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 consider the problem of finding low-complexity, bandwidth-efficient, and processing-power efficient transmission schemes for a downlink scenario under the framework of diversity combining. Capitalizing on recent results for joint adaptive modulation and diversity combining schemes (AMDC), we design and analyze two AMDC schemes that utilize power control to reduce the radiated power, and thus the potential interference to other systems/users. Based on knowledge of the channel fading, the proposed schemes adaptively select the signal constellation, diversity combiner structure, and transmit power level. We show that the novel schemes also provide significant average transmit power gains compared to existing joint adaptive QAM and diversity schemes. In particular, over a large signal to noise ratio range, the transmitted power is reduced by 30-50%, yielding a substantial decrease in interference to co-existing systems/users, while maintaining high average spectral efficiency, low combining complexity, and compliance with bit error rate constraints.

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.

In wireless communications, bandwidth is a scarce resource. By employing link adaptation we achieve bandwidth-efficient wireless transmission schemes. Using a fixed number of codes we propose a variable-power transmission scheme for slowly flat-fading channels. Assuming that capacity-achieving codes for AWGN channels are available, we develop new combined discrete-rate discrete-power adaptation algorithms with limited feedback for wireless systems. The adaptation schemes are optimized in order to maximize the average spectral efficiency (ASE) for any finite number of available rates. We show that the new transmission schemes can achieve significantly higher ASE when compared to constant power schemes, almost reaching the upper bound of continuous power adaptation. Specifically, using just four rates and four power levels per rate results in a spectral efficiency that is within 1 dB of the continuous-rate continuous-power Shannon capacity. Further, the novel discrete transmission schemes reduce the probability of outage and are more robust against imperfect channel estimation and prediction.

In wireless communications, bandwidth is a scarce resource. By employing link adaptation we achieve bandwidth-efficient wireless transmission schemes. We propose a variable-power transmission scheme for slowly varying flat-fading channels using a fixed number of codes. Assuming that capacity-achieving codes for AWGN channels are available, the proposed power adaptation scheme maximizes the average spectral efficiency (ASE) for any finite number N of available rates. We show that the power adapted transmission scheme, using just four different rates, achieves a spectral efficiency within 0.15 bits/s/Hz of the Shannon capacity for continuous rate and power adaptation. Further, when restricted to N optimally chosen rates, introducing power adaptation has significant ASE and outage probability gains over a constant power scheme