Long-term Channel State Information Research Articles

High-Mobility Multi-Modal Sensing for IoT Network via MIMO AirComp: A Mixed-Timescale Optimization Approach

Multiple-input-multiple-output (MIMO) over-the-air Computation (AirComp) is a promising technique for implementing high-mobility multi-modal sensing (HMS) to compute multiple target functions of distributed data by leveraging the superposition property of wireless multi-access channel, thereby significantly improving the computation efficiency as compared to the traditional orthogonal multi-access scheme. However, two obstacles constraining the potential gain of MIMO AirComp are high hardware cost and power consumption, and huge channel state information (CSI) signaling overhead. To overcome these challenges, we propose a novel mixed-timescale hybrid combining (MHC) scheme to minimize the average mean-square error (MSE) under some practical constraints in the uplink of a IoT network. Specifically, the radio frequency (RF) combiner is adapted to the long-term statistical CSI, while the baseband combiner is adapted to the real-time effective CSI. In order to tackle the resulting stochastic nonconvex optimization problem, we devise a mixed-timescale stochastic successive convex approximation (MSCA) algorithm. Simulation results are presented to verify the effectiveness of our proposed scheme over the state-of-the-art baselines.

Multi-User Linear Precoding in Massively Distributed Polarized Antenna Systems Under Imperfect CSIT

This paper presents multi-user (MU) linear precoding methods for massively distributed antenna system (DAS) with polarized antennas, where mobile stations (MSs) are spatially grouped and the distributed antenna (DA) ports with a polarized-antenna are connected to a central base station (BS). While the (slowly-changing) long-term channel state information (CSI) (i.e., large scale fading and polarization) can be accurately estimated at the transmit-side, the short-term CSI (instantaneous channel response) is available at the transmit-side with quantization errors due to the limited feedback. Accordingly, the utilization of a large number of DA ports is not always beneficial due to the imperfect short-term CSI at the transmitter (CSIT). In this paper, we first derive the asymptotic achievable rate and signal-to-leakage-and-noise ratio (SLNR) for the MU linear precoding in the massive DAS, which depends on the long-term CSIT and the number of feedback bits, and propose a transmit DA port selection based on the asymptotic SLNR performance. Here, the number of selected DA ports can be also optimized according to the long-term CSIT and the number of feedback bits. Furthermore, we also propose a polarization selection strategy at each DA port (equipped with multi-polarized antennas) so as to exploit the polarization diversity. Through the simulation, we confirm that the proposed MU precoding with the DA port selection outperforms the conventional precoding schemes for the DAS and, furthermore, the proposed polarization selection strategy achieves a rate performance comparable to the DAS with a (single polarized antenna) node density twice as large.

Electromagnetic Emission-Aware Scheduling for the Uplink of Multicell OFDM Wireless Systems

The increasing demand for data and multimedia services, as well as the ubiquitous nature of the current generation of mobile devices have resulted in continuous network upgrades to support an ever-increasing number of users. Given that wireless communication systems rely on radiofrequency waves, the electromagnetic (EM) emissions from these systems are increasingly becoming a concern, especially in terms of adverse health effects. In order to address these concerns, we propose a novel resource allocation scheme for minimizing the EM emission of users in the uplink of multicell orthogonal frequency division multiplexing (OFDM) systems, while ensuring the quality of service. Our scheme is based on the assumption that long-term channel state information of all the users in the network is available. A new multicell user grouping that uses the received interference powers of the users of different sectors is proposed. Furthermore, we propose two power allocation algorithms to minimize EM emission. The first power allocation algorithm performs multicell iterative optimization to obtain the transmit powers of each user in the system. On the other hand, our second power allocation algorithm uses the average channel gains of the users of different sectors to obtain an approximation of the transmit power of each user without multicell iterative optimization. As a result, this approach has a reduced complexity when compared to our first power allocation algorithm. Simulation results show that our scheme reduces EM emission by up to 70% when compared to a single-cell EM emission aware scheme and by over 3 to 4 orders of magnitude when compared to spectral efficiency maximization schemes.

Electromagnetic Emission-Aware Schedulers for the Uplink of OFDM Wireless Communication Systems

The popularity and convergence of wireless communications have resulted in continuous network upgrades to support the increasing demand for bandwidth. However, given that wireless communication systems operate on radio-frequency waves, the health effects of electromagnetic (EM) emission from these systems are increasingly becoming a concern due to the ubiquity of mobile communication devices. To address these concerns, we propose two schemes (offline and online) for minimizing the EM emission of users in the uplink of orthogonal frequency-division multiplexing (OFDM) systems, while maintaining an acceptable quality of service (QoS). We formulate our offline EM reduction scheme as a convex optimization problem and solve it through water filling. This is based on the assumption that the long-term channel state information (CSI) of all the users is known. Given that, in practice, long-term CSI of all the users cannot always be available, we propose our online EM emission reduction scheme, which is based on minimizing the instantaneous transmit energy per bit of each user. Simulation results show that both of our proposed schemes significantly minimize the EM emission when compared with the benchmark classic greedy spectral efficiency (SE)-based scheme and an energy efficiency (EE)-based scheme. Furthermore, our offline scheme proves to be very robust against channel prediction errors.

Energy‐efficient dual‐layer coordinated beamforming scheme in multi‐cell massive multiple‐input–multiple‐output systems

To improve the energy efficiency of multi-cell massive multiple-input–multiple-output system while guaranteeing the information transmission quality, this study proposes a dual-layer coordinated beamforming scheme. In the proposed scheme, the beamformer at each evolved node B (eNB) is divided into a cell-layer beamformer and a user-layer beamformer. The cell-layer beamformer is used to mitigate inter-cell interference (ICI) by exchanging long-term channel state information (CSI) among eNBs. The user-layer beamformer serves user according to the local real-time CSI at each eNB. On the basis of the dual-layer structure, the cell-layer beamformers, the user-layer beamformers, and power allocation are jointly optimised in order to minimise the total transmit power across all the eNBs subject to the signal-to-interference-plus-noise ratio requirements and single-antenna power constraints. To make the original problem solvable, the ICI is replaced by its upper bound. Then, the problem is partitioned into two convex sub-problems, and two iterative algorithms are proposed in order to find the sub-optimal solution to the original optimisation problem. Simulation results show that the proposed scheme performs better than two reference schemes including the existing zero-forcing scheme and coordinative multiple point schemes.

Distributed Long-Term Base Station Clustering in Cellular Networks Using Coalition Formation

Interference alignment (IA) is a promising technique for interference mitigation in multicell networks due to its ability to completely cancel the intercell interference through linear precoding and receive filtering. In small networks, the amount of required channel state information (CSI) is modest and IA is, therefore, typically applied jointly over all base stations. In large networks, where the channel coherence time is short in comparison to the time needed to obtain the required CSI, base station clustering must be applied however. We model such clustered multicell networks as a set of coalitions, where CSI acquisition and IA precoding are performed independently within each coalition. We develop a long-term throughput model, which includes both CSI acquisition overhead and the level of interference mitigation ability as a function of the coalition structure. Given the throughput model, we formulate a coalitional game where the involved base stations are the rational players. Allowing for individual deviations by the players, we formulate a distributed coalition formation algorithm with low complexity and low communication overhead that leads to an individually stable coalition structure. The dynamic clustering is performed using only long-term CSI, but we also provide a robust short-term precoding algorithm, which accounts for the intercoalition interference when spectrum sharing is applied between coalitions. Numerical simulations show that the distributed coalition formation is generally able to reach long-term sum throughputs within 10% of the global optimum.

Globally Optimal Base Station Clustering in Interference Alignment-Based Multicell Networks

Coordinated precoding based on interference alignment is a promising technique for improving the throughputs in future wireless multicell networks. In small networks, all base stations can typically jointly coordinate their precoding. In large networks however, base station clustering is necessary due to the otherwise overwhelmingly high channel state information (CSI) acquisition overhead. In this work, we provide a branch and bound algorithm for finding the globally optimal base station clustering. The algorithm is mainly intended for benchmarking existing suboptimal clustering schemes. We propose a general model for the user throughputs, which only depends on the long-term CSI statistics. The model assumes intracluster interference alignment and is able to account for the CSI acquisition overhead. By enumerating a search tree using a best-first search and pruning sub-trees in which the optimal solution provably cannot be, the proposed method converges to the optimal solution. The pruning is done using specifically derived bounds, which exploit some assumed structure in the throughput model. It is empirically shown that the proposed method has an average complexity which is orders of magnitude lower than that of exhaustive search.

Two Timescale Joint Beamforming and Routing for Multi-Antenna D2D Networks via Stochastic Cutting Plane

This paper proposes a dynamic resource allocation scheme to exploit the mixed timescale channel state information (CSI) knowledge structure in a multi-antenna base station-assisted device-to-device (D2D) network. The short-term multi-antenna beamforming control at each transmit device is adaptive to the local real-time CSI. The long-term routing and flow control is adaptive to the global topology and the long-term global CSI statistics of the D2D network. The design objective is to maximize a network utility function subject to the average transmit power constraint, the flow balance constraints and the instantaneous physical layer capacity constraints. The mixed timescale problem can be decomposed into a short-term beamforming control problem and a long-term flow and routing control problem. Using the stochastic cutting plane, we propose a low complexity, self-learning algorithm, which converges to the global optimal solution without explicit knowledge of the channel statistics. Simulation illustrates performance gains with several baselines.

Downlink massive distributed antenna systems scheduling

This study investigates the scheduling problem for a single-cell downlink distributed antenna systems (DASs) with a massive number of remote access units (RAUs). To reduce signalling overhead under limited backhaul capacity, the authors make use of local long-term channel state information (CSI) in coordinated scheduling design. They first derive the ergodic rate expressions for both the single RAU transmission and the cooperative RAU transmission modes as functions of long-term CSI. Then greedy scheduling algorithms (GSAs) aiming for the maximum ergodic sum rate for the massive DAS using long-term CSI are proposed. To mitigate the intra-cell interference, a two-stage GSA with hybrid transmission mode is devised. Asymptotic analysis reveals that as the number of RAUs goes to infinity, intra-cell interference can be effectively mitigated. Simulation results verify the analysis and demonstrate that the two-stage GSA exhibits a higher ergodic sum-rate.

A Coherent PMI Estimation Scheme for Double Codebook

The double codebook structures are proposed and applied in LTE-advanced, which can track both long-term and short-term channel state information. In traditional incoherent estimation, precoding matrix indicators (PMI) estimation is performed on each individual subcarrier and some form of averaging procedure then follows to combine the results from individual narrow-band processing. As a result, if there is a large deviation in any estimates of a subcarrier, it will lead to a larger deviation in the final result. Thus incoherent PMI estimation has a poor performance in low signal to noise ratio. This paper introduces a new PMI feedback scheme for wideband sources with coherent signal subspace method. This new technique estimates PMI based on an approximately coherent combination of each subcarrier by a focusing matrix. Simulation results show that the coherent estimation scheme has a better throughput performance than the traditional incoherent estimation.

Asynchronous Distributed Downlink Beamforming and Power Control in Multi-Cell Networks

In this paper, we consider a multi-cell network where every base station (BS) serves multiple users with an antenna array. Each user is associated with only one BS and has a single antenna. Assume that only long-term channel state information (CSI) is available in the system. The objective is to minimize the network downlink transmission power needed to meet the users' signal-to-interference-plus-noise ratio (SINR) requirements. For this objective, we propose an asynchronous distributed beamforming and power control algorithm, which provides the same optimal solution as given by centralized algorithms. To design the algorithm, the power minimization problem is formulated mathematically as a non-convex problem. For distributed implementation, the non-convex problem is cast into the dual decomposition framework. Resorting to the theory about matrix pencil, a novel asynchronous iterative method is proposed for solving the dual of the non-convex problem. The methods for beamforming and power control are obtained by investigating the primal problem. Finally, simulation results are provided to demonstrate the convergence and performance of the algorithm.

Minimax Robust Relay Selection Based on Uncertain Long-Term CSI

Cooperative communications via multiple relay nodes is known to provide the benefits of increase diversity and coverage. Simultaneous transmission via multiple relays, however, requires strong coordination between nodes either in terms of slot-based transmission or distributed space-time (ST) code implementation. Dynamically selecting a single best relay out of multiple relays and then using it alone for cooperative transmission alleviates the need for this strong coordination while still reaping the benefits of increased diversity and coverage. In this paper, we consider the design of relay selection (RS) under an imperfect knowledge of long-term channel state information (CSI) at the relay nodes, and we pursue minimax optimization to arrive at a robust RS approach that promises the best guarantee on the worst-case end-to-end signal-to-noise ratio (SNR). We provide some intuitive examples and extensive simulation results, not only in terms of worst-case SNR performance but also in terms of average bit-error-rate (BER) performance, to demonstrate the benefits of the proposed minimax robust RS scheme.

Equivalent Quasi-Convex Form of the Multicast Max–Min Beamforming Problem

Multicast downlink transmission in a multicell network with multiple users is investigated. Max–min beamforming (MB) enables a fair distribution of the signal-to-interference-plus-noise ratio (SINR) among all users in a network for given power constraints at the base stations (BSs) of the network. The multicast MB problem (MBP) is proven to be NP-hard and nonconvex in general. However, the MBP has an equivalent quasi-convex (QC) form and can be optimally solved with an efficient algorithm for special instances, depending on the structure of the available channel state information (CSI). This paper derives the equivalent QC form of the MBP for the practically relevant scenario of long-term CSI in the form of Hermitian positive semi-definite Toeplitz (HPST) matrices and per-antenna array power constraints. The optimization problem is then given by a convex feasibility check problem with finite autocorrelation sequences (FASs) as optimization variables. Using FASs, the MBP can be expressed as a QC fractional program (FP). Based on the theory of QC programming, this paper proposes a fast root-finding algorithm with superlinear convergence.

On the Duality of the Max–Min Beamforming Problem With Per-Antenna and Per-Antenna-Array Power Constraints

This paper considers a downlink unicast transmission in a multicell network with multiple users. In a network with frequency reuse factor of one, intercell interference is a limiting factor. The max-min beamforming technique enables a balancing of the signal-to-interference-plus-noise ratio (SINR) among all users in a network under a power budget. Thus, a fair distribution of the achievable rate can be achieved. The max-min beamforming problem (MBP) is nonconvex in general. However, if instantaneous channel-state information (CSI) is available, the MBP has an equivalent quasiconvex form and can optimally be solved with an efficient algorithm based on a convex solver. In addition to this convex-solver-based solution, this paper considers the so-called surrogate dual problem of the MBP with per-antenna and per-antenna-array power constraints. The surrogate dual problem combines multiple power constraints to a single power constraint. Furthermore, the surrogate dual problem can efficiently be solved for long-term CSI in the form of spatial correlation matrices. Strong duality is proved for instantaneous and long-term CSI in the form of higher rank spatial correlation matrices. With the surrogate dual problem, a fast algorithm for the MBP is presented. The convergence issue is discussed. Numerical results verify the convergence and the performance of the proposed algorithm.

Multiuser Resource Allocation Maximizing the Perceived Quality

Multiuser resource allocation for time/frequency slotted wireless communication systems is addressed. A framework for application driven cross-layer optimization (CLO) between the application (APP) layer and medium access control (MAC) layer is developed. The objective is to maximize the user-perceived quality by jointly optimizing the rate of the information bit-stream served by the APP layer and the adaptive resource assignment on the MAC layer. Assuming adaptive transmission with long-term channel state information at the transmitter (CSIT), we present a novel CLO algorithm that substantially reduces the amount of parameters to be exchanged between optimizer and layers. The proposed CLO framework supports user priorities where premium users perceive a superior service quality and have a higher chance to be served than ordinary users.

Finite-SNR Analysis and Optimization of Decode-and-Forward Relaying Over Slow-Fading Channels

We provide analytical results on the finite signal-to-noise ratio (SNR) outage performance of packet-based decode-and-forward relaying over a quasi-static fading channel, with different types of transmitter channel state information (CSI). At the relay, we consider repetition coding (RC) and parallel coding (PC). At the destination, we consider receivers based on selection combining (SC), code combining (CC), and maximum-ratio combining (MRC) (the latter only for the case of RC at the relay). Based on available CSI, we optimize the number of channel uses consumed by the source and by the relay for each packet. In doing so, we consider three different protocols that make use of different combinations of long-term CSI, 1-bit CSI, and complete CSI, respectively, at the source node. Several interesting observations emerge. For example, we show that for high SNRs, SC and CC provide the same outage probabilities when the source has perfect CSI.

Packet-wise vertical handover for unlicensed multi-standard spectrum access with cognitive radios

In this letter, packet-by-packet vertical handover is investigated as a means to improve the performance of unlicensed spectrum access. The analysis focuses on the optimization of a channel access strategy for a cognitive multi-standard radio node that has the capability to switch between two orthogonal uplink radio interfaces, based on long-term channel state information. The radio interfaces differ for coverage and quality of service (QoS) requirements of primary users. The proposed strategy prescribes a cross-layer selection of physical-layer (transmitting powers) and medium access control layer (handover probability) parameters and is designed to maximize secondary throughput while guaranteeing the primary QoS constraints. Analysis and numerical results bring insight into the impact of network topology, measurement errors and primary QoS constraints on the optimal system design and corresponding performance.

Multiuser adaptive transmission technique for time-varying frequency-selective fading channels

This paper proposes an efficient multiuser adaptive modulation and coding (AMC) scheme that considers inevitable feedback delay by employing short-term and long-term channel state information (CSI) in time-varying frequency-selective fading channels. By taking the statistic of the true signal-to-noise ratio (SNR) at a given predicted SNR value into account, the required transmit power to meet the target packet-error-rate (PER) can be obtained and used for user selection, power allocation, and modulation and coding set (MCS) selection. In addition, a simple and useful approximation method of obtaining the required transmit power is proposed. The performance of the proposed scheme is shown to be much better than that of conventional schemes without considering the feedback delay or the prediction error. The proposed scheme can also reduce the feedback resource while maintaining the system throughput by allocating different feedback resources to different users according to their prediction error variances.

Spectrum Leasing to Cooperating Secondary Ad Hoc Networks

The concept of cognitive radio (or secondary spectrum access) is currently under investigation as a promising paradigm to achieve efficient use of the frequency resource by allowing the coexistence of licensed (primary) and unlicensed (secondary) users in the same bandwidth. According to the property-rights model of cognitive radio, the primary terminals own a given bandwidth and may decide to lease it for a fraction of time to secondary nodes in exchange for appropriate remuneration. In this paper, we propose and analyze an implementation of this framework, whereby a primary link has the possibility to lease the owned spectrum to an ad hoc network of secondary nodes in exchange for cooperation in the form of distributed space-time coding. On one hand, the primary link attempts to maximize its quality of service in terms of either rate or probability of outage, accounting for the possible contribution from cooperation. On the other hand, nodes in the secondary ad hoc network compete among themselves for transmission within the leased time-slot following a distributed power control mechanism. The investigated model is conveniently cast in the framework of Stackelberg games. We consider both a baseline scenario with full channel state information and information-theoretic transmission strategies, and a more practical model with long-term channel state information and randomized distributed space-time coding. Analysis and numerical results show that spectrum leasing based on trading secondary spectrum access for cooperation is a promising framework for cognitive radio.

Linear Space-Time Precoding for OFDM Systems Based on Long-Term Channel State Information

This work addresses the severe performance degradation for OFDM systems when long channel delays exceed the guard interval. Such long delays can be seen as an interference source on the one hand and also as a valuable frequency diversity source on the other hand. Instead of simply suppressing such long delays, e.g. by beamforming at the transmitter, we propose a new precoding scheme, which attempts to find an optimum trade-off between the interference suppression and the diversity gain. An optimization problem is formulated by exploiting only long-term channel state information instead of instantaneous one. Then, it is solved based on the minimum mean square error criterion with a constraint on the transmit power. Computer simulation results show that the proposed scheme outperforms the conventional beamforming scheme.