The focus of this paper is on rate and power control algorithms in DSRC. We first design a utility maximization framework by leveraging the well-developed network congestion control, and formulate two subproblems, one on rate control with fixed transmit powers and the other on power control with fixed rates. Distributed rate control and power control algorithms are developed to solve these two subproblems, respectively, and are proved to be asymptotically optimal. Joint rate and power control can be done by using the two algorithms in an alternating fashion. The performance enhancement of our algorithms compared with a recent rate control algorithm, called EMBARC [1], is evaluated by using the network simulator ns2.

We consider the problem of precise range estimation between pairs of moving vehicles using periodic broadcast messages. The vehicles are not time synchronized and one needs to explicitly account for the clock offset and clock drift in addition to the vehicle motion to obtain accurate range estimates. We develop a broadcast range estimation algorithm based on local polynomial smoothing of the vehicle motion and experimentally verify that the performance is close to that obtained using unicast round-trip time ranging. This broadcast approach is of interest particularly in the context of dedicated short-range communication (DSRC) wherein periodic broadcast safety messages are exchanged between vehicles. We propose to exploit these broadcast messages to perform ranging. Our scheme requires additional timestamp information to be transmitted as part of the DSRC messages, and we develop a novel timestamp compression algorithm to minimize the resulting overhead. We validate our proposed algorithm on experimental data and show that it is able to achieve sub-meter ranging accuracies in vehicular scenarios.

A vehicular backbone network (VBN) has the potential to augment the Internet with high-throughput data flows for delay-tolerant traffic. High-throughput flows require a joint utilization of transportation capacity for carrying data packets through physical mobility and wireless capacity for switching data packets from one route to another. This paper establishes a model that incorporates both transportation mobility and wireless switching. Then, it characterizes the network capacity based on flow conservation, wireless communication capacity constraints and data storage limits, and solves a convex optimization that results in joint routing and congestion control. A variant with cost minimization reduces delay while maximizing throughput. Next, this paper develops a distributed algorithm that achieves the global objective with limited infrastructure support. Lastly, a packet-level simulation platform using real-world road map and traffic statistics is used to evaluate the distributed algorithm, and demonstrate the significant performance enhancement achieved.

An abstraction of the physical-layer coding using bit pipes that are coupled through data-rates is insufficient to capture notions such as node cooperation in cooperative relay networks. Consequently, network-stability analyses based on such abstractions are valid for non-cooperative schemes alone and meaningless for cooperative schemes. Motivated from this, this paper develops a framework that brings the information-theoretic coding scheme together with network-stability analysis. This framework does not constrain the system to any particular achievable scheme, i.e., the relays can use any cooperative coding strategy of its choice such as amplify/compress/quantize or any alter-and-forward scheme. The paper focuses on the scenario when coherence duration is of the same order of the packet/codeword duration, the channel distribution is unknown and the fading state is only known causally. The main contributions of this paper are two-fold: first, it develops a low-complexity queue-architecture to enable stable operation of cooperative relay networks, and, second, it establishes the throughput optimality of a simple network algorithm that utilizes this queue-architecture.

In traditional wireless protocols, medium-access-control and physical-layer rate-allocation are performed separately. This paper makes the case for combining the two into a single cross-layer framework. It presents the design, implementation, and evaluation of queue-based channel-measurement and rate-allocation (Q-CMRA) that is based on this single cross-layer framework. Q-CMRA's distributed algorithms utilize both queue-state and channel-state to jointly control medium-access and rate-allocation. Such joint control is essential to improving spatial-reuse and total network throughput. In our experiments, Q-CMRA outperforms traditional CSMA-CA and doubles the total network throughput in some setups.

This paper studies the robust weighted-sum rate optimization problem in the presence of channel uncertainty over a K-user Gaussian Interference Channel (GIFC), where multiple antennas are present at all transmitters and receivers. Motivated by recent results on interference alignment that show the optimality of linear precoders and simple receivers in achieving the maximum degrees-of-freedom available in the GIFC, we consider linear transmit precoding and two simple decoding schemes: single-stream decoding and single-user decoding. The resulting precoder design problems are then posed as specific optimization problems. Unfortunately, due to the hardness of these problems, optimal solutions cannot be efficiently obtained. Instead of resorting to ad-hoc algorithms, we show that it is possible to design algorithms using a systematic approach. Towards this end, this paper develops new provably convergent iterative algorithms for precoder design through ingenious sub-problem formulations such that each of these sub-problems can be solved optimally. The sub-problems are solved in closed-form for certain cases and formulated as standard convex problems for the rest. To complement these contributions on achievable schemes, we generalize the genie-MAC outer bounding technique to incorporate channel uncertainty using notions of compound-MAC capacity and then obtain computable outer bounds using an alternating optimization approach. Thus, we introduce one of the first approaches to obtain tighter outer bounds on the capacity region of the GIFC in the presence of channel uncertainty.

This paper develops a distributed algorithm for rate allocation in wireless networks that achieves the same throughput region as optimal centralized algorithms. This cross-layer algorithm jointly performs medium access control and physical-layer rate adaptation. The paper establishes that this algorithm is throughput-optimal for general rate regions. In contrast to on-off scheduling, rate allocation enables optimal utilization of physical-layer schemes by scheduling multiple rate levels. The algorithm is based on local queue-length information, and thus the algorithm is of significant practical value. An important application of this algorithm is in multiple-band multiple-radio throughput-optimal distributed scheduling for white-space networks. The algorithm requires that each link can determine the global feasibility of increasing its current data-rate. In many classes of networks, any one link's data-rate primarily impacts its neighbors and this impact decays with distance. Hence, local exchanges can provide the information needed to determine feasibility. Along these lines, the paper discusses the potential use of existing physical-layer control messages to determine feasibility. This can be considered as a technique analogous to carrier sensing in carrier sense multiple access (CSMA) networks.

This paper considers a multi-cell multiple antenna system with precoding used at the base stations for downlink transmission. Channel state information (CSI) is essential for precoding at the base stations. An effective technique for obtaining this CSI is time-division duplex (TDD) operation where uplink training in conjunction with reciprocity simultaneously provides the base stations with downlink as well as uplink channel estimates. This paper mathematically characterizes the impact that uplink training has on the performance of such multi-cell multiple antenna systems. When non-orthogonal training sequences are used for uplink training, the paper shows that the precoding matrix used by the base station in one cell becomes corrupted by the channel between that base station and the users in other cells in an undesirable manner. This paper analyzes this fundamental problem of pilot contamination in multi-cell systems. Furthermore, it develops a new multi-cell MMSE-based precoding method that mitigates this problem. In addition to being linear, this precoding method has a simple closed-form expression that results from an intuitive optimization. Numerical results show significant performance gains compared to certain popular single-cell precoding methods.

Analogous to the beneficial impact that mobility has on the throughput of unicast networks, this paper establishes that mobility can provide a similar gain in the order-wise growth-rate of the throughput for multicast networks. This paper considers an all-mobile multicast network, and characterizes its multicast capacity scaling. The scaling result shows that the growth-rate of the throughput in the all-mobile multicast network is order-wise higher compared to the all-static multicast network. Further, the paper considers a static-mobile hybrid multicast network, and establishes that, if there are sufficient number of mobile nodes (that is order-wise smaller than the total number of nodes) in the network, then mobile nodes can enhance the order behavior of the multicast throughput.

This paper studies a family of genie-MAC (multiple access channel) outer bounds for K-user Gaussian interference channels. This family is inspired by existing genie-aided bounding mechanisms, but differs from current approaches in its optimization problem formulation and application. The fundamental idea behind these bounds is to create a group of genie receivers that form multiple access channels that can decode a subset of the original interference channel's messages. The MAC sum capacity of each of the genie receivers provides an outer bound on the sum of rates for this subset. The genie-MAC outer bounds are used to derive new sum-capacity results. In particular, this paper derives sum-capacity in closed-form for the class of K-user Gaussian degraded interference channels. The sum-capacity achieving scheme is shown to be a successive interference cancellation scheme. This result generalizes a known result for two-user channels to K-user channels.

This paper considers a multi-cell multiple antenna system with precoding at the base stations for downlink transmission. To enable precoding, channel state information (CSI) is obtained via uplink training. This paper mathematically characterizes the impact that uplink training has on the performance of multi-cell multiple antenna systems. When non-orthogonal training sequences are used for uplink training, it is shown that the precoding matrix used by the base station in one cell becomes corrupted by the channel between that base station and the users in other cells. This problem of pilot contamination is analyzed in this paper. A multi-cell MMSE-based precoding is proposed that, when combined with frequency/time/pilot reuse techniques, mitigate this problem.

The downlink transmission in multi-user multiple- input multiple-output (MIMO) systems has been extensively studied from both communication-theoretic and information-theoretic perspectives. Most of these papers assume perfect/imperfect channel knowledge. In general, the problem of channel estimation is studied separately. However, in interference-limited communication systems with high mobility, the problem of channel estimation is tightly coupled with the problem of maximizing throughput of the system. In this paper, scheduling and preconditioning in the presence of reciprocal time-division duplex (TDD) training are considered. In the case of homogeneous users, a scheduling scheme is proposed and an improved lower bound on the sum capacity is derived. The problem of choosing training sequence length to maximize net throughput of the system is also studied. In the case of heterogeneous users, a modified pre-conditioning method is proposed and an optimized pre-conditioning matrix is derived. This method is combined with a scheduling scheme to further improve achievable weighted-sum rate.

Decision feedback equalizers (DFEs) are designed to deal with AWGN noise, and hence, generally perform very poorly in the presence of impulsive noise. Extreme value theory (EVT) is used to modify the DFE structure to handle impulsive noise. The received measurements are modified using EVT based weights before passing it to the equalizer. A modified maximal ratio combining (MRC) scheme which also uses EVT is developed to further improve the error-rate performance in the presence of impulsive interference. The proposed method performs much better than the conventional DFE-MRC technique which uses the simple MRC in conjunction with the DFE at low signal-to-interference (SIR) ratios.