In this paper, we investigate whether using a simple form of power allocation and relaying at the transmitters has the same order of performance gain as transmit cooperation coupled with complex coding techniques. We consider two different interference channel scenarios: a cellular system with two neighboring cells and an ad-hoc network with two interfering communication links over a given area. For transmit cooperation, we propose simple and practical linear pre-coding schemes based on Zero-Forcing (ZF) algorithm. We also propose to exploit relaying with distributed power allocation schemes. We see that in the high interference regime, using transmit cooperation coupled with complex coding schemes outperforms pure power allocation and relaying schemes. On the other hand for the moderate to low interference regime, the performance of the simpler schemes become comparable to that of transmit cooperation.

Network-wide optimization of transmit power with the goal of maximizing the total throughput, promises significant system capacity gains in interference-limited data networks. Finding distributed solutions to this global optimization problem however, remains a challenging task. In this work, we first focus on the maximization of the weighted sum-rate capacity, as this allows the incorporation of QoS criteria in the objective function. For the case of two links, we are able to analytically characterize the optimal solution to the weighted sum-rate maximization problem. However, computing the optimal solution requires centralized knowledge of network information. We thus formulate a framework for distributed power optimization valid for N mutually interfering links, based on the concept of channel state partitioning. By assuming instantaneous knowledge of local information and statistical knowledge of non-local information, we derive a distributed power allocation algorithm, which we first analyze for the case of N = 2. Although a gain is observed over equal power allocation, the distributed algorithm shows a performance gap as compared to a centralized solution, as expected. We show however, that minimal information message passing (in this case one bit) between interfering links can help reduce this gap substantially. Finally, we also propose a method to incorporate user scheduling into the distributed power allocation algorithm.

We consider allocating the transmit powers for a wireless multi-link (N-link) system, in order to maximize the total system throughput under interference and noise impairments, and short term power constraints. Employing dynamic spectral reuse, we allow for centralized control. In the two-link case, the optimal power allocation then has a remarkably simple nature termed binary power control: depending on the noise and channel gains, assign full power to one link and minimum to the other, or full power on both. Binary power control (BPC) has the advantage of leading towards simpler or even distributed power control algorithms. For N>2 we propose a strategy based on checking the corners of the domain resulting from the power constraints to perform BPC. We identify scenarios in which binary power allocation can be proven optimal also for arbitrary N. Furthermore, in the general setting for N>2, simulations demonstrate that a throughput performance with negligible loss, compared to the best non-binary scheme found by geometric programming, can be obtained by BPC. Finally, to reduce the complexity of optimal binary power allocation for large networks, we provide simple algorithms achieving 99% of the capacity promised by exhaustive binary search.

We address the problem of multicell co-channel scheduling in view of mitigating interference in a wireless data network with full spectrum reuse. The centralized joint multicell scheduling optimization problem, based on the complete co-channel gain information, has so far been justly considered impractical due to complexity and real-time cell-to-cell signaling overhead. However, we expose here the following remarkable result for a large network with a standard power control policy. The capacity maximizing joint multicell scheduling problem admits a simple and fully distributed solution. This result is proved analytically for an idealized network. From the constructive proof, we propose a practical algorithm that is shown to achieve near maximum capacity for realistic cases of simulated networks of even small sizes.

Recent results show that sum-rate maximizing multicell power allocation promises significant gains in interference- limited data networks. Finding practical, i.e. distributed, versions of this global optimization problem however remains a challenging task. In this work, we establish a general framework for the distributed power allocation problem for N mutually interfering links enabling us to derive a fully distributed power allocation algorithm. Although a gain for N = 2 is observed, a performance gap is still observed compared to a centralized algorithm. As a way to fill that gap, we propose minimal information (in this case 1 bit) message passing between interfering links to improve performance. Numerical results show these algorithms to exploit a substantial amount of the capacity gain offered by centralized optimization.

Joint optimization of transmit power and scheduling in wireless data networks promises significant system-wide capacity gains. However, this problem is known to be NP-hard and thus difficult to tackle in practice. We analyze this problem for the downlink of a multicell full reuse network with the goal of maximizing the overall network capacity. We propose a distributed power allocation and scheduling algorithm which provides significant capacity gain for any finite number of users. This distributed cell coordination scheme, in effect, achieves a form of dynamic spectral reuse, whereby the amount of reuse varies as a function of the underlying channel conditions and only limited inter-cell signaling is required.

A sensible design of wireless networks involves striking a good balance between an aggressive reuse of the spectral resource throughout the network and managing the resulting co-channel interference. Traditionally, this problem has been tackled using a ldquodivide and conquerrdquo approach. The latter consists in deploying the network with a static or semidynamic pattern of resource reutilization. The chosen reuse factor, while sacrificing a substantial amount of efficiency, brings the interference to a tolerable level. The resource can then be managed in each cell so as to optimize the per cell capacity using an advanced air interface design. In this paper, we focus our attention on the overall network capacity as a measure of system performance. We consider the problem of resource allocation and adaptive transmission in multicell scenarios. As a key instance, the problem of joint scheduling and power control simultaneously in multiple transmit-receive links, which employ capacity-achieving adaptive codes, is studied. In principle, the solution of such an optimization hinges on tough issues such as the computational complexity and the requirement for heavy receiver-to-transmitter feedback and, for cellular networks, cell-to-cell channel state information (CSI) signaling. We give asymptotic properties pertaining to rate-maximizing power control and scheduling in multicell networks. We then present some promising leads for substantial complexity and signaling reduction via the use of newly developed distributed and game theoretic techniques.

We analyze the sum capacity of multicell wireless networks with full resource reuse and channel-driven opportunistic scheduling in each cell. We address the problem of finding the co-channel (throughout the network) user assignment that results in the optimal joint multicell capacity, under a resource-fair constraint and a standard power control strategy. This problem in principle requires processing the complete co-channel gain information, and thus, has so far been justly considered unpractical due to complexity and channel gain signaling overhead. However, we expose here the following key result: the multicell optimal user scheduling problem admits a remarkably simple and fully distributed solution for large networks. This result is proved analytically for an idealized network. From this constructive proof, we propose a practical algorithm that is shown to achieve near maximum capacity for realistic cases of simulated networks of even small sizes