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The 802.11ac is a significant landmark in wireless communications, as it pushes towards new rate limits by utilizing downlink multiuser multiple-input multiple-output (MIMO) beamforming to transmit data to various locations simultaneously. However, successful beamforming relies on intelligent user selection which requires, in turn, extensive overhead of channel calibration between the AP and each of the candidate users. The large overhead involved in the user selection procedure overwhelms the multiuser gain and hinders the utilization of multiuser MIMO. The phenomenon is even more acute when APs handle large groups of mobile users, which frequently associate and disconnect, making the process of acquiring channel state from all users and selecting the appropriate group even harder. Thus, the subtle relation between the achievable rate of a scheduling algorithm and the overhead it requires is significant for the 802.11ac performance analysis. In this paper, we provide a rigorous analysis of distributed algorithms that schedule a group of users for the downlink. In particular, we accommodate common scheduling methods for the 802.11ac protocol and analyze both their achievable rate and their calibration process overhead. Both analysis and extensive simulations depict the superiority of simple threshold-based methods in terms of the throughput. [ABSTRACT FROM AUTHOR]

One of the key challenges in wireless networking is allocating the available radio resources to maximize key service delivery parameters such as the aggregate throughput and the multimedia quality of experience (QoE). We propose a novel and effective scheduling policy that allocates resource blocks, such that interference power is shifted toward capacity-saturated users, while improving the throughput of unsaturated users. The highlight of this paper is that the proposed scheme can dramatically improve the performance of cells that have a high discrepancy in its signal-to-noise ratio (SNR) distribution, which is typical in urban areas. The results show that a free-lunch (FL) solution is possible, whereby for negligible performance degradation in the saturated users, a large improvement in the nonsaturated users can be obtained. However, on average, the number of FL user pairings is low. By relaxing the degradation constraints, the non-FL (NFL) solution can yield a greater multiuser throughput gain. Motivated by a surge in mobile multimedia traffic, we further demonstrate that the proposed scheduling may have a profound impact on both energy efficiency and QoE of multimedia service delivery. [ABSTRACT FROM PUBLISHER]

This paper considers the transmission of scheduling information in orthogonal frequency-division multiple-access (OFDMA)-based cellular communication systems such as the Third-Generation Partnership Project (3GPP) long-term evolution (LTE). These systems provide efficient usage of radio resources by allowing users to be dynamically scheduled in both frequency and time. This requires considerable amounts of scheduling information to be sent to the users. This paper compares two basic transmission strategies: transmitting a separate scheduling message to each user versus broadcasting a joint scheduling message to all users. Different scheduling granularities are considered, as are different scheduling algorithms. The schemes are evaluated in the context of the LTE downlink using multiuser system simulations, assuming a full-buffer situation. The results show that separate transmission of the scheduling information requires a slightly lower overhead than joint broadcasting when proportional fair scheduling is employed and the users are spread out over the cell area. The results also indicate that the scheduling granularity standardized for LTE provides a good tradeoff between scheduling granularity and overhead. [ABSTRACT FROM PUBLISHER]

We present in this paper a novel proactive interference avoidance scheme for mobile-to-mobile (M2M) communication as an underlay in Long-Term Evolution (LTE) downlink networks. The proposed scheme introduces an intelligent user-selective resource allocation algorithm that aims to proactively avoid interference between M2M pairs and LTE regular users while achieving a satisfactory link budget for both M2M and LTE regular links. The proposed resource allocation algorithm is based on a novel low-complexity interference estimation approach that requires only channel quality indicator (CQI) information (CSI). Since the CQI reporting mechanism has been already implemented in current LTE systems, the proposed algorithm is indeed practical and standard compatible, compared with the existing works that require perfect knowledge of the channel state information (CSI). In this research, achievable data throughput with LTE-compliant adaptive modulation and coding has been adopted as the optimization measurement, rather than the information-theoretic capacity. Therefore, the performance of the proposed scheme is practically realizable in LTE communication systems. Since the performance of the proposed scheme highly relies on the accuracy of CQI feedback, we also carry out performance analysis to evaluate the impact of CQI estimation error and delay. The simulation results show that the total achievable data throughput for LTE networks is dramatically enhanced by M2M communication with this proposed interference avoidance resource allocation scheme. [ABSTRACT FROM PUBLISHER]

Interference and collisions greatly limit the throughput of mesh networks that use contention-based MAC protocols such as IEEE 802.11. Significantly higher throughput is achievable if transmissions are scheduled. However, traditional methods to compute optimal schedules are computationally intractable (unless co-channel interference is neglected). This paper presents a practical technique to compute optimal schedules. The resulting algorithm searches for a low-dimensional optimization problem that has the same solution as the full problem. Such a low-dimensional problem is shown to always exist. The resulting algorithm converges arithmetically fast or geometrically fast, depending on whether the objective is to maximize the proportional fair throughput or to maximize the minimum throughput, where the minimum is over all flows in the network. At each iteration of the algorithm, a graph-theoretic optimization known as the maximum weighted independent set (MWIS) problem must be solved. While the general MWIS problem is NP-hard in the worst case, we find that the MWIS can be solved efficiently. Specifically, computational experiments on over 17\,000 topologies indicate that the ratio of the time to solve the MWIS and the mean degree of the conflict graph grows polynomially with the number of nodes. [ABSTRACT FROM PUBLISHER]

This paper proposes an automatic control framework to address the problem of distributed and opportunistic transmission power control in wireless communication networks. The proposed framework allows high flexibility on the quality of service (QoS) provision by exploiting the link quality variation. It associates a standard target tracking algorithm with a dynamic target QoS determined by a linear quadratic regulator (LQR) controller that induces an opportunistic behavior. Strict QoS requirements can also be fulfilled by choosing appropriately the weighting parameters of the LQR synthesis. To prevent performance degradation due to measurement uncertainties, robust solutions are developed. The proposed mixed \cal H2/\cal H\infty distributed power control combines the optimal \cal H2 control and the robust \cal H\infty control to obtain trade-off solutions. All developed algorithms present low computational complexity, and the mixed \cal H2/\cal H\infty distributed power control can operate as the pure \cal H2 control if the robust constraint is relaxed according to the choice of a single parameter. The performance of proposed algorithms is assessed through computer simulations. [ABSTRACT FROM PUBLISHER]

This paper investigates transmit antenna selection for linearly precoded multiuser multiple-input–multiple-output (MU-MIMO) systems. First, in some precoded single-user MIMO systems, using all transmit antennas does not always lead to the best performance due to ill-conditioned channel matrices. This condition motivates us to investigate whether a similar result can be obtained in MU-MIMO systems. Based on the derived analytical results, we found that, for a given number of transmit antennas, decreasing the number of active transmit antennas [number of radio frequency (RF) units] always degrades system performance in the linearly precoded MU-MIMO systems. However, in practical systems, RF units are expensive. To reduce the hardware cost, antenna selection is usually used to reduce the number of RF units. Thus, we further analyze the performance loss due to transmit antenna selection (TAS). These analytical results provide good design references for using TAS in practical systems. Moreover, based on the analytical results, we proposed several simple TAS algorithms for linearly precoded MU-MIMO systems. Complexity analysis and simulation results show that the computational complexity of the proposed algorithms can significantly be reduced, whereas the performance is still comparable with the optimal selection scheme. As a result, the analyzed results enable us to better understand how TAS affects the MU-MIMO systems. In addition, the proposed algorithms make TAS more feasible to be used in practical systems. [ABSTRACT FROM PUBLISHER]

In this paper, a bidirectional Fano algorithm (BFA) is proposed, in which a forward decoder (FD) and a backward decoder (BD) search in the opposite direction in the code tree simultaneously. It is shown that the proposed BFA can achieve more than twice the decoding throughput compared to the conventional unidirectional Fano algorithm (UFA) and there is higher throughput improvement at low signal-to-noise ratio (SNR). This new BFA decoding technique is applied in the parallel convolutional decoding architecture in very high throughput systems, such as the WirelessHD system. Due to the variability in the decoding delays of the parallel codewords, a scheduler is introduced in the parallel Fano decoding architecture which can dynamically allocate the idle decoders to assist with decoding the other parallel codewords in a bidirectional manner. It is shown that the proposed parallel Fano decoding with scheduling can dramatically increase the decoding throughput compared to the parallel Fano decoding without scheduling, and its computational complexity is much lower than that of parallel Viterbi decoding, especially at high SNR. The performance of the parallel Fano decoding with different scheduling schemes is also compared and analyzed in detail in the paper. [ABSTRACT FROM AUTHOR]

Channel estimation is essential in a Multiple Input Multiple Output (MIMO) wireless communication in 5G. In the MIMO system, numerous antennas are utilized on the sender and receiver sides for enhancing spectral efficiency and reliability. The channel estimation can improve the exactness of the received signal. Increasing the number of channel coefficients can make channel estimation fairly complex. Transmitting multiple paths have some delay and signal echoes. Therefore, channel assessment is very necessary for efficiently receiving the transmitted signals. A novel Enhanced Convolution Neural African Buffalo approach was developed for channel estimation purposes to overcome such issues. Moreover, the additive white Gaussian noise channel is created for MIMO communication. The simulation of this research is done using the network simulator platform. Sequentially, the proposed system outcomes are compared with other techniques in terms of throughput, accuracy, signal to noise ratio, mean square error, and bit error rate. The comparison results also proved the effectiveness of the proposed approach. [ABSTRACT FROM AUTHOR]

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 that is orders of magnitude lower than that of exhaustive search. [ABSTRACT FROM PUBLISHER]

We develop a distributed throughput-optimal power allocation algorithm in wireless networks. The study of this problem has been limited due to the nonconvexity of the underlying optimization problems that prohibits an efficient solution even in a centralized setting. By generalizing the randomization framework originally proposed for input queued switches to SINR rate-based interference model, we characterize the throughput-optimality conditions that enable efficient and distributed implementation. Using gossiping algorithm, we develop a distributed power allocation algorithm that satisfies the optimality conditions, thereby achieving (nearly) 100 percent throughput. We illustrate the performance of our power allocation solution through numerical simulation. [ABSTRACT FROM PUBLISHER]

Multiuser diversity techniques are used in multicarrier data systems to enhance downlink cell throughput. This requires downlink channel information from the users that is opportunistically used by a base station to send data to the users with good channel condition. Channel feedback from the user to the base station incurs high overhead especially when many users are in the cell and each user needs to report channel information over multiple channels, as in OFDMA systems. To reduce the quantity of feedback information without significant throughput degradation, a practical strategy is to deliver feedback on a partial set of channels with the best channel quality. We call it best feedback, and this reporting scheme carried out for best four or five among 24 channels has been already adopted in the IEEE 802.16e standard. Considering real feedback conditions, we investigate the performance of a best feedback scheme and derive the optimal number of channels for which information needs to be fed back to keep the throughput gap (compared to a full feedback scheme) within a target margin. From the optimal condition, we propose an adaptive best feedback algorithm, where the number of reported channels is adjusted to adapt to the number of users in the cell. We also propose an adjusted periodic feedback algorithm, where users are divided into groups and scheduling is carried out group by group, so a user can report feedback information when his group is scheduled, thereby reducing the frequency of feedback transmission. To support differentiated performance, we further propose heterogeneous feedback algorithms where users are divided into heterogeneous groups, each with a different group size or a different feedback period. Numerical results validate our analysis and provide meaningful insights into the design of various channel feedback schemes. [ABSTRACT FROM AUTHOR]