Adaptive V-BLAST with partial channel state information feedback

Next generation wireless communication systems use massive Multi Input Multi Output (m-MIMO) antenna arrays for their enhanced beamforming capabilities. Providing accurate Channel State Information (CSI) is vital for optimizing m-MIMO communication systems. The complexity of channel reconstruction grows exponentially with the number of antennas, causing traditional methods to become increasingly complicated. Machine-learning techniques can be a useful alternative for channel reconstruction using partial CSI feedback. This paper presents the results of a simulation study built using the MATLAB 5G Toolbox and a neural network trained using the simulated data. The simulator emulates a 5G channel to generate its path delays and gains, and the realistic CSI feedback. This data was used to train and test a neural network to estimate the dominant path gains and delays. The models showed promising results while operating on limited CSI data.

Resource allocation for half-duplex bidirectional relaying networks using partial channel state information (CSI) at the transmitters is considered, with an emphasis on the diversity-multiplexing tradeoff (DMT). It is assumed that there is a direct link between the sources. It is shown that decode-and-forward (DF) two-way relaying does not suffer from a diversity loss when the multiplexing gain is high, a significant improvement over the dynamic DF protocol for one-way relaying. In contrast to the case of no direct link, power control with CSI feedback from relay to sources provides strictly better performance over systems without power control. The best DMT is obtained when each source broadcasts CSI to the other nodes in the network, allowing them to partially adapt their transmit powers.

We consider a server serving a time-slotted queued system of multiple packet-based flows, where not more than one flow can be serviced in a single time slot. The flows have exogenous packet arrivals and time-varying service rates. At each time, the server can observe instantaneous service rates for only a subset of flows (selected from a fixed collection of observable subsets) before scheduling a flow in the subset for service. We are interested in queue length aware scheduling to keep the queues short. The limited availability of instantaneous service rate information requires the scheduler to make a careful choice of which subset of service rates to sample. We develop scheduling algorithms that use only partial service rate information from subsets of channels, and that minimize the likelihood of queue overflow in the system. Specifically, we present a new joint subset-sampling and scheduling algorithm called Max-Exp that uses only the current queue lengths to pick a subset of flows, and subsequently schedules a flow using the Exponential rule. When the collection of observable subsets is disjoint, we show that Max-Exp achieves the best exponential decay rate, among all scheduling algorithms that base their decision on the current (or any finite past history of) system state, of the tail of the longest queue. To accomplish this, we employ novel analytical techniques for studying the performance of scheduling algorithms using partial state, which may be of independent interest. These include new sample-path large deviations results for processes obtained by non-random, predictable sampling of sequences of independent and identically distributed random variables. A consequence of these results is that scheduling with partial state information yields a rate function significantly different from scheduling with full channel information. In the special case when the observable subsets are singleton flows, i.e., when there is effectively no a priori channel state information, Max-Exp reduces to simply serving the flow with the longest queue; thus, our results show that to always serve the longest queue in the absence of any channel state information is large deviations optimal.

In this paper, we consider the downlink transmission over a single-cell massive multiple-input-multiple-output (MIMO) system in the presence of multiple single-antenna eavesdroppers (massive MIMOME). We concentrate on the practical scenario where partial channel state information (CSI) of legitimate users and no CSI of eavesdroppers are available at the base station and consider both types of eavesdroppers including the non-colluding and colluding eavesdroppers. Random unitary beamforming (RUB) based scheme is used to describe the partial CSI of legitimate users. We derive the closed-form expression of ergodic secrecy rate for RUB based massive MIMOME transmission, and its single legitimate user particular case. We also present numerical results to illustrate the performance-complexity tradeoff among different massive MIMO transmission schemes. We show that RUB based scheme can enhance secrecy performance of massive MIMO transmission with lower implementation complexity.

Next-generation Wi-Fi 8 (IEEE 802.11bn) targets ultra-high reliability (UHR) by introducing coordinated beamforming (CoBF). In dense networks with multiple access points (APs), simultaneous downlink (DL) multi-user MIMO (MU-MIMO) transmissions from multiple APs can cause severe intra-basic service set (intra-BSS) and inter-BSS interference. CoBF aided by only partial channel state information (CSI) feedback through medium access control (MAC) layer frame exchange is envisioned to support concurrent DL transmission with mitigated physical-(PHY-)layer interference. To improve the network throughput, not only the interference mitigation algorithm design requires careful design but also the selection of optimal AP CoBF clusters is crucial for dense AP deployments. This paper presents a cross-layer solution combining PHY and MAC layer design to optimize AP cluster formation for Wi-Fi 8 CoBF. At the PHY layer, we introduce two beamforming nulling strategies: full nulling, which completely cancels all intra-BSS and inter-BSS interference when sufficient spatial degrees of freedom are available, and partial nulling, which is used under limited degrees of freedom to reduce interference as much as possible. Based on this, we formulate the cross-layer problem that aims to optimize the network throughput, to which we propose an exact linear programming (LP) optimization to determine the optimal AP cluster formation. A greedy clustering algorithm is proposed as a low-complexity alternate. Simulation results demonstrate that the proposed CoBF approach significantly mitigates interference and achieves substantial throughput gains in dense AP scenarios. Furthermore, the LP-optimized AP clustering yields the higher network throughput than the greedy heuristic and mixed integer linear programming (MILP) by up to 12% and 26%, highlighting the benefits of global optimization in terms of performance and time complexity.

The performance of random beamforming (RBF) with partial channel state information (CSI) feedback is investigated here for millimeter wave (mmWave) multiuser multiple-input single-output (MU-MISO) downlink systems using the uniform random multipath (UR-MP) channel model. In particular, the required number of users in the cell for RBF to yield linear sum rate scaling with respect to the number of transmit antennas is identified under the UR-MP channel model for different levels of channel sparsity. Then, the problem of user scheduling in MU-MISO downlink is considered when the number of users in the cell is not sufficiently large (sparse user regime) for the system to operate in the identified sufficient user regime. By exploiting the sparsity of mmWave radio channels, several user scheduling algorithms based on beam aggregation with reasonable amount of feedback are proposed for the sparse user regime. Our numerical results show that the proposed algorithms yield very good sum-rate performance in sparse mmWave channels.

In this paper, a single input multiple output system is considered with L receive antennas and the underlying channels are assumed to be time varying with temporal correlation coefficient a and spatially correlated with correlation coefficient $$\rho $$ . Further, the channel is assumed to be identically distributed using Rayleigh fading channels and characterized by first order autoregressive model. For the detection, we assume partial channel state information (CSI) available at the receiver. The partial CSI is in the form of a known preamble (one symbol) only before beginning of a frame of size N symbols. Further, the preamble is imperfectly known with a variance of error $$\sigma _e^2$$ . For the assumed system, closed form expressions of symbol error rate (SER) are derived for M-PSK and M-QAM constellations under the compound effect of spatially correlated channels, temporally correlated channels and partial CSI at the Receiver. The derived expressions are functions of average SNR per symbol, $$\rho $$ , a, $$\sigma _e^2$$ , N, L and modulation order M. Further, these expressions are reduced for some special cases and compared with prevailing results in literature. The analytical expressions are also validated by comparing them with the corresponding simulation results. The derived expressions are very useful to select N, L and M to overcome the deterioration in SER due to adverse effects of $$\rho $$ , a and $$\sigma _e^2$$ .

In this letter, energy-efficient transmission and power allocation for fixed-gain amplify-and-forward relay networks with partial channel state information (CSI) are studied. In the energy-efficiency problem, the total power consumed is minimized while keeping the signal-to-noise-ratio (SNR) above a certain threshold. In the dual problem of power allocation, the end-to-end SNR is maximized under individual and global power constraints. Closed-form expressions for the optimal source and relay powers and the Lagrangian multiplier are obtained. Numerical results show that the optimal power allocation with partial CSI provides comparable performance as optimal power allocation with full CSI at low SNR.

This paper introduces a new metric, referred to as delay-sensitive area spectral efficiency (DASE), to analyze the performance of delay-constrained wireless networks. We study DASE in different point-to-point, spectrum sharing, interference, and Poisson-point process-based dense network configurations and with different levels of channel state information (CSI) at the transmitters. Also, we determine the optimal rates/powers optimizing DASE. Finally, we use some recent results on finite block-length codes to analyze the effect of the codewords length on the network DASE. As demonstrated, DASE is a useful metric for analyzing delay-sensitive green networks. Moreover, adaptive power allocation and partial CSI feedback are powerful tools for performance improvement in green networks.

The knowledge of the impact of imperfect channel state information (CSI) on data transmission techniques is helpful to improve the performance of wireless communication systems. In this paper, based on the proposed cooperative downlink (DL) transmission scheme applying adaptive scheduling of mobile stations (MSs), significant channel selection, and joint transmission (JT) with partial CSI, i.e., JT considering only part of the useful channels and the interference channels, a novel performance assessment is performed taking imperfectness of the CSI into consideration. The performance degradation caused by the imperfectness of the CSI which is used in the adaptive scheduling, the significant channel selection and the JT with partial CSI is investigated. The question about how much CSI should be taken into consideration as a function of the extent of imperfectness of the CSI in order to achieve optimum system performance is answered based on numerical results.

A novel approach is proposed for correlated multiple-input multiple-output (MIMO) channel estimation based on reduced-rank (RR) technique and partial channel state information (CSI). In contrast to previous proposals that used the channel correlation matrix (CCM) and its eigendecomposition, this paper shows that close linear minimum mean-square-error (LMMSE) performance can be achieved with the use of predefined bases derived from the knowledge of the maximum angular dispersion. A theoretical framework to synthesize a suitable set of bases is provided, from which discrete prolate spheroidal sequences (DPSSs) are identified as one of the appropriate predefined bases for spatial channel representation. The robustness of the proposed estimator allows changes in the propagation scenario to be managed according to the demands of realistic communications systems. The performance analysis of the channel estimator is shown and corroborated with simulation results.

Millimeter wave communication systems define a new paradigm for wireless communications. At such high frequencies, multi-input multi-output (MIMO) processing is done on the radio-frequency signals to limit the hardware complexity and the energy consumption. Consequently, the channel experienced by the receiver directly depends on the transmitter and receiver antenna array responses, making the determination of the analog precoder and combiner an intertwined problem. In this paper, we propose a procedure capable of calculating analog weights at the transmitter and at the receiver, which allows the maximization of the wideband spectral efficiency for single stream transmission single user MIMO (SU-MIMO). While solely relying on partial channel state information (CSI), our procedure has close performance to full CSI-dependent schemes.

Interference alignment (IA) is a linear precoding strategy that can achieve optimal capacity scaling at high SNR in interference networks. Most of the existing IA designs require full channel state information (CSI) at the transmitters, which induces a huge CSI signaling cost. Hence it is desirable to improve the feedback efficiency for IA and in this paper, we propose a novel IA scheme with a significantly reduced CSI feedback. To quantify the CSI feedback cost, we introduce a novel metric, namely the feedback dimension. This metric serves as a first-order measurement of CSI feedback overhead. Due to the partial CSI feedback constraint, conventional IA schemes can not be applied and hence, we develop a novel IA precoder/decorrelator design and establish new IA feasibility conditions. Via dynamic feedback profile design, the proposed IA scheme can also achieve a flexible tradeoff between the degree of freedom (DoF) requirements for data streams, the antenna resources and the CSI feedback cost. We show by analysis and simulations that the proposed scheme achieves substantial reductions of CSI feedback overhead under the same DoF requirement in MIMO interference networks.

In order to effectively utilise the spectrum of wireless network, secondary users (SUs) are often authorised to share spectrum with primary users (PUs) in cognitive networks. To guarantee effective transmission of PUs and SUs, the interference from the SUs to the PUs is limited and only the SUs that have the information rates (rates) than the corresponding thresholds are allowed to transmit their data. In order to select proper SUs to transmit and to maximise their achievable weighted sum rate (WSR) under rate requirement and interference constraint, the authors propose a joint admission control and beamforming design algorithm for partial channel state information (CSI) case. The WSR maximisation problem is first constructed by using approximation and convex relaxation, according to the statistical property of the CSI. Then a penalty factor is introduced to check the feasibility of such problem and instruct the admission of the SUs. Beyond the feasible SUs set, the WSR is maximised by updating the achievable rate of each user with rate profile technique and polyblock outer approximation method. Simulation results show that the proposed beamforming can achieve higher WSR than the beamforming of the existing baseline method.

The vertical Bell labs layered space-time (V-BLAST) system is a multi-input multioutput (MIMO) system designed to achieve good multiplexing gain. In recent literature, a precoder, which exploits channel information, has been added in the V-BLAST transmitter. This precoder forces each symbol stream to have an identical mean square error (MSE). It can be viewed as an alternative to the bit-loading method. In this paper, this precoded V-BLAST system is extended to the case of frequency-selective MIMO channels. Both the FIR and redundant types of transceivers, which use cyclic-prefixing and zero-padding, are considered. A fast algorithm for computing a cyclic-prefixing-based precoded V-BLAST transceiver is developed. Experiments show that the proposed methods with redundancy have better performance than the SVD-based system with optimal powerloading and bit loading for frequency-selective MIMO channels. The gain comes from the fact that the MSE-equalizing precoder has better bit-error rate performance than the optimal bitloading method.