Ying Jun Angela Zhang

Cooperative Beamforming for Cognitive Radio Networks: A Cross-Layer Design

Cognitive Radio (CR) can significantly improve the utilization of the precious radio spectrum by allowing Secondary Users (SUs) to borrow the licensed spectrum if they do not cause harmful interference to Primary Users (PUs). As a wireless technology, CR confronts the challenges of wireless channels inevitably and thus wishes to employ node cooperation to achieve spatial diversity gain. However, conventional cooperative diversity technologies require two idle timeslots for each transmission. This implies two temporal spectrum holes are needed for each transmission when the technologies are applied to CR Networks (CRNs). This can cause severe delay, as temporal spectrum holes are only available from time to time in CRNs. In this paper, we present a cross-layer approach, where cooperative beamforming is adopted to forward messages in busy timeslots without causing interference to PUs, so as to achieve cooperative diversity gain and improve Quality of Service (QoS) for SUs without consuming additional idle timeslots or temporal spectrum holes. In the physical layer, the beamforming weight vector and the cooperative diversity gain are obtained using a geometric approach. The MAC layer of the cooperative communication in CRNs can be modeled by a tandem queue, where the source queue is the bottleneck. Therefore, we propose an optimal opportunistic priority scheduling scheme in the MAC layer, the timeout probability of which is obtained using an absorbing Markov chain. A cross-layer optimization of the transmission rate is then carried out to jointly reduce the timeout and outage probabilities. Its significant QoS gain is demonstrated by simulations.

Distributed Nonconvex Power Control using Gibbs Sampling

Transmit power control in wireless networks has long been recognized as an effective mechanism to mitigate co-channel interference. Due to the highly non-convex nature, optimal power control is known to be difficult to achieve if a system utility is to be maximized. In our earlier paper , we have proposed a centralized optimal power control algorithm that obtains the global optimal solution for both concave and non-concave system utility functions. A question remained unanswered is whether such global optimal solution can be achieved in a distributed manner. This paper addresses the question by developing a Gibbs Sampling based Asynchronous distributed power control algorithm (referred to as GLAD). The proposed algorithm quickly converges to the global optimal solution regardless of the concavity, differentiability and monotonicity of the utility function. To further enhance the practicality of the algorithm, this paper proposes two variants of the GLAD algorithm, namely I-GLAD and NI-GLAD, to reduce message passing in two dimensions of communication complexity, i.e., time and space. In particular, I-GLAD, where the prefix I stands for Infrequent message passing, reduces the time overhead of message passing. The convergence of I-GLAD can be proved regardless of the reduction in the message passing rate. Meanwhile, NI-GLAD, where the prefix N stands for Neighborhood message passing, restricts the computation overhead related to message passing to a small neighborhood space. Our results show that the optimality of the solution obtained by NI-GLAD depends on the selection of the neighborhood size.

Nonpreemptive Constrained Link Scheduling in Wireless Mesh Networks

This paper considers the problem of link scheduling with non-preemptive constraint in wireless mesh networks. In real-world implementation, there is often a constraint that a link can only transmit once and occupy consecutive time slots during a frame. We refer to it as the non-preemptive constraint. To date, only few scheduling algorithms in the literature has taken such constraint into consideration. In this paper, we show that optimal non-preemptive link scheduling (NPLS) problems are generally NP-hard and are provably harder to solve than link scheduling without such a constraint. To tackle the problem, a low-complexity list link scheduling (LLS) algorithm is proposed to approximate the optimal NPLS. Our analysis shows that with a randomly selected link-ordering list, throughput degradation of LLS compared to the optimal NPLS is bounded even in the worst case. By carefully constructing the link-ordering list, the performance of LLS can be further greatly improved. In this paper, we propose three schemes to construct link-ordering lists. The performance of the proposed schemes is evaluated through simulations.

Scalable coordinated uplink processing in cloud radio access networks

Featured by centralized processing and cloud based infrastructure, Cloud Radio Access Network (C-RAN) is a promising solution to achieve an unprecedented system capacity in future wireless cellular networks. The huge capacity gain mainly comes from the centralized and coordinated signal processing at the cloud server. However, full-scale coordination in a large-scale C-RAN requires the processing of very large channel matrices, leading to high computational complexity and channel estimation overhead. To resolve this challenge, we show in this paper that the channel matrices can be greatly sparsified without substantially compromising the system capacity. Through rigorous analysis, we derive a simple threshold-based channel matrix sparsification approach. Based on this approach, for reasonably large networks, the non-zero entries in the channel matrix can be reduced to a very low percentage (say 0.13% ~ 2%) by compromising only 5% of SINR. This means each RRH only needs to obtain the CSI of a small number of closest users, resulting in a significant reduction in the channel estimation overhead. On the other hand, the high sparsity of the channel matrix allows us to design detection algorithms that are scalable in the sense that the average computational complexity per user does not grow with the network size.

Randomized Gaussian message passing for scalable uplink signal processing in C-RANs

In Cloud Radio Access Networks (C-RANs), the high computational complexity of signal processing becomes unaffordable due to the large number of remote radio heads (RRHs) and users. This paper proposes a randomized Gaussian message-passing (RGMP) algorithm to reduce the complexity of uplink signal processing in C-RANs. Specifically, we first propose to use Gaussian message passing to reduce the computational complexity. In C-RANs, RRHs only need to detect signals from nearby users as the signals from distant users are very weak and can be ignored. Thus, in message-passing algorithms, messages only need to be exchanged among nearby RRHs and users. This leads to a linear computational complexity with the number of RRHs and users. Then, to improve the convergence of message passing, we propose to exchange messages in a random order instead of exchanging them simultaneously or in a fixed order. Numerical results show that the proposed RGMP algorithm has better convergence performance than conventional message passing. The randomness of the message update schedule also simplifies the analysis, which allows us to derive some convergence conditions for the RGMP algorithm. Besides analysis, we also compare the convergence rate of RGMP with existing low-complexity algorithms through extensive simulations.

A two-stage spectrum leasing optimization framework for virtual mobile network operators

Wireless network visualization (WNV) allows mobile network operators (MNOs) to lease its network infrastructure or licensed spectrum to virtual mobile network operators (VMNOs). A VMNO in WNV pays the MNOs for leasing wireless resources and receives payments from its mobile subscribers (MSs) based on the qualities of communication services. In this paper, we propose a novel two-stage spectrum leasing framework to maximize the average profit of a VMNO. Specifically, a VMNO can first make long-term spectrum lease based on the prediction of the average user traffic intensity over a long time period. Besides, the VMNO can flexibly make additional short-term leases based on the actual realizations of MS locations. We adopt a general alpha-fairness utility function to evaluate the qualities of downlink services to MSs. Within the proposed framework, we derive a closed-form expression for the optimal short-term leasing strategy for the VMNO, based on which we then propose an efficient algorithm to calculate the optimal long-term lease strategy. Simulation results show that the proposed two-stage spectrum leasing strategies can effectively increase the profit of VMNOs.

Token-based opportunistic scheduling protocol for cognitive radios with distributed beamforming

The authors propose a cross-layer approach, which exploits distributed beamforming in the physical layer and token passing in the media access control (MAC) layer, to improve quality of service (QoS) for secondary users (SUs) with bursty traffics in cognitive relay systems. In this scheme, source-to-destination transmissions are relayed by some SU nodes, which can form a distributed beamformer to forward messages in busy timeslots while completely eliminating interference to primary users (PUs). In contrast with previous cognitive relaying protocols, this scheme can utilise more spectrum resources, namely idle timeslots (or temporal spectrum holes) as well as busy timeslots (or spatial spectrum holes). Based on a token passing mechanism, an opportunistic scheduling protocol is then developed to dynamically balance available spectrum holes between the source and the relays, and hence adapts to bursty arrival of secondary traffics and random presence of PUs. By formulating a tandem queueing analytical framework, the performance of the proposed scheme is then analysed using a multi-dimensional Markov chain model. Numerical results demonstrate that the proposed scheme can achieve significant QoS gains over conventional cognitive relaying protocols that utilise only idle timeslots.

Lattice precoding for MIMO multiway relay channel with full data exchange

We consider efficient communications over the multiple-input multiple-output (MIMO) multiway relay channel (mRC) with full data exchange, where each user, equipped with multiple antennas, broadcasts its message to all the other users via the help of a multi-antenna relay. We propose a lattice-precoding based nested lattice coding scheme for the considered MIMO mRC, and derive the achievable rates of the proposed scheme. The proposed scheme is generally applicable to MIMO mRCs with an arbitrary number of users. Particularly, we show that the proposed scheme achieves the asymptotic capacity of the MIMO mRC with two users (i.e., the MIMO two-way relay channel), as the signal-to-noise ratio tends to infinity. We also show that, for a general MIMO mRC, the proposed scheme achieves the capacity of the downlink, and performs close to the cut-set bound of the uplink.

Adaptive inter-cell interference cancellation in heterogeneous networks: Making smart use of multiple antennas at base stations

In downlink heterogeneous networks (HetNets), inter-cell interference management becomes more complicated and challenging than that in traditional homogeneous networks due to the coexistence of different types of base stations (BSs). Most existing interference management approaches, such as coordinated beamforming and joint transmission, require joint signal processing at a number of, if not all, BSs. The burden thus imposed on the backhaul network may be too heavy for practical systems. In this paper, we therefore propose a low-complexity inter-cell interference cancellation (ICIC) scheme, where each BS independently cancels its interference to a set of users in neighboring cells when transmitting to its own user. In particularly, the proposed ICIC scheme is implemented in two time-scales. In the slow time-scale, each BS is assigned to a set of users intended for ICIC, referred to as a nulling set, according to long-term statistics of the wireless channels. Then, in the fast time-scale, beamforming vectors are chosen at each BS to cancel its interference to the users in its nulling set based on the instantaneous channel state information (CSI). In this way, the overall system throughput is improved through slow adaptive assignment of nulling sets, while real-time signal processing is implemented locally at each BS. To assign nulling sets for BSs, we should carefully examine the trade-off between the gain from ICIC and the loss in the transmission diversity, such that the benefits of multiple antennas at BSs are exploited efficiently. To this end, we formulate the problem of finding the optimal assignment of nulling sets as an integer programming (IP), where the data rate is expressed as a function of the nulling set assignment. With the derived expression of the data rate, we develop a greedy algorithm to solve the IP efficiently. Simulation in a two-tier network shows that the proposed ICIC scheme can balance the data rates in pico and macro cells adaptively such that the total throughput is improved.

Throughput optimization for training-based large-scale virtual MIMO systems

We consider large-scale virtual multiple-input multiple-output (MIMO) systems, in which a large number of user terminals communicate with a large number of cooperative bases stations (BSs). We focus on a training-based scheme and investigate the throughput maximization over various system parameters including pilot symbols, the time allocation coefficient a, the power allocation coefficient γ, and the user number K. Our main contribution is to derive simple throughput expressions by utilizing the random matrix theory, based on which closed-form optimal solutions of (K, γ, α) are obtained. We show that, for a large but finite coherent time T, the optimal K for throughput optimization satisfies K > T/2 and converges to T/2 as the signal-to-noise ratio goes to infinity.