Feedback Load Research Articles

Cooperative Device-to-Device Communication With Network Coding for Machine Type Communication Devices

With the rapid development of the Internet of Things, it is pressing to improve wireless transmission efficiency, especially for machine type communications, due to the limited wireless spectrum. In this paper, we propose a downlink transmission scheme leveraging cooperative device-to-device (D2D) communications and network coding, which can largely reduce the cellular resource consumption and the total energy consumption. In the proposed scheme, the base station generates and broadcasts linear combinations based on the packets requested by different user equipments (UEs) until at least one mature UE can recover all the original packets. Then, a selected mature UE broadcasts new linear combinations based on the recovered original packets to neighbors via D2D until all UEs can decode their packets. A feasible and backward-compatible system design including the necessary revisions on the protocol stack based on the current cellular system architecture is also provided. Then, the closed-form probability mass functions of transmission times for both cellular and D2D transmissions are derived, where the error rates in both cellular and D2D transmissions have been considered. The feedback load is also analyzed. Simulation results with different block error rate (BLER) settings are given, which can be used as references for the cellular network to decide the target BLER and adapt the modulation and coding.

An Efficient Rate Compatible Modulation With Variable Weight Sets

Rate Compatible Modulation (RCM) has been shown to be capable of achieving smooth rate adaptive transmission in highly dynamic channel scenarios. In this paper, the capacity of RCM is analyzed for different channel conditions, and an important result is obtained, i.e., in low-to-middle signal to noise ratio (SNR), the same achievable capacity in RCMs may be obtained for different weight sets being used. Based on this result, instead of employing fixed-weight sets with large values or big cardinalities for all channel conditions as in conventional RCM, a novel RCM scheme with variable weight sets (RCM-VWS) is proposed by employing different weight sets for different SNR ranges such that the demodulation complexity can be significantly reduced. In RCM-VWS, the SNR value of the received signal is divided into five unoverlapped ranges, which are estimated and encoded into 3-bit information at the receiver. This 3-bit information is delivered back to the transmitter for weight set selection when the SNR value of the received signal changes from one range to another or for some transmission interval. Thus, the cost of the feedback load is trivial in practice. Theoretical analysis and simulations show that compared with conventional RCM with fixed weight sets, the computational complexity of RCM-VWS can be reduced by as much as 97% at SNR < 6 dB and 90% at 6 dB < SNR < 9 dB while maintaining the same transmission rate. In addition, the proposed scheme achieves a better BER performance than the conventional RCM.

An adaptive dynamic feedback load balancing algorithm based on QoS in distributed file system

An adaptive dynamic load balancing algorithm based on QoS is proposed to improve the performance of load balancing in distributed file system, combining the advantages of a variety of load balancing algorithms. The new algorithm uses a tuple containing the number of files and the total file size as the QoS measure for the requested task. The master node sets a threshold for the requested task based on the QoS to filter storage nodes that meet the requirements of the task. In order to guarantee the reliability of the new algorithm, we consider the impact of CPU utilization, memory usage, disk IO occupancy rate, network bandwidth usage and hard disk usage on load balancing performance when calculating the real-time load balancing of storage nodes. The heterogeneity of the network is considered when the master node schedule task assignments to ensure the fairness of the algorithm. The comprehensive evaluation value is determined based the performance load ratio, which is calculated from the real-time load value of the storage node and a performance value after normalization. The master node assigns tasks to the storage node with the highest comprehensive evaluation value. The storage nodes provide adaptive feedback based on changes in the degree of connectivity, rather than periodic update of the load information. The actual distributed file system environment is set up on the server cluster, the performance of the new algorithm is tested through a contrast experiment. The experimental results show that the new algorithm can effectively reduce the average response time of the system, improve throughput, and enable the system load to reach a good balance.

Quantized feedback scheduling for MIMO-OFDM broadcast networks with subcarrier clustering

Feedback of user channel quality information (CQI) to base station (BS) is essential for efficient scheduling of multi-user (MU) multiple-input multiple-output orthogonal frequency division multiplexing (MIMO-OFDM) broadcast networks. This feedback overload increases linearly with the cardinality of users, transmit antennas and subcarriers present in MIMO-OFDM systems. Hence, clustering of adjacent subcarriers has been considered in literature for reduction in feedback load. Here users communicate the CQI of the center subcarrier of the clusters assuming that subcarriers of a cluster experience similar channel conditions. However, this method leads to inefficient resource scheduling for higher cluster sizes as it does not take advantage of CQI of other subcarriers that experience non-uniform channel conditions. This reason motivated us to develop a new limited feedback scheduling scheme where quantization of CQI with multiple bits is implemented along with clustering of adjacent subcarriers. In this scheme, the multi-bit quantized CQI of all the subcarriers of a cluster are examined for the efficient scheduling along with further reduction in the feedback overhead. Moreover, we proposed a less time consuming method to find the optimum quantization thresholds that play significant role in multi-bit quantization process by using genetic algorithm (GA). This GA methodology is computationally more efficient than exhaustive search process.

Decentralized bounded input bounded output stabilization of perturbed interconnected time-delay power systems with energy storages

This paper presents a new decentralized bounded input bounded output (BIBO) stabilization problem for a class of interconnected time-delay systems and its application to power systems with energy storages. We first provide conditions for the derivation of an ellipsoid that bounds a given linear functions of the state vector. Then, a design procedure is proposed to synthesize decentralized static output feedback controllers. The designed controllers guarantee that a given linear functions of the state vector, starting from any initial condition, converges exponentially to its prescribed zones. To deal with the time-delay issue, we use an improved weighted integral inequality recently reported in the literature to derive less conservative exponential stability conditions. Then, our presented control approach is applied to an interconnected power system integrated energy storages with multiple time delays. We synthesis decentralized static output feedback load frequency controllers to guarantee that the system frequency and interchanged power converge to their prescribed zones exponentially from any initial conditions. The controller’s construction is simpler and easier for implementation due to only the local output measurements are required. In order to systematically obtain the controller gains, an effective procedure using linear matrix inequality based stabilisation criteria, which can be solved by various computation tools, is provided. Finally, the effectiveness of the proposed control scheme is verified by comprehensive simulations.

Per-antenna signal to leakage noise ratio precoding for MU-MIMO-OFDM system with reduced feedback

In this work, multi-user multiple input multiple outputs orthogonal frequency division multiplexing (MU-MIMO-OFDM) precoding is introduced. The proposed method makes use of per-antenna signal to leakage plus noise ratio (PA-SLNR) as optimisation criteria. This optimisation criterion takes both the inter-user and inter-antennas interference cancellation into account. Unlike the conventional proposed methods in which simple frequency flat fading is assumed, in the proposed method we generalised the precoding problem to more realistic frequency selective MU-MIMOOFDM Broadcast channel where OFDM is used to mitigate inter-symbol-interference (ISI) and the MU-MIMO is used for spatial multiplexing. Conventionally, each individual user has to feedback the channel frequency response of each subcarrier to the central node base station for precoding matrix design which leads to high traffic load in the feedback channel. In this work we also introduce a simple frequency response channel factorisation such that each user is required to feedback only one subcarrier time domain channel impulse response which dramatically reduces the amount of the feedback load.

Beam-Blocked Channel Estimation for FDD Massive MIMO With Compressed Feedback

To fully exploit both multiplexing gain and array gain of massive multiple-input–multiple-output (MIMO), the channel state information must be obtained accurately at transmitter side (CSIT). However, conventional channel estimation solutions are not suitable for frequency-division duplexing (FDD) multiuser massive MIMO because of overwhelming pilot and feedback overhead. To reduce the pilot and feedback overhead of channel estimation in FDD systems, we propose a compressive channel estimation scheme for FDD massive MIMO systems in this paper, where the beam-blocked sparsity of massive MIMO channels in beamspace is leveraged. Particularly, we first propose a beam-blocked compressive channel estimation scheme, which can reduce the overhead for downlink training. Then, an optimal block orthogonal matching pursuit algorithm at the BS is proposed to acquire reliable CSIT from the limited number of pilots. Furthermore, an efficient algorithm for channel matrix recovery from separately quantized amplitude and phase of received signals is developed to efficiently decrease feedback load. Simulation results demonstrate that our proposed scheme outperforms conventional solutions.

Per-antenna signal to leakage noise ratio precoding for MU-MIMO-OFDM system with reduced feedback

In this work, multi-user multiple input multiple outputs orthogonal frequency division multiplexing (MU-MIMO-OFDM) precoding is introduced. The proposed method makes use of per-antenna signal to leakage plus noise ratio (PA-SLNR) as optimisation criteria. This optimisation criterion takes both the inter-user and inter-antennas interference cancellation into account. Unlike the conventional proposed methods in which simple frequency flat fading is assumed, in the proposed method we generalised the precoding problem to more realistic frequency selective MU-MIMOOFDM Broadcast channel where OFDM is used to mitigate inter-symbol-interference (ISI) and the MU-MIMO is used for spatial multiplexing. Conventionally, each individual user has to feedback the channel frequency response of each subcarrier to the central node base station for precoding matrix design which leads to high traffic load in the feedback channel. In this work we also introduce a simple frequency response channel factorisation such that each user is required to feedback only one subcarrier time domain channel impulse response which dramatically reduces the amount of the feedback load.

Position-Based Limited Feedback Scheme for Railway MU-MIMO Systems

In future railway communications, the system architecture is highly likely to be based on the mobile relay structure. However, during the transition period, onboard passenger equipment can only directly connect to wayside public mobile networks to get wireless services. As an efficient technology to increase the capacity widely used in Long-Term Evolution (LTE) public mobile networks, multiuser multiple input–multiple output (MU-MIMO) requires channel state information (CSI) of users at base stations to suppress interuser interference, which poses a bandwidth burden on feedback links. Thus, how to reduce the MU-MIMO feedback load becomes an urgent problem. In railway scenarios, onboard passenger equipment of a user set almost has the same signal-to-noise ratio (SNR), which provides a new way to reduce the MU-MIMO feedback overheads. Accordingly, we propose a position-based limited feedback scheme for railway MU-MIMO systems, in which dedicated feedback resource positions are preassigned to codebook vectors. A resource position where users feed back channel quality indicators (CQIs) indicates a special codebook vector. Then, overheads due to precoding matrix index (PMIs) are saved. Even if bandwidth resources assigned to feedback positions may be insufficient to supply every user with a feedback chance, it still has little impact on the total capacity for whether the user with the largest CQI in an active user set is selected in highly populated railway scenarios. Moreover, a novel performance criterion called feedback efficiency is proposed. Simulation results demonstrate that the proposed scheme can achieve higher feedback efficiency compared with the conventional scheme.

Ergodic capacity of multiuser scheduling in cognitive radio networks: analysis and comparison

AbstractThis paper analyzes the ergodic capacity of secondary point‐to‐multipoint communications under the outage constraint of multiple primary user receivers (PU‐Rxs) and the secondary user (SU) maximum transmit power limit. We first derive analytical expressions of the ergodic capacity for three scheduling schemes, that is, round robin, Max‐signal‐to‐interference‐plus‐noise‐ratio (Max‐SINR) and Min‐SINR, and compare their performance. Numerical examples show that when the number of SU receivers (SU‐Rxs) increases in the presence of a single PU‐Rx and at high SINR, the Min‐SINR outperforms the Max‐SINR scheme. As the number of PU‐Rxs increases, the Max‐SINR performs better than the Min‐SINR scheduling. When the number of SU‐Rxs becomes large, the system capacity is enhanced but so is the feedback load. To exploit the Max‐SINR transmission while reducing the feedback load, we assume a threshold based on the channel quality where the SU‐Rxs with the instantaneous SINR above the threshold participate in the Max‐SINR scheduling; otherwise, an SU‐Rx is selected randomly. Then, an analytical expression of the average capacity is derived. Numerical results illustrate that the capacity with limited feedback is approximately the same as for the case of Max‐SINR with full feedback when the SU threshold for feedback condition is set to low and medium values. Copyright © 2016 John Wiley &amp; Sons, Ltd.

User selection scheme with limited feedback processing and outdated CSI in multiuser relay networks

AbstractA new user selection strategy is investigated and analyzed in a multiuser relaying environment in the presence of co‐channel interference. The proposed selection scheme aims at avoiding unnecessary feedback load processing, in cases where a target threshold, in the received instantaneous signal‐to‐noise ratio, is exceeded. Assuming that perfect channel state information is available, closed‐form lower bound expressions are derived for the cumulative distribution function of the output signal to interference plus noise ratio. Moreover, the impact of outdated channel state information on the system's performance is also investigated. In addition, under the assumption of high signal‐to‐noise ratio conditions, simplified approximated expressions are also provided for the cumulative distribution functions of the output signal to interference plus noise ratio, which are employed to study the outage probability and bit error probability performance of the system. It is shown that with the proposed approach, a significant reduction in feedback load processing is achieved, with only a slight loss in performance. Copyright © 2016 John Wiley &amp; Sons, Ltd.

Opportunistic vector channel estimation based on scalar feedback

AbstractMulti‐user diversity exploits channel fluctuations by favouring the user with the best channel. In slow fading environments, channel fluctuations are artificially created by the use of random beamforming at the transmitter, a technique called opportunistic beamforming. One drawback of opportunistic beamforming is that, it needs large number of users to achieve the capacity of a true beamforming system. On the other hand, if partial channel state information is available at the transmitter, this can be used to reduce the necessary number of users to reach this capacity. Opportunistic beamforming systems require scalar feedback from users in the system instead of the complete channel vector to reduce the feedback load. In conventional systems, these feedback values are used for scheduling only. In this letter, we propose three channel estimation methods that solely rely on the scalar feedback already available at the transmitter. We also propose an accompanying scheduling method where these estimates are used to form the beamforming vectors. Although we use the same information available in a conventional opportunistic beamforming system, the proposed scheduling scheme in conjunction with channel estimation is able to achieve higher throughput and also achieve better fairness which is verified by simulations. Copyright © 2016 John Wiley &amp; Sons, Ltd.

Efficient Resource Allocation Based on Layered Coding in Single Frequency Network with Limited Feedback Scheme

Single frequency network (SFN) can provide Multimedia Broadcast Multicast Service (MBMS) over a large coverage area, so it receives more and more attention from both academia and industry. However, the application of SFN is still restricted by a large number of feedbacks. Therefore, we propose a novel multicast resource allocation algorithm based on limited feedback scheme. In the algorithm, we first design a user limited feedback scheme based on channel gain threshold to effectively reduce feedback load. The scheme determines to which base stations users should report channel state information. Next, to overcome the MBMS capacity limitation drawback, we encode the MBMS data into a base layer and multiple enhancement layers and develop a joint subcarrier and power allocation strategy to maximize the throughput of enhancement layers while guaranteeing the rate requirement of base layer. Simulation results show that the proposed algorithm significantly reduces 83 % of the feedback overhead while achieving a comparable multicast throughput performance to the case of full feedback.

Reduced feedback link design for device‐relaying enhanced multiple‐input single‐output orthogonal frequency‐division multiple access wireless systems

Device-relaying enhanced multiple-input single-output (MISO) orthogonal frequency-division multiple access (OFDMA) systems are one of the promising and cost-effective methods to satisfy data rate requirements of future wireless networks. To implement device relaying in MISO-OFDMA systems efficiently, the channel state information belonging to all links is required at the base station. However, the feedback load increases with the number of users, device-relay candidates, antennas and subcarriers. A novel scheduling and a joint feedback link design and relay selection algorithm in device-relaying enhanced downlink MISO-OFDMA systems are proposed, while reducing the feedback load significantly without sacrificing the system performance.

Interwoven random unitary beamforming for MIMO broadcast channels

AbstractWe consider a multiple‐input multiple‐output broadcast channel where the transmitter performs unitary beamforming using the channel state information obtained through limited feedback from the receivers. A new feedback structure is proposed which considerably boosts the performance of the system compared with the conventional beamforming techniques like zero forcing beamforming or per user unitary and rate control. Because of the structure of the proposed feedback design, the new scheme is named interwoven random unitary beamforming or IRUB. In IRUB, the multiple‐input multiple‐output channel is in fact continuously decomposed into M orthogonal channels where the signal transmissions over them have timing offsets. User scheduling and beamforming in IRUB is very simple. User selection in IRUB is accomplished only based on the SINR feedbacks, and in each scheduling phase, only the scheduled users are inquired to send their channel direction information to the transmitter. Thereby, the feedback load of the system is considerably reduced. We provide an analysis for the achievable rate and its scaling law for the proposed scheme. Also, a 1‐bit feedback strategy is applied over IRUB to further reduce the total feedback load of the system. We further generalize IRUB for multi antenna receivers. The effectiveness of the IRUB method is validated through simulations. Copyright © 2016 John Wiley &amp; Sons, Ltd.

A Channel State Information Feedback Algorithm for Massive MIMO Systems

To improve spectral efficiency and link reliability of a massive multiple-input multiple-output system, the downlink channel state information (CSI) should be fed back to the base station (BS). However, it is difficult to implement the feedback in practical applications due to the large number of antennas at the BS. We propose a compressive sensing-based method to reduce the feedback load without decreasing the reconstruction accuracy. By exploiting the correlation of CSI, a quasi-signal-independent dictionary is designed to improve the accuracy of the CSI reconstruction at the BS. The analysis and simulation results show that the proposed approach outperforms the conventional methods in terms of reconstruction accuracy and feedback load.

A limited feedback based network coding retransmission scheme for machine‐to‐machine wireless broadcasting

ABSTRACTIn this paper, we focus on network coding based wireless broadcasting problem in a machine‐to‐machine (M2M) scenario where massive M2M devices (MDs) exist. On the finding that numerous feedback messages are required in traditional full feedback based network coding schemes, which imposes a heavy feedback load to the system, we propose a Limited Feedback based Network Coding Retransmission (LFNCR) scheme on the purpose of achieving a practical coverage rate CR (CR &lt; 100%). Instead of the accurate feedback‐by‐packet mechanism, in LFNCR, a feedback‐by‐stage mechanism is applied, that is, each MD feeds back a generation for pseudo‐random network coded packets in a next retransmission stage; thus, feedback load is significantly decreased. In order to maximise broadcast efficiency, we further propose a two‐step generation optimization procedure. In particular, in the first step, each MD determines a local optimal generation and sends it to the base station; in the second step, base station selects a global optimal generation among local optimal generations corresponding to the target coverage rate. Theoretical analysis and simulation results show that in the proposed LFNCR, only extreme few feedback is required, compared with traditional full feedback based network coding retransmission schemes, meanwhile acceptable broadcast efficiency could be achieved. Copyright © 2015 John Wiley &amp; Sons, Ltd.

Efficient multicast resource allocation algorithm with limited feedback scheme in single‐frequency networks

Single-frequency network (SFN) can provide multimedia broadcast multicast service (MBMS) over a large coverage area, so it receives more and more attention from both academia and industry. However, the application of SFN is still restricted by a large number of feedbacks. Therefore, the authors propose a novel multicast resource allocation algorithm based on limited feedback scheme. In the algorithm, they first design a user limited feedback scheme based on channel gain threshold to effectively reduce feedback load. The scheme determines to which base stations users should report channel state information. Next, to overcome the MBMS capacity limitation drawback, they encode the MBMS data into a base layer and multiple enhancement layers and develop a joint subcarrier and power allocation strategy to maximise the throughput of enhancement layers while guaranteeing the rate requirement of base layer. Simulation results show that the proposed algorithm significantly reduces 83% of the feedback overhead while achieving a comparable multicast throughput performance to the case of full feedback.

Layered Multicast Resource Allocation with Limited Feedback Scheme in Single Frequency Networks

Single frequency network (SFN) can provide Multimedia Broadcast Multicast Service (MBMS) over a large coverage area, so it receives more and more attention from both academia and industry. However, the application of SFN is still restricted by a large number of feedbacks. Therefore, we propose a novel multicast resource allocation algorithm based on limited feedback scheme. In the algorithm, we first design a user limited feedback scheme based on channel gain threshold to effectively reduce feedback load. The scheme determines to which base stations users should report channel state information. Next, to overcome the MBMS capacity limitation drawback, we encode the MBMS data into a base layer and multiple enhancement layers and develop a joint subcarrier and power allocation strategy to maximize the throughput of enhancement layers while guaranteeing the rate requirement of base layer. Simulation results show that the proposed algorithm significantly reduces 83 % of the feedback overhead while achieving a comparable multicast throughput performance to the case of full feedback.

Opportunistic Relay Selection With Limited Feedback

Relay selection is a simple technique that achieves spatial diversity in cooperative relay networks. Generally, relay selection algorithms require channel state information (CSI) feedback from all cooperating relays to make a selection decision. This requirement poses two important challenges, which are often neglected in the literature. Firstly, the fed back channel information is usually corrupted by additive noise. Secondly, CSI feedback generates a great deal of feedback overhead (air-time) that could result in significant performance hits. In this paper, we propose a compressive sensing (CS) based relay selection algorithm that reduces the feedback overhead of relay networks under the assumption of noisy feedback channels. The proposed algorithm exploits CS to first obtain the identity of a set of relays with favorable channel conditions. Following that, the CSI of the identified relays is estimated using least squares estimation without any additional feedback. Both single and multiple relay selection cases are considered. After deriving closed-form expressions for the asymptotic end-to-end SNR at the destination and the feedback load for different relaying protocols, we show that CS-based selection drastically reduces the feedback load and achieves a rate close to that obtained by selection algorithms with dedicated error-free feedback.