on gradient descent and golden section search methods. Moreover, we mathematically derive the scaling property and numerically obtain request probability and delivery delay thresholds for content pushing. Simulations show that the presented pushing policies achieve higher EE than on-demand transmissions, especially when the content item is popular and the tolerable delivery delay is small.

Towards Cost Minimization for Wireless Caching Networks With Recommendation and Uncharted Users’ Feature Information

Caching popular contents at the network edge has been considered as a promising enabler to relieve the pressure on networks due to the fact that a substantial portion of global data traffic is repeatedly requested by many subscribers and thus redundantly generated. Recommendation, on the other hand, has attracted spiraling attention for its capability of reshaping users’ contents demand patterns. In this paper, we examine the practicability of recommendation in boosting the gains of edge caching with uncharted users’ feature information. To this end, we first characterize the average system cost for a generic network model, disclosing its dependence on the recommendation and caching strategies. Then, we formulate the joint caching and recommendation decision oriented cost minimization problem, taking the constraints on each content provider’s cache capacity budget, each individual user’s recommendation size and recommendation quality into account. However, the implicit information regarding users’ preference makes the problem inextricable. To address this issue, a versatile long short term memory (LSTM) network assisted prediction paradigm is proposed to attain the preference schema of users with the assistance of their historical behavior data. Based on that, we rigorously prove the NP-hardness of obtaining the optimal recommendation and caching policies that jointly minimize the system cost. Therewith, an iterative suboptimal algorithm is developed, which has provable polynomial time complexity and convergence guarantee. Extensive simulation results validate the effectiveness of our proposed LSTM enabled feature information prediction approach and the convergence performance of the devised joint decision making methodology. In addition, it is shown that the proposed scheme outperforms numerous benchmarks significantly.