On the macroscopic optimization of multicell wireless systems with multiuser detection and multiple antennas - uplink analysis

In this paper, we consider a system with K single-antenna client users, n/sub B/ base stations (each base station has n/sub R/ antennas), as well as a centralized controller. A client user could be associated with a single base station at any time. All the base stations operate at the same frequency and have optimal multiuser detection per base station which cancels intracell interference only. We consider a general problem of uplink macroscopic resource management where the centralized controller dynamically determines an appropriate association mapping of the K users with respect to the n/sub B/ base stations over a macroscopic time scale. We propose a novel analytical framework for the above macroscopic scheduling problems. A simple rule is to associate a user with the strongest base station (camp-on-the-strongest-cell), and this has been widely employed in conventional cellular systems. However, based on the optimization framework, we found that this conventional approach is in fact not optimal when multiuser detection is employed at the base station. We show that the optimal macroscopic scheduling algorithm is of exponential complexity, and we propose a simple greedy algorithm as a feasible solution.     

A theoretical framework of uplink macroscopic optimization for multicell systems with multiuser detection

In this paper, we consider a system with K client users (single antenna), n/sub B/ base stations (each has single antenna), as well as a centralized controller. All the base stations operate at the same frequency and have optimal multiuser detection (MUD) per base station. The MUD at the base station is able to cancel only the intracell interference but not the intercell interference. A client user is associated with a single base station at any time. We consider a general problem of uplink macroscopic optimization (or macroscopic resource management) where the centralized controller dynamically determines an appropriate association mapping of the K users with respect to the n/sub B/ base stations over a macroscopic time scale. We propose a novel multicell capacity region as well as an analytical framework for the above macroscopic optimization problem. A simple conventional rule is to associate a user with the strongest base station (camp-on-the-strongest-cell) and this has been widely employed in conventional cellular systems. However, based on the optimization framework, we found that this conventional approach is in fact not optimal when MUD is employed at the base station. We show that the optimal macroscopic optimization algorithm is of exponential complexity and we propose a simple greedy algorithm as a feasible solution. It is shown that the macroscopic optimization gain over the conventional approach increases with decreasing path loss exponent due to large area of overlapping.     

Proportional Fair Space–Time Scheduling for Wireless Communications

We extend the proportional fair (PF) scheduling algorithm to systems with multiple antennas. There are$K$client users (each with a single antenna) and one base station (with$n_R$antennas). We focus on the reverse link of the system, and assume a slow-fading channel where clients are moving with pedestrian speed. Qualcomm's original PF scheduling algorithm satisfies the PF criteria only when the communication is constrained to one user at a time with no power waterfilling. However, the original PF algorithm does not generalize easily when we have$n_R$receive antennas at the base station. In this paper, we shall formulate the PF scheduling design as a convex optimization problem. One challenge is in the optimal power allocation over the multiantenna multiaccess capacity region, which is still an open problem. For practical consideration, we consider multiuser minimum mean-square error processing at the base station. To obtain first-order insight, we propose an asymptotically optimal PF scheduling solution. Using the proposed PF solution for a multiantenna base station, the system capacity is enhanced by exploiting the multiuser selection diversity, as well as the distributed multiple-input multiple-output configuration. It is found that the PF scheduler achieves a good balance between fairness and system capacity gain.     

Pilot scheduling algorithm based on cell classification-cross entropy for massive MIMO

The massive MIMO system is equipped with a large number of antennas at the base station to serve many users, but it is affected by the inter-cell interference which called pilot contamination. A pilot scheduling scheme was designed to reduce the effect of pilot contamination. Pilot scheduling problem belongs to the arrangement optimization problem, and the algorithm computational complexity of greedy algorithm to search the optimal solution is greatly. In order to reduce the influence of the interference of the community, a new pilot scheduling schemes was proposed by so this article using cell classification and cross entropy(CE) mechanism to reduce the effect of pilot contamination on the system. The simulation results show that the proposed algorithm can not only improve the performance of the system, but also reduce the computational complexity.     

Cross layer design of uplink multi-antenna wireless systems with outdated CSI

This letter studies the performance of uplink cross layer design for multi-antenna systems with outdated channel state information (CSI). We consider a multi-user system with one base station (with n/sub R/ receive antennas) and K mobile users (each with single transmit antenna). The multi-user physical layer is modeled based on information theoretical framework and the cross layer design can be cast as an optimization problem. In view of the high computation complexity in the optimal solution, we propose a low complexity genetic algorithm as suboptimal solution. We found that with outdated CSI, there is significant degradation in the spatial multiplexing and multi-user diversity gain due to potential packet transmission outage as well as misscheduling. To address the poor performance in the presence of outdated CSI, we propose two simple but effective empirical solutions, namely the rate quantization and rate discounting, to tackle the packet outage problem. For instance, it is well-known that rate quantization imposes system capacity loss in systems with perfect CSI. However, we found that rate quantization can enhance the robustness of system capacity with respect to outdated CSI.     

Resource scheduling in downlink LTE-advanced system with carrier aggregation

In this paper,we focus on the resource scheduling in the downlink of long term evolution advanced (LTE-A) assuming equal power allocation among subcarriers.Considering the backward compatibility,the LT...     

On the design of MIMO block-fading channels with feedback-link capacity constraint

In this paper, we propose a combined adaptive power control and beamforming framework for optimizing multiple-input/multiple-output (MIMO) link capacity in the presence of feedback-link capacity constraint. The feedback channel is used to carry channel state information only. It is assumed to be noiseless and causal with a feedback capacity constraint in terms of maximum number of feedback bits per fading block. We show that the hybrid design could achieve the optimal MIMO link capacity, and we derive a computationally efficient algorithm to search for the optimal design under a specific average power constraint. Finally, we shall illustrate that a minimum mean-square error spatial processor with a successive interference canceller at the receiver could be used to realize the optimal capacity. We found that feedback effectively enhances the forward channel capacity for all signal-to-noise ratio (SNR) values when the number of transmit antennas (n/sub T/) is larger than the number of receive antennas (n/sub R/). The SNR gain with feedback is contributed by focusing transmission power on active eigenchannel and temporal power waterfilling . The former factor contributed, at most, 10log/sub 10/(n/sub T//n/sub R/) dB SNR gain when n/sub T/>n/sub R/, while the latter factor's SNR gain is significant only for low SNR values.     

Low complexity MIMO scheduling with channel decomposition using capacity upperbound

In multiuser MIMO systems, the base station schedules transmissions to a group of users simultaneously. Since the data transmitted to each user are different, in order to avoid the inter-user interference, a transmit preprocessing technique which decomposes the multiuser MIMO downlink channel into multiple parallel independent single-user MIMO channels can be used. When the number of users is larger than the maximum that the system can support simultaneously, the base station selects a subset of users who have the best instantaneous channel quality to maximize the system throughput. Since the exhaustive search for the optimal user set is computationally prohibitive, a low complexity scheduling algorithm which aims to maximize the capacity upper bound is proposed. Simulation results show that the proposed scheduling algorithm achieves comparable total throughput as the optimal algorithm with much lower complexity.     

Distributed closed-loop spatial multiplexing for uplink multiuser systems

Focusing on the uplink, where mobile users (each with a single transmit antenna) communicate with a base station with multiple antennas, we treat multiple users as antennas to enable spatial multiplexing across users. Introducing distributed closed-loop spatial multiplexing with threshold-based user selection, we propose two uplink channel-assigning strategies with limited feedback. We prove that the proposed system also outperforms the standard greedy scheme with respect to the degree of fairness, measured by the variance of the time averaged throughput. For uplink multi-antenna systems, we show that the proposed scheduling is a better choice than the greedy scheme in terms of the average BER, feedback complexity, and fairness. The numerical results corroborate our findings.     

Sum capacity of the reverse link for multi-cell multi-user cellular systems

This letter studies the information-theoretic sum capacity of the reverse link for multi-cell, multi-user cellular systems subjected to a peak power constraint. It is proven that, for the optimal scheduling, there will be at most one user transmitting at part of the peak power within each cell. Therefore, we approximate the optimization scheduling problem to a combination optimization problem which can be solved by standard simulated annealing algorithm. Further, we propose a low complexity cell greedy scheduling algorithm which can achieve almost the same performance as simulated annealing algorithm.     

Optimizing MIMO antenna systems with channel covariance feedback

We consider a narrowband point-to-point communication system with n/sub T/ transmitters and n/sub R/ receivers. We assume the receiver has perfect knowledge of the channel, while the transmitter has no channel knowledge. We consider the case where the receiving antenna array has uncorrelated elements, while the elements of the transmitting array are arbitrarily correlated. Focusing on the case where n/sub T/=2, we derive simple analytic expressions for the ergodic average and the cumulative distribution function of the mutual information for arbitrary input (transmission) signal covariance. We then determine the ergodic and outage capacities and the associated optimal input signal covariances. We thus show how a transmitter with covariance knowledge should correlate its transmissions to maximize throughput. These results allow us to derive an exact condition (both necessary and sufficient) that determines when beamforming is optimal for systems with arbitrary number of transmitters and receivers.     

Capacity achieving high rate space-time block codes

It is well known, that the Alamouti scheme is the only space-time code from orthogonal design achieving the capacity of multiple-input multiple-output (MIMO) wireless communication system with n/sub T/=2 transmit antennas and n/sub R/=1 receive antenna. In this work, we propose the n-times stacked Alamouti scheme for n/sub T/=2n transmit antennas and show that this scheme achieves the capacity in the case of n/sub R/=1 receive antenna. For the more general case of more than one receive antenna, we show that if the number of transmit antennas is higher than the number of receive antennas we achieve a high portion of the capacity with this scheme.     

Design differentiated service multicast with selfish agents

Differentiated service (DiffServ) is a mechanism to provide the quality-of-service (QoS) with a certain performance guarantee. In this paper, we study how to design DiffServ multicast when every relay link is an independent selfish agent. We assume that each link e/sub i/ is associated with a (privately known) cost coefficient c/sub i/ such that the cost of e/sub i/ to provide a transmission service with bandwidth demand x is c/sub i//spl middot/x. Further, we assume that there is a fixed source node s and a set R of receivers, each of which requires from s data with a minimum bandwidth demand. The DiffServ multicast problem is to compute a link-weighted tree rooted at s and spanning R such that the receivers' demands are met. This generalizes the traditional link-weighted Steiner tree problem. We first show that a previous approximation algorithm does not directly induce a strategyproof mechanism. We then give a new polynomial time algorithm to construct a DiffServ multicast tree whose total cost is no more than eight times the optimal total cost when the cost coefficient of each link is known. Based on this tree, we design a truthful mechanism for DiffServ multicast, i.e., we give a polynomial-time computable payment scheme to compensate all chosen relay links such that each link maximizes its profit when it declares its cost coefficient truthfully.     

A multidimensional phase-locked loop for blind multiuser detection

This paper concerns the problem of blind multiuser detection, a special case of the blind source separation problem in which the source signals have finite alphabets. Specifically, we address the problem of identifying and resolving the n/spl times/n unitary matrix ambiguity U that results from whitening the receiver observations, where n is the number of sources. We propose the multidimensional phase-locked loop (MPLL) as a generalization of a scalar decision-directed PLL to vector-valued signals. The MPLL adapts an estimate of U according to the recursion U/spl circ//sub k+1/=U/spl circ//sub k/R/sub k/, where R/sub k/ is an n-dimensional Householder-like rotation depending on only the kth receiver observation. The O(n/sup 2/) complexity of an efficient implementation of the algorithm is extremely low. Nevertheless, simulation results demonstrate good convergence properties and superior steady-state performance when compared with prior techniques. The algorithm is also able to accommodate large alphabets and shaped alphabets.     

基于列生成的毫米波多连接链路配置调度算法

多连接技术允许用户同时建立和保持与多个小区/接入点的连接,通过网络元素之间的协调在吞吐量和可靠性方面大幅提高网络性能。针对毫米波通信中超高频段的链路中断问题,研究了多连接基于链路配置的调度算法,以提高链路调度效率,降低复杂度。首先,在系统模型中采用链路配置作为优化变量;其次,设计了多连接比例公平的调度准则;最后,提出一种基于列生成算法的链路配置调度优化算法,利用Dantzig-Wolfe分解将原问题分解为限制主问题和定价问题,并结合分支定界方法获得最优解。仿真结果表明,所提算法能够在数值上逼近全局最优,并且比现有的毫米波蜂窝网络链路调度方案增益平均提高40%以上。     

Guaranteed scheduling for switches with configuration overhead

We present three algorithms that provide performance guarantees for scheduling switches, such as optical switches, with configuration overhead. Each algorithm emulates an unconstrained (zero overhead) switch by accumulating a batch of configuration requests and generating a corresponding schedule for a constrained switch. Speedup is required both to cover the configuration overhead of the switch and to compensate for empty slots left by the scheduling algorithm. Scheduling algorithms are characterized by the number of configurations N/sub s/ they require to cover a batch of requests and the speedup required to compensate for empty slots S/sub min/. Initially, all switch reconfiguration is assumed to occur simultaneously. We show that a well-known exact matching algorithm, EXACT, leaves no empty slots (i.e., S/sub min/=1), but requires N/sub s//spl ap/N/sup 2/ configurations for an N-port switch leading to high configuration overhead or large batches and, hence, high delay. We present two new algorithms that reduce the number of configurations required substantially. MIN covers a batch of requests in the minimum possible number of configurations, N/sub s/=N, but at the expense of many empty slots, S/sub min//spl ap/4log/sub 2/N. DOUBLE strikes a balance, requiring twice as many configurations, N/sub s/=2N, while reducing the number of empty slots so that S/sub min/=2. Loosening the restriction on reconfiguration times, the scheduling problem is cast as an open shop. The best known practical scheduling algorithm for open shops, list scheduling (LIST), gives the same emulation requirements as DOUBLE. Therefore, we conclude that our architecture gains no advantages from allowing arbitrary switch reconfiguration. Finally, we show that DOUBLE and LIST offer the lowest required speedup to emulate an unconstrained switch across a wide range of port count and delay.     

静态分簇下的大规模分布式天线系统用户调度算法

在大规模分布式天线系统中，静态分簇和用户调度带来的簇间干扰问题会导致系统和速率下降。针对这个问题，提出了一种两阶段贪婪用户调度算法。首先，每个簇内并行实施贪婪用户调度；然后，从全局上再次利用贪婪算法来剔除簇间干扰较大、服务质量较差的用户，使得系统和速率进一步提升。仿真结果表明，随着不同系统参数的改变，两阶段贪婪用户调度算法可有效提高系统和速率。     

Channel exploration for aggregation in cognitive radio system

In this paper we focus on channel exploration problem in the cognitive radio (CR) system, where the exploration process consumes time. The explored channels can be aggregated by CR user for its transmission. CR user adopts a synchronous slotted structure with primary users. Usually, the channel exploration problem is formulated as an optimal stopping problem. However, most previous related works are based on the assumption that channel state changes randomly across slots. In the system, channel state contains channel availability and link quality on the channel. When channel state’s transition across slots follows a Markov process, the problem becomes different. Then we introduce a two-dimension Partially Observable Markov Decision Process framework into the optimal stopping problem. We concentrate on the myopic rule of the new problem. By exploring the structure property of the myopic rule, we can achieve the optimal performance under the myopic rule with a lower computation complexity. To further reduce the computation complexity for a practical application, we then propose a greedy approach. The simulation results show CR user can obtain a near-optimal performance by the greedy approach. The validity of our proposed approaches is also verified by simulation.     

Genetic algorithm‐based scheduling in cognitive radio networks under interference temperature constraints

The proliferation of wireless technologies and services has intensified the demand for the radio spectrum. However, the currently existing fixed spectrum assignment policy leads to an inefficient and unevenly distributed spectrum utilization. Cognitive radio paradigm has been proposed to alleviate these drawbacks by employing dynamic spectrum access (DSA) methodology. Federal Communications Commission (FCC) has proposed the interference temperature model, which enables the unlicensed users to utilize the licensed frequencies simultaneously with the licensed users as long as they conform to the interference temperature constraints. Recently, throughput and delay optimal schedulers that meet the interference temperature constraints in cognitive radio networks have been formulated in the literature. However, these schedulers have high computational complexity. In this paper, we propose genetic algorithm (GA)‐based suboptimal methods addressing these throughput and delay optimal scheduling problems. The simulation results corroborate that our GA‐based approach yields very close performance to the optimal solutions and operates with much lower complexity. Copyright © 2010 John Wiley & Sons, Ltd.     

Coding‐aware routing and scheduling in WiMAX‐based mesh networks: a cross‐layer design approach

In this paper, we propose a cross‐layer design framework for the joint problem of coding‐aware routing and scheduling in WiMAX‐based mesh networks with unicast sessions. The model attempts to maximize the system throughput by exploiting opportunistic coding opportunities through appropriate routing and by achieving efficient spectrum reuse through appropriate link scheduling. We assume centralized scheduling at the base station and focus on minimizing the total schedule length to satisfy a certain traffic demand. Minimizing the schedule length is equivalent to maximizing the system throughput. We present a linear programming optimization model for the joint problem, which relies on the enumeration of all possible schedules. Given its complexity, we decompose the problem using a column generation approach. Our numerical results show that significant gains may be achieved when network coding is incorporated into the design. We compare the performance with that of a joint coding‐oblivious model with and without transmission power control. Copyright © 2011 John Wiley & Sons, Ltd.