Total Achievable Rate Research Articles

Coalitional game based sub-channel allocation for full-duplex-enabled mmWave IAB network in B5G

The combination of full-duplex communication (FDC) and millimeter wave integrated access and backhaul (mmWave IAB) is a highly implementable technique in beyond 5G era. However, the technical challenges such as sub-channel allocation and interference coordination must be effectively handled before the attractive benefits of the commercial implementation are enjoyed. In particular, the multiple user interference (MUI) is caused when the same sub-channel is shared by the concurrent links sourced from different transmitters, and the residual self-interference (RSI) is resulted from self-interference cancellation (SIC) in FDC. To this end, a coalitional game based sub-channel allocation mechanism is proposed for the full-duplex-enabled mmWave IAB network to maximize the total actual achievable rate. Firstly, the maximization problem of the sum actual achievable rate is formulated as an integer non-convex and non-linear optimization problem under the constriction of MUI and RSI, which is a non-polynomial hard (NP-Hard) problem, and it is intractable to obtain the optimal solution. Secondly, the formulated problem is transformed as a cooperative game, and a coalitional game based sub-channel allocation (CGSA) algorithm with lower computational complexity is proposed to obtain the sub-optimal solution. Thirdly, the properties of the proposed CGSA algorithm are discussed and analyzed from the aspects of stability and convergence, and it is proved that the final coalition partition formed by the proposed CGSA algorithm is Nash-stable. Fourthly, the proposed CGSA algorithm is compared with the reference algorithms under different scenarios, extensive simulations show that superiorities of the proposed CGSA algorithm are obvious including sum of achievable rate, number-ratio of user equipments with satisfied quality-of-service (QoS)requirement and iterations of algorithm convergence.

Impact of Channel Aging on Dual-Function Radar-Communication Systems: Performance Analysis and Resource Allocation

In conventional dual-function radar-communication (DFRC) systems, the radar and communication channels are routinely estimated at fixed time intervals based on their worst-case operation scenarios. Such situation-agnostic repeated estimations cause significant training overhead and dramatically degrade the system performance, especially for applications with dynamic sensing/communication demands and limited radio resources. In this paper, we leverage the channel aging characteristics to reduce training overhead and to design a situation-dependent channel re-estimation interval optimization-based resource allocation in a multi-target tracking DFRC system. Specifically, we exploit the channel temporal correlation to predict radar and communication channels for reducing the need for training preamble retransmission. Then, we characterize the channel aging effects on the Cramer-Rao lower bounds (CRLBs) for radar tracking performance analysis and achievable rates with maximum ratio transmission (MRT) and zero-forcing (ZF) transmit beamforming for communication performance analysis. In particular, the aged CRLBs and achievable rates are derived as closed-form expressions with respect to the channel aging time, bandwidth, and power. Based on the analyzed results, we optimize these factors to maximize the average total aged achievable rate subject to individual target tracking precision demand, communication rate requirement, and other practical constraints. Since the formulated problem belongs to a non-convex problem, we develop an efficient one-dimensional search based optimization algorithm to obtain its suboptimal solutions. Finally, simulation results are presented to validate the correctness of the derived theoretical results and the effectiveness of the proposed allocation scheme.

Deep Reinforcement Learning for Practical Phase-Shift Optimization in RIS-Aided MISO URLLC Systems

We study the joint active/passive beamforming and channel blocklength (CBL) allocation in a non-ideal reconfigurable intelligent surface (RIS)-aided ultra-reliable and low-latency communication (URLLC) system. The considered scenario is a finite blocklength (FBL) regime and the problem is solved by leveraging a deep reinforcement learning (DRL) algorithm named twin-delayed deep deterministic policy gradient (TD3). First, assuming an industrial automation system, the signal-to-interference-plus-noise ratio and achievable rate in the FBL regime are identified for each actuator. Next, the joint active/passive beamforming and CBL optimization problem is formulated where the objective is to maximize the total achievable FBL rate in all actuators, subject to non-linear amplitude response at the RIS elements, BS transmit power budget and total available CBL. Since the formulated problem is highly non-convex and non-linear, we resort to employing an actor-critic policy gradient DRL algorithm based on TD3. The considered method relies on interacting RIS with the industrial automation environment by taking actions which are the phase shifts at the RIS elements, CBL variables, and BS beamforming to maximize the expected observed reward, i.e., the total FBL rate. We assess the performance loss of the system when the RIS is non-ideal, i.e., with non-linear amplitude response, and compare it with ideal RIS without impairments. The numerical results show that optimizing the RIS phase shifts, BS beamforming, and CBL variables via the TD3 method with deterministic policy outperforms conventional methods and it is highly beneficial for improving the network total FBL rate considering finite CBL size.

Research on RIS-assisted PMU Short Packet Communication for Active Distribution Networks

Phasor measurement unit (PMU) has been widely applied in power grids. For these applications, there is usually a high requirement for data transmission delay and reliability; therefore, short data packets are often used for transmission. Therefore, a downlink communication model for reconfigurable intelligent surface (RIS) assisted multi-PMU is proposed in the paper. We propose a joint optimization problem to maximize the total achievable system rate. Finally, an alternate iterative algorithm based on SCA is proposed. The results show that the proposed algorithm can effectively improve the sum rate.

Performance Analysis of Non-Orthogonal Multiple Access (NOMA) Enabled Cloud Radio Access Networks

In this paper, the performance analysis of non-orthogonal multiple access (NOMA) in a cloud radio access networks (C-RAN) is carried out. The problem of jointly optimizing user association, muting and power-bandwidth allocation is formulated for NOMA-enabled C-RANs. To solve the mixed integer programming problem, the joint problem is decomposed into two subproblems as 1) user association and muting 2) power-bandwidth allocation optimization. To deal with the first subproblem, we propose a centralized and heuristic algorithm to obtain a feasible solution to the remote radio head (RRH) muting problem for given bandwidth and transmit power. The second subproblem is then reformulated for tractibility purpose and a low-complexity algorithm is proposed to bandwidth and power allocation subject to users data rate constraints. Moreover, for given user association and muting states, the near optimal power allocation is derived in a closed-form expression. Simulation results show that the proposed NOMA-enabled C-RAN outperforms orthogonal multiple access (OMA)-based C-RANs in terms of total achievable rate, interference mitigation and can achieve significant fairness improvement.

A Novel Design of Hybrid Precoder in Multicell, Multiuser Heterogeneous Network

In this paper, the precoders capable of maximizing the weighted sum rate (WSR) for multicell scenarios under power budget constraint conditions per cell were considered based on the MIMO system performance. This rate is strongly affected by intracellular interference, so we recommend using coordinated beam forming (CBF). If the number of user equipment provided per cell is small enough to the number of transmit antennas, simple linear beamformers, such as matched filter (MF), achieve higher performance rates. In general cases, two algorithms meet the best performance in terms of the total achievable rate; these are KG (Kim Giannakis) and WSMSE (Weighted Sum Minimum Squared Error) algorithms. In the KG method, the objective function of the sum rate is suggested to be divided into two functions, one function for the desired user rate and the other for the sum rate of the remaining users. In the WSMSE algorithm, maximizing the sum rate is solved by redefining it as the problem of minimizing the MSE (Mean Square Error) function. These functions are convex and nonconvex functions, respectively. In the proposed method, the WSMSEs were modified to reduce the complexity and calculations to provide optimal performance in beamforming.

Hybrid Beamforming and Adaptive RF Chain Activation for Uplink Cell-Free Millimeter-Wave Massive MIMO Systems

In this work, we investigate hybrid analog–digital beamforming (HBF) architectures for uplink cell-free (CF) millimeter-wave (mmWave) massive multiple-input multiple-output (MIMO) systems. We first propose two HBF schemes, namely, decentralized HBF (D-HBF) and semi-centralized HBF (SC-HBF). In the former, both the digital and analog beamformers are generated independently at each AP based on the local channel state information (CSI). In contrast, in the latter, only the digital beamformer is obtained locally at the access point (AP), whereas the analog beamforming matrix is generated at the central processing unit (CPU) based on the global CSI received from all APs. We show that the analog beamformers generated in these two HBF schemes provide approximately the same achievable rates despite the lower complexity of D-HBF and its lack of CSI requirement. Furthermore, to reduce the power consumption, we propose a novel adaptive radio frequency (RF) chain-activation (ARFA) scheme, which dynamically activates/deactivates RF chains and their connected analog-to-digital converters (ADCs) and phase shifters (PSs) at the APs based on the CSI. For the activation of RF chains, low-complexity algorithms are proposed, which can achieve significant improvement in energy efficiency (EE) with only a marginal loss in the total achievable rate.

Low-Complexity Antenna Selection and Discrete Phase-Shifts Design in IRS-Assisted Multiuser Massive MIMO Networks

We propose two novel antenna selection (AS) and discrete phase-shifts design (PSD) schemes for use in intelligent reflecting surface (IRS) assisted multiuser massive multiple-input multiple-output (mMIMO) networks. The first AS and PSD method aims at maximizing the gain of the channels; while the second method is an iterative sum-rate maximization (ISM) scheme that aims at maximizing the total achievable rate. For the AS part, we demonstrate that the ISM method achieves near optimal performance with much lower complexity compared to benchmark AS schemes, and can be utilized with any precoder at the mMIMO base station. For the PSD, our proposed successive-refinement optimization methods are not only efficient, but their complexities scale linearly with the number of elements at the IRS, making them highly attractive when dealing with large surfaces. A thorough complexity analysis for the proposed methods is carried out in terms of the number of floating point operations required for their implementations. Finally, extensive numerical results are provided and some key points are highlighted on the performance of the proposed schemes with both conjugate beamforming and zero-forcing precoders.

Interference management via cooperative beam biasing and power control for mmWave satellite clusters

SummaryIn order to tackle the problem of spectrum scarcity, spectrum sharing among satellite communication systems becomes a common phenomenon. Due to the spectrum sharing, mmWave satellite cluster, which is considered as a prominent part of the space information networks, may suffer severe mutual interference among inter‐satellite data links. In this paper, we firstly conduct the interference analysis for satellite clusters. Then, we formulate the interference problem as a constrained optimization problem in the performance metric of the total achievable data rate. To alleviate the interference between satellite clusters, a particle swarm optimization (PSO)‐based cooperative beam biasing and power control scheme (CBP‐PSO) from the perspectives of the joint beam domain and power domain is proposed. And the interference management problem is transformed to find the best combination of the boresight direction and transmission power based on the improved PSO algorithm. Specially, in the interference analysis and proposed scheme, different satellite cluster formations and antenna configurations are taken into account. Finally, simulation results show that the proposed CBP‐PSO scheme based on cooperative beam biasing and power control can effectively improve the achievable data rate of the interfered data links, even for the severe in‐line interference.

Dynamic Unequally Sub-Connected Hybrid Beamforming Architecture for Massive MIMO Systems

In this work, we propose a novel dynamic unequal sub-array (DUESA) architecture for massive multiple-input multiple-output (MIMO) systems with partially connected hybrid beamforming (PC-HBF) that relaxes the antenna-allocation constraint in the conventional unequal sub-array (UESA) architecture. The proposed DUESA architecture dynamically allocates antennas to sub-arrays based on channel state information, which leads to a combinatorial problem to optimize the sum rate. The exact solution to this problem requires an exhaustive search; however, the total number of candidates to search through can be extremely large in massive MIMO systems. To address this challenge, we propose three sub-optimal algorithms. The first is a tabu search-based algorithm, which examines the neighbors of the current solution to antenna-to-sub-array allocation and moves to the best one in each iteration. The second is a sequential antenna-reallocation algorithm with reduced complexity, which updates an antenna-to-sub-array assignment in each iteration. The third algorithm, that is, greedy antenna allocation, is composed of two sub-optimal algorithms. In its first part, only a single antenna is allocated to each sub-array based on a conventional algorithm. Then, in the second part, the antennas are added sequentially to each sub-array to optimize the system sum rate. The numerical results demonstrate that the DUESA architecture achieves substantially higher total achievable rates than conventional PC-HBF schemes. Furthermore, it has approximately 10%–20% higher energy efficiency than the conventional UESA architecture.

Hybrid NOMA and ZF Pre-Coding Transmission for Multi-Cell VLC Networks

Though visible-light communication (VLC) channels are contained by opaque boundaries, they present unique challenges in the development of multi-user/multi-cell scenarios. In this paper, two hybrid transmission schemes are proposed for managing multiple users in multi-cell VLC networks. The proposed schemes are based on using non-orthogonal multiple access (NOMA) in the network access points (APs), while applying zero-forcing (ZF) pre-coding to the cell edge users' signals, which are cooperatively broadcast from the APs. The proposed approach allows a reduction of the inter-cell interference affecting the cell-edge users thanks to ZF pre-coding, while dealing with inter-user interference for cell-center users via NOMA signaling. Considering different transmission scenarios, we show the improvement in the network total achievable data rate as well as fairness, as compared to conventional NOMA. For example, for a typical scenario considered, an improvement of up to 39% in total achievable rate and up to 112% in the network fairness is achieved. The proposed approach also presents a clear advantage over the conventional ZF pre-coding, for which the maximum number of users is constrained to the number of APs.

A Hybrid Downlink NOMA With OFDM and OFDM-IM for Beyond 5G Wireless Networks

In this paper, a hybrid power domain non-orthogonal multiple accessing (NOMA) scheme by the superposition of orthogonal frequency division multiple accessing (OFDM) and index modulated OFDM (OFDM-IM) technologies is presented and named IM-NOMA. It is shown via both computer-based simulations and mathematical analysis that IM-NOMA outperforms the classical OFDM-NOMA in terms of bit error rate (BER) under a total power constraint and achievable sum rate. The system performance of IM-NOMA not only depends on the power difference between the overlapping users but also on features of the OFDM-IM signal. Hence, this scheme is robust against possible catastrophic error performance in case similar power is assigned to the users.

Joint User Association and UAV Location Optimization for UAV-Aided Communications

This letter is concerned with investigating a joint user association and unmanned aerial vehicle (UAV) location optimization problem for UAV-aided communications. Different from the majority of existing studies, this letter aims at deriving solutions to the joint optimization problem that is modeled as a mixed-integer non-convex optimization problem (MINCOP) with a goal of maximizing users’ total achievable data rates. A novel iterative optimization scheme is proposed to handle the MINCOP. Specifically, the MINCOP is first decomposed into an integer optimization subproblem and a non-convex optimization subproblem. An iterative algorithm is then developed to optimize these two separated subproblems alternatively. Besides, the non-convexity of the non-convex subproblem is handled via successive convex approximation. Based on approximate results, Karush-Kuhn-Tucker (KKT) conditions are leveraged to obtain the evolutionary-form solution to the joint optimization problem. The comparison with the state-of-the-art verifies that the developed algorithm is convergent and is able to achieve good performance.

Unequally Sub-Connected Architecture for Hybrid Beamforming in Massive MIMO Systems

A variety of hybrid analog-digital beamforming architectures have recently been proposed for massive multiple-input multiple-output (MIMO) systems to reduce energy consumption and the cost of implementation. In the analog processing network of these architectures, the practical sub-connected structure requires lower power consumption and hardware complexity than the fully connected structure but cannot fully exploit the beamforming gains, which leads to a loss in overall performance. In this work, we propose a novel unequal sub-connected architecture for hybrid combining at the receiver of a massive MIMO system that employs unequal numbers of antennas in sub-antenna arrays. The optimal design of the proposed architecture is analytically derived, and includes antenna allocation and channel ordering schemes. Simulation results show that an enhancement of up to 10% can be attained in the total achievable rate by unequally assigning antennas to sub-arrays in the sub-connected system at the cost of a marginal increase in power consumption. Furthermore, in order to reduce the computational complexity involved in finding the optimal number of antennas connected to each radio frequency (RF) chain, we propose three low-complexity antenna allocation algorithms. The simulation results show that they can yield a significant reduction in complexity while achieving near-optimal performance.

Full-duplex user-centric communication using non-orthogonal multiple access

This paper proposes an improved user-centric Non-Orthogonal Multiple Access (NOMA) communication in two-base station networks with in-band full duplex (IBFD) user. We derive the achievable rates of the proposed user-centric NOMA systems. For benchmarking purposes, we also derive the achievable rate for the user-centric system deploying conventional NOMA schemes, Orthogonal Multiple Access (OMA) schemes and point-point communication systems. We then analyze and simulate the performance of the proposed and all the benchmarked systems. We found that our proposed user-centric NOMA approach has a 64% improvement in the total achievable rate when compared to the benchmarked approach under similar power constraint.

Millimeter-Wave Massive MIMO Systems Relying on Generalized Sub-Array-Connected Hybrid Precoding

In this paper, we consider a generalized sub-array-connected (GSAC) architecture for arbitrary radio frequency (RF) chain and antenna configurations, where the number of RF chains connected to a sub-array and the number of antennas in each sub-array can be arbitrary. Our design objective is to improve the energy-efficiency of the hybrid precoder of millimeter-wave massive multiple input multiple output (MIMO) systems. We firstly propose a successive interference cancellation based hybrid precoding algorithm to maximize the achievable rate for any given RF chain and antenna configuration. This algorithm firstly decomposes the total achievable rate optimization problem into multiple sub-rate optimization problems, then it successively maximizes these sub-rates. Since the number of RF chains is limited, we can afford using an exhaustive search scheme to evaluate all configurations and identify the one having the best energy efficiency. Moreover, to rely on an attractive limited feedback, we also propose a beamsteering codebook for our hybrid precoding aided GSAC architecture. Our simulation results demonstrate that the proposed scheme achieves a similar rate as the corresponding optimal unconstrained precoder. Furthermore, we show that the energy-efficiency of the proposed scheme is better than that of the existing schemes in the fully-connected and sub-array-connected architectures.

Performance Analysis and Resource Allocation for Cooperative D2D Communication in Cellular Networks With Multiple D2D Pairs

In this letter, we investigate cooperative device-to-device (D2D) communication in an uplink cellular network, where D2D users act as relays for cellular users. We derive the outage probability of a cellular user and the average achievable rate from a D2D transmitter to a D2D receiver in analytic form. We obtain optimal spectrum and power allocation to maximize the total average achievable rate under the outage probability constraint. The validity of the analysis is verified by computer simulations.

Adaptive Power Allocation Scheme for Mobile NOMA Visible Light Communication System

Recently, due to its higher spectral efficiency and enhanced user experience, non-orthogonal multiple access (NOMA) has been widely studied in visible light communication (VLC) systems. As a main concern in NOMA-VLC systems, the power allocation scheme greatly affects the tradeoff between the total achievable data rate and user fairness. In this context, our main aim in this work was to find a more balanced power allocation scheme. To this end, an adaptive power allocation scheme based on multi-attribute decision making (MADM), which flexibly chooses between conventional power allocation or inverse power allocation (IPA) and the optimal power allocation factor, has been proposed. The concept of IPA is put forward for the first time and proves to be beneficial to achieving a higher total achievable data rate at the cost of user fairness. Moreover, considering users’ mobility along certain trajectories, we derived a fitting model of the optimal power allocation factor. The feasibility of the proposed adaptive scheme was verified through simulation and the fitting model was approximated to be the sum of three Gaussian functions.

Secret-Key Capacity Regions for Multiple Enrollments With an SRAM-PUF

We introduce the multiple enrollment scheme for SRAM-physical unclonable functions (PUFs). During each enrollment, the binary power-on values of the SRAM are observed, and a corresponding key and helper data are generated. Each key can later be reconstructed from an additional observation and the helper data. The helper data do not reveal information about the keys to an attacker. It is our goal to use the additional enrollments to consecutively increase the entropy of the generated key material. We analyze two alternative settings. First, we present a regular setting, where each additional key is independent of all previous keys. Second, we introduce a key-replacement setting, where instead of an additional independent key, a new key (of increased length) is generated that replaces the old key. We characterize the capacity regions for both the settings. We show that the total achievable secret-key rate is equal to the mutual information between all enrollment observations and a single (reconstruction) observation. We derive our results based on a statistical model for the SRAM-PUF that has been proposed in the literature. This model implies a permutation symmetry property of the SRAM-PUF which plays a key role in our proofs.

Energy-Efficient Hybrid Precoding With Low Complexity for mmWave Massive MIMO Systems

Millimeter-wave (mmWave) massive multiple-input multiple-output (MIMO) utilizes large antenna arrays and is considered a promising technology for fifth-generation (5G) and beyond wireless communication systems. However, the high-power consumption of the radio-frequency (RF) chains makes it infeasible. To solve this problem, hybrid precoding is proposed, which is a combination of analog and digital precoding. The fully connected architecture hybrid precoding still requires a large number of phase shifters (PSs). The sub-connected architecture can greatly reduce the required power consumption, and however, it cannot obtain a satisfactory achievable rate. To avoid the high energy consumption and obtain a high resolution, we propose a novel partly connected architecture in this paper. In addition, we propose an energy-efficient successive interference cancelation (SIC) hybrid precoding based on the partly connected architecture, which transforms the problem of maximizing the total achievable rate with non-convex constraints into a series of sub-rate optimization problems. Furthermore, a low-complexity energy-efficient SIC hybrid precoding based on the partly connected architecture is developed, which uses the partial singular value decomposition (SVD) to realize the sub-rate optimization and significantly reduce the complexity. Theoretical analysis demonstrates the superiority of the proposed hybrid precoding in terms of complexity. The simulation results indicate that the proposed hybrid precoding algorithms enjoy better energy efficiency and achievable rate performance than some recently proposed hybrid precoding algorithms.