Rami Mochaourab

Optimal Beamforming in Interference Networks with Perfect Local Channel Information

We consider settings in which T multi-antenna transmitters and K single-antenna receivers concurrently utilize the available communication resources. Each transmitter sends useful information only to its intended receivers and can degrade the performance of unintended systems. Here, we assume the performance measures associated with each receiver are monotonic with the received power gains. In general, the joint performance of the systems is desired to be Pareto optimal. However, designing Pareto optimal resource allocation schemes is known to be difficult. In order to reduce the complexity of achieving efficient operating points, we show that it is sufficient to consider rank-1 transmit covariance matrices and propose a framework for determining the efficient beamforming vectors. These beamforming vectors are thereby also parameterized by T(K-1) real-valued parameters each between zero and one. The framework is based on analyzing each transmitters power gain-region which is composed of all jointly achievable power gains at the receivers. The efficient beamforming vectors are on a specific boundary section of the power gain-region, and in certain scenarios it is shown that it is necessary to perform additional power allocation on the beamforming vectors. Two examples which include broadcast and multicast data as well as a cognitive radio application scenario illustrate the results.

Distributed Channel Assignment in Cognitive Radio Networks: Stable Matching and Walrasian Equilibrium

We consider a set of secondary transmitter-receiver pairs in a cognitive radio setting. Based on channel sensing and access performances, we consider the problem of assigning channels orthogonally to secondary users through distributed coordination and cooperation algorithms. Two economic models are applied for this purpose: matching markets and competitive markets. In the matching market model, secondary users and channels build two agent sets. We implement a stable matching algorithm in which each secondary user, based on his achievable rate, proposes to the coordinator to be matched with desirable channels. The coordinator accepts or rejects the proposals based on the channel preferences which depend on interference from the secondary user. The coordination algorithm is of low complexity and can adapt to network dynamics. In the competitive market model, channels are associated with prices and secondary users are endowed with monetary budget. Each secondary user, based on his utility function and current channel prices, demands a set of channels. A Walrasian equilibrium maximizes the sum utility and equates the channel demand to their supply. We prove the existence of Walrasian equilibrium and propose a cooperative mechanism to reach it. The performance and complexity of the proposed solutions are illustrated by numerical simulations.

Effective Capacity Maximization in Multi-Antenna Channels with Covariance Feedback

The tradeoff between average transmission rate and average delay is important for the system design of future wireless communication systems. In double-correlated multiple antenna channels, the spatial degrees of freedom allow to optimize the transmit strategy under throughput/delay priority. In this work, we maximize the effective capacity of a MIMO system with covariance feedback. Interestingly, the larger the delay requirement is, the more spatial degrees of freedom are used to avoid low instantaneous transmission rates. This fact is shown analytically by deriving a closed-form expression for the beamforming optimality range as a function of the spatial correlation, the SNR, and the QoS exponent. Numerical simulations illustrate the average effective capacity optimization and confirm the theoretical results.

Exchange Economy in Two-User Multiple-Input Single-Output Interference Channels

We study the conflict between two links in a multiple-input single-output interference channel. This setting is strictly competitive and can be related to perfectly competitive market models. In such models, general equilibrium theory is used to determine equilibrium measures that are Pareto optimal. First, we consider the links to be consumers that can trade goods within themselves. The goods in our setting correspond to beamforming vectors. We utilize the conflict representation of the consumers in the Edgeworth box, a graphical tool that depicts the allocation of the goods for the two consumers, to provide closed-form solution to all Pareto optimal outcomes. Afterwards, we model the situation between the links as a competitive market which additionally defines prices for the goods. The equilibrium in this economy is called Walrasian and corresponds to the prices that equate the demand to the supply of goods. We calculate the unique Walrasian equilibrium and propose a coordination process that is realized by an arbitrator which distributes the Walrasian prices to the consumers. The consumers then calculate in a decentralized manner their optimal demand corresponding to beamforming vectors that achieve the Walrasian equilibrium. This outcome is Pareto optimal and dominates the noncooperative outcome of the systems. Thus, based on the game theoretic model and solution concept, an algorithm for a distributed implementation of the beamforming problem in multiple-input single-output interference channels is provided.

Stable Matching for Adaptive Cross-Layer Scheduling in the LTE Downlink

Resource allocation from base stations to mobile users in realistic MIMO-OFDMA systems such as the 3GPP Long Term Evolution (LTE) downlink is based on limited and quantized channel feedback over a fine-granular resource grid of multiple dimensions. This allows for opportunistic scheduling but impedes application of enhanced cross-layer strategies due to the discrete and combinatorial problem space. Integer optimization for this allocation problem is strongly complex and prohibits use of efficient algorithms. Provided solutions in practice are given by sub-optimal greedy heuristics. In this paper, we apply twosided stable matchings for adaptive multi-user scheduling. Our framework gives Pareto-efficient allocations and yields a tunable tradeoff between system throughput and user fairness. We form stable pairings of system resources and users based on queue- and channel-aware lists of preferred matches. The derived concept aims to find a stable matching state under presence of nonstrict preference relations if such exist or redefines the allocation problem to a solvable strict problem instance. A performance evaluation for scheduling is done by system level simulations for high traffic loads in a realistically modeled LTE deployment.

Resource allocation in protected and shared bands: uniqueness and efficiency of Nash equilibria

Motivated by the question, is non-cooperative spectrum sharing desirable or not, we consider a scenario utilizing protected and shared bands. In a static non-cooperative setting consisting of two communication system pairs, we study the existence, uniqueness and efficiency of a fixed point of the iterative water-filling algorithm which corresponds to the Nash equilibrium. There exist several sufficient conditions for the convergence of the algorithm in the literature mostly based on the contraction mapping theorem. We derive necessary and sufficient conditions for convergence by relating the game to supermodular games. There, the best response dynamics is globally convergent when a unique Nash equilibrium exists. In order to understand the loss in efficiency due to non-cooperation, we study the Price of Anarchy of the system. We show that the performance of the non-cooperative system cannot fall below two third of that of the cooperative system in the high signal to noise ratio regime. Theoretical results are illustrated by numerical simulations for a simplified system scenario.

Coalitional Games in MISO Interference Channels: Epsilon-Core and Coalition Structure Stable Set

The multiple-input single-output interference channel is considered. Each transmitter is assumed to know the channels between itself and all receivers perfectly and the receivers are assumed to treat interference as additive noise. In this setting, noncooperative transmission does not take into account the interference generated at other receivers which generally leads to inefficient performance of the links. To improve this situation, we study cooperation between the links using coalitional games. The players (links) in a coalition either perform zero forcing transmission or Wiener filter precoding to each other. The ε-core is a solution concept for coalitional games that takes into account the overhead required in coalition deviation. We provide necessary and sufficient conditions for the strong and weak ε-core of our coalitional game not to be empty with zero forcing transmission. Since, the ε-core only considers the possibility of joint cooperation of all links, we study coalitional games in partition form in which several distinct coalitions can form. We propose a polynomial-time distributed coalition formation algorithm based on coalition merging and prove that its solution lies in the coalition structure stable set of our coalition formation game. Simulation results reveal the cooperation gains for different coalition formation complexities and deviation overhead models.

Alternating Rate Profile Optimization in Single Stream MIMO Interference Channels

The multiple-input multiple-output interference channel is considered with perfect channel information at the transmitters and single-user decoding receivers. With all transmissions restricted to single stream beamforming, we consider the problem of finding all Pareto optimal rate-tuples in the achievable rate region. The problem is cast as a rate profile optimization problem. Due to its nonconvexity, we resort to an alternating approach: For fixed receivers, optimal transmission is known. For fixed transmitters, we show that optimal receive beamforming is a solution to an inverse field of values problem. We prove the solutions stationarity and compare it with existing approaches.

A fair and energy-efficient topology control protocol for wireless sensor networks

In energy constrained wireless sensor networks, energy conservation techniques are to be applied in order to maximize the system lifetime. We tackle the problem of increasing network lifetime through the topology control assignment. In a two-dimensional random sensor deployment, the nodes can estimate the distances to their neighbors and can vary their transmission ranges accordingly. Supporting self-organization of the sensor nodes, each node locally selects its appropriate neighbors according to a neighbor eligibility metric. Here, we introduce the notion of weighted relaying regions defined over the plane of a searching node. This is aimed at dropping out inefficient links in the network in order to reduce the overall energy consumption. Contrary to most topology control protocols that rely on nearest neighbor approaches, we use a distance measure that is radio characteristic and channel condition dependent. This in turn, proves more adequate for energy conservation in dense network deployments. Considering network dynamics that might arise due to node mobility or node failures, our topology control protocol is to be run periodically. Fairness between the nodes can be increased in updating the topology considering the changing energy reserves of the nodes. We verify the performance of the protocol through simulation results on network graph properties and energy consumption.

Adaptive Pilot Clustering in Heterogeneous Massive MIMO Networks

We consider the uplink of a cellular massive multiple-input multiple-output network. Acquiring channel state information at the base stations (BSs) requires uplink pilot signaling. Since the number of orthogonal pilot sequences is limited by the channel coherence, pilot reuse across cells is necessary to achieve high spectral efficiency. However, finding efficient pilot reuse patterns is non-trivial, especially in practical asymmetric BS deployments. We approach this problem using the coalitional game theory. Each BS has a few unique pilots and can form coalitions with other BSs to gain access to more pilots. The BSs in a coalition, thus, benefit from serving more users in their cells at the expense of higher pilot contamination and interference. Given that a cells average spectral efficiency depends on the overall pilot reuse pattern, the suitable coalitional game model is in the partition form. We develop a low-complexity distributed coalition formation based on individual stability. By incorporating a BS intercommunication budget constraint, we are able to control the overhead in message exchange between the BSs and ensure the algorithms convergence to a solution of the game called individually stable coalition structure. Simulation results reveal fast algorithmic convergence and substantial performance gains over the baseline schemes with no pilot reuse, full pilot reuse, or random pilot reuse pattern.