Design and analysis of random access networks with interference alignment

A multibeam satellite communications network serving multiple zones with random access uplinks and one concentrating transponder in the downlinks is studied. Two different models are described. By the first model the random access protocol S-ALOHA is used in the uplink, and by the second one - the protocol R-ALOHA. The performance of such a random access system is analysed with the aid of the equilibrium point analysis (EPA). Channel error effects are also included in the analysis to reflect applications in satellite communications using very small aperture terminals (VSAT). The synthesised analytical models allow to design the values of communication satellite system parameters.

In this paper we analyze a multiple-input-multiple-output interference channel where nodes randomly distributed on a plane utilize interference alignment to reduce the point-to-point outage. We model the spatial distribution of the nodes as a spatial Poisson cluster point process with equal sized clusters. Each cluster uses intra-cluster interference alignment to suppress interference. We link the accuracy of channel state information to the distance between the nodes, i.e., for a fixed SNR, the quality of CSI degrades with increasing distance. Accounting for the inter-cluster unaligned interference, we compare intra-cluster interference alignment with open-loop spatial multiplexing. In our analysis we find common system setups where the benefits of using interference alignment over spatial multiplexing degrade the most due to the imperfect channel state information.

Massive number of the uncoordinated machine-type communication (MTC) terminals accessing the satellite hub overloads the CDMA slotted Aloha (SA) random access system with a large capacity, whose performance is drastically reduced, and introduce a long service time for the activated terminals. To improve the performance of the random access scheme, we propose a Least Squares Support Vector Machines (LS-SVM) regression based random access control scheme with a sliding window. By using one step and multistep LS-SVM predicting algorithm, satellite hub predicts the channel loads of the future slots, and computes the access probability, then broadcasts to all MTC devices. Simulation results show that the proposed scheme approaches to the perfect control scheme, and the throughput is more than 2.5 times, and the total service time for all the activated terminals is less than 0.4 times that of the available EKF based control scheme in the satellite network.

We consider a secondary user with energy harvesting capability. We design access schemes for the secondary user which incorporate random spectrum sensing and random access, and which make use of the primary automatic repeat request (ARQ) feedback. The sensing and access probabilities are obtained such that the secondary throughput is maximized under the constraints that both the primary and secondary queues are stable and that the primary queueing delay is kept lower than a specified value needed to guarantee a certain quality of service (QoS) for the primary user. We consider spectrum sensing errors and assume multipacket reception (MPR) capabilities. Numerical results are presented to show the enhanced performance of our proposed system over a random access system, and to demonstrate the benefit of leveraging the primary feedback.

In wireless interference networks, orthogonal medium access techniques like time division multiple access (TDMA) and frequency division multiple access (FDMA) achieve merely one degree of freedom (DoF). However, by using interference alignment (IA) the achievable DoF can be linearly scaled up with the number of users. Nevertheless, this outperformance of IA heavily depends on the availability of perfect channel state information (CSI), which is not a realistic assumption in practice. Since under imperfect CSI, the performance of IA may become severely degraded, design of enhanced IA algorithms by relying on the availability of merely imperfect CSI is of particular interest. In this paper, we propose a least squares (LS)-based IA algorithm which adaptively designs the beamformers based on the knowledge of the channel estimation error variance, which is possible to be known in advance. Then, we compare the performance of the proposed scheme with interference leakage minimization algorithms. It is shown that under both perfect and imperfect CSI, the proposed LS-based IA outperforms interference leakage minimization algorithms.

We study the throughput capacity of wireless networks which employ (asynchronous) random-access scheduling as opposed to deterministic scheduling. The central question we answer is: how should we set the channel-access probability for each link in the network so that the network operates close to its optimal throughput capacity? We design simple and distributed channel-access strategies for random-access networks which are provably competitive with respect to the optimal scheduling strategy, which is deterministic, centralized, and computationally infeasible. We show that the competitiveness of our strategies are nearly the best achievable via random-access scheduling, thus establishing fundamental limits on the performance of random- access. A notable outcome of our work is that random access compares well with deterministic scheduling when link transmission durations differ by small factors, and much worse otherwise. The distinguishing aspects of our work include modeling and rigorous analysis of asynchronous communication, asymmetry in link transmission durations, and hidden terminals under arbitrary link-conflict based wireless interference models.

For slotted random access systems, the slotted ALOHA protocol provides the maximum throughput of 0.368 (packets/slot) while in the category of splitting (or tree) algorithms, the maximum achievable throughput can reach up to 0.487 with the first-come first-serve (FCFS) algorithm. It has been so far demonstrated that the FCFS algorithm can achieve this maximum throughput only for Poisson traffic. This may limit its application in practical systems, where packet arrivals may not be Poissonian. In this paper, we propose a novel online transmission control framework that introduces random splitting upon collisions and controls the transmission probabilities optimally at each slot by estimating the number of active users in the system. The proposed algorithm is said to be online as it estimates the number of active users slot by slot recursively, and thus can adapt to network dynamics. We first show that the splitting algorithm of our interest can achieve the throughput of 0.532 if the number of users involved in a collision could be known, which serves as a guideline for the upper limit for the random access systems with splitting. Then, when the information on the number of collided users is not available, we show that the proposed algorithm can achieve the maximum throughput of 0.487 for Poisson arrivals while achieving shorter access delay than FCFS. When more bursty traffic than Poisson process is applied, the proposed algorithm shows much better throughput and delay performance than FCFS.

We consider connectionless packet switched CDMA systems where all users have to share a common bandwidth and signature sequence on a contention basis. Multiple access is achieved by the simultaneous acquisition of different users' spread spectrum transmissions. The throughput of a pure random access system of this kind suffers from acquisition errors and timing mismatches of the active users as well as instability issues. We consider schemes that use information available at the physical layer, i.e., at the output of the multiuser access detector (MUAD) designed for this random access system, to improve and stabilize the throughput. We consider timing randomization, pseudo-Bayesian stabilization and collision resolution algorithms and report the corresponding numerical results.

A finite user model for stability analysis of announced retransmission random access (ARRA) systems is presented. It is shown that as in other ALOHA-type random access protocols, bistable behavior and saturation can occur unless a suitably long retransmission delay is used. The higher expected delay associated with an unconditionally stable nonadaptive system can be avoided by adaptively varying retransmission parameters with system backlog. Optimal retransmission control (ORC) policies of this type are derived and evaluated.

With the rapid growth of network users, how to increase the system capacity has become an urgent problem for the current communication system in the case of limited spectrum resources. The introduction of multi-user systems has increased system capacity, but it has also led to inter-user interference, which has further affected system capacity. To solve the multi-user interference problem, interference alignment is introduced. Interference Alignment (IA) is an interference cancellation technique that effectively eliminates the effects of interfering signals by compressing the interfering signal into a space independent of the desired signal and then forcing the interfering signal to zero at the receiving end. However, in practical applications, interference-aligned transceivers require a joint design, which is often difficult to achieve. The traditional approach is to mathematically expect it, but it also leads to some degree of irrationality in the transceiver design. In this paper, based on the traditional least square interference alignment (LS-IA) algorithm, a symbol-detection-assisted least square interference alignment (SDA-LS-IA) algorithm is proposed for its shortcomings in transceiver algorithm design. Firstly, based on the precoding matrix and the zero-forcing matrix of the transceiver designed by the traditional LS-IA, the symbol detection is performed, and then the transceiver is designed again according to the detection symbol, and then the symbol detection is performed. The computer simulation proves that the proposed algorithm has better anti-interference performance than the traditional LS-IA.

The use of the stack protocol (also called tree protocol) to introduce a priority mechanism on the random access stage in wideband code-division multiple access (W-CDMA) seems more robust when compared with slotted ALOHA. In fact, the protocol stability in infinite population and the guaranteed low-contention delay appear very attractive for a random-access system proposal. Also, for third-generation networks, defining and offering priorities in wireless communication systems appears to be more important for operators and system users. Indeed, after voice only 2G networks, 3G systems (UMTS) should offer more services with quality and priority. However, all priorities in the UMTS system are based on the dedicated channel, which is offered after the random-access mechanism that uses the weak access protocol, slotted ALOHA. We analyze theoretically the possibility of applying the tree random-access protocol for the W-CDMA part of the UTRA radio interface proposition. We also study a priority system applied for random access directly. This seems to be very useful to propose priority for some applications, especially emergency applications. The analytical model uses generating functions and an algebraic method in order to show the stack protocol performance. Also, numerical and simulation results are presented and show the predominance of this protocol compared with the slotted-ALOHA mechanism.

A new channel coding approach was proposed in [1] for random multiple access communication over the discrete-time memoryless channel. The coding approach allows users to choose their communication rates independently without sharing the rate information among each other or with the receiver. The receiver will either decode the message or report a collision depending on whether reliable message recovery is possible. It was shown that, asymptotically as the codeword length goes to infinity, the set of communication rates supporting reliable message recovery can be characterized by an achievable region which equals Shannon's information rate region possibly without a convex hull operation. In this paper, we derive achievable bounds on error probabilities, including the decoding error probability and the collision miss detection probability, of random multiple access systems with a finite codeword length. Achievable error exponents are obtained by taking the codeword length to infinity.

Multiple-input multiple-output (MIMO) wireless communication systems can achieve higher throughput through interference alignment. For a small number of users, determining the maximum possible degrees of freedom as well as the feasibility of interference alignment in MIMO systems is well studied. However, the issues of scheduling in systems employing interference alignment and serving a large number of users have received little attention so far. In this paper, we study the problem of joint scheduling, interference alignment, and packet admission control in MIMO wireless systems with the goal of maximizing system throughput subject to stability constraints. We formulate a stochastic network optimization problem and propose a scheduling and interference alignment (SIA) algorithm. In each time slot, SIA schedules some users among many competing ones to transmit data, and determines encoding and decoding matrices for the selected users. Packet admission control is performed in each time slot. In addition, we propose a heuristic semi-distributed algorithm (SDSIA), which has a lower computational complexity than the SIA algorithm. Via simulation, we evaluate the performance of SIA and SDSIA for different algorithm parameters and different numbers of users. We also compare the performance of SDSIA with other approaches which do not simultaneously exploit interference alignment and scheduling and find that the combination of these two techniques increases the achievable data rate dramatically.

Interference alignment (IA) is a multi-user wireless communication strategy that enables several transmitters simultaneous communicate with their paired receivers without interfering each other. IA is a multiplexing gain optimal strategy at the expense of requiring the global channel state information (CSI) at the transmitters. Most previous works assume the availability of perfect CSI which is not possible in practice. The performance of IA algorithms based on perfect CSI drop dramatically in circumstance where CSI errors exist. In this paper we propose a robust minimum mean square error (MMSE) IA algorithm with imperfect CSI. Based on a Gaussian statistic channel error model, we give the mean square error objective function with per transmitter power constraint. The objective function is not joint convex in precoders and decoders, but convex in precoders with fixed decoders and vice versa. We convert the problem to two convex sub problems and iteratively find the solutions. Numerical results are presented to validate the effectiveness of proposed algorithm when CSI errors exist.

Some basic concepts about MRI acquisition are reviewed. The foundations here described will be necessary to understand the different assumptions about signal and noise used along the book; the statistical models defined in following chapters are based on the premises here set. We especially focus on modeling the k-space and x-space from a signal processing point of view. Sequences and specifics on acquisition modalities will be left aside to confine us to an upper level modeling of the acquired signal. First, we review the formation process in single-coil systems and then we extrapolate the results to multiple-coil systems. For the sake of illustration, two methods are considered in order to fuse the information from the different antennae into one single magnitude image: the sum of squares (SoS) and the spatial matched filter (SMF). The chapter concludes with the analysis of some parallel MRI methods, focusing on two of the most used ones, SENSE and GRAPPA.